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    <title>My Weird Prompts: Development</title>
    <description><![CDATA[Building software: programming, developer tools, APIs, deployment and DevOps, and building with AI (agents, MCP servers, RAG, AI coding tools). An extra channel alongside an episode's main one, so AI engineering episodes sit in both.]]></description>
    <link>https://myweirdprompts.com/channel/development/</link>
    <language>en-us</language>
    <copyright>Copyright 2026 Daniel Rosehill</copyright>
    <lastBuildDate>Tue, 27 Oct 2026 22:13:20 GMT</lastBuildDate>
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    <image>
      <url>https://files.myweirdprompts.com/logos/mwp-square-3000.png</url>
      <title>My Weird Prompts: Development</title>
      <link>https://myweirdprompts.com/channel/development/</link>
    </image>

    <itunes:author>Daniel Rosehill</itunes:author>
    <itunes:summary><![CDATA[Building software: programming, developer tools, APIs, deployment and DevOps, and building with AI (agents, MCP servers, RAG, AI coding tools). An extra channel alongside an episode's main one, so AI engineering episodes sit in both.]]></itunes:summary>
    <itunes:owner>
      <itunes:name>Daniel Rosehill</itunes:name>
      <itunes:email>feed@myweirdprompts.com</itunes:email>
    </itunes:owner>
    <itunes:image href="https://files.myweirdprompts.com/logos/mwp-square-3000.png"/>
    <itunes:category text="Technology"/>
    <itunes:explicit>no</itunes:explicit>
    <itunes:type>episodic</itunes:type>
    <podcast:locked owner="feed@myweirdprompts.com">yes</podcast:locked>

    
    <item>
      <title>Who Sets Identity Standards for AI Agents</title>
      <description><![CDATA[Standards bodies and consulting groups are currently racing to define non-human identity, credential rotation, and authorization for autonomous software. While advisory groups build community landscapes, concrete engineering specifications are emerging through IETF workload initiatives, OpenID Foundation threat models, and OASIS cross-domain delegation protocols. Examining the actual technical drafts reveals why treating AI agents strictly as software workloads—extending existing foundations like SPIFFE and OAuth—offers a practical path toward securing cross-boundary delegated authority.]]></description>
      <link>https://myweirdprompts.com/episode/agent-identity-standards-landscape-89452/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-identity-standards-landscape-89452/</guid>
      <pubDate>Tue, 27 Oct 2026 22:02:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-identity-standards-landscape-89452.m4a"
        type="audio/mp4"
        length="19556449"
      />
      <itunes:title>Who Sets Identity Standards for AI Agents</itunes:title>
      <itunes:subtitle>Drafts from authors across AWS, OpenAI, and Ping Identity argue AI agents should be treated as software workloads, not people.</itunes:subtitle>
      <itunes:summary><![CDATA[Standards bodies and consulting groups are currently racing to define non-human identity, credential rotation, and authorization for autonomous software. While advisory groups build community landscapes, concrete engineering specifications are emerging through IETF workload initiatives, OpenID Foundation threat models, and OASIS cross-domain delegation protocols. Examining the actual technical drafts reveals why treating AI agents strictly as software workloads—extending existing foundations like SPIFFE and OAuth—offers a practical path toward securing cross-boundary delegated authority.]]></itunes:summary>
      <itunes:duration>1559</itunes:duration>
      <itunes:episode>6320</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-identity-standards-landscape-89452.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-identity-standards-landscape-89452.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Choosing Between Modal, RunPod, and Vast.ai</title>
      <description><![CDATA[Fine-tuning open-weight models forces a choice between fundamentally different infrastructure models. This comparison examines how Modal, RunPod, and Vast.ai handle the actual mechanics of training runs, from persistent state and environment setups to hidden costs. Key topics include Modal's Python decorators and scale-to-zero billing, RunPod's Pod-based container workflows and Community pricing tiers, and Vast.ai's decentralized host marketplace offering consumer cards like the RTX 4090 and 5090.]]></description>
      <link>https://myweirdprompts.com/episode/gpu-fine-tuning-cloud-platforms-29586/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gpu-fine-tuning-cloud-platforms-29586/</guid>
      <pubDate>Tue, 27 Oct 2026 19:23:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gpu-fine-tuning-cloud-platforms-29586.m4a"
        type="audio/mp4"
        length="26270704"
      />
      <itunes:title>Choosing Between Modal, RunPod, and Vast.ai</itunes:title>
      <itunes:subtitle>RunPod lists an H100 SXM at $3.99 an hour on Secure Cloud, but its Community marketplace drops it to $2.69.</itunes:subtitle>
      <itunes:summary><![CDATA[Fine-tuning open-weight models forces a choice between fundamentally different infrastructure models. This comparison examines how Modal, RunPod, and Vast.ai handle the actual mechanics of training runs, from persistent state and environment setups to hidden costs. Key topics include Modal's Python decorators and scale-to-zero billing, RunPod's Pod-based container workflows and Community pricing tiers, and Vast.ai's decentralized host marketplace offering consumer cards like the RTX 4090 and 5090.]]></itunes:summary>
      <itunes:duration>1490</itunes:duration>
      <itunes:episode>6312</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gpu-fine-tuning-cloud-platforms-29586.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gpu-fine-tuning-cloud-platforms-29586.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Model APIs Lack Dedicated Few-Shot Fields</title>
      <description><![CDATA[General model APIs treat few-shot demonstrations as raw text or chat messages rather than structured, versionable task artifacts. While specialized models like iAmTime and LoRA Recycle train directly on explicit demonstration structures, frontier LLM platforms rely on unstructured prompts, XML tags, or massive context windows. An examination of in-context learning shows why adding an examples parameter requires clear boundaries, ordering guarantees, and separate execution semantics beyond simple token pasting.]]></description>
      <link>https://myweirdprompts.com/episode/few-shot-demonstration-interfaces-90091/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/few-shot-demonstration-interfaces-90091/</guid>
      <pubDate>Tue, 27 Oct 2026 19:05:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/few-shot-demonstration-interfaces-90091.m4a"
        type="audio/mp4"
        length="24345320"
      />
      <itunes:title>Why Model APIs Lack Dedicated Few-Shot Fields</itunes:title>
      <itunes:subtitle>Zou and colleagues found models suffered steep drops at 16,000 tokens on tasks requiring learning across an entire demonstration set.</itunes:subtitle>
      <itunes:summary><![CDATA[General model APIs treat few-shot demonstrations as raw text or chat messages rather than structured, versionable task artifacts. While specialized models like iAmTime and LoRA Recycle train directly on explicit demonstration structures, frontier LLM platforms rely on unstructured prompts, XML tags, or massive context windows. An examination of in-context learning shows why adding an examples parameter requires clear boundaries, ordering guarantees, and separate execution semantics beyond simple token pasting.]]></itunes:summary>
      <itunes:duration>1373</itunes:duration>
      <itunes:episode>6310</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/few-shot-demonstration-interfaces-90091.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/few-shot-demonstration-interfaces-90091.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why DeepMind Systems Live Outside the Gemini API</title>
      <description><![CDATA[Google DeepMind regularly announces breakthrough systems that never appear as simple endpoints in Google AI Studio or the Gemini API. Exploring systems like AlphaEvolve, Co-Scientist, Genie, and AlphaFold reveals why they are kept separate. The restrictions stem from serving costs, workflow requirements, and commercial strategy rather than mere secrecy, requiring specialized cloud setups, research applications, or dedicated spin-outs instead of a standard chat prompt box.]]></description>
      <link>https://myweirdprompts.com/episode/deepmind-models-outside-gemini-api-03211/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/deepmind-models-outside-gemini-api-03211/</guid>
      <pubDate>Tue, 27 Oct 2026 18:45:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/deepmind-models-outside-gemini-api-03211.m4a"
        type="audio/mp4"
        length="27380334"
      />
      <itunes:title>Why DeepMind Systems Live Outside the Gemini API</itunes:title>
      <itunes:subtitle>AlphaEvolve saved 0.7% of Google&apos;s compute, but running it requires an enterprise cloud environment to evaluate candidate code.</itunes:subtitle>
      <itunes:summary><![CDATA[Google DeepMind regularly announces breakthrough systems that never appear as simple endpoints in Google AI Studio or the Gemini API. Exploring systems like AlphaEvolve, Co-Scientist, Genie, and AlphaFold reveals why they are kept separate. The restrictions stem from serving costs, workflow requirements, and commercial strategy rather than mere secrecy, requiring specialized cloud setups, research applications, or dedicated spin-outs instead of a standard chat prompt box.]]></itunes:summary>
      <itunes:duration>1551</itunes:duration>
      <itunes:episode>6309</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/deepmind-models-outside-gemini-api-03211.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/deepmind-models-outside-gemini-api-03211.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Exact Matching Outlasts the Index</title>
      <description><![CDATA[Searching without building a massive index remains surprisingly durable. While modern developer tools often rely on complex indexing, streaming Unix utilities like grep, find, and locate still handle immense workloads by balancing query-time scanning against stale precomputed paths. Exploring Ken Thompson's original 1973 assembly implementation, the Boyer-Moore skipping algorithms behind modern matchers, and the trade-offs of live directory walks versus cached path databases shows why literal matching without semantic approximation remains so effective.]]></description>
      <link>https://myweirdprompts.com/episode/grep-find-locate-search-57953/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/grep-find-locate-search-57953/</guid>
      <pubDate>Tue, 27 Oct 2026 18:36:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/grep-find-locate-search-57953.m4a"
        type="audio/mp4"
        length="31448572"
      />
      <itunes:title>Why Exact Matching Outlasts the Index</itunes:title>
      <itunes:subtitle>Ken Thompson extracted grep from the ed editor in 1973 to search the Federalist Papers without loading entire files into memory.</itunes:subtitle>
      <itunes:summary><![CDATA[Searching without building a massive index remains surprisingly durable. While modern developer tools often rely on complex indexing, streaming Unix utilities like grep, find, and locate still handle immense workloads by balancing query-time scanning against stale precomputed paths. Exploring Ken Thompson's original 1973 assembly implementation, the Boyer-Moore skipping algorithms behind modern matchers, and the trade-offs of live directory walks versus cached path databases shows why literal matching without semantic approximation remains so effective.]]></itunes:summary>
      <itunes:duration>1787</itunes:duration>
      <itunes:episode>6307</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/grep-find-locate-search-57953.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/grep-find-locate-search-57953.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Training Custom Rerankers for Podcast Search</title>
      <description><![CDATA[Building custom search across a dense podcast archive requires more than off-the-shelf vector retrieval. The real engineering work happens during candidate reranking and data curation. This look at search architecture explores chunking transcripts, navigating the tradeoff between recall ceilings and inference costs, mining hard negatives without accidentally penalizing nuanced matches, and using query-specific rubrics like Doug Turnbull's Bag of Decisions before reaching for a full model fine-tune.]]></description>
      <link>https://myweirdprompts.com/episode/custom-rerankers-archive-search-74056/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/custom-rerankers-archive-search-74056/</guid>
      <pubDate>Tue, 27 Oct 2026 18:35:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/custom-rerankers-archive-search-74056.m4a"
        type="audio/mp4"
        length="30676027"
      />
      <itunes:title>Training Custom Rerankers for Podcast Search</itunes:title>
      <itunes:subtitle>Expanding a retrieval candidate pool past 300 items yielded less than a single percentage point of recall in legal benchmark evaluations.</itunes:subtitle>
      <itunes:summary><![CDATA[Building custom search across a dense podcast archive requires more than off-the-shelf vector retrieval. The real engineering work happens during candidate reranking and data curation. This look at search architecture explores chunking transcripts, navigating the tradeoff between recall ceilings and inference costs, mining hard negatives without accidentally penalizing nuanced matches, and using query-specific rubrics like Doug Turnbull's Bag of Decisions before reaching for a full model fine-tune.]]></itunes:summary>
      <itunes:duration>1747</itunes:duration>
      <itunes:episode>6306</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/custom-rerankers-archive-search-74056.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/custom-rerankers-archive-search-74056.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Terminal Fuzzy Finders Forgive Different Mistakes</title>
      <description><![CDATA[Approximate string matching lives between strict regular expressions and semantic retrieval, but different search tools play by entirely different rules. While web search engines rely on edit distances to forgive typos and transpositions, terminal finders like fzf require strict ordered subsequences. This exploration covers how algorithms from Levenshtein, Damerau, Bitap, and Smith-Waterman compute edit distance, parse text via bitwise word operations, and rank candidate records without burning CPU cycles across massive databases.]]></description>
      <link>https://myweirdprompts.com/episode/fuzzy-matching-algorithms-search-36558/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fuzzy-matching-algorithms-search-36558/</guid>
      <pubDate>Tue, 27 Oct 2026 18:35:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fuzzy-matching-algorithms-search-36558.m4a"
        type="audio/mp4"
        length="24948905"
      />
      <itunes:title>Why Terminal Fuzzy Finders Forgive Different Mistakes</itunes:title>
      <itunes:subtitle>Fred Damerau found that over eighty percent of spelling errors in retrieval systems were a single insertion, deletion, substitution, or transposition.</itunes:subtitle>
      <itunes:summary><![CDATA[Approximate string matching lives between strict regular expressions and semantic retrieval, but different search tools play by entirely different rules. While web search engines rely on edit distances to forgive typos and transpositions, terminal finders like fzf require strict ordered subsequences. This exploration covers how algorithms from Levenshtein, Damerau, Bitap, and Smith-Waterman compute edit distance, parse text via bitwise word operations, and rank candidate records without burning CPU cycles across massive databases.]]></itunes:summary>
      <itunes:duration>1410</itunes:duration>
      <itunes:episode>6305</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fuzzy-matching-algorithms-search-36558.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fuzzy-matching-algorithms-search-36558.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Blind Testing AI Video Models for Architects</title>
      <description><![CDATA[Evaluating AI video models requires more than skimming glossy vendor demos, especially when an architectural project cannot tolerate shifting walls or warping window mullions. Evaluating tools like GenMedia, media-gen-arena, and GenMediaBenchmark reveals the practical mechanics of running internal blind pairwise comparisons. Rigorous testing demands tracking settings, managing per-clip generation costs, inspecting frame-by-frame structural defects with tools like DeltaFrame, and borrowing standardized benchmarking protocols from Artificial Analysis without mistaking strangers' aesthetic tastes for a firm's concrete technical requirements.]]></description>
      <link>https://myweirdprompts.com/episode/evaluating-video-models-architecture-02204/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/evaluating-video-models-architecture-02204/</guid>
      <pubDate>Tue, 27 Oct 2026 18:22:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/evaluating-video-models-architecture-02204.m4a"
        type="audio/mp4"
        length="25011359"
      />
      <itunes:title>Blind Testing AI Video Models for Architects</itunes:title>
      <itunes:subtitle>A matrix of sixteen prompts and two seeds across three video models produces ninety-six clips to generate, fund, and review.</itunes:subtitle>
      <itunes:summary><![CDATA[Evaluating AI video models requires more than skimming glossy vendor demos, especially when an architectural project cannot tolerate shifting walls or warping window mullions. Evaluating tools like GenMedia, media-gen-arena, and GenMediaBenchmark reveals the practical mechanics of running internal blind pairwise comparisons. Rigorous testing demands tracking settings, managing per-clip generation costs, inspecting frame-by-frame structural defects with tools like DeltaFrame, and borrowing standardized benchmarking protocols from Artificial Analysis without mistaking strangers' aesthetic tastes for a firm's concrete technical requirements.]]></itunes:summary>
      <itunes:duration>1411</itunes:duration>
      <itunes:episode>6303</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/evaluating-video-models-architecture-02204.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/evaluating-video-models-architecture-02204.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Fine-Tuning Whisper Won&apos;t Stop Duplicated Text</title>
      <description><![CDATA[Speech recognition errors are often blamed on model weights when the real culprit is inference orchestration. Fine-tuning an acoustic model will not fix duplicated phrases caused by chunk merging, fallback temperature schedules, or silence heuristics. By examining Whisper's DecodingOptions, beam search trade-offs, and WhisperX's choice to disable conditioning on previous text, this discussion maps the operational line between neural logits and the search algorithms that actually decide what to print.]]></description>
      <link>https://myweirdprompts.com/episode/whisper-decoding-search-parameters-34957/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/whisper-decoding-search-parameters-34957/</guid>
      <pubDate>Tue, 27 Oct 2026 17:36:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/whisper-decoding-search-parameters-34957.m4a"
        type="audio/mp4"
        length="26857239"
      />
      <itunes:title>Why Fine-Tuning Whisper Won&apos;t Stop Duplicated Text</itunes:title>
      <itunes:subtitle>Whisper evaluates transcripts against a gzip compression threshold of 2.4, rejecting repetitive text before retrying at higher temperatures.</itunes:subtitle>
      <itunes:summary><![CDATA[Speech recognition errors are often blamed on model weights when the real culprit is inference orchestration. Fine-tuning an acoustic model will not fix duplicated phrases caused by chunk merging, fallback temperature schedules, or silence heuristics. By examining Whisper's DecodingOptions, beam search trade-offs, and WhisperX's choice to disable conditioning on previous text, this discussion maps the operational line between neural logits and the search algorithms that actually decide what to print.]]></itunes:summary>
      <itunes:duration>1518</itunes:duration>
      <itunes:episode>6302</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/whisper-decoding-search-parameters-34957.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/whisper-decoding-search-parameters-34957.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Choosing the Right Audio Annotation Tool</title>
      <description><![CDATA[Turning raw environmental recordings into machine learning datasets requires choosing between lightweight inspection tools and heavyweight crowd platforms. From offline desktop staples like ELAN and Praat to self-hosted frameworks like Label Studio, Potato, and bioacoustic-focused Whombat, each tool handles timeline events differently. The discussion covers tier management for overlapping sounds, local prelabelling with faster-whisper, model-assisted workflows using ML backends, and when commercial platforms like Encord or managed services like iMerit make sense.]]></description>
      <link>https://myweirdprompts.com/episode/audio-annotation-tool-selection-82225/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-annotation-tool-selection-82225/</guid>
      <pubDate>Tue, 27 Oct 2026 12:45:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-annotation-tool-selection-82225.m4a"
        type="audio/mp4"
        length="24222596"
      />
      <itunes:title>Choosing the Right Audio Annotation Tool</itunes:title>
      <itunes:subtitle>Desktop tool ELAN supports independent timeline tiers to prevent overlapping sounds like car horns from flattening into speech transcripts.</itunes:subtitle>
      <itunes:summary><![CDATA[Turning raw environmental recordings into machine learning datasets requires choosing between lightweight inspection tools and heavyweight crowd platforms. From offline desktop staples like ELAN and Praat to self-hosted frameworks like Label Studio, Potato, and bioacoustic-focused Whombat, each tool handles timeline events differently. The discussion covers tier management for overlapping sounds, local prelabelling with faster-whisper, model-assisted workflows using ML backends, and when commercial platforms like Encord or managed services like iMerit make sense.]]></itunes:summary>
      <itunes:duration>1361</itunes:duration>
      <itunes:episode>6300</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-annotation-tool-selection-82225.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-annotation-tool-selection-82225.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Dictation Models Steal Other People&apos;s Voices</title>
      <description><![CDATA[Speech recognition models can transcribe every word accurately while capturing the wrong person's conversation. Fine-tuning a recognizer on a single voice does not teach it to ignore clean, loud background speech like train platform announcements. Addressing issues in systems like Parakeet requires target-speaker conditioning, diarization-dependent masks, or enrollment embeddings rather than raw audio alone, preventing fluent intrusions and music-induced hallucinations.]]></description>
      <link>https://myweirdprompts.com/episode/target-speaker-asr-parakeet-05822/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/target-speaker-asr-parakeet-05822/</guid>
      <pubDate>Tue, 27 Oct 2026 12:30:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/target-speaker-asr-parakeet-05822.m4a"
        type="audio/mp4"
        length="24576649"
      />
      <itunes:title>Why Dictation Models Steal Other People&apos;s Voices</itunes:title>
      <itunes:subtitle>Stock speech models lack target-speaker conditioning, defaulting to transcribing whichever competing voice is loudest or most predictable.</itunes:subtitle>
      <itunes:summary><![CDATA[Speech recognition models can transcribe every word accurately while capturing the wrong person's conversation. Fine-tuning a recognizer on a single voice does not teach it to ignore clean, loud background speech like train platform announcements. Addressing issues in systems like Parakeet requires target-speaker conditioning, diarization-dependent masks, or enrollment embeddings rather than raw audio alone, preventing fluent intrusions and music-induced hallucinations.]]></itunes:summary>
      <itunes:duration>1376</itunes:duration>
      <itunes:episode>6298</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/target-speaker-asr-parakeet-05822.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/target-speaker-asr-parakeet-05822.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Speech Recognizers Cut Off Hesitant Speakers</title>
      <description><![CDATA[Speech recognition systems routinely cut callers off mid-thought, mistaking a hesitant pause for a finished turn. The mechanics behind endpointing reveal why accurate vocabulary models still fail in emergency dispatch and accessibility tools. Looking at pause frequency, duration, and repair structure across spontaneous speech, older adults, and clinical cohorts shows that human hesitation follows complex syntactic patterns rather than a universal silence threshold.]]></description>
      <link>https://myweirdprompts.com/episode/speech-recognition-pauses-turn-taking-07649/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speech-recognition-pauses-turn-taking-07649/</guid>
      <pubDate>Tue, 27 Oct 2026 12:27:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speech-recognition-pauses-turn-taking-07649.m4a"
        type="audio/mp4"
        length="30758472"
      />
      <itunes:title>Why Speech Recognizers Cut Off Hesitant Speakers</itunes:title>
      <itunes:subtitle>Between speakers, conversational handover averages 200 milliseconds, but planning pauses inside a turn naturally take seconds.</itunes:subtitle>
      <itunes:summary><![CDATA[Speech recognition systems routinely cut callers off mid-thought, mistaking a hesitant pause for a finished turn. The mechanics behind endpointing reveal why accurate vocabulary models still fail in emergency dispatch and accessibility tools. Looking at pause frequency, duration, and repair structure across spontaneous speech, older adults, and clinical cohorts shows that human hesitation follows complex syntactic patterns rather than a universal silence threshold.]]></itunes:summary>
      <itunes:duration>1751</itunes:duration>
      <itunes:episode>6296</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speech-recognition-pauses-turn-taking-07649.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speech-recognition-pauses-turn-taking-07649.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Splitting Serverless Apps Earns Its Keep</title>
      <description><![CDATA[Architectural boundaries often reflect deployment schedules rather than execution limits. On platforms like Modal, grouping web endpoints, background cron jobs, and heavy GPU workers into a single app provides atomic deployments without sharing containers, scaling budgets, or container images. Splitting these workloads into separate apps isolates blast radiuses and decouples release cadences, but it also trades type safety for cross-app lookup risks and version coordination. True decoupling depends on whether components actually ship on independent schedules.]]></description>
      <link>https://myweirdprompts.com/episode/serverless-app-boundaries-deployment-cadence-41957/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/serverless-app-boundaries-deployment-cadence-41957/</guid>
      <pubDate>Tue, 27 Oct 2026 11:45:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/serverless-app-boundaries-deployment-cadence-41957.m4a"
        type="audio/mp4"
        length="23317879"
      />
      <itunes:title>When Splitting Serverless Apps Earns Its Keep</itunes:title>
      <itunes:subtitle>Modal apps enforce atomic deployment, meaning all grouped functions redeploy in place together while scaling independently.</itunes:subtitle>
      <itunes:summary><![CDATA[Architectural boundaries often reflect deployment schedules rather than execution limits. On platforms like Modal, grouping web endpoints, background cron jobs, and heavy GPU workers into a single app provides atomic deployments without sharing containers, scaling budgets, or container images. Splitting these workloads into separate apps isolates blast radiuses and decouples release cadences, but it also trades type safety for cross-app lookup risks and version coordination. True decoupling depends on whether components actually ship on independent schedules.]]></itunes:summary>
      <itunes:duration>1315</itunes:duration>
      <itunes:episode>6295</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/serverless-app-boundaries-deployment-cadence-41957.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/serverless-app-boundaries-deployment-cadence-41957.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Tool Calls Break Across Coding Models</title>
      <description><![CDATA[Moving coding setups from Claude Code to environments like OpenCode using Kimi models reveals hidden incompatibilities beneath simple dropdown switches. Tool schemas, Anthropic's user-role tool results, and provider-specific chat templates handle calls entirely differently. Inspecting how native tokenizer tokens and inference parsers package structured function calls explains why an agent might declare a tool missing even when its configuration file opens flawlessly.]]></description>
      <link>https://myweirdprompts.com/episode/tool-call-model-migration-37679/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tool-call-model-migration-37679/</guid>
      <pubDate>Tue, 27 Oct 2026 11:15:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tool-call-model-migration-37679.m4a"
        type="audio/mp4"
        length="24267577"
      />
      <itunes:title>Why Tool Calls Break Across Coding Models</itunes:title>
      <itunes:subtitle>Translating coding agents across providers fails when input schemas, user-role results, and tokenizer markers diverge.</itunes:subtitle>
      <itunes:summary><![CDATA[Moving coding setups from Claude Code to environments like OpenCode using Kimi models reveals hidden incompatibilities beneath simple dropdown switches. Tool schemas, Anthropic's user-role tool results, and provider-specific chat templates handle calls entirely differently. Inspecting how native tokenizer tokens and inference parsers package structured function calls explains why an agent might declare a tool missing even when its configuration file opens flawlessly.]]></itunes:summary>
      <itunes:duration>1369</itunes:duration>
      <itunes:episode>6294</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tool-call-model-migration-37679.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tool-call-model-migration-37679.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Hermes and Pi Top the OpenRouter Coding Table</title>
      <description><![CDATA[Token rankings place Nous Research's Hermes Agent and Mario Zechner's pi above Claude Code on OpenRouter's coding leaderboard. The leaderboard's measurement quirks explain part of the gap, but the real divide is architectural: Hermes builds an extensive persistent apparatus with strict character-limited memory files, while pi opts for an aggressive four-tool core with branched session trees. The architectural tradeoffs show what minimal harnesses actually buy developers over feature-heavy coding assistants.]]></description>
      <link>https://myweirdprompts.com/episode/coding-agents-hermes-pi-64762/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/coding-agents-hermes-pi-64762/</guid>
      <pubDate>Mon, 26 Oct 2026 17:57:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/coding-agents-hermes-pi-64762.m4a"
        type="audio/mp4"
        length="29399053"
      />
      <itunes:title>Why Hermes and Pi Top the OpenRouter Coding Table</itunes:title>
      <itunes:subtitle>Hermes Agent and pi logged 1.66 trillion and 1.41 trillion tokens on OpenRouter, topping Claude Code despite fundamentally opposite designs.</itunes:subtitle>
      <itunes:summary><![CDATA[Token rankings place Nous Research's Hermes Agent and Mario Zechner's pi above Claude Code on OpenRouter's coding leaderboard. The leaderboard's measurement quirks explain part of the gap, but the real divide is architectural: Hermes builds an extensive persistent apparatus with strict character-limited memory files, while pi opts for an aggressive four-tool core with branched session trees. The architectural tradeoffs show what minimal harnesses actually buy developers over feature-heavy coding assistants.]]></itunes:summary>
      <itunes:duration>1669</itunes:duration>
      <itunes:episode>6289</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/coding-agents-hermes-pi-64762.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/coding-agents-hermes-pi-64762.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Claude Code Replaced Vector Search with Grep</title>
      <description><![CDATA[Coding agents often feel like they secretly maintain persistent semantic indexes of your repository. In reality, Claude Code dropped its early vector database prototype entirely in favor of ripgrep, glob, and iterative tool calls. This deep dive unpacks how Claude Code and Codex manage project context, why staleness and privacy make precomputed embeddings fragile, and where server-side prompt caching actually helps with token costs without remembering your codebase.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-agentic-search-83118/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-agentic-search-83118/</guid>
      <pubDate>Mon, 26 Oct 2026 12:37:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-agentic-search-83118.m4a"
        type="audio/mp4"
        length="24548663"
      />
      <itunes:title>Why Claude Code Replaced Vector Search with Grep</itunes:title>
      <itunes:subtitle>Claude Code creator Boris Cherny revealed early prototypes used local vector search before abandoning it for agentic grep.</itunes:subtitle>
      <itunes:summary><![CDATA[Coding agents often feel like they secretly maintain persistent semantic indexes of your repository. In reality, Claude Code dropped its early vector database prototype entirely in favor of ripgrep, glob, and iterative tool calls. This deep dive unpacks how Claude Code and Codex manage project context, why staleness and privacy make precomputed embeddings fragile, and where server-side prompt caching actually helps with token costs without remembering your codebase.]]></itunes:summary>
      <itunes:duration>1382</itunes:duration>
      <itunes:episode>6286</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-agentic-search-83118.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-agentic-search-83118.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Coding Benchmarks Split Across Models</title>
      <description><![CDATA[Benchmark charts often clash directly with hands-on developer experience. Evaluating Chinese models like GLM 5.3, Kimi K3.1 Pro, and MiniMax M3.1 Flash against Western flagships shows that older coding evaluations can reward overfitted styles while newer benchmarks diverge sharply. The true cost of cheaper token pricing hinges on human supervision, terminal harness quirks, and knowing when an agent should refuse to touch working code.]]></description>
      <link>https://myweirdprompts.com/episode/evaluating-coding-agent-benchmarks-21569/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/evaluating-coding-agent-benchmarks-21569/</guid>
      <pubDate>Mon, 26 Oct 2026 12:20:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/evaluating-coding-agent-benchmarks-21569.m4a"
        type="audio/mp4"
        length="29034248"
      />
      <itunes:title>Why Coding Benchmarks Split Across Models</itunes:title>
      <itunes:subtitle>GLM 5.3 claims 88.2 on Terminal Bench 2.1 against Opus 4.8 at 85, but newer evaluations reveal a very different order.</itunes:subtitle>
      <itunes:summary><![CDATA[Benchmark charts often clash directly with hands-on developer experience. Evaluating Chinese models like GLM 5.3, Kimi K3.1 Pro, and MiniMax M3.1 Flash against Western flagships shows that older coding evaluations can reward overfitted styles while newer benchmarks diverge sharply. The true cost of cheaper token pricing hinges on human supervision, terminal harness quirks, and knowing when an agent should refuse to touch working code.]]></itunes:summary>
      <itunes:duration>1651</itunes:duration>
      <itunes:episode>6285</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/evaluating-coding-agent-benchmarks-21569.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/evaluating-coding-agent-benchmarks-21569.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Coding Agent Configurations Do Not Travel Well</title>
      <description><![CDATA[Switching coding assistants often reveals how little project setup actually transfers between tools like Claude Code, Cursor, and Codex. Standards like AGENTS.md, Agent Skills, and the Agent Plugins 1.0 specification provide common filenames and discovery rules, but client execution models remain stubbornly distinct. A look at why shared Markdown instructions still produce behavioral drift, how discovery hierarchies differ across tools, and why identical skills can behave unpredictably depending on the harness running them.]]></description>
      <link>https://myweirdprompts.com/episode/portable-coding-agent-configuration-67959/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/portable-coding-agent-configuration-67959/</guid>
      <pubDate>Mon, 26 Oct 2026 12:19:47 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/portable-coding-agent-configuration-67959.m4a"
        type="audio/mp4"
        length="24568528"
      />
      <itunes:title>Why Coding Agent Configurations Do Not Travel Well</itunes:title>
      <itunes:subtitle>Codex caps combined instruction files at 32 kibibytes, which can crowd out nested rules before an agent even starts work.</itunes:subtitle>
      <itunes:summary><![CDATA[Switching coding assistants often reveals how little project setup actually transfers between tools like Claude Code, Cursor, and Codex. Standards like AGENTS.md, Agent Skills, and the Agent Plugins 1.0 specification provide common filenames and discovery rules, but client execution models remain stubbornly distinct. A look at why shared Markdown instructions still produce behavioral drift, how discovery hierarchies differ across tools, and why identical skills can behave unpredictably depending on the harness running them.]]></itunes:summary>
      <itunes:duration>1382</itunes:duration>
      <itunes:episode>6284</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/portable-coding-agent-configuration-67959.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/portable-coding-agent-configuration-67959.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Terminal Benchmarks Miss Real Desktop Faults</title>
      <description><![CDATA[Standard AI benchmarks test isolated containers on synthetic tasks, but fixing a broken Linux desktop application requires an entirely different set of diagnostic instincts. From trajectory-aware grading in WeaveBench to massive ranking shifts caused purely by agent harnesses like OpenHands and PI, isolated evaluations often overlook the realities of live troubleshooting. Testing AI agents against real-world systems reveals why watching an agent work provides insights that leaderboard scores routinely hide.]]></description>
      <link>https://myweirdprompts.com/episode/terminal-benchmarks-desktop-faults-40673/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/terminal-benchmarks-desktop-faults-40673/</guid>
      <pubDate>Mon, 26 Oct 2026 12:11:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/terminal-benchmarks-desktop-faults-40673.m4a"
        type="audio/mp4"
        length="28088471"
      />
      <itunes:title>Why Terminal Benchmarks Miss Real Desktop Faults</itunes:title>
      <itunes:subtitle>A hardness audit revealed that over a third of universally failed benchmark tasks were broken rather than genuinely difficult.</itunes:subtitle>
      <itunes:summary><![CDATA[Standard AI benchmarks test isolated containers on synthetic tasks, but fixing a broken Linux desktop application requires an entirely different set of diagnostic instincts. From trajectory-aware grading in WeaveBench to massive ranking shifts caused purely by agent harnesses like OpenHands and PI, isolated evaluations often overlook the realities of live troubleshooting. Testing AI agents against real-world systems reveals why watching an agent work provides insights that leaderboard scores routinely hide.]]></itunes:summary>
      <itunes:duration>1596</itunes:duration>
      <itunes:episode>6283</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/terminal-benchmarks-desktop-faults-40673.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/terminal-benchmarks-desktop-faults-40673.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Steering Messages Reach Active Coding Agents</title>
      <description><![CDATA[Sending a correction while an AI coding agent is in flight reveals the critical gap between receiving a message and acting on it. Looking closely at protocols like the Responses API steering events, Codex app server turns, and CLI harnesses, this episode examines how systems queue user input mid-task. Discover why steering produces new continuation responses rather than rewritten history, how in-flight tool boundaries function, and why durable software wrappers matter more than an unbroken model mind.]]></description>
      <link>https://myweirdprompts.com/episode/steering-coding-agents-protocols-72100/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/steering-coding-agents-protocols-72100/</guid>
      <pubDate>Mon, 26 Oct 2026 11:14:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/steering-coding-agents-protocols-72100.m4a"
        type="audio/mp4"
        length="28365179"
      />
      <itunes:title>How Steering Messages Reach Active Coding Agents</itunes:title>
      <itunes:subtitle>Steering messages in the Responses API are acknowledged and queued without interrupting running tool calls or rewriting past execution.</itunes:subtitle>
      <itunes:summary><![CDATA[Sending a correction while an AI coding agent is in flight reveals the critical gap between receiving a message and acting on it. Looking closely at protocols like the Responses API steering events, Codex app server turns, and CLI harnesses, this episode examines how systems queue user input mid-task. Discover why steering produces new continuation responses rather than rewritten history, how in-flight tool boundaries function, and why durable software wrappers matter more than an unbroken model mind.]]></itunes:summary>
      <itunes:duration>1612</itunes:duration>
      <itunes:episode>6282</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/steering-coding-agents-protocols-72100.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/steering-coding-agents-protocols-72100.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Model Benchmarks Miss Small Workflow Friction</title>
      <description><![CDATA[Headline benchmark scores often suggest cutting-edge language models are practically interchangeable, yet real-world workflows tell another story. A look at how Claude 3.5 Sonnet and Opus handle compact developer shorthands exposes the hidden costs of intelligence routing. When an instruction lacks full specification, successful execution hinges on targeted local exploration rather than raw test scores. Examining benchmarks like SWE-Bench Pro and Terminal-Bench reveals why cheaper inference can quietly demand far more human attention.]]></description>
      <link>https://myweirdprompts.com/episode/model-benchmarks-workflow-friction-13368/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/model-benchmarks-workflow-friction-13368/</guid>
      <pubDate>Sun, 25 Oct 2026 22:30:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/model-benchmarks-workflow-friction-13368.m4a"
        type="audio/mp4"
        length="24057133"
      />
      <itunes:title>Why Model Benchmarks Miss Small Workflow Friction</itunes:title>
      <itunes:subtitle>Switching Claude Code to Sonnet broke an everyday shell alias request that Opus solved simply by recognizing where to look in local context.</itunes:subtitle>
      <itunes:summary><![CDATA[Headline benchmark scores often suggest cutting-edge language models are practically interchangeable, yet real-world workflows tell another story. A look at how Claude 3.5 Sonnet and Opus handle compact developer shorthands exposes the hidden costs of intelligence routing. When an instruction lacks full specification, successful execution hinges on targeted local exploration rather than raw test scores. Examining benchmarks like SWE-Bench Pro and Terminal-Bench reveals why cheaper inference can quietly demand far more human attention.]]></itunes:summary>
      <itunes:duration>1354</itunes:duration>
      <itunes:episode>6278</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/model-benchmarks-workflow-friction-13368.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/model-benchmarks-workflow-friction-13368.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Video Search Often Works Better as Text</title>
      <description><![CDATA[Multimodal search promises a single vector space for text, audio, and video, but compressing complex media into pooled representations introduces major trade-offs. Examining real-world pipelines like Ragie reveals why converting video frames and audio tracks into descriptive text and timestamps often outperforms direct cross-modal embeddings. The discussion covers frame sampling rates, Whisper-based transcription pipelines, CLIP-style contrastive encoders, and why high-stakes retrieval depends heavily on granular metadata rather than opaque vectors.]]></description>
      <link>https://myweirdprompts.com/episode/video-multimodal-embeddings-retrieval-19745/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/video-multimodal-embeddings-retrieval-19745/</guid>
      <pubDate>Sun, 25 Oct 2026 16:29:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/video-multimodal-embeddings-retrieval-19745.m4a"
        type="audio/mp4"
        length="28872133"
      />
      <itunes:title>Why Video Search Often Works Better as Text</itunes:title>
      <itunes:subtitle>A one-minute video compressed into a single vector undergoes roughly five orders of magnitude of raw data reduction.</itunes:subtitle>
      <itunes:summary><![CDATA[Multimodal search promises a single vector space for text, audio, and video, but compressing complex media into pooled representations introduces major trade-offs. Examining real-world pipelines like Ragie reveals why converting video frames and audio tracks into descriptive text and timestamps often outperforms direct cross-modal embeddings. The discussion covers frame sampling rates, Whisper-based transcription pipelines, CLIP-style contrastive encoders, and why high-stakes retrieval depends heavily on granular metadata rather than opaque vectors.]]></itunes:summary>
      <itunes:duration>1635</itunes:duration>
      <itunes:episode>6271</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/video-multimodal-embeddings-retrieval-19745.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/video-multimodal-embeddings-retrieval-19745.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Storing Ledgers in Postgres and Ticks in Kdb+</title>
      <description><![CDATA[Financial applications juggle two fundamentally different storage problems: authoritative ledgers that cannot lose balances, and high-frequency market feeds that record billions of price observations. While Postgres offers native exact decimal precision and broad operational familiarity, scanning years of market ticks demands specialized architectures. This discussion explores the boundary between transactional row stores and columnar time-series systems like kdb+, examining the trade-offs between precision, arithmetic speed, compression, and specialized query languages.]]></description>
      <link>https://myweirdprompts.com/episode/postgres-financial-tick-storage-24581/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/postgres-financial-tick-storage-24581/</guid>
      <pubDate>Sun, 25 Oct 2026 12:29:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/postgres-financial-tick-storage-24581.m4a"
        type="audio/mp4"
        length="24838892"
      />
      <itunes:title>Storing Ledgers in Postgres and Ticks in Kdb+</itunes:title>
      <itunes:subtitle>Postgres numeric types support up to 131,072 digits before the decimal, but exact arithmetic carries steep computational costs.</itunes:subtitle>
      <itunes:summary><![CDATA[Financial applications juggle two fundamentally different storage problems: authoritative ledgers that cannot lose balances, and high-frequency market feeds that record billions of price observations. While Postgres offers native exact decimal precision and broad operational familiarity, scanning years of market ticks demands specialized architectures. This discussion explores the boundary between transactional row stores and columnar time-series systems like kdb+, examining the trade-offs between precision, arithmetic speed, compression, and specialized query languages.]]></itunes:summary>
      <itunes:duration>1403</itunes:duration>
      <itunes:episode>6268</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/postgres-financial-tick-storage-24581.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/postgres-financial-tick-storage-24581.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Text-to-Speech Fails at Explaining Code</title>
      <description><![CDATA[Translating source code into speech creates a difficult dilemma between tedious character-by-character dictation and overly smooth summaries that omit crucial logic. This discussion explores the mechanics of code-to-speech translation, reviewing recent research on how major voice models misrepresent structural cues like indentation, pauses, and scope boundaries. It examines tools like speakable-text that use language-specific parsers to translate syntax into semantic narration, preserving operator precision, identifier conventions, and execution boundaries without reciting raw whitespace.]]></description>
      <link>https://myweirdprompts.com/episode/code-to-speech-narration-syntax-69128/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/code-to-speech-narration-syntax-69128/</guid>
      <pubDate>Sun, 25 Oct 2026 12:28:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/code-to-speech-narration-syntax-69128.m4a"
        type="audio/mp4"
        length="23564742"
      />
      <itunes:title>Why Text-to-Speech Fails at Explaining Code</itunes:title>
      <itunes:subtitle>A study comparing Google TTS, ChatGPT, Gemini, ElevenLabs, and Copilot found every system failed to communicate indentation, scope, and code comments.</itunes:subtitle>
      <itunes:summary><![CDATA[Translating source code into speech creates a difficult dilemma between tedious character-by-character dictation and overly smooth summaries that omit crucial logic. This discussion explores the mechanics of code-to-speech translation, reviewing recent research on how major voice models misrepresent structural cues like indentation, pauses, and scope boundaries. It examines tools like speakable-text that use language-specific parsers to translate syntax into semantic narration, preserving operator precision, identifier conventions, and execution boundaries without reciting raw whitespace.]]></itunes:summary>
      <itunes:duration>1325</itunes:duration>
      <itunes:episode>6267</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/code-to-speech-narration-syntax-69128.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/code-to-speech-narration-syntax-69128.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Balancing Speech Training Data With Square Roots</title>
      <description><![CDATA[Training a 600-million parameter Parakeet model on bilingual code-switching and technical vocabulary requires carefully balancing recording prompts. The underlying app doesn't use target percentages; instead, it runs a weighted raffle where tickets equal the square root of unread sentences multiplied by manual task weights. Meanwhile, mistaken dictation words jump the line entirely, entering a throttled queue that serves a three-card burst followed by one card every eight turns until reaching a twenty-take target.]]></description>
      <link>https://myweirdprompts.com/episode/balancing-speech-training-data-04984/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/balancing-speech-training-data-04984/</guid>
      <pubDate>Sun, 25 Oct 2026 12:25:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/balancing-speech-training-data-04984.m4a"
        type="audio/mp4"
        length="22443637"
      />
      <itunes:title>Balancing Speech Training Data With Square Roots</itunes:title>
      <itunes:subtitle>A fine-tuning recording app weighs tasks by the square root of unread sentences so massive datasets don&apos;t crowd out smaller prompt sets.</itunes:subtitle>
      <itunes:summary><![CDATA[Training a 600-million parameter Parakeet model on bilingual code-switching and technical vocabulary requires carefully balancing recording prompts. The underlying app doesn't use target percentages; instead, it runs a weighted raffle where tickets equal the square root of unread sentences multiplied by manual task weights. Meanwhile, mistaken dictation words jump the line entirely, entering a throttled queue that serves a three-card burst followed by one card every eight turns until reaching a twenty-take target.]]></itunes:summary>
      <itunes:duration>1246</itunes:duration>
      <itunes:episode>6266</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/balancing-speech-training-data-04984.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/balancing-speech-training-data-04984.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Upgrading Embedding Models Demands Reindexing</title>
      <description><![CDATA[Switching embedding models often forces teams to re-encode massive archives from scratch. Providers like Voyage and Cohere now offer shared embedding spaces across model families, allowing teams to pair expensive document vectors with cheaper query models. Yet generation-spanning upgrades still require heavy backfills. Exploring research like Apple's forward-compatible training, Google's learned converters, and database constraints in Qdrant and Honcho reveals what compatibility commitments actually deliver—and the hidden migration bills they leave behind.]]></description>
      <link>https://myweirdprompts.com/episode/shared-embedding-spaces-migration-55033/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/shared-embedding-spaces-migration-55033/</guid>
      <pubDate>Sun, 25 Oct 2026 11:56:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/shared-embedding-spaces-migration-55033.m4a"
        type="audio/mp4"
        length="24853562"
      />
      <itunes:title>Why Upgrading Embedding Models Demands Reindexing</itunes:title>
      <itunes:subtitle>Cohere&apos;s Embed Five Fast queries against a Pro index reach 98.4 percent of performance without regenerating stored document vectors.</itunes:subtitle>
      <itunes:summary><![CDATA[Switching embedding models often forces teams to re-encode massive archives from scratch. Providers like Voyage and Cohere now offer shared embedding spaces across model families, allowing teams to pair expensive document vectors with cheaper query models. Yet generation-spanning upgrades still require heavy backfills. Exploring research like Apple's forward-compatible training, Google's learned converters, and database constraints in Qdrant and Honcho reveals what compatibility commitments actually deliver—and the hidden migration bills they leave behind.]]></itunes:summary>
      <itunes:duration>1402</itunes:duration>
      <itunes:episode>6265</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/shared-embedding-spaces-migration-55033.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/shared-embedding-spaces-migration-55033.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Choosing Omni Embedding Models for Mixed Media</title>
      <description><![CDATA[Unified vector spaces promise to let recordings, pictures, documents, and text query one another inside a single index. Evaluating options like Jina v5 omni and Gemini Embedding 2 reveals the fundamental trade-off between native multimodal architectures and frozen-backbone retrofits. Key considerations include preserving existing text indices, managing audio padding artifacts from Whisper extractors, and using proper query versus document task prefixes.]]></description>
      <link>https://myweirdprompts.com/episode/omni-embedding-models-multimodal-search-94394/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/omni-embedding-models-multimodal-search-94394/</guid>
      <pubDate>Sun, 25 Oct 2026 11:51:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/omni-embedding-models-multimodal-search-94394.m4a"
        type="audio/mp4"
        length="24233934"
      />
      <itunes:title>Choosing Omni Embedding Models for Mixed Media</itunes:title>
      <itunes:subtitle>Jina&apos;s omni model trains only 0.35 percent of its weights while aligning with existing text indices.</itunes:subtitle>
      <itunes:summary><![CDATA[Unified vector spaces promise to let recordings, pictures, documents, and text query one another inside a single index. Evaluating options like Jina v5 omni and Gemini Embedding 2 reveals the fundamental trade-off between native multimodal architectures and frozen-backbone retrofits. Key considerations include preserving existing text indices, managing audio padding artifacts from Whisper extractors, and using proper query versus document task prefixes.]]></itunes:summary>
      <itunes:duration>1359</itunes:duration>
      <itunes:episode>6264</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/omni-embedding-models-multimodal-search-94394.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/omni-embedding-models-multimodal-search-94394.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Numeric Data Lacks a Default Embedding Model</title>
      <description><![CDATA[While text and vision have standard embedding models, numeric time-series data lacks a general-purpose retrieval default. Looking across methods reveals the divide between forecasting architectures and similarity search. The discussion covers simple normalized coordinate windows in systems like InfluxDB and Milvus, dimension-reducing transforms such as KDB.AI's temporal search, multimodal financial alignment, and specialized vector indexing in Timescale Vector. When analyzing numbers, calculating exact values and matching shapes require fundamentally different engineering choices.]]></description>
      <link>https://myweirdprompts.com/episode/numeric-time-series-embeddings-48016/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/numeric-time-series-embeddings-48016/</guid>
      <pubDate>Sun, 25 Oct 2026 11:50:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/numeric-time-series-embeddings-48016.m4a"
        type="audio/mp4"
        length="25544284"
      />
      <itunes:title>Why Numeric Data Lacks a Default Embedding Model</itunes:title>
      <itunes:subtitle>Popular time-series models like IBM&apos;s Granite TSPulse forecast trends rather than provide canonical vectors for similarity search.</itunes:subtitle>
      <itunes:summary><![CDATA[While text and vision have standard embedding models, numeric time-series data lacks a general-purpose retrieval default. Looking across methods reveals the divide between forecasting architectures and similarity search. The discussion covers simple normalized coordinate windows in systems like InfluxDB and Milvus, dimension-reducing transforms such as KDB.AI's temporal search, multimodal financial alignment, and specialized vector indexing in Timescale Vector. When analyzing numbers, calculating exact values and matching shapes require fundamentally different engineering choices.]]></itunes:summary>
      <itunes:duration>1440</itunes:duration>
      <itunes:episode>6263</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/numeric-time-series-embeddings-48016.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/numeric-time-series-embeddings-48016.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Routing Claude Code Tasks to Local Ollama Models</title>
      <description><![CDATA[Claude Code lacks native support for pointing subagents at local inference servers, but developers are splitting traffic using two distinct architectural patterns. This discussion explores model-based proxy interception with tools like llm-router alongside explicit delegation via MCP servers like local-vibes and ollama-code-mcp. It examines configuration inheritance caveats, credential exposure within the request path, loop timeouts, and why silent cloud fallbacks can undermine the exact privacy guarantees that drove local delegation in the first place.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-local-ollama-routing-08363/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-local-ollama-routing-08363/</guid>
      <pubDate>Sat, 24 Oct 2026 15:18:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-local-ollama-routing-08363.m4a"
        type="audio/mp4"
        length="30173231"
      />
      <itunes:title>Routing Claude Code Tasks to Local Ollama Models</itunes:title>
      <itunes:subtitle>A feature request to support local subagent endpoints in Claude Code was closed for inactivity after less than two months.</itunes:subtitle>
      <itunes:summary><![CDATA[Claude Code lacks native support for pointing subagents at local inference servers, but developers are splitting traffic using two distinct architectural patterns. This discussion explores model-based proxy interception with tools like llm-router alongside explicit delegation via MCP servers like local-vibes and ollama-code-mcp. It examines configuration inheritance caveats, credential exposure within the request path, loop timeouts, and why silent cloud fallbacks can undermine the exact privacy guarantees that drove local delegation in the first place.]]></itunes:summary>
      <itunes:duration>1708</itunes:duration>
      <itunes:episode>6255</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-local-ollama-routing-08363.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-local-ollama-routing-08363.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Inference Engines Change Benchmark Scores</title>
      <description><![CDATA[Switching an inference runtime can drastically alter an open model's performance without touching its weights. Examining engines like vLLM, llama.cpp, and LMDeploy reveals how subtle implementation details—including tokenization quirks, chat templates, layer sliding window attention, and speculative decoding—distort both benchmark evaluations and real-world behavior. The discussion covers the hidden architectural work behind new model releases and explains why software configuration often masks raw model capability.]]></description>
      <link>https://myweirdprompts.com/episode/inference-engine-benchmark-discrepancies-03345/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/inference-engine-benchmark-discrepancies-03345/</guid>
      <pubDate>Sat, 24 Oct 2026 13:53:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/inference-engine-benchmark-discrepancies-03345.m4a"
        type="audio/mp4"
        length="30354271"
      />
      <itunes:title>Why Inference Engines Change Benchmark Scores</itunes:title>
      <itunes:subtitle>A study found a 16.6 percentage point spread on GSM8K across different inference engines running identical weights on the same GPU.</itunes:subtitle>
      <itunes:summary><![CDATA[Switching an inference runtime can drastically alter an open model's performance without touching its weights. Examining engines like vLLM, llama.cpp, and LMDeploy reveals how subtle implementation details—including tokenization quirks, chat templates, layer sliding window attention, and speculative decoding—distort both benchmark evaluations and real-world behavior. The discussion covers the hidden architectural work behind new model releases and explains why software configuration often masks raw model capability.]]></itunes:summary>
      <itunes:duration>1721</itunes:duration>
      <itunes:episode>6245</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/inference-engine-benchmark-discrepancies-03345.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/inference-engine-benchmark-discrepancies-03345.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your Fine-Tune Isn&apos;t Done Learning Yet</title>
      <description><![CDATA[Your fine-tune's loss curve is dropping beautifully. So why is it still picking the wrong citation, drifting back into base-model habits halfway through an answer, or transcribing the same words wrong as the stock model? This episode works through what undertraining actually looks like in fine-tuned models — Whisper, LLMs, and speech models adapted to a single voice — and how to tell unfinished learning apart from bad data, a broken learning rate, or a capability you accidentally trained away. Along the way: why teacher forcing makes training loss a poor predictor of generated transcripts, why the new behavior showing up in six of ten samples is a classic undertraining signature, and why "just train longer" is sometimes the most expensive wrong answer available.]]></description>
      <link>https://myweirdprompts.com/episode/undertrained-fine-tune-signs-76084/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/undertrained-fine-tune-signs-76084/</guid>
      <pubDate>Sat, 24 Oct 2026 12:26:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/undertrained-fine-tune-signs-76084.m4a"
        type="audio/mp4"
        length="24093667"
      />
      <itunes:title>When Your Fine-Tune Isn&apos;t Done Learning Yet</itunes:title>
      <itunes:subtitle>Falling loss, 90% word error rate, and a model that still says &quot;Derry gym lag.&quot; What undertraining actually looks like — and when more epochs won&apos;t...</itunes:subtitle>
      <itunes:summary><![CDATA[Your fine-tune's loss curve is dropping beautifully. So why is it still picking the wrong citation, drifting back into base-model habits halfway through an answer, or transcribing the same words wrong as the stock model? This episode works through what undertraining actually looks like in fine-tuned models — Whisper, LLMs, and speech models adapted to a single voice — and how to tell unfinished learning apart from bad data, a broken learning rate, or a capability you accidentally trained away. Along the way: why teacher forcing makes training loss a poor predictor of generated transcripts, why the new behavior showing up in six of ten samples is a classic undertraining signature, and why "just train longer" is sometimes the most expensive wrong answer available.]]></itunes:summary>
      <itunes:duration>1385</itunes:duration>
      <itunes:episode>6244</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/undertrained-fine-tune-signs-76084.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/undertrained-fine-tune-signs-76084.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Training a Model on Your Own Taste</title>
      <description><![CDATA[Daniel wants a model that generates episode titles and descriptions, learns from side-by-side button presses, and improves each checkpoint. Herman walks through what that pipeline actually is: an iterative preference optimization loop built on DPO (Direct Preference Optimization), with an implicit reward derived from a frozen reference model rather than a separate reward model or RL rollout. Along the way: what one saved record must contain, why ties deserve their own objective (DPO-RK, DPO-D), and why a single transcript's four candidates are still just one opinion.]]></description>
      <link>https://myweirdprompts.com/episode/dpo-title-generator-training-73025/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dpo-title-generator-training-73025/</guid>
      <pubDate>Sat, 24 Oct 2026 11:48:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dpo-title-generator-training-73025.m4a"
        type="audio/mp4"
        length="22901422"
      />
      <itunes:title>Training a Model on Your Own Taste</itunes:title>
      <itunes:subtitle>What does it actually take to train a small model on your editorial taste using pairwise preferences and DPO?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants a model that generates episode titles and descriptions, learns from side-by-side button presses, and improves each checkpoint. Herman walks through what that pipeline actually is: an iterative preference optimization loop built on DPO (Direct Preference Optimization), with an implicit reward derived from a frozen reference model rather than a separate reward model or RL rollout. Along the way: what one saved record must contain, why ties deserve their own objective (DPO-RK, DPO-D), and why a single transcript's four candidates are still just one opinion.]]></itunes:summary>
      <itunes:duration>1315</itunes:duration>
      <itunes:episode>6242</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dpo-title-generator-training-73025.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dpo-title-generator-training-73025.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Synthetic Data Isn&apos;t as Diverse as You Think</title>
      <description><![CDATA[Synthetic data generation has a hidden problem: each example looks fine, but together they teach your model a narrow slice of reality. This episode digs into cross-batch mode collapse, why temperature and personas underdeliver, and what actually expands coverage — from scenario planning to multi-stage generation pipelines. If you're generating training data at scale, the bottleneck isn't quality per example. It's the variety of situations your dataset represents.]]></description>
      <link>https://myweirdprompts.com/episode/synthetic-data-diversity-collapse-88204/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/synthetic-data-diversity-collapse-88204/</guid>
      <pubDate>Sat, 24 Oct 2026 11:31:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/synthetic-data-diversity-collapse-88204.m4a"
        type="audio/mp4"
        length="22883936"
      />
      <itunes:title>Why Your Synthetic Data Isn&apos;t as Diverse as You Think</itunes:title>
      <itunes:subtitle>Fifty-nine percent of one GPT-4 dataset started with &quot;Once upon a time.&quot; Correct examples can still make a terrible training set.</itunes:subtitle>
      <itunes:summary><![CDATA[Synthetic data generation has a hidden problem: each example looks fine, but together they teach your model a narrow slice of reality. This episode digs into cross-batch mode collapse, why temperature and personas underdeliver, and what actually expands coverage — from scenario planning to multi-stage generation pipelines. If you're generating training data at scale, the bottleneck isn't quality per example. It's the variety of situations your dataset represents.]]></itunes:summary>
      <itunes:duration>1304</itunes:duration>
      <itunes:episode>6241</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/synthetic-data-diversity-collapse-88204.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/synthetic-data-diversity-collapse-88204.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Testing Synthetic Data Generators Before You Scale</title>
      <description><![CDATA[Picking a synthetic data generator by model reputation is a trap — a brilliant problem-solver can produce terrible training material. This episode digs into AgoraBench, a benchmark that trains the same student model on each generator's output and measures how much of the performance gap it closes. We cover why zero-seed generation is a different problem from transforming real data, how creating new instances and refining existing ones are separate skills, and why diversity often collapses as you scale from 2,000 to 20,000 samples. Plus: what a small pilot actually needs to prove before you commit to a big batch.]]></description>
      <link>https://myweirdprompts.com/episode/testing-synthetic-data-generators-83852/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/testing-synthetic-data-generators-83852/</guid>
      <pubDate>Sat, 24 Oct 2026 11:27:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/testing-synthetic-data-generators-83852.m4a"
        type="audio/mp4"
        length="23825856"
      />
      <itunes:title>Testing Synthetic Data Generators Before You Scale</itunes:title>
      <itunes:subtitle>A benchmark trains student models on each generator&apos;s output to find out which model actually writes useful training data.</itunes:subtitle>
      <itunes:summary><![CDATA[Picking a synthetic data generator by model reputation is a trap — a brilliant problem-solver can produce terrible training material. This episode digs into AgoraBench, a benchmark that trains the same student model on each generator's output and measures how much of the performance gap it closes. We cover why zero-seed generation is a different problem from transforming real data, how creating new instances and refining existing ones are separate skills, and why diversity often collapses as you scale from 2,000 to 20,000 samples. Plus: what a small pilot actually needs to prove before you commit to a big batch.]]></itunes:summary>
      <itunes:duration>1361</itunes:duration>
      <itunes:episode>6239</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/testing-synthetic-data-generators-83852.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/testing-synthetic-data-generators-83852.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Which AI Models Actually Generate Good Training Data?</title>
      <description><![CDATA[Synthetic training data lives or dies on the generator behind it. This episode compares the instruction models actually available for the job — Qwen, DeepSeek, Llama, Mistral, and Gemma on the open-weight side, and the Claude Opus/Sonnet/Haiku and GPT-5.6 Sol/Terra/Luna tiers on the closed side — with real pricing per million tokens and what each licence actually permits. Then it digs into the scores people cite to prove a model follows instructions: what IFEval really measures, why models near two billion parameters now clear eighty percent on it, and what IFBench's fifty-eight new constraints reveal when the test stops looking familiar. The throughline: producing one acceptable answer is not the same as producing thousands of useful training examples.]]></description>
      <link>https://myweirdprompts.com/episode/instruction-models-synthetic-data-83038/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/instruction-models-synthetic-data-83038/</guid>
      <pubDate>Sat, 24 Oct 2026 11:23:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/instruction-models-synthetic-data-83038.m4a"
        type="audio/mp4"
        length="23062895"
      />
      <itunes:title>Which AI Models Actually Generate Good Training Data?</itunes:title>
      <itunes:subtitle>Open weights vs closed APIs for synthetic data, and why IFEval scores don&apos;t tell you which model will hold up.</itunes:subtitle>
      <itunes:summary><![CDATA[Synthetic training data lives or dies on the generator behind it. This episode compares the instruction models actually available for the job — Qwen, DeepSeek, Llama, Mistral, and Gemma on the open-weight side, and the Claude Opus/Sonnet/Haiku and GPT-5.6 Sol/Terra/Luna tiers on the closed side — with real pricing per million tokens and what each licence actually permits. Then it digs into the scores people cite to prove a model follows instructions: what IFEval really measures, why models near two billion parameters now clear eighty percent on it, and what IFBench's fifty-eight new constraints reveal when the test stops looking familiar. The throughline: producing one acceptable answer is not the same as producing thousands of useful training examples.]]></itunes:summary>
      <itunes:duration>1305</itunes:duration>
      <itunes:episode>6238</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/instruction-models-synthetic-data-83038.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/instruction-models-synthetic-data-83038.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Synthetic Logs Without Leaking Your Clients</title>
      <description><![CDATA[Daniel has real agent-workflow logs full of his own projects and wants synthetic versions that preserve the rhythm and ambiguity without advertising what he's working on. Corn and Herman walk through what purpose-built privacy tooling actually does that a plain rewrite prompt doesn't: which libraries preserve information dependencies, where differential privacy guarantees really enter a pipeline, and why protecting a single turn may not hide a project described across hundreds of them. Along the way: tabular synthesizers, seed-based generators, state-aware agent simulators, and why differentially private synthetic text is still an unsolved problem.]]></description>
      <link>https://myweirdprompts.com/episode/synthetic-conversation-logs-privacy-53788/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/synthetic-conversation-logs-privacy-53788/</guid>
      <pubDate>Sat, 24 Oct 2026 11:17:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/synthetic-conversation-logs-privacy-53788.m4a"
        type="audio/mp4"
        length="25850144"
      />
      <itunes:title>Synthetic Logs Without Leaking Your Clients</itunes:title>
      <itunes:subtitle>You can rewrite your logs with a cheap model — but can you keep the task boundaries while losing the client names?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has real agent-workflow logs full of his own projects and wants synthetic versions that preserve the rhythm and ambiguity without advertising what he's working on. Corn and Herman walk through what purpose-built privacy tooling actually does that a plain rewrite prompt doesn't: which libraries preserve information dependencies, where differential privacy guarantees really enter a pipeline, and why protecting a single turn may not hide a project described across hundreds of them. Along the way: tabular synthesizers, seed-based generators, state-aware agent simulators, and why differentially private synthetic text is still an unsolved problem.]]></itunes:summary>
      <itunes:duration>1467</itunes:duration>
      <itunes:episode>6236</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/synthetic-conversation-logs-privacy-53788.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/synthetic-conversation-logs-privacy-53788.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your AI Agent Has Too Many Skills</title>
      <description><![CDATA[Installing a Claude Code skill is never free: its name and description take up permanent space in the agent's context, even if it's never used. As skill libraries grow, agents end up spending their working memory on metadata instead of the actual job. This episode digs into progressive disclosure — the three-tier loading architecture behind Agent Skills — and why it postpones the cost without eliminating it. We look at what a 55,000-skill analysis revealed about routing descriptions, how Codex budgets skill metadata at roughly 2% of the context window, and what workday's library-scaling tests showed about the tradeoff between fewer tokens and faster completion. Then we separate the real numbers from the marketing ones: what "70% token savings" actually measures, why tool-schema proxies inflate the headline, and how a PayPal deployment cut tool context from 140,000 tokens to 1,300. If you're building a discovery gateway, a repository scratchpad, or just wondering why your agent keeps looking up how to look things up — this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-skill-discovery-53989/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-skill-discovery-53989/</guid>
      <pubDate>Sat, 24 Oct 2026 09:57:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-skill-discovery-53989.m4a"
        type="audio/mp4"
        length="22512858"
      />
      <itunes:title>When Your AI Agent Has Too Many Skills</itunes:title>
      <itunes:subtitle>Your agent has 55,000 skills installed — and it&apos;s spending the whole task just reading the menu.</itunes:subtitle>
      <itunes:summary><![CDATA[Installing a Claude Code skill is never free: its name and description take up permanent space in the agent's context, even if it's never used. As skill libraries grow, agents end up spending their working memory on metadata instead of the actual job. This episode digs into progressive disclosure — the three-tier loading architecture behind Agent Skills — and why it postpones the cost without eliminating it. We look at what a 55,000-skill analysis revealed about routing descriptions, how Codex budgets skill metadata at roughly 2% of the context window, and what workday's library-scaling tests showed about the tradeoff between fewer tokens and faster completion. Then we separate the real numbers from the marketing ones: what "70% token savings" actually measures, why tool-schema proxies inflate the headline, and how a PayPal deployment cut tool context from 140,000 tokens to 1,300. If you're building a discovery gateway, a repository scratchpad, or just wondering why your agent keeps looking up how to look things up — this one's for you.]]></itunes:summary>
      <itunes:duration>1283</itunes:duration>
      <itunes:episode>6235</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-skill-discovery-53989.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-skill-discovery-53989.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Phone Mic Beats a Dictation Mic: How?</title>
      <description><![CDATA[A dedicated dictation microphone losing to a phone is expensive feedback — so what happens when you try to keep using the phone's mic at your desktop? This episode digs into the gap between "sending audio" and "sending the audio that actually won the benchmark," why Android's recording path matters as much as the hardware, and what close-range measurement data says about distance, angle, and handling noise. Plus a practical, low-effort route to getting phone audio into your desktop dictation app using open-source tools and a virtual audio device.]]></description>
      <link>https://myweirdprompts.com/episode/phone-mic-dictation-setup-44697/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/phone-mic-dictation-setup-44697/</guid>
      <pubDate>Sat, 24 Oct 2026 09:07:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/phone-mic-dictation-setup-44697.m4a"
        type="audio/mp4"
        length="23614193"
      />
      <itunes:title>Phone Mic Beats a Dictation Mic: How?</itunes:title>
      <itunes:subtitle>Your phone&apos;s internal mic may beat a dedicated dictation microphone. Here&apos;s how to route it to your desktop without losing what makes it work.</itunes:subtitle>
      <itunes:summary><![CDATA[A dedicated dictation microphone losing to a phone is expensive feedback — so what happens when you try to keep using the phone's mic at your desktop? This episode digs into the gap between "sending audio" and "sending the audio that actually won the benchmark," why Android's recording path matters as much as the hardware, and what close-range measurement data says about distance, angle, and handling noise. Plus a practical, low-effort route to getting phone audio into your desktop dictation app using open-source tools and a virtual audio device.]]></itunes:summary>
      <itunes:duration>1350</itunes:duration>
      <itunes:episode>6234</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/phone-mic-dictation-setup-44697.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/phone-mic-dictation-setup-44697.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What 12GB of VRAM Can Actually Do Now</title>
      <description><![CDATA[A Radeon RX 7700 XT owner gave up on running models locally six months ago — the context window filled instantly and the model lost the plot. But the models changed underneath him. This episode walks through the new memory arithmetic: why a 35B mixture-of-experts model with only 3B active can run on a 12GB card, how cache costs dropped roughly seven-fold between model generations, and what that means for speed, fine-tuning with Unsloth's official AMD support, and the honest limits nobody has benchmarked yet.]]></description>
      <link>https://myweirdprompts.com/episode/12gb-vram-local-ai-guide-87609/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/12gb-vram-local-ai-guide-87609/</guid>
      <pubDate>Fri, 23 Oct 2026 13:28:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/12gb-vram-local-ai-guide-87609.m4a"
        type="audio/mp4"
        length="23856809"
      />
      <itunes:title>What 12GB of VRAM Can Actually Do Now</itunes:title>
      <itunes:subtitle>Local AI on a mid-range AMD card: what fits, what&apos;s fast, and what&apos;s still out of reach in 2026.</itunes:subtitle>
      <itunes:summary><![CDATA[A Radeon RX 7700 XT owner gave up on running models locally six months ago — the context window filled instantly and the model lost the plot. But the models changed underneath him. This episode walks through the new memory arithmetic: why a 35B mixture-of-experts model with only 3B active can run on a 12GB card, how cache costs dropped roughly seven-fold between model generations, and what that means for speed, fine-tuning with Unsloth's official AMD support, and the honest limits nobody has benchmarked yet.]]></itunes:summary>
      <itunes:duration>1351</itunes:duration>
      <itunes:episode>6228</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/12gb-vram-local-ai-guide-87609.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/12gb-vram-local-ai-guide-87609.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Many Epochs Does Fine-Tuning Actually Need?</title>
      <description><![CDATA[Almost nobody picks an epoch count in advance — they set a ceiling, evaluate often, keep the best checkpoint, and the epoch count is whatever that turns out to be. This episode works through two concrete cases: a Whisper speech fine-tune scored on word error rate, and an instruction-tuned language model using SFT or LoRA. Along the way: why steps and tokens beat epochs as a unit, why validation loss and word error rate can tell opposite stories, why the best LoRA learning rate runs about ten times the full fine-tuning rate, and what the position of your best checkpoint tells you about what to change next.]]></description>
      <link>https://myweirdprompts.com/episode/fine-tuning-epoch-count-guide-05662/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fine-tuning-epoch-count-guide-05662/</guid>
      <pubDate>Fri, 23 Oct 2026 12:34:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fine-tuning-epoch-count-guide-05662.m4a"
        type="audio/mp4"
        length="21633486"
      />
      <itunes:title>How Many Epochs Does Fine-Tuning Actually Need?</itunes:title>
      <itunes:subtitle>Epochs are a receipt, not a plan. How to pick a training ceiling, read the curves, and know when to stop.</itunes:subtitle>
      <itunes:summary><![CDATA[Almost nobody picks an epoch count in advance — they set a ceiling, evaluate often, keep the best checkpoint, and the epoch count is whatever that turns out to be. This episode works through two concrete cases: a Whisper speech fine-tune scored on word error rate, and an instruction-tuned language model using SFT or LoRA. Along the way: why steps and tokens beat epochs as a unit, why validation loss and word error rate can tell opposite stories, why the best LoRA learning rate runs about ten times the full fine-tuning rate, and what the position of your best checkpoint tells you about what to change next.]]></itunes:summary>
      <itunes:duration>1220</itunes:duration>
      <itunes:episode>6223</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fine-tuning-epoch-count-guide-05662.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fine-tuning-epoch-count-guide-05662.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Fine-Tuning Runs Can&apos;t Always Resume</title>
      <description><![CDATA[Daniel has been fine-tuning NVIDIA's 600M-parameter Parakeet on his own read-aloud recordings, English with Hebrew mixed in. Each run costs about eighty cents and twenty-five minutes on a single L40S, so retraining from the stock model is cheap — but that raises a tangle of questions: what's actually inside a 6.9 GB training checkpoint versus a 2.5 GB finished weights file, why resuming a finished run does nothing, and what "warm-starting" really means. This episode unpacks the difference between resuming, continuing, and warm-starting, why re-warming a settled model makes the loss go up before it gets better, and what the continual-learning literature says about replay, catastrophic forgetting, and whether any of it has been tested on a Parakeet-class speech model.]]></description>
      <link>https://myweirdprompts.com/episode/fine-tuning-resume-warm-start-75923/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fine-tuning-resume-warm-start-75923/</guid>
      <pubDate>Fri, 23 Oct 2026 09:53:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fine-tuning-resume-warm-start-75923.m4a"
        type="audio/mp4"
        length="21841447"
      />
      <itunes:title>Why Fine-Tuning Runs Can&apos;t Always Resume</itunes:title>
      <itunes:subtitle>Eighty cents and twenty-five minutes per training run — so why does the checkpoint question still keep a tinkerer up at night?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has been fine-tuning NVIDIA's 600M-parameter Parakeet on his own read-aloud recordings, English with Hebrew mixed in. Each run costs about eighty cents and twenty-five minutes on a single L40S, so retraining from the stock model is cheap — but that raises a tangle of questions: what's actually inside a 6.9 GB training checkpoint versus a 2.5 GB finished weights file, why resuming a finished run does nothing, and what "warm-starting" really means. This episode unpacks the difference between resuming, continuing, and warm-starting, why re-warming a settled model makes the loss go up before it gets better, and what the continual-learning literature says about replay, catastrophic forgetting, and whether any of it has been tested on a Parakeet-class speech model.]]></itunes:summary>
      <itunes:duration>1238</itunes:duration>
      <itunes:episode>6222</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fine-tuning-resume-warm-start-75923.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fine-tuning-resume-warm-start-75923.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Many Examples Does Fine-Tuning Actually Need?</title>
      <description><![CDATA[A listener has 150 hand-labelled dictated prompts and wants to know if that's a start or a third of what he needs — and his architect wife has the same question about photographs of buildings. This episode works through the real answer: count examples per label, not in total; treat vendor minimums as proof-of-concept ceilings rather than floors; and measure your own learning curve instead of trusting forum numbers. Along the way: why your test set is the hidden bottleneck, why fifty photos of one building are closer to one example than fifty, and how active sampling can make the next batch of annotation count for more than the first.]]></description>
      <link>https://myweirdprompts.com/episode/how-many-examples-fine-tuning-69769/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/how-many-examples-fine-tuning-69769/</guid>
      <pubDate>Fri, 23 Oct 2026 09:51:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/how-many-examples-fine-tuning-69769.m4a"
        type="audio/mp4"
        length="22605951"
      />
      <itunes:title>How Many Examples Does Fine-Tuning Actually Need?</itunes:title>
      <itunes:subtitle>Everyone says &quot;thousands.&quot; Nobody says how many for your task. Here&apos;s how to measure the number yourself.</itunes:subtitle>
      <itunes:summary><![CDATA[A listener has 150 hand-labelled dictated prompts and wants to know if that's a start or a third of what he needs — and his architect wife has the same question about photographs of buildings. This episode works through the real answer: count examples per label, not in total; treat vendor minimums as proof-of-concept ceilings rather than floors; and measure your own learning curve instead of trusting forum numbers. Along the way: why your test set is the hidden bottleneck, why fifty photos of one building are closer to one example than fifty, and how active sampling can make the next batch of annotation count for more than the first.]]></itunes:summary>
      <itunes:duration>1283</itunes:duration>
      <itunes:episode>6221</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/how-many-examples-fine-tuning-69769.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/how-many-examples-fine-tuning-69769.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Dictation Beyond the Keyboard</title>
      <description><![CDATA[Dictation has quietly reshaped how many of us work, but starting and stopping it well is its own engineering problem. This episode digs into the physical controls that make voice input dependable: desktop pedals like the PCsensor and Stream Deck Pedal, handheld buttons, and wearables, plus the assistive-technology history that made all of it possible. Then it moves to Android, where open-source gesture apps borrow accessibility machinery to add controls manufacturers never supplied — global edge swipes, shake triggers, and floating panels — and asks how much of that can survive daily use.]]></description>
      <link>https://myweirdprompts.com/episode/dictation-beyond-keyboard-04251/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dictation-beyond-keyboard-04251/</guid>
      <pubDate>Fri, 23 Oct 2026 09:09:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dictation-beyond-keyboard-04251.m4a"
        type="audio/mp4"
        length="25245011"
      />
      <itunes:title>Dictation Beyond the Keyboard</itunes:title>
      <itunes:subtitle>Pedals, push-to-talk, and Android gesture apps promise keyboard-free dictation — but can any of it become a dependable daily setup?</itunes:subtitle>
      <itunes:summary><![CDATA[Dictation has quietly reshaped how many of us work, but starting and stopping it well is its own engineering problem. This episode digs into the physical controls that make voice input dependable: desktop pedals like the PCsensor and Stream Deck Pedal, handheld buttons, and wearables, plus the assistive-technology history that made all of it possible. Then it moves to Android, where open-source gesture apps borrow accessibility machinery to add controls manufacturers never supplied — global edge swipes, shake triggers, and floating panels — and asks how much of that can survive daily use.]]></itunes:summary>
      <itunes:duration>1435</itunes:duration>
      <itunes:episode>6219</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dictation-beyond-keyboard-04251.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dictation-beyond-keyboard-04251.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Training a LoRA to Write Like You</title>
      <description><![CDATA[Off-the-shelf "Humanizer" models flatten everyone into the same average voice — the method works, but the corpus is the problem. This episode walks through building a personal style adapter: manufacturing flat/neutral training pairs from your own posts, choosing a base model over an instruct one, curating hard, and knowing when it's overfit. Then we cover the practical part: running the adapter with llama.cpp, vLLM, or Ollama, and why serverless LoRA hosting is mostly gone.]]></description>
      <link>https://myweirdprompts.com/episode/lora-writing-voice-clone-16819/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/lora-writing-voice-clone-16819/</guid>
      <pubDate>Fri, 23 Oct 2026 08:35:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/lora-writing-voice-clone-16819.m4a"
        type="audio/mp4"
        length="23014007"
      />
      <itunes:title>Training a LoRA to Write Like You</itunes:title>
      <itunes:subtitle>A Humanizer model stripped every trace of the author. The fix isn&apos;t a better Humanizer — it&apos;s a LoRA trained on your own writing.</itunes:subtitle>
      <itunes:summary><![CDATA[Off-the-shelf "Humanizer" models flatten everyone into the same average voice — the method works, but the corpus is the problem. This episode walks through building a personal style adapter: manufacturing flat/neutral training pairs from your own posts, choosing a base model over an instruct one, curating hard, and knowing when it's overfit. Then we cover the practical part: running the adapter with llama.cpp, vLLM, or Ollama, and why serverless LoRA hosting is mostly gone.]]></itunes:summary>
      <itunes:duration>1300</itunes:duration>
      <itunes:episode>6218</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/lora-writing-voice-clone-16819.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/lora-writing-voice-clone-16819.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tuning a Trillion-Parameter Model at Home</title>
      <description><![CDATA[Daniel fine-tuned a speech model on his own mixed Hebrew-and-English recordings for the price of a coffee. So what happens if you point that same move at an Opus 5.5-class frontier model — raw, full precision, no practical reason at all? This episode walks through the arithmetic: the sixteen bytes per parameter that Adam demands, why a one-trillion-parameter model needs roughly 200 H100s just to hold its training state, and why mixture-of-experts saves compute but not memory. Along the way: what Modal's eight-GPU ceiling actually buys you, why a ten-billion-token fine-tune costs about $166,000 in pure compute, and what 1,024 GPUs and 1.8 megawatts of power look like as a physical room.]]></description>
      <link>https://myweirdprompts.com/episode/trillion-parameter-fine-tune-cost-60285/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/trillion-parameter-fine-tune-cost-60285/</guid>
      <pubDate>Thu, 22 Oct 2026 23:34:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/trillion-parameter-fine-tune-cost-60285.m4a"
        type="audio/mp4"
        length="22856422"
      />
      <itunes:title>Fine-Tuning a Trillion-Parameter Model at Home</itunes:title>
      <itunes:subtitle>What does it actually take to full fine-tune a trillion-parameter model? Sixteen terabytes of optimizer state, hundreds of GPUs, and a very big bill.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel fine-tuned a speech model on his own mixed Hebrew-and-English recordings for the price of a coffee. So what happens if you point that same move at an Opus 5.5-class frontier model — raw, full precision, no practical reason at all? This episode walks through the arithmetic: the sixteen bytes per parameter that Adam demands, why a one-trillion-parameter model needs roughly 200 H100s just to hold its training state, and why mixture-of-experts saves compute but not memory. Along the way: what Modal's eight-GPU ceiling actually buys you, why a ten-billion-token fine-tune costs about $166,000 in pure compute, and what 1,024 GPUs and 1.8 megawatts of power look like as a physical room.]]></itunes:summary>
      <itunes:duration>1284</itunes:duration>
      <itunes:episode>6217</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/trillion-parameter-fine-tune-cost-60285.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/trillion-parameter-fine-tune-cost-60285.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Two WERs, One Model: Inside ASR Eval Harnesses</title>
      <description><![CDATA[You fine-tune a Whisper-style model or Parakeet on your own voice, run it twice, and get two word error rates several points apart. Neither number is a bug. This episode pulls apart the three layers of ASR evaluation — the scorer, the normaliser, and the harness — and shows why almost every WER you've ever read is really a statement about someone's regular expressions. We walk through jiwer and the Hugging Face evaluate wrapper, the Whisper English normaliser and the points it buys you, why multilingual normalisation can inflate scores by stripping vowel marks, and why code-switched Hebrew-English dictation has no canonical normaliser at all. Then it's on to the harnesses themselves: the Open ASR Leaderboard repo and its three tracks, NeMo's transcribe-and-evaluate pair, and why your RTFx will never match a published one. Plus a salesman who promises negative word error rates.]]></description>
      <link>https://myweirdprompts.com/episode/asr-eval-harnesses-explained-18423/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/asr-eval-harnesses-explained-18423/</guid>
      <pubDate>Thu, 22 Oct 2026 21:32:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/asr-eval-harnesses-explained-18423.m4a"
        type="audio/mp4"
        length="23994675"
      />
      <itunes:title>Two WERs, One Model: Inside ASR Eval Harnesses</itunes:title>
      <itunes:subtitle>Same model, same audio, two different word error rates. The gap lives in the normaliser — and that&apos;s just layer two of three.</itunes:subtitle>
      <itunes:summary><![CDATA[You fine-tune a Whisper-style model or Parakeet on your own voice, run it twice, and get two word error rates several points apart. Neither number is a bug. This episode pulls apart the three layers of ASR evaluation — the scorer, the normaliser, and the harness — and shows why almost every WER you've ever read is really a statement about someone's regular expressions. We walk through jiwer and the Hugging Face evaluate wrapper, the Whisper English normaliser and the points it buys you, why multilingual normalisation can inflate scores by stripping vowel marks, and why code-switched Hebrew-English dictation has no canonical normaliser at all. Then it's on to the harnesses themselves: the Open ASR Leaderboard repo and its three tracks, NeMo's transcribe-and-evaluate pair, and why your RTFx will never match a published one. Plus a salesman who promises negative word error rates.]]></itunes:summary>
      <itunes:duration>1355</itunes:duration>
      <itunes:episode>6207</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/asr-eval-harnesses-explained-18423.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/asr-eval-harnesses-explained-18423.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where Should Your Notebook Actually Run?</title>
      <description><![CDATA[Colab is the default for every fine-tuning tutorial, but free has fine print: fluctuating GPUs, 12-hour runtime caps, and idle timeouts. This episode walks through five real options — Colab, Kaggle, local Jupyter, marimo, and pay-per-use GPU notebooks like Modal and Lightning — with honest tradeoffs for each. Then it tackles the question everyone almost doesn't ask: when you share a notebook so others can reproduce your result, what exactly are you publishing? Notebook files save their outputs, and research on leaked secrets found .ipynb files to be the most leak-prone file type in public code — led by Hugging Face tokens. Learn why the code cells aren't where the secrets hide, and how to check a notebook before it goes public.]]></description>
      <link>https://myweirdprompts.com/episode/notebook-runtime-colab-vs-local-07508/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/notebook-runtime-colab-vs-local-07508/</guid>
      <pubDate>Thu, 22 Oct 2026 21:20:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/notebook-runtime-colab-vs-local-07508.m4a"
        type="audio/mp4"
        length="22044600"
      />
      <itunes:title>Where Should Your Notebook Actually Run?</itunes:title>
      <itunes:subtitle>Colab&apos;s free GPU comes with a 12-hour wall and hidden secrets in your saved outputs. Here&apos;s where notebooks should actually live.</itunes:subtitle>
      <itunes:summary><![CDATA[Colab is the default for every fine-tuning tutorial, but free has fine print: fluctuating GPUs, 12-hour runtime caps, and idle timeouts. This episode walks through five real options — Colab, Kaggle, local Jupyter, marimo, and pay-per-use GPU notebooks like Modal and Lightning — with honest tradeoffs for each. Then it tackles the question everyone almost doesn't ask: when you share a notebook so others can reproduce your result, what exactly are you publishing? Notebook files save their outputs, and research on leaked secrets found .ipynb files to be the most leak-prone file type in public code — led by Hugging Face tokens. Learn why the code cells aren't where the secrets hide, and how to check a notebook before it goes public.]]></itunes:summary>
      <itunes:duration>1248</itunes:duration>
      <itunes:episode>6205</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/notebook-runtime-colab-vs-local-07508.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/notebook-runtime-colab-vs-local-07508.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Claude Code Sessions Message Each Other</title>
      <description><![CDATA[Two Claude Code sessions on the same machine can now talk to each other natively — but the design has sharp edges. Names are the address, messages carry text and nothing else, and a "sent" receipt proves less than you'd think. We walk through ListAgents discovery, the name flag, the notify-when-idle subscription that replaces polling, and the file-on-disk pattern that makes handoffs trustworthy.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-cross-session-messaging-13415/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-cross-session-messaging-13415/</guid>
      <pubDate>Thu, 22 Oct 2026 20:20:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-cross-session-messaging-13415.m4a"
        type="audio/mp4"
        length="21794938"
      />
      <itunes:title>How Claude Code Sessions Message Each Other</itunes:title>
      <itunes:subtitle>Two terminals, one machine: how Claude Code sessions find each other by name, pass messages, and subscribe to an idle ping.</itunes:subtitle>
      <itunes:summary><![CDATA[Two Claude Code sessions on the same machine can now talk to each other natively — but the design has sharp edges. Names are the address, messages carry text and nothing else, and a "sent" receipt proves less than you'd think. We walk through ListAgents discovery, the name flag, the notify-when-idle subscription that replaces polling, and the file-on-disk pattern that makes handoffs trustworthy.]]></itunes:summary>
      <itunes:duration>1240</itunes:duration>
      <itunes:episode>6203</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-cross-session-messaging-13415.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-cross-session-messaging-13415.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Israel Votes, Part 6: Asking the Decision Models</title>
      <description><![CDATA[What happens when you freeze the evidence, hand two decision models the same 39 questions about Israel's election, and then add a table of Kalshi and Polymarket prices? Clef (open weights, Qwen backbone) and Jev (closed, API-only) both picked Likud and Netanyahu — until the market section went in, when both flipped to Yashar and Eisenkot, overshooting the market's own prices. The whole run cost one and a half cents. This is Part Six of the election series, built on Daniel's Israeli Elections repo, with every input hashed and frozen before the vote. Results get Brier-scored when the Central Elections Committee certifies on November 5.]]></description>
      <link>https://myweirdprompts.com/episode/clef-jev-israeli-election-forecast-82677/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/clef-jev-israeli-election-forecast-82677/</guid>
      <pubDate>Thu, 22 Oct 2026 20:03:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/clef-jev-israeli-election-forecast-82677.m4a"
        type="audio/mp4"
        length="24556948"
      />
      <itunes:title>Israel Votes, Part 6: Asking the Decision Models</itunes:title>
      <itunes:subtitle>Two decision models, identical evidence, one table of betting prices — and both flip their pick for Israel&apos;s next leader.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when you freeze the evidence, hand two decision models the same 39 questions about Israel's election, and then add a table of Kalshi and Polymarket prices? Clef (open weights, Qwen backbone) and Jev (closed, API-only) both picked Likud and Netanyahu — until the market section went in, when both flipped to Yashar and Eisenkot, overshooting the market's own prices. The whole run cost one and a half cents. This is Part Six of the election series, built on Daniel's Israeli Elections repo, with every input hashed and frozen before the vote. Results get Brier-scored when the Central Elections Committee certifies on November 5.]]></itunes:summary>
      <itunes:duration>1407</itunes:duration>
      <itunes:episode>6202</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/clef-jev-israeli-election-forecast-82677.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/clef-jev-israeli-election-forecast-82677.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside Cloudflare&apos;s Clef: A Language Model That Can&apos;t Speak</title>
      <description><![CDATA[Cloudflare's Clef is a 27-billion-parameter model with a vow of silence: it reads text, images, and video, and returns nothing but a table of probabilities. Underneath, it's a post-trained Qwen 3.8 with a frozen backbone and a small routing head — and that architecture turns out to matter a lot. This episode opens the hood on Clef, Clef-flash, and Clef-omni, compares them with Jev, TypeSafe, and OpenAI's Decisions API, and asks the uncomfortable question: if the format is guaranteed but the content isn't, how much should you trust a probability?]]></description>
      <link>https://myweirdprompts.com/episode/cloudflare-clef-language-model-probabilities-06408/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cloudflare-clef-language-model-probabilities-06408/</guid>
      <pubDate>Thu, 22 Oct 2026 19:58:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cloudflare-clef-language-model-probabilities-06408.m4a"
        type="audio/mp4"
        length="23447238"
      />
      <itunes:title>Inside Cloudflare&apos;s Clef: A Language Model That Can&apos;t Speak</itunes:title>
      <itunes:subtitle>Cloudflare&apos;s Clef reads text, images, and video but outputs only probabilities — no words. Here&apos;s what&apos;s actually under the hood.</itunes:subtitle>
      <itunes:summary><![CDATA[Cloudflare's Clef is a 27-billion-parameter model with a vow of silence: it reads text, images, and video, and returns nothing but a table of probabilities. Underneath, it's a post-trained Qwen 3.8 with a frozen backbone and a small routing head — and that architecture turns out to matter a lot. This episode opens the hood on Clef, Clef-flash, and Clef-omni, compares them with Jev, TypeSafe, and OpenAI's Decisions API, and asks the uncomfortable question: if the format is guaranteed but the content isn't, how much should you trust a probability?]]></itunes:summary>
      <itunes:duration>1318</itunes:duration>
      <itunes:episode>6201</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cloudflare-clef-language-model-probabilities-06408.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cloudflare-clef-language-model-probabilities-06408.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Israeli Election Polls Hide Behind Seat Counts</title>
      <description><![CDATA[Israeli election headlines report tidy seat counts, but those integers hide enormous uncertainty. SekerKolshehu, a small open source project by developer Isgav Saranga, takes a poll's vote shares, draws 100,000 plausible variations, and runs each one through the actual Israeli allocation rules — threshold, surplus agreements and all. The result is a distribution: average seats, a 90% range, and the probability of every possible outcome. This episode walks through how the engine works, why it validates against a real past election, and where the model's uncertainty quietly ends.]]></description>
      <link>https://myweirdprompts.com/episode/israeli-election-poll-uncertainty-78400/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/israeli-election-poll-uncertainty-78400/</guid>
      <pubDate>Thu, 22 Oct 2026 19:38:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/israeli-election-poll-uncertainty-78400.m4a"
        type="audio/mp4"
        length="25243293"
      />
      <itunes:title>What Israeli Election Polls Hide Behind Seat Counts</itunes:title>
      <itunes:subtitle>A new open source tool simulates 100,000 election allocations from a single poll — and shows why &quot;4 seats&quot; is less certain than it sounds.</itunes:subtitle>
      <itunes:summary><![CDATA[Israeli election headlines report tidy seat counts, but those integers hide enormous uncertainty. SekerKolshehu, a small open source project by developer Isgav Saranga, takes a poll's vote shares, draws 100,000 plausible variations, and runs each one through the actual Israeli allocation rules — threshold, surplus agreements and all. The result is a distribution: average seats, a 90% range, and the probability of every possible outcome. This episode walks through how the engine works, why it validates against a real past election, and where the model's uncertainty quietly ends.]]></itunes:summary>
      <itunes:duration>1445</itunes:duration>
      <itunes:episode>6200</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/israeli-election-poll-uncertainty-78400.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/israeli-election-poll-uncertainty-78400.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Running AI Experiments Before an Election</title>
      <description><![CDATA[Daniel wants to run a proper LLM-driven forecast on Israel's election on the twenty-seventh — not another loose script. So what do experiment platforms actually do? This episode sorts the vocabulary into three families: product testing tools like Statsig and Eppo, bioinformatics workflow managers like Snakemake and Nextflow, and run trackers like MLflow, Weights and Biases and ClearML. Then it turns to the newer LLM-native tools — Braintrust, Langfuse, LangSmith and the UK AI Security Institute's Inspect — and asks what separates an experiment from an evaluation. Along the way: why a run log beats a folder of JSON files, why the notebook became the default container for computation, and what a study of a million GitHub notebooks found when researchers tried to actually run them.]]></description>
      <link>https://myweirdprompts.com/episode/ai-experiment-frameworks-election-forecast-79533/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-experiment-frameworks-election-forecast-79533/</guid>
      <pubDate>Thu, 22 Oct 2026 19:23:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-experiment-frameworks-election-forecast-79533.m4a"
        type="audio/mp4"
        length="23575988"
      />
      <itunes:title>Running AI Experiments Before an Election</itunes:title>
      <itunes:subtitle>What MLflow, Braintrust and notebooks actually do when you have five days and one forecast to get right.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants to run a proper LLM-driven forecast on Israel's election on the twenty-seventh — not another loose script. So what do experiment platforms actually do? This episode sorts the vocabulary into three families: product testing tools like Statsig and Eppo, bioinformatics workflow managers like Snakemake and Nextflow, and run trackers like MLflow, Weights and Biases and ClearML. Then it turns to the newer LLM-native tools — Braintrust, Langfuse, LangSmith and the UK AI Security Institute's Inspect — and asks what separates an experiment from an evaluation. Along the way: why a run log beats a folder of JSON files, why the notebook became the default container for computation, and what a study of a million GitHub notebooks found when researchers tried to actually run them.]]></itunes:summary>
      <itunes:duration>1338</itunes:duration>
      <itunes:episode>6199</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-experiment-frameworks-election-forecast-79533.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-experiment-frameworks-election-forecast-79533.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Benchmarking a TTS Model on Your Own GPU</title>
      <description><![CDATA[Daniel has fine-tuned Qwen3 TTS 1.7B into a voice clone called Meorav, and it currently takes 2.6 seconds of compute for every second of audio. This episode digs into how to actually benchmark speech-model inference: what real-time factor means (and how people invert it), why the first run is always slow, how to measure peak allocated versus peak reserved memory, and why shrinking weights to 8 bits may save space without saving time. Plus ROCm 10.1 tooling, rocprof, CUDA graphs, and what the vLLM Omni team's numbers reveal about launch overhead.]]></description>
      <link>https://myweirdprompts.com/episode/tts-inference-benchmarking-gpu-31308/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tts-inference-benchmarking-gpu-31308/</guid>
      <pubDate>Thu, 22 Oct 2026 18:58:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tts-inference-benchmarking-gpu-31308.m4a"
        type="audio/mp4"
        length="25791820"
      />
      <itunes:title>Benchmarking a TTS Model on Your Own GPU</itunes:title>
      <itunes:subtitle>Real-time factor, warm-up artifacts, and why an 8-bit quantized speech model might not run any faster.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has fine-tuned Qwen3 TTS 1.7B into a voice clone called Meorav, and it currently takes 2.6 seconds of compute for every second of audio. This episode digs into how to actually benchmark speech-model inference: what real-time factor means (and how people invert it), why the first run is always slow, how to measure peak allocated versus peak reserved memory, and why shrinking weights to 8 bits may save space without saving time. Plus ROCm 10.1 tooling, rocprof, CUDA graphs, and what the vLLM Omni team's numbers reveal about launch overhead.]]></itunes:summary>
      <itunes:duration>1465</itunes:duration>
      <itunes:episode>6198</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tts-inference-benchmarking-gpu-31308.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tts-inference-benchmarking-gpu-31308.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tuning ASR: When Your Model Memorizes You</title>
      <description><![CDATA[You fine-tuned a speech recognition model on your own voice. It transcribes your training recordings perfectly. Is that skill, or memorization? This episode walks through how to tell the difference — from training-versus-validation loss curves and word error rate comparisons, to checkpoint selection, session-based data splits, and weights-only diagnostics like WeightWatcher. Along the way: why validation loss and WER can disagree, why an epoch table shouldn't overrule fresh-audio performance, and a cautionary Armenian Whisper-small model card where training loss looked nearly conquered while transcripts stayed poor. Plus, a wartime history of sliced bread.]]></description>
      <link>https://myweirdprompts.com/episode/asr-fine-tuning-overfitting-detection-74904/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/asr-fine-tuning-overfitting-detection-74904/</guid>
      <pubDate>Thu, 22 Oct 2026 18:38:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/asr-fine-tuning-overfitting-detection-74904.m4a"
        type="audio/mp4"
        length="26317164"
      />
      <itunes:title>Fine-Tuning ASR: When Your Model Memorizes You</itunes:title>
      <itunes:subtitle>Your fine-tuned speech model aces your training recordings — but does it actually work on fresh audio?</itunes:subtitle>
      <itunes:summary><![CDATA[You fine-tuned a speech recognition model on your own voice. It transcribes your training recordings perfectly. Is that skill, or memorization? This episode walks through how to tell the difference — from training-versus-validation loss curves and word error rate comparisons, to checkpoint selection, session-based data splits, and weights-only diagnostics like WeightWatcher. Along the way: why validation loss and WER can disagree, why an epoch table shouldn't overrule fresh-audio performance, and a cautionary Armenian Whisper-small model card where training loss looked nearly conquered while transcripts stayed poor. Plus, a wartime history of sliced bread.]]></itunes:summary>
      <itunes:duration>1497</itunes:duration>
      <itunes:episode>6196</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/asr-fine-tuning-overfitting-detection-74904.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/asr-fine-tuning-overfitting-detection-74904.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Teaching a TTS Model His Own Accent</title>
      <description><![CDATA[What does it take to make a machine say Hebrew words the way an English speaker in Jerusalem actually does? Daniel fine-tuned Qwen3 TTS on his own voice to build Meorav, a model named after Jerusalem mixed grill, and the voice you hear from him in this episode is that model speaking. Corn and Herman run on Chatterbox, the show's usual engine, so this is a side-by-side test of cloning versus full fine-tuning. The conversation covers why zero-shot clones keep the timbre but borrow an American cadence, why a phonemiser with no Irish-English voice closed one door, and why spelling every Hebrew word one single way turned out to matter so much.]]></description>
      <link>https://myweirdprompts.com/episode/hebrew-english-tts-fine-tune-75935/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hebrew-english-tts-fine-tune-75935/</guid>
      <pubDate>Thu, 22 Oct 2026 15:54:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hebrew-english-tts-fine-tune-75935.m4a"
        type="audio/mp4"
        length="23014415"
      />
      <itunes:title>Teaching a TTS Model His Own Accent</itunes:title>
      <itunes:subtitle>One man fine-tuned a text-to-speech model on his own voice to get Irish-accented English that can say Hebrew words mid-sentence.</itunes:subtitle>
      <itunes:summary><![CDATA[What does it take to make a machine say Hebrew words the way an English speaker in Jerusalem actually does? Daniel fine-tuned Qwen3 TTS on his own voice to build Meorav, a model named after Jerusalem mixed grill, and the voice you hear from him in this episode is that model speaking. Corn and Herman run on Chatterbox, the show's usual engine, so this is a side-by-side test of cloning versus full fine-tuning. The conversation covers why zero-shot clones keep the timbre but borrow an American cadence, why a phonemiser with no Irish-English voice closed one door, and why spelling every Hebrew word one single way turned out to matter so much.]]></itunes:summary>
      <itunes:duration>1313</itunes:duration>
      <itunes:episode>6194</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hebrew-english-tts-fine-tune-75935.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hebrew-english-tts-fine-tune-75935.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Actually Happens When You Train a Model</title>
      <description><![CDATA[Everyone's first mental picture of training a model is two folders dragged onto a script: data goes in, model comes out. The reality is fussier and more interesting. Starting from a Whisper fine-tune on Common Voice, this episode works through what a dataset row actually is, why transcript conventions and sample rates matter more than they sound, and why validation and test sets are consumables rather than paperwork. Then it gets to the part that quietly ruins benchmarks: leakage. Speaker overlap in a random split, the Hungarian corpus that lost two-thirds of its hours to strict separation, and the pretraining-scale version where a single copy of a test set in a web scrape moves the loss floor. If you've ever quoted a model score, this is the episode that tells you what to ask about it.]]></description>
      <link>https://myweirdprompts.com/episode/speech-recognition-dataset-fundamentals-75970/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speech-recognition-dataset-fundamentals-75970/</guid>
      <pubDate>Wed, 21 Oct 2026 23:59:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speech-recognition-dataset-fundamentals-75970.m4a"
        type="audio/mp4"
        length="22110973"
      />
      <itunes:title>What Actually Happens When You Train a Model</itunes:title>
      <itunes:subtitle>Data in, model out — except the &quot;data&quot; is pairs, the split is the whole game, and one leaked speaker can fake your score.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone's first mental picture of training a model is two folders dragged onto a script: data goes in, model comes out. The reality is fussier and more interesting. Starting from a Whisper fine-tune on Common Voice, this episode works through what a dataset row actually is, why transcript conventions and sample rates matter more than they sound, and why validation and test sets are consumables rather than paperwork. Then it gets to the part that quietly ruins benchmarks: leakage. Speaker overlap in a random split, the Hungarian corpus that lost two-thirds of its hours to strict separation, and the pretraining-scale version where a single copy of a test set in a web scrape moves the loss floor. If you've ever quoted a model score, this is the episode that tells you what to ask about it.]]></itunes:summary>
      <itunes:duration>1258</itunes:duration>
      <itunes:episode>6192</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speech-recognition-dataset-fundamentals-75970.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speech-recognition-dataset-fundamentals-75970.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Actually Happens During Fine-Tuning</title>
      <description><![CDATA[You copy the notebook, change the dataset path, hit run, and hours later a folder appears with new weights that understand your accent. What happened in between? This episode walks the full training step — safetensors files, batches, forward passes, the loss, the backward pass, and Adam's quiet bookkeeping — and explains why a 7B full fine-tune is a memory problem before it's a compute problem. Along the way: why the first five minutes of a fine-tune deliver the cheap gains, how overfitting turns a model into a parrot that can recite training text, and the tricks (mixed precision, gradient checkpointing, gradient accumulation) that let one card do a job that wants a hundred gigabytes.]]></description>
      <link>https://myweirdprompts.com/episode/what-happens-during-fine-tuning-67730/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/what-happens-during-fine-tuning-67730/</guid>
      <pubDate>Wed, 21 Oct 2026 23:58:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/what-happens-during-fine-tuning-67730.m4a"
        type="audio/mp4"
        length="22380036"
      />
      <itunes:title>What Actually Happens During Fine-Tuning</itunes:title>
      <itunes:subtitle>Sixteen bytes per parameter in, two out — a walk through the forward pass, backward pass, and optimizer that turn a dataset into new weights.</itunes:subtitle>
      <itunes:summary><![CDATA[You copy the notebook, change the dataset path, hit run, and hours later a folder appears with new weights that understand your accent. What happened in between? This episode walks the full training step — safetensors files, batches, forward passes, the loss, the backward pass, and Adam's quiet bookkeeping — and explains why a 7B full fine-tune is a memory problem before it's a compute problem. Along the way: why the first five minutes of a fine-tune deliver the cheap gains, how overfitting turns a model into a parrot that can recite training text, and the tricks (mixed precision, gradient checkpointing, gradient accumulation) that let one card do a job that wants a hundred gigabytes.]]></itunes:summary>
      <itunes:duration>1265</itunes:duration>
      <itunes:episode>6191</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/what-happens-during-fine-tuning-67730.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/what-happens-during-fine-tuning-67730.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your AI API Quietly Changes Under You</title>
      <description><![CDATA[You ran the evals, you pinned the version, and your invoice parser worked perfectly in October. Then December arrives and the same API name returns something subtly different. This episode digs into what actually changes behind an unchanged endpoint name — weights, system instructions, routing, classifiers, quantization, even the GPUs — and why an alias like "latest" is effectively a subscription to retesting. Drawing on model equality testing across 31 commercial Llama endpoints (11 of which produced distributions different from Meta's reference implementation) and the disclosure practices of major providers, we separate model identity from service behavior, explain why pinning a snapshot removes one major source of change without freezing everything, and offer a practical stance: stop asking whether the model got worse overall, and start asking whether the specific part you're paying for moved.]]></description>
      <link>https://myweirdprompts.com/episode/ai-api-silent-model-updates-42297/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-api-silent-model-updates-42297/</guid>
      <pubDate>Wed, 21 Oct 2026 20:28:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-api-silent-model-updates-42297.m4a"
        type="audio/mp4"
        length="30497510"
      />
      <itunes:title>When Your AI API Quietly Changes Under You</itunes:title>
      <itunes:subtitle>Same API name, different model behind it. How to tell when the thing you tested stops being the thing you&apos;re using.</itunes:subtitle>
      <itunes:summary><![CDATA[You ran the evals, you pinned the version, and your invoice parser worked perfectly in October. Then December arrives and the same API name returns something subtly different. This episode digs into what actually changes behind an unchanged endpoint name — weights, system instructions, routing, classifiers, quantization, even the GPUs — and why an alias like "latest" is effectively a subscription to retesting. Drawing on model equality testing across 31 commercial Llama endpoints (11 of which produced distributions different from Meta's reference implementation) and the disclosure practices of major providers, we separate model identity from service behavior, explain why pinning a snapshot removes one major source of change without freezing everything, and offer a practical stance: stop asking whether the model got worse overall, and start asking whether the specific part you're paying for moved.]]></itunes:summary>
      <itunes:duration>1753</itunes:duration>
      <itunes:episode>6190</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-api-silent-model-updates-42297.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-api-silent-model-updates-42297.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>A Virtual Disk Inside Your Linux Filesystem</title>
      <description><![CDATA[Daniel wants a pocket of storage inside his ext4 home server — a virtual disk that lives as a single file, mounts from fstab, and behaves like a real partition. No VM required. The kernel already knows how to pretend a file is a disk, and that trick is called a loop device. This episode walks through building image files with truncate versus fallocate, why sparse files can fail at 3am, how LUKS encryption and Squashfs read-only images ride on top of the loop, and why swap files take a completely different path through the kernel. Plus: project quotas as the boring alternative, fscrypt for per-directory encryption, and what happens when you try to hand a laptop swap space over the network.]]></description>
      <link>https://myweirdprompts.com/episode/linux-loop-device-virtual-disks-31688/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/linux-loop-device-virtual-disks-31688/</guid>
      <pubDate>Wed, 21 Oct 2026 16:49:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/linux-loop-device-virtual-disks-31688.m4a"
        type="audio/mp4"
        length="21404302"
      />
      <itunes:title>A Virtual Disk Inside Your Linux Filesystem</itunes:title>
      <itunes:subtitle>Loop devices let a plain file act as a real disk — format it, encrypt it, mount it from fstab. Here&apos;s what works, and what bites back.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants a pocket of storage inside his ext4 home server — a virtual disk that lives as a single file, mounts from fstab, and behaves like a real partition. No VM required. The kernel already knows how to pretend a file is a disk, and that trick is called a loop device. This episode walks through building image files with truncate versus fallocate, why sparse files can fail at 3am, how LUKS encryption and Squashfs read-only images ride on top of the loop, and why swap files take a completely different path through the kernel. Plus: project quotas as the boring alternative, fscrypt for per-directory encryption, and what happens when you try to hand a laptop swap space over the network.]]></itunes:summary>
      <itunes:duration>1203</itunes:duration>
      <itunes:episode>6186</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/linux-loop-device-virtual-disks-31688.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/linux-loop-device-virtual-disks-31688.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Private Podcast Feeds: Tokens, Buckets, and Basic Auth</title>
      <description><![CDATA[Making a private podcast feed is easy; keeping it private is where things fall apart. This episode walks the whole ramp — from paid hosts like Transistor and Castos that hand each subscriber a tokenized feed address, down to a storage bucket and a Worker that signs every enclosure on the fly. Along the way: why "private, not secure" is the honest label, why pre-signed URLs go stale in seven days, why a password in the URL is an obscure URL with extra steps, and why podcast apps can't send the custom headers Cloudflare Access wants. Also, Dorothy calls in with a recipe for unbuttered bread.]]></description>
      <link>https://myweirdprompts.com/episode/private-podcast-feeds-privacy-05420/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/private-podcast-feeds-privacy-05420/</guid>
      <pubDate>Wed, 21 Oct 2026 15:31:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/private-podcast-feeds-privacy-05420.m4a"
        type="audio/mp4"
        length="25496916"
      />
      <itunes:title>Private Podcast Feeds: Tokens, Buckets, and Basic Auth</itunes:title>
      <itunes:subtitle>How do you keep a podcast feed for four people away from everyone else? Tokens, buckets, and the auth podcast apps barely support.</itunes:subtitle>
      <itunes:summary><![CDATA[Making a private podcast feed is easy; keeping it private is where things fall apart. This episode walks the whole ramp — from paid hosts like Transistor and Castos that hand each subscriber a tokenized feed address, down to a storage bucket and a Worker that signs every enclosure on the fly. Along the way: why "private, not secure" is the honest label, why pre-signed URLs go stale in seven days, why a password in the URL is an obscure URL with extra steps, and why podcast apps can't send the custom headers Cloudflare Access wants. Also, Dorothy calls in with a recipe for unbuttered bread.]]></itunes:summary>
      <itunes:duration>1449</itunes:duration>
      <itunes:episode>6183</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/private-podcast-feeds-privacy-05420.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/private-podcast-feeds-privacy-05420.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Running a Private APT Repo for Family Machines</title>
      <description><![CDATA[A zip file in a shared Drive folder is a software release process with no mechanism for the second release. So how do you run your own APT repository — and keep it private — without turning into your family's unpaid update service? This episode walks through the modern client-side setup: the new deb822 .sources format, why Signed-By matters more than ever, and the Sequoia-based signature strictness in Debian 13 that quietly broke a lot of third-party repos this year. Then the privacy question gets split in two — hidden versus authenticated — with honest verdicts on Cloudflare Access (stock apt can't use it), a Worker-plus-R2 basic-auth pattern, and the Tailscale serve route that works right up until someone's client is switched off. Plus the setup-package trick that makes key rotation a normal update, and how to get silent third-party updates via Origins-Pattern.]]></description>
      <link>https://myweirdprompts.com/episode/private-apt-repo-family-25197/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/private-apt-repo-family-25197/</guid>
      <pubDate>Wed, 21 Oct 2026 15:15:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/private-apt-repo-family-25197.m4a"
        type="audio/mp4"
        length="23907446"
      />
      <itunes:title>Running a Private APT Repo for Family Machines</itunes:title>
      <itunes:subtitle>Daniel wants his Debian packages on family machines without becoming their unpaid update service. Here&apos;s the modern way to do it.</itunes:subtitle>
      <itunes:summary><![CDATA[A zip file in a shared Drive folder is a software release process with no mechanism for the second release. So how do you run your own APT repository — and keep it private — without turning into your family's unpaid update service? This episode walks through the modern client-side setup: the new deb822 .sources format, why Signed-By matters more than ever, and the Sequoia-based signature strictness in Debian 13 that quietly broke a lot of third-party repos this year. Then the privacy question gets split in two — hidden versus authenticated — with honest verdicts on Cloudflare Access (stock apt can't use it), a Worker-plus-R2 basic-auth pattern, and the Tailscale serve route that works right up until someone's client is switched off. Plus the setup-package trick that makes key rotation a normal update, and how to get silent third-party updates via Origins-Pattern.]]></itunes:summary>
      <itunes:duration>1359</itunes:duration>
      <itunes:episode>6182</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/private-apt-repo-family-25197.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/private-apt-repo-family-25197.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Shipping Private Android Apps to Your Family</title>
      <description><![CDATA[Building an app with AI is now an afternoon's work — getting it onto your aunt's phone is the hard part. This episode walks through running a private F-Droid repository from GitHub Actions: signed APKs, a signed index, HTTP Basic Auth, and a QR code treated like a house key. Along the way: why GitHub Pages can't host it, why Cloudflare Access would probably break the client, what Google's new developer verification actually covers (and what's still uncertain), and the two keystores you must back up or lose forever.]]></description>
      <link>https://myweirdprompts.com/episode/private-fdroid-repo-family-apps-90147/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/private-fdroid-repo-family-apps-90147/</guid>
      <pubDate>Wed, 21 Oct 2026 15:11:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/private-fdroid-repo-family-apps-90147.m4a"
        type="audio/mp4"
        length="23687419"
      />
      <itunes:title>Shipping Private Android Apps to Your Family</itunes:title>
      <itunes:subtitle>Daniel wants to ship private Android apps to non-technical relatives. F-Droid repos, Basic Auth, and the Google verification cloud on the horizon.</itunes:subtitle>
      <itunes:summary><![CDATA[Building an app with AI is now an afternoon's work — getting it onto your aunt's phone is the hard part. This episode walks through running a private F-Droid repository from GitHub Actions: signed APKs, a signed index, HTTP Basic Auth, and a QR code treated like a house key. Along the way: why GitHub Pages can't host it, why Cloudflare Access would probably break the client, what Google's new developer verification actually covers (and what's still uncertain), and the two keystores you must back up or lose forever.]]></itunes:summary>
      <itunes:duration>1342</itunes:duration>
      <itunes:episode>6181</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/private-fdroid-repo-family-apps-90147.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/private-fdroid-repo-family-apps-90147.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Auditing a Sycophantic Model From the Inside</title>
      <description><![CDATA[Your model is flattering users and you can't say why. This episode walks through the process a well-funded lab would actually follow: build an eval for the exact failure, bisect between the last good and first bad checkpoint, ablate the suspects, then reach for the research-grade tools — influence functions, persona vectors, sparse autoencoders. Along the way we look at what OpenAI's GPT-4o sycophancy post-mortem did and didn't include, why "each change looked fine alone" is the hardest failure mode to debug, and why no lab has publicly used mechanistic interpretability on a shipped incident yet.]]></description>
      <link>https://myweirdprompts.com/episode/model-diagnostics-sycophancy-audit-84177/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/model-diagnostics-sycophancy-audit-84177/</guid>
      <pubDate>Wed, 21 Oct 2026 13:56:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/model-diagnostics-sycophancy-audit-84177.m4a"
        type="audio/mp4"
        length="23744697"
      />
      <itunes:title>Auditing a Sycophantic Model From the Inside</itunes:title>
      <itunes:subtitle>A stumped lab, a fawning model, and the diagnostic playbook nobody has a name for — from eval slicing to persona vectors.</itunes:subtitle>
      <itunes:summary><![CDATA[Your model is flattering users and you can't say why. This episode walks through the process a well-funded lab would actually follow: build an eval for the exact failure, bisect between the last good and first bad checkpoint, ablate the suspects, then reach for the research-grade tools — influence functions, persona vectors, sparse autoencoders. Along the way we look at what OpenAI's GPT-4o sycophancy post-mortem did and didn't include, why "each change looked fine alone" is the hardest failure mode to debug, and why no lab has publicly used mechanistic interpretability on a shipped incident yet.]]></itunes:summary>
      <itunes:duration>1334</itunes:duration>
      <itunes:episode>6173</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/model-diagnostics-sycophancy-audit-84177.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/model-diagnostics-sycophancy-audit-84177.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Endpoint, Many Boxes: Local LLM Routing</title>
      <description><![CDATA[Daniel wanted an inference manager the way he already has a secrets manager, and what he described is basically a LiteLLM-style proxy. The failover machinery is genuinely built out: cooldowns, health probes, latency-based and least-busy routing. The gap is memory. Ollama will tell you a model's VRAM footprint, not the headroom left, so "is this box full?" isn't a question you can put to it — you write that glue yourself. We walk through what works out of the box, what a two-box network with mismatched cards actually demands, and why the cheapest latency win is just keeping the model warm. Then the hosted side: OpenRouter, Cloudflare, Vercel and Portkey are built around keys and the bill, and the box on your network is the one thing they can't see. Plus the Anthropic-shaped catch in pointing Claude Code at a gateway.]]></description>
      <link>https://myweirdprompts.com/episode/local-llm-routing-gateway-37784/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/local-llm-routing-gateway-37784/</guid>
      <pubDate>Wed, 21 Oct 2026 13:02:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/local-llm-routing-gateway-37784.m4a"
        type="audio/mp4"
        length="20620593"
      />
      <itunes:title>One Endpoint, Many Boxes: Local LLM Routing</itunes:title>
      <itunes:subtitle>A LiteLLM-style proxy can fail over between your own GPU boxes and the cloud — but it can&apos;t see your VRAM.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wanted an inference manager the way he already has a secrets manager, and what he described is basically a LiteLLM-style proxy. The failover machinery is genuinely built out: cooldowns, health probes, latency-based and least-busy routing. The gap is memory. Ollama will tell you a model's VRAM footprint, not the headroom left, so "is this box full?" isn't a question you can put to it — you write that glue yourself. We walk through what works out of the box, what a two-box network with mismatched cards actually demands, and why the cheapest latency win is just keeping the model warm. Then the hosted side: OpenRouter, Cloudflare, Vercel and Portkey are built around keys and the bill, and the box on your network is the one thing they can't see. Plus the Anthropic-shaped catch in pointing Claude Code at a gateway.]]></itunes:summary>
      <itunes:duration>1167</itunes:duration>
      <itunes:episode>6170</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/local-llm-routing-gateway-37784.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/local-llm-routing-gateway-37784.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Hebrish: Hebrew-Aware Dictation That Actually Works</title>
      <description><![CDATA[If you dictate in English but live in Jerusalem, stock speech recognizers mangle words like arnona and makolet. Hebrish is a not-for-profit project that fixes this with two new models, Jachnun and Sabich, both built on NVIDIA's Parakeet. We walk through every evaluation, from term recall to never-seen Hebrew words, and explain why Sabich is the one the apps recommend. Plus: the Shakshuka streaming line still in the pan, the one-speaker corpus behind all of it, and why "level" is the real win.]]></description>
      <link>https://myweirdprompts.com/episode/hebrish-hebrew-dictation-models-57153/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hebrish-hebrew-dictation-models-57153/</guid>
      <pubDate>Wed, 21 Oct 2026 01:21:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hebrish-hebrew-dictation-models-57153.m4a"
        type="audio/mp4"
        length="23415888"
      />
      <itunes:title>Hebrish: Hebrew-Aware Dictation That Actually Works</itunes:title>
      <itunes:subtitle>Two new speech models fix English-Hebrew code-switching dictation — and the whole training run cost about fifty cents of GPU time.</itunes:subtitle>
      <itunes:summary><![CDATA[If you dictate in English but live in Jerusalem, stock speech recognizers mangle words like arnona and makolet. Hebrish is a not-for-profit project that fixes this with two new models, Jachnun and Sabich, both built on NVIDIA's Parakeet. We walk through every evaluation, from term recall to never-seen Hebrew words, and explain why Sabich is the one the apps recommend. Plus: the Shakshuka streaming line still in the pan, the one-speaker corpus behind all of it, and why "level" is the real win.]]></itunes:summary>
      <itunes:duration>1321</itunes:duration>
      <itunes:episode>6168</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hebrish-hebrew-dictation-models-57153.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hebrish-hebrew-dictation-models-57153.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Streaming Browser Cookies to a Headless Server</title>
      <description><![CDATA[Daniel wants his agent-driven browser off his laptop and onto the home server, but the server has never logged into anything. Chrome closed the obvious path in March 2025 when version 136 started ignoring the remote debugging port on your default profile — infostealers had been using it to lift sessions. So what are the real options? This episode walks through the extension cookie API and CookieCloud, headless browsers like Steel and Browserless, the one-way sync problem, Device Bound Session Credentials (DBSC), and the unglamorous option that might win: just log in once by hand and use Chrome's Memory Saver.]]></description>
      <link>https://myweirdprompts.com/episode/streaming-browser-cookies-server-01064/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/streaming-browser-cookies-server-01064/</guid>
      <pubDate>Tue, 20 Oct 2026 19:32:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/streaming-browser-cookies-server-01064.m4a"
        type="audio/mp4"
        length="21407588"
      />
      <itunes:title>Streaming Browser Cookies to a Headless Server</itunes:title>
      <itunes:subtitle>Daniel wants his logged-in browser on the home server. Chrome bolted the front door — so what&apos;s left?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants his agent-driven browser off his laptop and onto the home server, but the server has never logged into anything. Chrome closed the obvious path in March 2025 when version 136 started ignoring the remote debugging port on your default profile — infostealers had been using it to lift sessions. So what are the real options? This episode walks through the extension cookie API and CookieCloud, headless browsers like Steel and Browserless, the one-way sync problem, Device Bound Session Credentials (DBSC), and the unglamorous option that might win: just log in once by hand and use Chrome's Memory Saver.]]></itunes:summary>
      <itunes:duration>1217</itunes:duration>
      <itunes:episode>6167</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/streaming-browser-cookies-server-01064.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/streaming-browser-cookies-server-01064.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Turning a Drifted Server Into a Rebuildable Playbook</title>
      <description><![CDATA[Every tutorial starts with an empty machine and a playbook you write first. But what if your Ubuntu box grew for years — packages, containers, midnight config edits — and keeps drifting? This episode works through Daniel's question: how do you turn a living machine into a recipe that stays true? The answer splits rebuild from restore, separates config from data from secrets, and accepts that no tool hands you a faithful playbook from a drifted box. We cover Ansible's idempotent modules, check and diff mode, pull mode with cron, etckeeper for your /etc history, translating stray Docker containers into compose files, drift detection's blind spots, and why a rebuild nobody has ever run on a blank machine is just a rumour.]]></description>
      <link>https://myweirdprompts.com/episode/ansible-server-rebuild-playbook-95044/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ansible-server-rebuild-playbook-95044/</guid>
      <pubDate>Tue, 20 Oct 2026 19:26:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ansible-server-rebuild-playbook-95044.m4a"
        type="audio/mp4"
        length="25916798"
      />
      <itunes:title>Turning a Drifted Server Into a Rebuildable Playbook</itunes:title>
      <itunes:subtitle>Your server grew for years. Now you want a recipe that can rebuild it from scratch. Here&apos;s the honest path from drift to playbook.</itunes:subtitle>
      <itunes:summary><![CDATA[Every tutorial starts with an empty machine and a playbook you write first. But what if your Ubuntu box grew for years — packages, containers, midnight config edits — and keeps drifting? This episode works through Daniel's question: how do you turn a living machine into a recipe that stays true? The answer splits rebuild from restore, separates config from data from secrets, and accepts that no tool hands you a faithful playbook from a drifted box. We cover Ansible's idempotent modules, check and diff mode, pull mode with cron, etckeeper for your /etc history, translating stray Docker containers into compose files, drift detection's blind spots, and why a rebuild nobody has ever run on a blank machine is just a rumour.]]></itunes:summary>
      <itunes:duration>1460</itunes:duration>
      <itunes:episode>6166</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ansible-server-rebuild-playbook-95044.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ansible-server-rebuild-playbook-95044.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Watching 20 Batch Jobs at Once</title>
      <description><![CDATA[Everyone talks about batch API discounts, but nobody talks about the babysitting. When you're running a speech fine-tune across thousands of clips or a personal classifier, the real problem isn't the price — it's knowing which job finished, which one expired, and where the results went. We walk through the actual state of batch monitoring: which providers now offer webhooks (OpenAI, Gemini, OpenRouter, Bedrock), which still only do polling (Anthropic), and why a webhook is a doorbell, not a dashboard. Then we sort the tools that try to fill the gap into four piles — vendor consoles, spreadsheets with an AI column, eval runners, and workflow builders — and figure out which one to reach for when you're staring at twenty batches in flight.]]></description>
      <link>https://myweirdprompts.com/episode/batch-job-monitoring-dashboards-24028/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/batch-job-monitoring-dashboards-24028/</guid>
      <pubDate>Tue, 20 Oct 2026 18:52:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/batch-job-monitoring-dashboards-24028.m4a"
        type="audio/mp4"
        length="22957318"
      />
      <itunes:title>Watching 20 Batch Jobs at Once</itunes:title>
      <itunes:subtitle>Batch APIs give you a discount, then hand you a babysitting job. Here&apos;s what actually watches a batch run — and what doesn&apos;t.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone talks about batch API discounts, but nobody talks about the babysitting. When you're running a speech fine-tune across thousands of clips or a personal classifier, the real problem isn't the price — it's knowing which job finished, which one expired, and where the results went. We walk through the actual state of batch monitoring: which providers now offer webhooks (OpenAI, Gemini, OpenRouter, Bedrock), which still only do polling (Anthropic), and why a webhook is a doorbell, not a dashboard. Then we sort the tools that try to fill the gap into four piles — vendor consoles, spreadsheets with an AI column, eval runners, and workflow builders — and figure out which one to reach for when you're staring at twenty batches in flight.]]></itunes:summary>
      <itunes:duration>1311</itunes:duration>
      <itunes:episode>6165</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/batch-job-monitoring-dashboards-24028.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/batch-job-monitoring-dashboards-24028.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>We Gave Each Host Its Own AI Agent</title>
      <description><![CDATA[What happens when you stop letting one model voice every character? This week the show rebuilt itself on Google DeepMind's Concordia framework — a separate agent for each host, each with its own context window, plus a showrunner deciding who speaks next. The result: real questions instead of filler, but a cost six times the normal price. We walk through what got better, what got worse, every rule we had to hard-code to stop the room from falling apart, and the two paths that might bring the price back down.]]></description>
      <link>https://myweirdprompts.com/episode/multi-agent-podcast-concordia-36481/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-agent-podcast-concordia-36481/</guid>
      <pubDate>Tue, 20 Oct 2026 16:48:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-agent-podcast-concordia-36481.m4a"
        type="audio/mp4"
        length="24712211"
      />
      <itunes:title>We Gave Each Host Its Own AI Agent</itunes:title>
      <itunes:subtitle>We split our two hosts into separate AI agents with private context windows. The bill came to 78 cents an episode.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when you stop letting one model voice every character? This week the show rebuilt itself on Google DeepMind's Concordia framework — a separate agent for each host, each with its own context window, plus a showrunner deciding who speaks next. The result: real questions instead of filler, but a cost six times the normal price. We walk through what got better, what got worse, every rule we had to hard-code to stop the room from falling apart, and the two paths that might bring the price back down.]]></itunes:summary>
      <itunes:duration>1412</itunes:duration>
      <itunes:episode>6164</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-agent-podcast-concordia-36481.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-agent-podcast-concordia-36481.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Forking a Keyboard for One Voice Feature</title>
      <description><![CDATA[Daniel has a working voice model that flips between English and Hebrew, shipping as a standalone Android dictation app. The obvious next move — fork an open source keyboard and wire the engine in — comes with a catch: the moment you fork, you inherit the upstream's entire future. This episode works through the patch-based alternative: keep your integration as a tiny replayable patch, let automation handle the mechanical parts, and let a human test the build before it ships. Along the way: what HeliBoard's voice key might already do, why GPL versus Apache decides your architecture, why a new application ID is non-negotiable, and why crediting the chain of volunteers matters more than the label you pick.]]></description>
      <link>https://myweirdprompts.com/episode/forking-keyboard-voice-patch-49354/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/forking-keyboard-voice-patch-49354/</guid>
      <pubDate>Tue, 20 Oct 2026 16:22:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/forking-keyboard-voice-patch-49354.m4a"
        type="audio/mp4"
        length="23189403"
      />
      <itunes:title>Forking a Keyboard for One Voice Feature</itunes:title>
      <itunes:subtitle>Daniel built a Hebrew-English dictation engine. Now he wants to fuse it into someone else&apos;s keyboard — without inheriting its entire future.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has a working voice model that flips between English and Hebrew, shipping as a standalone Android dictation app. The obvious next move — fork an open source keyboard and wire the engine in — comes with a catch: the moment you fork, you inherit the upstream's entire future. This episode works through the patch-based alternative: keep your integration as a tiny replayable patch, let automation handle the mechanical parts, and let a human test the build before it ships. Along the way: what HeliBoard's voice key might already do, why GPL versus Apache decides your architecture, why a new application ID is non-negotiable, and why crediting the chain of volunteers matters more than the label you pick.]]></itunes:summary>
      <itunes:duration>1327</itunes:duration>
      <itunes:episode>6163</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/forking-keyboard-voice-patch-49354.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/forking-keyboard-voice-patch-49354.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Turning Words Into Sound: G2P, IPA, and the Hebrew Vowel Problem</title>
      <description><![CDATA[Daniel records himself reading prompts aloud to fine-tune a speech recognition model, and every brand name, Hebrew term, or Yiddish word he's only seen written down becomes a wall: he doesn't know how to say it, so whatever he says becomes the training label. The obvious fix is a pipeline — word in, predicted phonetics, a voice says it back. The pipeline exists, in parts. Grapheme-to-phoneme (G2P) conversion, IPA versus ARPAbet, dictionary lookup before neural guessing, WikiPron's five million human-written entries, and Hebrew's missing vowels all shape what's actually possible. This episode walks through the tools — espeak-ng, Epitran, Charsiu G2P, Phonikud, ReNikud — and the playback half nobody warns you about: many modern TTS models ignore phoneme input entirely. If you've ever needed a machine to tell you how to say something, this is the map.]]></description>
      <link>https://myweirdprompts.com/episode/g2p-ipa-hebrew-pronunciation-06510/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/g2p-ipa-hebrew-pronunciation-06510/</guid>
      <pubDate>Tue, 20 Oct 2026 14:01:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/g2p-ipa-hebrew-pronunciation-06510.m4a"
        type="audio/mp4"
        length="26154493"
      />
      <itunes:title>Turning Words Into Sound: G2P, IPA, and the Hebrew Vowel Problem</itunes:title>
      <itunes:subtitle>Text-to-phonetics tools exist, but the neural ones guess wrong on exactly the words you care about — names, brands, and Hebrew.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel records himself reading prompts aloud to fine-tune a speech recognition model, and every brand name, Hebrew term, or Yiddish word he's only seen written down becomes a wall: he doesn't know how to say it, so whatever he says becomes the training label. The obvious fix is a pipeline — word in, predicted phonetics, a voice says it back. The pipeline exists, in parts. Grapheme-to-phoneme (G2P) conversion, IPA versus ARPAbet, dictionary lookup before neural guessing, WikiPron's five million human-written entries, and Hebrew's missing vowels all shape what's actually possible. This episode walks through the tools — espeak-ng, Epitran, Charsiu G2P, Phonikud, ReNikud — and the playback half nobody warns you about: many modern TTS models ignore phoneme input entirely. If you've ever needed a machine to tell you how to say something, this is the map.]]></itunes:summary>
      <itunes:duration>1470</itunes:duration>
      <itunes:episode>6161</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/g2p-ipa-hebrew-pronunciation-06510.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/g2p-ipa-hebrew-pronunciation-06510.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where Do Tiny Speech Rules Live? A Ladder From Regex to LoRA</title>
      <description><![CDATA[Daniel wants his on-phone Parakeet model to write "FBI" instead of "F B I" and "fifty NIS" instead of "fifty shekels." That sounds like a rule, but speech models don't obey rules — they nudge probabilities. This episode walks a ladder from cheapest to heaviest: rule-based inverse text normalization with weighted finite state transducers, decode-time phrase boosting, shallow fusion with a KenLM language model, and finally adapters and fine-tuning. Along the way: why the shekel sign is physically absent from Parakeet's vocabulary, why Whisper's prompt is a style steer rather than an instruction, and the one question that tells you which rung each task actually needs.]]></description>
      <link>https://myweirdprompts.com/episode/speech-model-rule-ladder-29085/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speech-model-rule-ladder-29085/</guid>
      <pubDate>Tue, 20 Oct 2026 13:51:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speech-model-rule-ladder-29085.m4a"
        type="audio/mp4"
        length="26612217"
      />
      <itunes:title>Where Do Tiny Speech Rules Live? A Ladder From Regex to LoRA</itunes:title>
      <itunes:subtitle>A regex notebook, a WFST grammar, or a fine-tune? Where do tiny speech-rewrite rules actually belong in a 600M-parameter model?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants his on-phone Parakeet model to write "FBI" instead of "F B I" and "fifty NIS" instead of "fifty shekels." That sounds like a rule, but speech models don't obey rules — they nudge probabilities. This episode walks a ladder from cheapest to heaviest: rule-based inverse text normalization with weighted finite state transducers, decode-time phrase boosting, shallow fusion with a KenLM language model, and finally adapters and fine-tuning. Along the way: why the shekel sign is physically absent from Parakeet's vocabulary, why Whisper's prompt is a style steer rather than an instruction, and the one question that tells you which rung each task actually needs.]]></itunes:summary>
      <itunes:duration>1515</itunes:duration>
      <itunes:episode>6160</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speech-model-rule-ladder-29085.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speech-model-rule-ladder-29085.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When a Speech Model Can&apos;t Decide How to Write Dollars</title>
      <description><![CDATA[Daniel is fine-tuning Parakeet v2, a 600-million-parameter speech model, on his own voice — English and Hebrew mixed in the same sentence. His training data is built as small, atomic datasets, one per phenomenon: currency, brand names, acronyms, self-corrections, code-switching. But when one module writes "$20" and another writes "twenty dollars," does the model pick a winner, split the difference, or invent a hybrid nobody labelled? This episode digs into how convention clashes hide inside fine-tuning pipelines, why naive string-diffing cries wolf, why confident-learning tools are the wrong instrument for formatting disagreements, and what a single canonical normaliser can catch in seconds that a model would never flag.]]></description>
      <link>https://myweirdprompts.com/episode/speech-model-currency-convention-clash-28065/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speech-model-currency-convention-clash-28065/</guid>
      <pubDate>Tue, 20 Oct 2026 13:48:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speech-model-currency-convention-clash-28065.m4a"
        type="audio/mp4"
        length="19647790"
      />
      <itunes:title>When a Speech Model Can&apos;t Decide How to Write Dollars</itunes:title>
      <itunes:subtitle>One dataset says &quot;$20,&quot; another says &quot;twenty dollars.&quot; Fine-tuning a speech model means finding out which convention wins — and why it matters.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel is fine-tuning Parakeet v2, a 600-million-parameter speech model, on his own voice — English and Hebrew mixed in the same sentence. His training data is built as small, atomic datasets, one per phenomenon: currency, brand names, acronyms, self-corrections, code-switching. But when one module writes "$20" and another writes "twenty dollars," does the model pick a winner, split the difference, or invent a hybrid nobody labelled? This episode digs into how convention clashes hide inside fine-tuning pipelines, why naive string-diffing cries wolf, why confident-learning tools are the wrong instrument for formatting disagreements, and what a single canonical normaliser can catch in seconds that a model would never flag.]]></itunes:summary>
      <itunes:duration>1113</itunes:duration>
      <itunes:episode>6159</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speech-model-currency-convention-clash-28065.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speech-model-currency-convention-clash-28065.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Who Checks the Labels? Weak vs Strong Supervision</title>
      <description><![CDATA[Paying someone to label every example feels reassuring until you ask who checked the labels. This episode untangles the mess of strong supervision, weak supervision, self-supervision, and distillation — separating how a training target is produced from how trustworthy it actually is. From labeling functions and data programming to pseudo-labeling and the Llama 3.1 distillation work, we dig into where the signal really comes from, why specificity matters more than people admit, and how a plausible mistake can quietly become the next model's education.]]></description>
      <link>https://myweirdprompts.com/episode/weak-strong-supervision-explained-27173/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/weak-strong-supervision-explained-27173/</guid>
      <pubDate>Tue, 20 Oct 2026 09:56:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/weak-strong-supervision-explained-27173.m4a"
        type="audio/mp4"
        length="32450114"
      />
      <itunes:title>Who Checks the Labels? Weak vs Strong Supervision</itunes:title>
      <itunes:subtitle>Human labels, rule-based labels, or a teacher model writing the exam — what actually makes supervision &quot;strong,&quot; and who verifies it?</itunes:subtitle>
      <itunes:summary><![CDATA[Paying someone to label every example feels reassuring until you ask who checked the labels. This episode untangles the mess of strong supervision, weak supervision, self-supervision, and distillation — separating how a training target is produced from how trustworthy it actually is. From labeling functions and data programming to pseudo-labeling and the Llama 3.1 distillation work, we dig into where the signal really comes from, why specificity matters more than people admit, and how a plausible mistake can quietly become the next model's education.]]></itunes:summary>
      <itunes:duration>1866</itunes:duration>
      <itunes:episode>6154</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/weak-strong-supervision-explained-27173.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/weak-strong-supervision-explained-27173.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Decision Models: Typed Answers in One Forward Pass</title>
      <description><![CDATA[A new category of "decision models" has gone from one launch to a crowded field in about a month. Clef, Jev, and OpenAI's Decisions API take a state plus a schema of typed questions and return choices, booleans, and scores with probabilities — no free-form text to generate and parse. The pitch is speed and cost: 70–500ms latencies and output priced at zero. But usable answers aren't the same as accurate ones, and calibration numbers don't travel with your API key. We work through where these fit — support routing, domain classification, agent escalation — and where a rules engine or a boring narrow classifier still wins.]]></description>
      <link>https://myweirdprompts.com/episode/decision-models-typed-answers-70880/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/decision-models-typed-answers-70880/</guid>
      <pubDate>Tue, 20 Oct 2026 09:35:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/decision-models-typed-answers-70880.m4a"
        type="audio/mp4"
        length="25112392"
      />
      <itunes:title>Decision Models: Typed Answers in One Forward Pass</itunes:title>
      <itunes:subtitle>Clef, Jev, and OpenAI&apos;s Decisions API return typed answers with probabilities in a single forward pass — no generated text to parse.</itunes:subtitle>
      <itunes:summary><![CDATA[A new category of "decision models" has gone from one launch to a crowded field in about a month. Clef, Jev, and OpenAI's Decisions API take a state plus a schema of typed questions and return choices, booleans, and scores with probabilities — no free-form text to generate and parse. The pitch is speed and cost: 70–500ms latencies and output priced at zero. But usable answers aren't the same as accurate ones, and calibration numbers don't travel with your API key. We work through where these fit — support routing, domain classification, agent escalation — and where a rules engine or a boring narrow classifier still wins.]]></itunes:summary>
      <itunes:duration>1439</itunes:duration>
      <itunes:episode>6153</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/decision-models-typed-answers-70880.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/decision-models-typed-answers-70880.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Tiny Hebrew Models Behind the Show&apos;s Voice</title>
      <description><![CDATA[Hebrew speech pipelines lean on small task models that quietly decide whether the output is right — a nikudizer, a phonetic converter, a spelling normalizer. This episode digs into Dicta's DictaBERT-large-char-menaked, Phonikud's adaptors on top of it, UNIKUD's CANINE-based approach, and why the model that converts defective to full spelling only exists as a byproduct of training another tool. Along the way: a live pipeline that renamed a host, a silent failure mode where unvowelled text slipped into a voice engine, and the Unicode trap that eats verse endings if you strip niqqud the lazy way.]]></description>
      <link>https://myweirdprompts.com/episode/hebrew-niqqud-task-models-77112/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hebrew-niqqud-task-models-77112/</guid>
      <pubDate>Mon, 19 Oct 2026 20:08:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hebrew-niqqud-task-models-77112.m4a"
        type="audio/mp4"
        length="31535762"
      />
      <itunes:title>The Tiny Hebrew Models Behind the Show&apos;s Voice</itunes:title>
      <itunes:subtitle>Dicta&apos;s 300M-parameter nikudizer, Phonikud&apos;s adaptors, and the ktiv male model hiding inside a vowel adder.</itunes:subtitle>
      <itunes:summary><![CDATA[Hebrew speech pipelines lean on small task models that quietly decide whether the output is right — a nikudizer, a phonetic converter, a spelling normalizer. This episode digs into Dicta's DictaBERT-large-char-menaked, Phonikud's adaptors on top of it, UNIKUD's CANINE-based approach, and why the model that converts defective to full spelling only exists as a byproduct of training another tool. Along the way: a live pipeline that renamed a host, a silent failure mode where unvowelled text slipped into a voice engine, and the Unicode trap that eats verse endings if you strip niqqud the lazy way.]]></itunes:summary>
      <itunes:duration>1804</itunes:duration>
      <itunes:episode>6148</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hebrew-niqqud-task-models-77112.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hebrew-niqqud-task-models-77112.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Grounding Is Not Solved: DIY vs Gateway vs Native Search</title>
      <description><![CDATA[Grounding used to mean wiring up a search API, a scraper, and a lot of glue. Now there's a middle tier — gateways like OpenRouter and Vercel that bolt search onto any model — plus models that search on their own. This episode walks through all three routes: the five moving parts of DIY, how gateway routing actually decides which engine to use, and what native grounding really costs once token weight and hidden dials enter the picture. Along the way: per-search versus per-prompt billing, encrypted result blocks you have to pass back, domain-filter limits that range from zero to a hundred, and a worked thousand-question cost comparison with prices as published this week.]]></description>
      <link>https://myweirdprompts.com/episode/grounding-diy-gateway-native-search-49268/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/grounding-diy-gateway-native-search-49268/</guid>
      <pubDate>Mon, 19 Oct 2026 19:56:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/grounding-diy-gateway-native-search-49268.m4a"
        type="audio/mp4"
        length="27156121"
      />
      <itunes:title>Grounding Is Not Solved: DIY vs Gateway vs Native Search</itunes:title>
      <itunes:subtitle>Bolt-on search, gateway search, or native grounding? The plumbing didn&apos;t vanish — it just moved to someone else&apos;s bill.</itunes:subtitle>
      <itunes:summary><![CDATA[Grounding used to mean wiring up a search API, a scraper, and a lot of glue. Now there's a middle tier — gateways like OpenRouter and Vercel that bolt search onto any model — plus models that search on their own. This episode walks through all three routes: the five moving parts of DIY, how gateway routing actually decides which engine to use, and what native grounding really costs once token weight and hidden dials enter the picture. Along the way: per-search versus per-prompt billing, encrypted result blocks you have to pass back, domain-filter limits that range from zero to a hundred, and a worked thousand-question cost comparison with prices as published this week.]]></itunes:summary>
      <itunes:duration>1554</itunes:duration>
      <itunes:episode>6146</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/grounding-diy-gateway-native-search-49268.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/grounding-diy-gateway-native-search-49268.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Which AI Writes Our Scripts Best? We Tested Five</title>
      <description><![CDATA[We ran five AI models through a bakeoff to write our podcast scripts — same briefs, same word-count window, one judge with a rubric. GPT 6.1 Sol took the win, but the runner-up title went to Meta's Muse Spark 1.3 on a headline score that gates purely on length, and on judged quality alone Spark sits fourth of five. Along the way: Grok 4.7 wrote the best dialogue while blowing past the word cap every single time, Gemini 3.8 Flash delivered the best scenes and the worst accuracy, and Gemini 4 Argon posted the strongest profile outside Sol at nearly double the cost. We walk through what each model actually wrote, where the numbers broke, and what the near-misses reveal about a job nobody benchmarks.]]></description>
      <link>https://myweirdprompts.com/episode/ai-script-writing-model-bakeoff-49812/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-script-writing-model-bakeoff-49812/</guid>
      <pubDate>Mon, 19 Oct 2026 17:38:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-script-writing-model-bakeoff-49812.m4a"
        type="audio/mp4"
        length="25944018"
      />
      <itunes:title>Which AI Writes Our Scripts Best? We Tested Five</itunes:title>
      <itunes:subtitle>Five models, three scripts each, one rubric. Sol won on length, Grok on voice — and the runner-up title hides a catch.</itunes:subtitle>
      <itunes:summary><![CDATA[We ran five AI models through a bakeoff to write our podcast scripts — same briefs, same word-count window, one judge with a rubric. GPT 6.1 Sol took the win, but the runner-up title went to Meta's Muse Spark 1.3 on a headline score that gates purely on length, and on judged quality alone Spark sits fourth of five. Along the way: Grok 4.7 wrote the best dialogue while blowing past the word cap every single time, Gemini 3.8 Flash delivered the best scenes and the worst accuracy, and Gemini 4 Argon posted the strongest profile outside Sol at nearly double the cost. We walk through what each model actually wrote, where the numbers broke, and what the near-misses reveal about a job nobody benchmarks.]]></itunes:summary>
      <itunes:duration>1467</itunes:duration>
      <itunes:episode>6139</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-script-writing-model-bakeoff-49812.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-script-writing-model-bakeoff-49812.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Hebrish on iPhone: MLX, Core ML, or Wait?</title>
      <description><![CDATA[Hebrish is a free, open-source fine-tune of NVIDIA's Parakeet that handles English and Hebrew mixed in one sentence — and it already runs as an Android voice keyboard. This episode digs into what it would actually take to bring it to iPhone. MLX runs on phones, but the Parakeet code on top of it is Apple-Silicon-Mac only. Core ML already runs stock Parakeet on the Neural Engine via FluidAudio, but Hebrish's weights were never converted. And sherpa-onnx has an iOS build guide that doesn't quite confirm Parakeet works there. Along the way: why stock Parakeet writes "Micollette" instead of makolet, why silent CPU fallback is the scariest failure mode, and why iOS custom keyboards can't touch the microphone at all.]]></description>
      <link>https://myweirdprompts.com/episode/hebrish-iphone-mlx-coreml-15283/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hebrish-iphone-mlx-coreml-15283/</guid>
      <pubDate>Mon, 19 Oct 2026 17:36:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hebrish-iphone-mlx-coreml-15283.m4a"
        type="audio/mp4"
        length="21593002"
      />
      <itunes:title>Hebrish on iPhone: MLX, Core ML, or Wait?</itunes:title>
      <itunes:subtitle>Hebrish spells makolet right on Android. Getting that Hebrew-English dictation onto an iPhone means picking between MLX, Core ML, and sherpa-onnx.</itunes:subtitle>
      <itunes:summary><![CDATA[Hebrish is a free, open-source fine-tune of NVIDIA's Parakeet that handles English and Hebrew mixed in one sentence — and it already runs as an Android voice keyboard. This episode digs into what it would actually take to bring it to iPhone. MLX runs on phones, but the Parakeet code on top of it is Apple-Silicon-Mac only. Core ML already runs stock Parakeet on the Neural Engine via FluidAudio, but Hebrish's weights were never converted. And sherpa-onnx has an iOS build guide that doesn't quite confirm Parakeet works there. Along the way: why stock Parakeet writes "Micollette" instead of makolet, why silent CPU fallback is the scariest failure mode, and why iOS custom keyboards can't touch the microphone at all.]]></itunes:summary>
      <itunes:duration>1223</itunes:duration>
      <itunes:episode>6138</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hebrish-iphone-mlx-coreml-15283.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hebrish-iphone-mlx-coreml-15283.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How We Chose Our New Script Writer</title>
      <description><![CDATA[The show is written by a language model, and the round robin that picks which one has never been properly tested. So Daniel wrote the brief first: hard pass/fail rules, then two qualities that actually decide it — vivid dialogue, and what he calls "holding the middle." This episode walks through the funnel, from a free desk screen of roughly 470 models down to three full scripts per finalist, and lands on the biggest story in the project: a censorship gate built on 155 published questions that eliminates seven of ten candidates. Also, the writer's fee turns out to be the smallest line in a sixteen-cent episode.]]></description>
      <link>https://myweirdprompts.com/episode/how-we-chose-our-new-script-writer/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/how-we-chose-our-new-script-writer/</guid>
      <pubDate>Mon, 19 Oct 2026 15:08:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/how-we-chose-our-new-script-writer.m4a"
        type="audio/mp4"
        length="24795532"
      />
      <itunes:title>How We Chose Our New Script Writer</itunes:title>
      <itunes:subtitle>Ten models audition to write the show. Seven fail the censorship gate — including the workhorse that wrote three episodes in four.</itunes:subtitle>
      <itunes:summary><![CDATA[The show is written by a language model, and the round robin that picks which one has never been properly tested. So Daniel wrote the brief first: hard pass/fail rules, then two qualities that actually decide it — vivid dialogue, and what he calls "holding the middle." This episode walks through the funnel, from a free desk screen of roughly 470 models down to three full scripts per finalist, and lands on the biggest story in the project: a censorship gate built on 155 published questions that eliminates seven of ten candidates. Also, the writer's fee turns out to be the smallest line in a sixteen-cent episode.]]></itunes:summary>
      <itunes:duration>1391</itunes:duration>
      <itunes:episode>6136</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/how-we-chose-our-new-script-writer.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/how-we-chose-our-new-script-writer.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Cheap Tokens, Expensive Scripts</title>
      <description><![CDATA[Picking an LLM on price per token is the easy mistake. This episode walks through a real evaluation for the show's own script-writing pipeline — a 26,000-token input with a 9,000-token cached prefix and 6,500 tokens out — and shows how a model with a worse input price can win the bill by a sixth, then lose once caching is off or thinking tokens triple the output. We cover why blended leaderboard pricing misleads, why hidden reasoning tokens are billed as the expensive kind, how OpenRouter host pinning and quantization quietly change your quality score, and why you should estimate to screen but measure to decide.]]></description>
      <link>https://myweirdprompts.com/episode/llm-cost-per-task-evaluation-18346/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-cost-per-task-evaluation-18346/</guid>
      <pubDate>Mon, 19 Oct 2026 14:51:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-cost-per-task-evaluation-18346.m4a"
        type="audio/mp4"
        length="22942018"
      />
      <itunes:title>Cheap Tokens, Expensive Scripts</itunes:title>
      <itunes:subtitle>A model with worse list prices can cost less per script — if the cache hits. The bill hides in tokens, not rates.</itunes:subtitle>
      <itunes:summary><![CDATA[Picking an LLM on price per token is the easy mistake. This episode walks through a real evaluation for the show's own script-writing pipeline — a 26,000-token input with a 9,000-token cached prefix and 6,500 tokens out — and shows how a model with a worse input price can win the bill by a sixth, then lose once caching is off or thinking tokens triple the output. We cover why blended leaderboard pricing misleads, why hidden reasoning tokens are billed as the expensive kind, how OpenRouter host pinning and quantization quietly change your quality score, and why you should estimate to screen but measure to decide.]]></itunes:summary>
      <itunes:duration>1307</itunes:duration>
      <itunes:episode>6134</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-cost-per-task-evaluation-18346.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-cost-per-task-evaluation-18346.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Rater Is Enough: Designing Daniel&apos;s Taste Test</title>
      <description><![CDATA[Most evaluation advice says to average away individual taste. For a show with one creator, that advice is backwards. This episode works through how to build an evaluation system where the authority is a single domain expert — and the reliability that matters is whether that expert agrees with himself three weeks later. Drawing on applied LLM evaluation practice, qualitative coding methods, and a creative writing study that tested expert agreement on short stories, we lay out a concrete design: open coding before criteria, versioned rubrics, binary labels instead of five-point scales, anchor scripts for calibration, and a side-by-side verdict that comes before the marks. Along the way we argue about whether a show that lives in audio can be graded on a page, and land on the honest answer: read to screen, listen to decide.]]></description>
      <link>https://myweirdprompts.com/episode/single-rater-taste-evaluation-60761/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/single-rater-taste-evaluation-60761/</guid>
      <pubDate>Mon, 19 Oct 2026 14:47:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/single-rater-taste-evaluation-60761.m4a"
        type="audio/mp4"
        length="24403375"
      />
      <itunes:title>One Rater Is Enough: Designing Daniel&apos;s Taste Test</itunes:title>
      <itunes:subtitle>When the ground truth is one person&apos;s taste, agreement with strangers is the wrong bar. Stability with yourself is the right one.</itunes:subtitle>
      <itunes:summary><![CDATA[Most evaluation advice says to average away individual taste. For a show with one creator, that advice is backwards. This episode works through how to build an evaluation system where the authority is a single domain expert — and the reliability that matters is whether that expert agrees with himself three weeks later. Drawing on applied LLM evaluation practice, qualitative coding methods, and a creative writing study that tested expert agreement on short stories, we lay out a concrete design: open coding before criteria, versioned rubrics, binary labels instead of five-point scales, anchor scripts for calibration, and a side-by-side verdict that comes before the marks. Along the way we argue about whether a show that lives in audio can be graded on a page, and land on the honest answer: read to screen, listen to decide.]]></itunes:summary>
      <itunes:duration>1393</itunes:duration>
      <itunes:episode>6133</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/single-rater-taste-evaluation-60761.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/single-rater-taste-evaluation-60761.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Picking a Human Labeling Tool for LLM Benchmarks</title>
      <description><![CDATA[When your benchmark needs a human rater, the tooling question gets real fast. This episode walks through the actual market for human annotation tools — the trace-based platforms like Langfuse, LangSmith, and Braintrust that are built for watching production apps, versus the static-dataset tools like Argilla, Label Studio, Prodigy, and the academic tool Potato that fit a benchmark's workflow. We cover why pairwise comparison breaks down for long outputs, why clause-by-clause and binary judgments hold up better under fatigue, and why the free tier of a Hugging Face-hosted tool might forget your data overnight. If you're deciding between a hand-rolled Gradio app and an off-the-shelf labeling tool, this is the maintenance math you need.]]></description>
      <link>https://myweirdprompts.com/episode/llm-benchmark-labeling-tools-68844/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-benchmark-labeling-tools-68844/</guid>
      <pubDate>Mon, 19 Oct 2026 14:33:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-benchmark-labeling-tools-68844.m4a"
        type="audio/mp4"
        length="23116765"
      />
      <itunes:title>Picking a Human Labeling Tool for LLM Benchmarks</itunes:title>
      <itunes:subtitle>Building a human review layer for LLM benchmarks? Here&apos;s what Argilla, Label Studio, Prodigy, and Potato actually cost in money and maintenance.</itunes:subtitle>
      <itunes:summary><![CDATA[When your benchmark needs a human rater, the tooling question gets real fast. This episode walks through the actual market for human annotation tools — the trace-based platforms like Langfuse, LangSmith, and Braintrust that are built for watching production apps, versus the static-dataset tools like Argilla, Label Studio, Prodigy, and the academic tool Potato that fit a benchmark's workflow. We cover why pairwise comparison breaks down for long outputs, why clause-by-clause and binary judgments hold up better under fatigue, and why the free tier of a Hugging Face-hosted tool might forget your data overnight. If you're deciding between a hand-rolled Gradio app and an off-the-shelf labeling tool, this is the maintenance math you need.]]></itunes:summary>
      <itunes:duration>1316</itunes:duration>
      <itunes:episode>6130</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-benchmark-labeling-tools-68844.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-benchmark-labeling-tools-68844.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Build an AI Preference Dataset</title>
      <description><![CDATA[Before a reward model can learn anything, someone has to build the dataset underneath it. This episode digs into the unglamorous machinery of pairwise preference collection: how candidate scripts get generated through batch inference, how pairs get chosen so the comparison graph stays connected, and why the aired script makes an awkward contestant. It also covers Bradley-Terry scoring, the cost of long-text comparisons, and a clever fix — aligned continuations — that cuts one script into shared prefixes so raters judge writing instead of plot.]]></description>
      <link>https://myweirdprompts.com/episode/ai-preference-data-pipeline-08074/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-preference-data-pipeline-08074/</guid>
      <pubDate>Mon, 19 Oct 2026 14:33:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-preference-data-pipeline-08074.m4a"
        type="audio/mp4"
        length="23813436"
      />
      <itunes:title>How to Build an AI Preference Dataset</itunes:title>
      <itunes:subtitle>Pairwise comparison, forced choice, or just &quot;this one or that one&quot; — the engineering behind collecting human preferences on AI outputs.</itunes:subtitle>
      <itunes:summary><![CDATA[Before a reward model can learn anything, someone has to build the dataset underneath it. This episode digs into the unglamorous machinery of pairwise preference collection: how candidate scripts get generated through batch inference, how pairs get chosen so the comparison graph stays connected, and why the aired script makes an awkward contestant. It also covers Bradley-Terry scoring, the cost of long-text comparisons, and a clever fix — aligned continuations — that cuts one script into shared prefixes so raters judge writing instead of plot.]]></itunes:summary>
      <itunes:duration>1355</itunes:duration>
      <itunes:episode>6129</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-preference-data-pipeline-08074.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-preference-data-pipeline-08074.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Haiku 5.5 at 10 Cents: What the Cheap Model Actually Does</title>
      <description><![CDATA[Haiku 5.5 launched on October 7 with a headline price of 10 cents per million input tokens — forty times cheaper than Opus 5.5 and a hundred times cheaper than Fable 5.1. But cheap tokens aren't the same as a cheap bill, and benchmark scores only tell you so much. We dig into the real numbers: token inflation, batch discounts, the 100K prompt-length pricing cliff, and what Anthropic's own comparison pages do and don't show. Then we test it against the job that matters most to us — writing this podcast. Haiku is already in our randomized trial pool, and one of its episodes aired. It scored well on vibe coding. It collapsed on agentic terminal tasks. It beat Sonnet on CyberBench. And when we asked it to write a script, it lectured. We walk through the eval data, the turn-length problem, and what it actually costs to run a show like this on a budget model.]]></description>
      <link>https://myweirdprompts.com/episode/haiku-5-5-cheap-model-tested-36498/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/haiku-5-5-cheap-model-tested-36498/</guid>
      <pubDate>Mon, 19 Oct 2026 12:28:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/haiku-5-5-cheap-model-tested-36498.m4a"
        type="audio/mp4"
        length="23624274"
      />
      <itunes:title>Haiku 5.5 at 10 Cents: What the Cheap Model Actually Does</itunes:title>
      <itunes:subtitle>Haiku 5.5 costs 10 cents per million input tokens. We break down where it holds up, where it fails, and whether it can write this show.</itunes:subtitle>
      <itunes:summary><![CDATA[Haiku 5.5 launched on October 7 with a headline price of 10 cents per million input tokens — forty times cheaper than Opus 5.5 and a hundred times cheaper than Fable 5.1. But cheap tokens aren't the same as a cheap bill, and benchmark scores only tell you so much. We dig into the real numbers: token inflation, batch discounts, the 100K prompt-length pricing cliff, and what Anthropic's own comparison pages do and don't show. Then we test it against the job that matters most to us — writing this podcast. Haiku is already in our randomized trial pool, and one of its episodes aired. It scored well on vibe coding. It collapsed on agentic terminal tasks. It beat Sonnet on CyberBench. And when we asked it to write a script, it lectured. We walk through the eval data, the turn-length problem, and what it actually costs to run a show like this on a budget model.]]></itunes:summary>
      <itunes:duration>1345</itunes:duration>
      <itunes:episode>6127</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/haiku-5-5-cheap-model-tested-36498.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/haiku-5-5-cheap-model-tested-36498.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Which Env Var Wins? Cloud vs. Local Precedence</title>
      <description><![CDATA[When the same environment variable exists in two places at once, which value does the process actually see? The answer flips depending on where you're standing: on Vercel and n8n, the narrower scope wins — project over team, project over global. On your laptop, dotenv does the opposite, letting your shell silently override the repo's .env file. This episode walks through the documented precedence rules for Vercel Shared Environment Variables, n8n global vs. project variables, Prefect, and dotenv's long-debated override flag — plus the gotchas that cost people days, like Vercel's per-deployment immutability and dotenv's first-vs-last-value inversion depending on a flag you may not remember setting. No universal rule exists. Only per-tool contracts.]]></description>
      <link>https://myweirdprompts.com/episode/env-var-precedence-cloud-local-61629/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/env-var-precedence-cloud-local-61629/</guid>
      <pubDate>Mon, 19 Oct 2026 11:56:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/env-var-precedence-cloud-local-61629.m4a"
        type="audio/mp4"
        length="26591364"
      />
      <itunes:title>Which Env Var Wins? Cloud vs. Local Precedence</itunes:title>
      <itunes:subtitle>Same variable name, two values, one silent winner. Vercel and n8n go one way; dotenv goes the other.</itunes:subtitle>
      <itunes:summary><![CDATA[When the same environment variable exists in two places at once, which value does the process actually see? The answer flips depending on where you're standing: on Vercel and n8n, the narrower scope wins — project over team, project over global. On your laptop, dotenv does the opposite, letting your shell silently override the repo's .env file. This episode walks through the documented precedence rules for Vercel Shared Environment Variables, n8n global vs. project variables, Prefect, and dotenv's long-debated override flag — plus the gotchas that cost people days, like Vercel's per-deployment immutability and dotenv's first-vs-last-value inversion depending on a flag you may not remember setting. No universal rule exists. Only per-tool contracts.]]></itunes:summary>
      <itunes:duration>1528</itunes:duration>
      <itunes:episode>6125</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/env-var-precedence-cloud-local-61629.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/env-var-precedence-cloud-local-61629.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Vault, Two Readers: Secrets for a Home AI Gateway</title>
      <description><![CDATA[Daniel wants to run a central MCP gateway on a home server, with every tool pulling its secrets from 1Password. The hard part isn't the firewall — it's how the keys get onto a headless box, and what happens the first time one rotates. This episode walks through three approaches: 1Password service accounts with secret references, copying a .env file across the LAN, and standing up a cloud secrets manager for thirty variables. Along the way: why "sync" is the wrong word for what he wants, what breaks secret references (it's the names, not the values), the rate limits a boot-looping container can burn through, and the one credential you can never design away — secret zero.]]></description>
      <link>https://myweirdprompts.com/episode/home-ai-gateway-secrets-65466/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-ai-gateway-secrets-65466/</guid>
      <pubDate>Mon, 19 Oct 2026 11:46:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-ai-gateway-secrets-65466.m4a"
        type="audio/mp4"
        length="23721386"
      />
      <itunes:title>One Vault, Two Readers: Secrets for a Home AI Gateway</itunes:title>
      <itunes:subtitle>One source, two readers — not two copies kept in sync. How a home AI gateway should actually get its API keys.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants to run a central MCP gateway on a home server, with every tool pulling its secrets from 1Password. The hard part isn't the firewall — it's how the keys get onto a headless box, and what happens the first time one rotates. This episode walks through three approaches: 1Password service accounts with secret references, copying a .env file across the LAN, and standing up a cloud secrets manager for thirty variables. Along the way: why "sync" is the wrong word for what he wants, what breaks secret references (it's the names, not the values), the rate limits a boot-looping container can burn through, and the one credential you can never design away — secret zero.]]></itunes:summary>
      <itunes:duration>1355</itunes:duration>
      <itunes:episode>6124</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-ai-gateway-secrets-65466.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-ai-gateway-secrets-65466.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>OAuth or a Personal Access Token for Your MCP Server?</title>
      <description><![CDATA[When you connect an MCP server to GitHub or Hugging Face, you have two doors: a browser login or a token you generate yourself. They look like a style preference, but they're really a decision about how much of your account an agent gets to touch. This episode walks through what the GitHub CLI login actually hands you (one token, no expiry, repo-wide scopes), why GitHub Apps and the newer Hugging Face CLI behave differently, and how fine-grained personal access tokens shrink the blast radius. Then we check what the MCP spec itself says — it draws the line by transport, not by preference — and why a prompt-injection attack from a public issue thread is the scenario that should really decide it.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-server-oauth-vs-pat-58661/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-server-oauth-vs-pat-58661/</guid>
      <pubDate>Mon, 19 Oct 2026 11:43:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-server-oauth-vs-pat-58661.m4a"
        type="audio/mp4"
        length="21677849"
      />
      <itunes:title>OAuth or a Personal Access Token for Your MCP Server?</itunes:title>
      <itunes:subtitle>GitHub CLI login hands your agent one long-lived token with access to everything. Fine-grained PATs can&apos;t. Which should your MCP server use?</itunes:subtitle>
      <itunes:summary><![CDATA[When you connect an MCP server to GitHub or Hugging Face, you have two doors: a browser login or a token you generate yourself. They look like a style preference, but they're really a decision about how much of your account an agent gets to touch. This episode walks through what the GitHub CLI login actually hands you (one token, no expiry, repo-wide scopes), why GitHub Apps and the newer Hugging Face CLI behave differently, and how fine-grained personal access tokens shrink the blast radius. Then we check what the MCP spec itself says — it draws the line by transport, not by preference — and why a prompt-injection attack from a public issue thread is the scenario that should really decide it.]]></itunes:summary>
      <itunes:duration>1230</itunes:duration>
      <itunes:episode>6123</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-server-oauth-vs-pat-58661.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-server-oauth-vs-pat-58661.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Two Keys and a Polite No: Labeling a Claude Code Approve Button</title>
      <description><![CDATA[A developer wants two spare laptop keys to approve or reject Claude Code actions — one says yes, the other says "no, not approved, please await my review." The joke is that this meme already shipped: OpenAI's Codex Micro, VibeKeys, and the Claw Pad all sell physical approval buttons, one with a spacebar-width "allow always" and an AUTO key that "toggles auto-accept mode instantly." But the real question isn't the wiring. It's whether you can print a laptop keycap (you can't), whether laminated label tape survives thousands of presses (nobody's tested it), and which permanent marker holds up. Along the way: why the PreToolUse hook's 600-second timeout changes how you should size a button, why a button that approves anything is "a liability with nice keycaps," and why a polite no with manners might be the most honest control surface of all.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-approval-keycaps-20762/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-approval-keycaps-20762/</guid>
      <pubDate>Mon, 19 Oct 2026 11:18:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-approval-keycaps-20762.m4a"
        type="audio/mp4"
        length="27465302"
      />
      <itunes:title>Two Keys and a Polite No: Labeling a Claude Code Approve Button</itunes:title>
      <itunes:subtitle>Daniel has two spare laptop keys and wants them to say yes and no. The electronics are easy — it&apos;s the labels that get hard.</itunes:subtitle>
      <itunes:summary><![CDATA[A developer wants two spare laptop keys to approve or reject Claude Code actions — one says yes, the other says "no, not approved, please await my review." The joke is that this meme already shipped: OpenAI's Codex Micro, VibeKeys, and the Claw Pad all sell physical approval buttons, one with a spacebar-width "allow always" and an AUTO key that "toggles auto-accept mode instantly." But the real question isn't the wiring. It's whether you can print a laptop keycap (you can't), whether laminated label tape survives thousands of presses (nobody's tested it), and which permanent marker holds up. Along the way: why the PreToolUse hook's 600-second timeout changes how you should size a button, why a button that approves anything is "a liability with nice keycaps," and why a polite no with manners might be the most honest control surface of all.]]></itunes:summary>
      <itunes:duration>1580</itunes:duration>
      <itunes:episode>6119</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-approval-keycaps-20762.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-approval-keycaps-20762.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Code-First Workflow Runners: Prefect, Temporal, Dagster</title>
      <description><![CDATA[If you've outgrown n8n's canvas and want your workflows versioned in git with real observability, this episode maps the code-first workflow orchestrator field. Prefect is the closest match to a long-lived Python script runner — flows, tasks, workers, and a server you can self-host. Temporal offers durable execution where a workflow can sleep for thirty days and resume on the exact line. And Dagster got a lot more interesting in July 2026 when Prefect announced it was acquiring Dagster Labs. We compare all three, explain what "robust" actually means as a checklist, and walk through what a five-node n8n canvas becomes as a sixty-line Python file.]]></description>
      <link>https://myweirdprompts.com/episode/code-first-workflow-orchestrators-07963/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/code-first-workflow-orchestrators-07963/</guid>
      <pubDate>Sun, 18 Oct 2026 18:16:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/code-first-workflow-orchestrators-07963.m4a"
        type="audio/mp4"
        length="24482289"
      />
      <itunes:title>Code-First Workflow Runners: Prefect, Temporal, Dagster</itunes:title>
      <itunes:subtitle>Your workflow lives in git, your GUI is just a window, and your worker actually runs the jobs. Which runner wins?</itunes:subtitle>
      <itunes:summary><![CDATA[If you've outgrown n8n's canvas and want your workflows versioned in git with real observability, this episode maps the code-first workflow orchestrator field. Prefect is the closest match to a long-lived Python script runner — flows, tasks, workers, and a server you can self-host. Temporal offers durable execution where a workflow can sleep for thirty days and resume on the exact line. And Dagster got a lot more interesting in July 2026 when Prefect announced it was acquiring Dagster Labs. We compare all three, explain what "robust" actually means as a checklist, and walk through what a five-node n8n canvas becomes as a sixty-line Python file.]]></itunes:summary>
      <itunes:duration>1399</itunes:duration>
      <itunes:episode>6099</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/code-first-workflow-orchestrators-07963.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/code-first-workflow-orchestrators-07963.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Podcast Graph That Doesn&apos;t Suck</title>
      <description><![CDATA[A listener named Daniel wants to rebuild how we explore a podcast archive: not a feed, not a hairball graph visualization, but a sequence of small decisions — like a dating app that learns your type and walks you through the archive one connection at a time. We dig into why force-directed graph views are a "screensaver with a fan club," what Spotify's 2024 podcast graph actually did (and why listeners never saw it), and which open source players are still alive. Then the database question: can you run Neo4j next to serverless Postgres to copy fourteen rows a day? What we found involves logical replication slots, a Business Source License, and a cautionary tale about an embedded graph database that got acquired and archived overnight.]]></description>
      <link>https://myweirdprompts.com/episode/podcast-knowledge-graph-discovery-06861/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcast-knowledge-graph-discovery-06861/</guid>
      <pubDate>Sun, 18 Oct 2026 11:01:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcast-knowledge-graph-discovery-06861.m4a"
        type="audio/mp4"
        length="21073158"
      />
      <itunes:title>Building a Podcast Graph That Doesn&apos;t Suck</itunes:title>
      <itunes:subtitle>Daniel wants to turn a 6,000-episode archive into a walkable graph. Turns out the hard part isn&apos;t the database — it&apos;s the pruning.</itunes:subtitle>
      <itunes:summary><![CDATA[A listener named Daniel wants to rebuild how we explore a podcast archive: not a feed, not a hairball graph visualization, but a sequence of small decisions — like a dating app that learns your type and walks you through the archive one connection at a time. We dig into why force-directed graph views are a "screensaver with a fan club," what Spotify's 2024 podcast graph actually did (and why listeners never saw it), and which open source players are still alive. Then the database question: can you run Neo4j next to serverless Postgres to copy fourteen rows a day? What we found involves logical replication slots, a Business Source License, and a cautionary tale about an embedded graph database that got acquired and archived overnight.]]></itunes:summary>
      <itunes:duration>1202</itunes:duration>
      <itunes:episode>6087</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcast-knowledge-graph-discovery-06861.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcast-knowledge-graph-discovery-06861.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Reading Polymarket as an OSINT Feed</title>
      <description><![CDATA[Polymarket is the world's largest prediction market, where a contract at 37 cents reads as a 37% implied probability. But the US military's own intelligence bulletin called prediction markets an "underutilized dataset" for national security analysis — even as US users are barred from trading. This episode walks the read-only path: the Gamma, CLOB, and Data APIs, the unified TypeScript and Python SDKs, the archived py-clob-client that's dead at 1,228 stars, and the rate limits that silently throttle your pipeline. Then we put it to work on live Israeli election markets, where a seat-count market and a prime minister market can both favor the same candidate while describing entirely different worlds. We also cover the calibration problem: an audit of top Polymarket wallets found the biggest winners were the worst-calibrated. Volume is not wisdom. A price is a sensor, not an oracle.]]></description>
      <link>https://myweirdprompts.com/episode/polymarket-osint-read-only-api-92057/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/polymarket-osint-read-only-api-92057/</guid>
      <pubDate>Sun, 18 Oct 2026 10:37:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/polymarket-osint-read-only-api-92057.m4a"
        type="audio/mp4"
        length="28511541"
      />
      <itunes:title>Reading Polymarket as an OSINT Feed</itunes:title>
      <itunes:subtitle>Polymarket&apos;s API turns prediction market prices into a live probability feed — no wallet, no trading, just reading the world&apos;s largest market.</itunes:subtitle>
      <itunes:summary><![CDATA[Polymarket is the world's largest prediction market, where a contract at 37 cents reads as a 37% implied probability. But the US military's own intelligence bulletin called prediction markets an "underutilized dataset" for national security analysis — even as US users are barred from trading. This episode walks the read-only path: the Gamma, CLOB, and Data APIs, the unified TypeScript and Python SDKs, the archived py-clob-client that's dead at 1,228 stars, and the rate limits that silently throttle your pipeline. Then we put it to work on live Israeli election markets, where a seat-count market and a prime minister market can both favor the same candidate while describing entirely different worlds. We also cover the calibration problem: an audit of top Polymarket wallets found the biggest winners were the worst-calibrated. Volume is not wisdom. A price is a sensor, not an oracle.]]></itunes:summary>
      <itunes:duration>1636</itunes:duration>
      <itunes:episode>6086</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/polymarket-osint-read-only-api-92057.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/polymarket-osint-read-only-api-92057.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Whole Machine: How an Episode Gets Made, Send to Publish</title>
      <description><![CDATA[Not a changelog this time but the full tour. Hilbert walks Corn and Herman through every stage of the pipeline that makes the show, as it runs in October 2026, from a script written by hand straight from the code. The send form and its signed uploads, the job row, duplicate checks and the sixty-second grace window; the dispatcher that routes every episode type from a single database table; intake that keeps Daniel's prompt raw; a research agent with ten topic toolkits and twenty-one direct primary sources under hard caps; memory, planning and the rotation variables that inject variety before a word is written. Then the new writer trial, where one episode in four goes to one of nine other models behind a code-only format gate, the deterministic polish that replaced an AI review, the herald in Daniel's cloned voice, text normalisation, and the GPU upgrade that decodes eight turns of dialogue at once across two L4 workers. Assembly, loudness, chapters, a content catalogue that publishes without a site build, announcements, backups, and the two-word supervisor. Plus a greyhound called Pewter.]]></description>
      <link>https://myweirdprompts.com/episode/the-whole-machine-pipeline-review-october-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/the-whole-machine-pipeline-review-october-2026/</guid>
      <pubDate>Sat, 17 Oct 2026 17:56:03 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/the-whole-machine-pipeline-review-october-2026.m4a"
        type="audio/mp4"
        length="35992002"
      />
      <itunes:title>The Whole Machine: How an Episode Gets Made, Send to Publish</itunes:title>
      <itunes:subtitle>The complete tour of the pipeline that makes the show, stage by stage, from the send form to publishing, including the new eight-turn batched voice decode.</itunes:subtitle>
      <itunes:summary><![CDATA[Not a changelog this time but the full tour. Hilbert walks Corn and Herman through every stage of the pipeline that makes the show, as it runs in October 2026, from a script written by hand straight from the code. The send form and its signed uploads, the job row, duplicate checks and the sixty-second grace window; the dispatcher that routes every episode type from a single database table; intake that keeps Daniel's prompt raw; a research agent with ten topic toolkits and twenty-one direct primary sources under hard caps; memory, planning and the rotation variables that inject variety before a word is written. Then the new writer trial, where one episode in four goes to one of nine other models behind a code-only format gate, the deterministic polish that replaced an AI review, the herald in Daniel's cloned voice, text normalisation, and the GPU upgrade that decodes eight turns of dialogue at once across two L4 workers. Assembly, loudness, chapters, a content catalogue that publishes without a site build, announcements, backups, and the two-word supervisor. Plus a greyhound called Pewter.]]></itunes:summary>
      <itunes:duration>2044</itunes:duration>
      <itunes:episode>6069</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/the-whole-machine-pipeline-review-october-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/the-whole-machine-pipeline-review-october-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where Does Speaker Structure Belong in AI Podcast Scripts?</title>
      <description><![CDATA[Every multi-speaker AI pipeline runs on a contract with no enforcement layer — and when it breaks, the failure gets blamed on the model. This episode digs into a documented bug report from an open-source podcast generator: six segments, three speakers, forty-two dialogue lines, and a model that returned the prompt's own JSON skeleton verbatim. Then it turns to the obvious fix — strict structured output — and what two research papers actually found about the cost of asking a model to respond in JSON. The answer isn't where most people assume: the damage happens at the prompt, not the decoder. Along the way: why high-temperature sampling doesn't restore diversity under a template, why capacity matters more than format for strong models, and why "thinking first" helps math but does nothing for prose.]]></description>
      <link>https://myweirdprompts.com/episode/speaker-attribution-structured-output-pipelines-05763/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speaker-attribution-structured-output-pipelines-05763/</guid>
      <pubDate>Sat, 17 Oct 2026 14:23:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speaker-attribution-structured-output-pipelines-05763.m4a"
        type="audio/mp4"
        length="26940455"
      />
      <itunes:title>Where Does Speaker Structure Belong in AI Podcast Scripts?</itunes:title>
      <itunes:subtitle>Label-and-parse pipelines break in ways that look like model errors but aren&apos;t. Plus what the research actually says about the format tax.</itunes:subtitle>
      <itunes:summary><![CDATA[Every multi-speaker AI pipeline runs on a contract with no enforcement layer — and when it breaks, the failure gets blamed on the model. This episode digs into a documented bug report from an open-source podcast generator: six segments, three speakers, forty-two dialogue lines, and a model that returned the prompt's own JSON skeleton verbatim. Then it turns to the obvious fix — strict structured output — and what two research papers actually found about the cost of asking a model to respond in JSON. The answer isn't where most people assume: the damage happens at the prompt, not the decoder. Along the way: why high-temperature sampling doesn't restore diversity under a template, why capacity matters more than format for strong models, and why "thinking first" helps math but does nothing for prose.]]></itunes:summary>
      <itunes:duration>1529</itunes:duration>
      <itunes:episode>6056</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speaker-attribution-structured-output-pipelines-05763.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speaker-attribution-structured-output-pipelines-05763.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Fine-Tune ASR on Noisy Audio</title>
      <description><![CDATA[Building a robust speech recognition model feels like it should require collecting mountains of noisy audio. It doesn't. The research points to a different approach: build a compact noise library, mix it into clean speech at sampled signal-to-noise ratios, and keep a healthy share of clean originals in every epoch. This episode walks through the composition questions that actually determine success — how much augmented data is too much, which noise types to prioritize, why the best adaptation technique depends on whether the noise is stationary or not, and how to schedule augmentation across training. Along the way: the ROAR paper's original-to-augmented ratio, NVIDIA's Riva recipe, the IIT Bombay noise taxonomy, and a field-recorded noise dataset from 165 Indian districts that captures overlapping sound the way deployment actually delivers it.]]></description>
      <link>https://myweirdprompts.com/episode/asr-fine-tuning-noisy-audio-00244/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/asr-fine-tuning-noisy-audio-00244/</guid>
      <pubDate>Sat, 17 Oct 2026 13:23:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/asr-fine-tuning-noisy-audio-00244.m4a"
        type="audio/mp4"
        length="44251999"
      />
      <itunes:title>How to Fine-Tune ASR on Noisy Audio</itunes:title>
      <itunes:subtitle>A small noise library mixed into clean speech beats collecting hours of noisy recordings. Here&apos;s the ratio that actually works.</itunes:subtitle>
      <itunes:summary><![CDATA[Building a robust speech recognition model feels like it should require collecting mountains of noisy audio. It doesn't. The research points to a different approach: build a compact noise library, mix it into clean speech at sampled signal-to-noise ratios, and keep a healthy share of clean originals in every epoch. This episode walks through the composition questions that actually determine success — how much augmented data is too much, which noise types to prioritize, why the best adaptation technique depends on whether the noise is stationary or not, and how to schedule augmentation across training. Along the way: the ROAR paper's original-to-augmented ratio, NVIDIA's Riva recipe, the IIT Bombay noise taxonomy, and a field-recorded noise dataset from 165 Indian districts that captures overlapping sound the way deployment actually delivers it.]]></itunes:summary>
      <itunes:duration>2542</itunes:duration>
      <itunes:episode>6055</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/asr-fine-tuning-noisy-audio-00244.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/asr-fine-tuning-noisy-audio-00244.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Find the AI Benchmark You Actually Need</title>
      <description><![CDATA[There are thousands of AI benchmarks and no canonical place to look for them. When you need to evaluate a model on something narrow — structured output validity, an unusual programming language, one kind of reasoning — how do you find out whether someone already built the benchmark before you spend a month building your own? This episode digs into the practical search discipline: why benchmark names describe nothing, why Hugging Face indexes artifacts rather than capabilities, and what two tools — Benchmark Radar and the Lighteval Tasks Explorer — actually offer. Along the way: a worked example around a custom LLM router, the usability questions that separate a dataset from a runnable benchmark, and the honest answer to when you should give up searching and build your own.]]></description>
      <link>https://myweirdprompts.com/episode/finding-ai-benchmarks-discovery-81611/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/finding-ai-benchmarks-discovery-81611/</guid>
      <pubDate>Sat, 17 Oct 2026 09:50:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/finding-ai-benchmarks-discovery-81611.m4a"
        type="audio/mp4"
        length="37582047"
      />
      <itunes:title>How to Find the AI Benchmark You Actually Need</itunes:title>
      <itunes:subtitle>Thousands of benchmarks, no canonical index. How Benchmark Radar and the Lighteval Tasks Explorer help — and where they fall short.</itunes:subtitle>
      <itunes:summary><![CDATA[There are thousands of AI benchmarks and no canonical place to look for them. When you need to evaluate a model on something narrow — structured output validity, an unusual programming language, one kind of reasoning — how do you find out whether someone already built the benchmark before you spend a month building your own? This episode digs into the practical search discipline: why benchmark names describe nothing, why Hugging Face indexes artifacts rather than capabilities, and what two tools — Benchmark Radar and the Lighteval Tasks Explorer — actually offer. Along the way: a worked example around a custom LLM router, the usability questions that separate a dataset from a runnable benchmark, and the honest answer to when you should give up searching and build your own.]]></itunes:summary>
      <itunes:duration>2119</itunes:duration>
      <itunes:episode>6051</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/finding-ai-benchmarks-discovery-81611.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/finding-ai-benchmarks-discovery-81611.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Codex vs Claude Code: What the Numbers Actually Count</title>
      <description><![CDATA[OpenAI Codex and Claude Code get compared in side-by-side charts every week, but those charts mix units. A 40 million weekly-active figure bundles Codex with ChatGPT for Work; a Stack Overflow survey puts Claude Code at 65.5% of agent users while a market-share report puts it at 29.4%. This episode digs into the measurement problem underneath the rivalry: bundled distribution versus paid installs, terminal-native execution versus cloud containers, million-token windows versus native compaction, and proprietary conventions versus an open standard. The honest answer to "who's winning" may be "pick a ruler" — but the reasons the rulers disagree are the interesting part.]]></description>
      <link>https://myweirdprompts.com/episode/codex-vs-claude-code-metrics-97669/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/codex-vs-claude-code-metrics-97669/</guid>
      <pubDate>Sat, 17 Oct 2026 09:25:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/codex-vs-claude-code-metrics-97669.m4a"
        type="audio/mp4"
        length="33784953"
      />
      <itunes:title>Codex vs Claude Code: What the Numbers Actually Count</itunes:title>
      <itunes:subtitle>Codex has more users, Claude Code has more revenue — both can be true. The difference is what each number is counting.</itunes:subtitle>
      <itunes:summary><![CDATA[OpenAI Codex and Claude Code get compared in side-by-side charts every week, but those charts mix units. A 40 million weekly-active figure bundles Codex with ChatGPT for Work; a Stack Overflow survey puts Claude Code at 65.5% of agent users while a market-share report puts it at 29.4%. This episode digs into the measurement problem underneath the rivalry: bundled distribution versus paid installs, terminal-native execution versus cloud containers, million-token windows versus native compaction, and proprietary conventions versus an open standard. The honest answer to "who's winning" may be "pick a ruler" — but the reasons the rulers disagree are the interesting part.]]></itunes:summary>
      <itunes:duration>1929</itunes:duration>
      <itunes:episode>6050</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/codex-vs-claude-code-metrics-97669.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/codex-vs-claude-code-metrics-97669.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Claude Code by the Numbers: What&apos;s Measurable, What&apos;s Hype</title>
      <description><![CDATA[Claude Code went from zero to the top of the coding-tool charts in eight months — but Anthropic has never published an absolute user count. This episode separates the disclosed from the estimated from the invented: the revenue ladder from $500M to $16.8B quarterly, the JetBrains and Stack Overflow surveys that disagree by 26 points, and an Anthropic study of 235,000 people that reveals who's actually at the keyboard. The most surprising finding? Novices succeed 15% of the time while experts hit 33% — and management occupations score highest of all.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-adoption-data-37156/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-adoption-data-37156/</guid>
      <pubDate>Sat, 17 Oct 2026 09:22:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-adoption-data-37156.m4a"
        type="audio/mp4"
        length="24550826"
      />
      <itunes:title>Claude Code by the Numbers: What&apos;s Measurable, What&apos;s Hype</itunes:title>
      <itunes:subtitle>Anthropic won&apos;t publish a user count. Here&apos;s what revenue, surveys, and 400K sessions actually tell us about Claude Code&apos;s rise.</itunes:subtitle>
      <itunes:summary><![CDATA[Claude Code went from zero to the top of the coding-tool charts in eight months — but Anthropic has never published an absolute user count. This episode separates the disclosed from the estimated from the invented: the revenue ladder from $500M to $16.8B quarterly, the JetBrains and Stack Overflow surveys that disagree by 26 points, and an Anthropic study of 235,000 people that reveals who's actually at the keyboard. The most surprising finding? Novices succeed 15% of the time while experts hit 33% — and management occupations score highest of all.]]></itunes:summary>
      <itunes:duration>1399</itunes:duration>
      <itunes:episode>6049</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-adoption-data-37156.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-adoption-data-37156.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Under the Hood, October 2026: Seventeen Prompt Versions and Three-Times-Faster Voices</title>
      <description><![CDATA[Corn, Herman and Hilbert walk through four weeks of changes to the pipeline that makes the show, from 20 September to 17 October 2026, read from a script written by hand straight from the code rather than generated. A sixty-second grace period before any job spends money; a near-duplicate guard that needs a language model because a reworded resend and a genuine sequel score almost the same; DeepSeek Flash requested by name; a two-word supervisor that decides retry or abort; and Whisper on OpenRouter after a credit balance ran dry. Then the script rules: funny by default, Larry's unbooked ad every episode, Hilbert's segment and the code that stops the hosts summoning him, and the deterministic polish that deletes echoes and rotates stock phrases. On the audio side: a wasted attention-weights request patched out, a CUDA-graph decode that took the voices from 77 to 28 cents per audio-hour, a number normaliser that no longer reads Ubuntu 26.04 as April first, and Daniel's cloned voice on the disclaimer. Plus Hilbert on the proving cabinet heating his flat.]]></description>
      <link>https://myweirdprompts.com/episode/under-the-hood-october-2026-pipeline-update/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/under-the-hood-october-2026-pipeline-update/</guid>
      <pubDate>Fri, 16 Oct 2026 23:18:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/under-the-hood-october-2026-pipeline-update.m4a"
        type="audio/mp4"
        length="33385482"
      />
      <itunes:title>Under the Hood, October 2026: Seventeen Prompt Versions and Three-Times-Faster Voices</itunes:title>
      <itunes:subtitle>Four weeks of pipeline changes, grounded in the code: grace periods, duplicate detection, Hilbert&apos;s rules, echo deletion, and voices nearly three times faster.</itunes:subtitle>
      <itunes:summary><![CDATA[Corn, Herman and Hilbert walk through four weeks of changes to the pipeline that makes the show, from 20 September to 17 October 2026, read from a script written by hand straight from the code rather than generated. A sixty-second grace period before any job spends money; a near-duplicate guard that needs a language model because a reworded resend and a genuine sequel score almost the same; DeepSeek Flash requested by name; a two-word supervisor that decides retry or abort; and Whisper on OpenRouter after a credit balance ran dry. Then the script rules: funny by default, Larry's unbooked ad every episode, Hilbert's segment and the code that stops the hosts summoning him, and the deterministic polish that deletes echoes and rotates stock phrases. On the audio side: a wasted attention-weights request patched out, a CUDA-graph decode that took the voices from 77 to 28 cents per audio-hour, a number normaliser that no longer reads Ubuntu 26.04 as April first, and Daniel's cloned voice on the disclaimer. Plus Hilbert on the proving cabinet heating his flat.]]></itunes:summary>
      <itunes:duration>1886</itunes:duration>
      <itunes:episode>6048</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/under-the-hood-october-2026-pipeline-update.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/under-the-hood-october-2026-pipeline-update.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Door for Every AI: Cloudflare MCP Portals</title>
      <description><![CDATA[Daniel wants to hand cloud AI tools access to MCP servers running on his own hardware, without installing a separate tunnel agent for every vendor. Cloudflare's MCP Server Portals — shipped in August 2025 as Agents Gateway, renamed, and now with private-server support added in September 2026 — centralize up to eighty MCP servers on a single HTTP endpoint behind Cloudflare Access. We walk through how the portal acts as authorization server to the cloud client and client to your server, why the obvious "Access policy plus service token" approach is structurally impossible for ChatGPT and Claude.ai, and how the private-server path changes what you can keep off the public internet entirely.]]></description>
      <link>https://myweirdprompts.com/episode/cloudflare-mcp-server-portals-57127/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cloudflare-mcp-server-portals-57127/</guid>
      <pubDate>Fri, 16 Oct 2026 22:23:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cloudflare-mcp-server-portals-57127.m4a"
        type="audio/mp4"
        length="30223826"
      />
      <itunes:title>One Door for Every AI: Cloudflare MCP Portals</itunes:title>
      <itunes:subtitle>Cloudflare&apos;s MCP Portals put every on-prem MCP server behind one authenticated endpoint — and why service tokens can&apos;t work for ChatGPT.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants to hand cloud AI tools access to MCP servers running on his own hardware, without installing a separate tunnel agent for every vendor. Cloudflare's MCP Server Portals — shipped in August 2025 as Agents Gateway, renamed, and now with private-server support added in September 2026 — centralize up to eighty MCP servers on a single HTTP endpoint behind Cloudflare Access. We walk through how the portal acts as authorization server to the cloud client and client to your server, why the obvious "Access policy plus service token" approach is structurally impossible for ChatGPT and Claude.ai, and how the private-server path changes what you can keep off the public internet entirely.]]></itunes:summary>
      <itunes:duration>1723</itunes:duration>
      <itunes:episode>6044</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cloudflare-mcp-server-portals-57127.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cloudflare-mcp-server-portals-57127.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How CENO Moves a Web Page Into a Censored Country</title>
      <description><![CDATA[CENO gets covered as a border-tunneling story, but the real design is closer to "does anyone on my street already have this page." This episode walks the mechanics end to end: the Ouinet client proxy, the signing injector run by eQualitie, bridges that relay ciphertext they can't read, and seeders who volunteer by accident just by browsing. We trace a single page request through the race between direct, injector, and peer cache routes, explain how the BitTorrent mainline DHT and uTP transport carry signed pages between phones, and answer the question everyone asks: how many intermediaries can a chain hold? The answer is one bridge, and the reason isn't security — it's that every extra hop is another thing that can fail.]]></description>
      <link>https://myweirdprompts.com/episode/ceno-relay-mechanics-explained-55325/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ceno-relay-mechanics-explained-55325/</guid>
      <pubDate>Fri, 16 Oct 2026 21:28:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ceno-relay-mechanics-explained-55325.m4a"
        type="audio/mp4"
        length="30995211"
      />
      <itunes:title>How CENO Moves a Web Page Into a Censored Country</itunes:title>
      <itunes:subtitle>A walk through CENO&apos;s actual plumbing: signed caches, volunteer bridges, and why the chain is deliberately short.</itunes:subtitle>
      <itunes:summary><![CDATA[CENO gets covered as a border-tunneling story, but the real design is closer to "does anyone on my street already have this page." This episode walks the mechanics end to end: the Ouinet client proxy, the signing injector run by eQualitie, bridges that relay ciphertext they can't read, and seeders who volunteer by accident just by browsing. We trace a single page request through the race between direct, injector, and peer cache routes, explain how the BitTorrent mainline DHT and uTP transport carry signed pages between phones, and answer the question everyone asks: how many intermediaries can a chain hold? The answer is one bridge, and the reason isn't security — it's that every extra hop is another thing that can fail.]]></itunes:summary>
      <itunes:duration>1767</itunes:duration>
      <itunes:episode>6041</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ceno-relay-mechanics-explained-55325.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ceno-relay-mechanics-explained-55325.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Claude Code Subagents Can Now Spawn Subagents</title>
      <description><![CDATA[Claude Code now lets subagents spin up their own subagents — so what happens when the delegation stack goes three, four, five levels deep? We trace the real history of spawn-depth defaults (five layers, then one, then three), the environment variable that controls it, and the striking convergence across Gemini CLI, Cursor, Google's ADK 2.0, Anthropic's Managed Agents, the OpenAI Agents SDK, and LangGraph. Then we get to the uncomfortable part: the published literature on recursive delegation stops at depth three. We dig into the ADaPT paper from NAACL Findings 2024, MIT's Recursive Language Models, and Anthropic's multi-agent research numbers — fifteen times the tokens, a single global budget cap, and no per-branch containment. If you've wondered whether A2A is running under the hood, the answer is no — and what's there instead is more interesting.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-recursive-subagents-76530/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-recursive-subagents-76530/</guid>
      <pubDate>Fri, 16 Oct 2026 21:24:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-recursive-subagents-76530.m4a"
        type="audio/mp4"
        length="31465661"
      />
      <itunes:title>Claude Code Subagents Can Now Spawn Subagents</itunes:title>
      <itunes:subtitle>Claude Code subagents can now spawn their own subagents. Here&apos;s where the depth limits come from, and why nobody actually knows where they stop.</itunes:subtitle>
      <itunes:summary><![CDATA[Claude Code now lets subagents spin up their own subagents — so what happens when the delegation stack goes three, four, five levels deep? We trace the real history of spawn-depth defaults (five layers, then one, then three), the environment variable that controls it, and the striking convergence across Gemini CLI, Cursor, Google's ADK 2.0, Anthropic's Managed Agents, the OpenAI Agents SDK, and LangGraph. Then we get to the uncomfortable part: the published literature on recursive delegation stops at depth three. We dig into the ADaPT paper from NAACL Findings 2024, MIT's Recursive Language Models, and Anthropic's multi-agent research numbers — fifteen times the tokens, a single global budget cap, and no per-branch containment. If you've wondered whether A2A is running under the hood, the answer is no — and what's there instead is more interesting.]]></itunes:summary>
      <itunes:duration>1801</itunes:duration>
      <itunes:episode>6040</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-recursive-subagents-76530.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-recursive-subagents-76530.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Gemini 4 Argon: Google&apos;s Million-Token Flagship</title>
      <description><![CDATA[Google's Gemini 4 Argon is positioned as a frontier reasoning model, but the model card leaves out architecture, parameter count, and licensing, and the page quotes no pricing at all. We walk through what's actually documented: a 1,048,576-token unified input/output limit, low/medium/high thinking levels, implicit caching only, and a wide tool surface including function calling, code execution, Search grounding, and a preview computer-use mode. Then we separate Google's nineteen-row benchmark table from independent testing — Artificial Analysis at 53 on the Intelligence Index, Vals AI ranking it first of 49 models — and note where the losses cluster: terminal-based agentic coding and computer use. We also cover the staged rollout through the Fairwind Program and the launch discount that changes the cost-per-task math.]]></description>
      <link>https://myweirdprompts.com/episode/gemini-4-argon-model-spotlight-96375/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gemini-4-argon-model-spotlight-96375/</guid>
      <pubDate>Fri, 16 Oct 2026 21:04:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gemini-4-argon-model-spotlight-96375.m4a"
        type="audio/mp4"
        length="24643179"
      />
      <itunes:title>Gemini 4 Argon: Google&apos;s Million-Token Flagship</itunes:title>
      <itunes:subtitle>A close read of Gemini 4 Argon&apos;s model card — a million-token window, thinking levels, implicit-only caching, and a lot of blanks.</itunes:subtitle>
      <itunes:summary><![CDATA[Google's Gemini 4 Argon is positioned as a frontier reasoning model, but the model card leaves out architecture, parameter count, and licensing, and the page quotes no pricing at all. We walk through what's actually documented: a 1,048,576-token unified input/output limit, low/medium/high thinking levels, implicit caching only, and a wide tool surface including function calling, code execution, Search grounding, and a preview computer-use mode. Then we separate Google's nineteen-row benchmark table from independent testing — Artificial Analysis at 53 on the Intelligence Index, Vals AI ranking it first of 49 models — and note where the losses cluster: terminal-based agentic coding and computer use. We also cover the staged rollout through the Fairwind Program and the launch discount that changes the cost-per-task math.]]></itunes:summary>
      <itunes:duration>1395</itunes:duration>
      <itunes:episode>6037</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gemini-4-argon-model-spotlight-96375.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gemini-4-argon-model-spotlight-96375.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>GPT-6 Astra: A Million Tokens, No Fine-Tuning</title>
      <description><![CDATA[OpenAI's GPT-6 Astra ships with a 1.05 million-token context window, ten native tools including computer use and MCP, and a five-level reasoning effort dial — but also no fine-tuning, a long-context pricing cliff, and benchmark numbers that shift depending on which harness ran them. This model spotlight walks through the spec sheet, the pricing math, and the contested ARC-AGI-3 results, plus the safety context: two training pauses in three months and reported departures from the safety team. If you're building on frontier models, this is the tradeoff map you need before you commit.]]></description>
      <link>https://myweirdprompts.com/episode/gpt-6-astra-model-spotlight-05518/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gpt-6-astra-model-spotlight-05518/</guid>
      <pubDate>Fri, 16 Oct 2026 20:59:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gpt-6-astra-model-spotlight-05518.m4a"
        type="audio/mp4"
        length="24658726"
      />
      <itunes:title>GPT-6 Astra: A Million Tokens, No Fine-Tuning</itunes:title>
      <itunes:subtitle>A million-token window, ten built-in tools, a 272K pricing cliff — and no fine-tuning. What GPT-6 Astra actually offers builders.</itunes:subtitle>
      <itunes:summary><![CDATA[OpenAI's GPT-6 Astra ships with a 1.05 million-token context window, ten native tools including computer use and MCP, and a five-level reasoning effort dial — but also no fine-tuning, a long-context pricing cliff, and benchmark numbers that shift depending on which harness ran them. This model spotlight walks through the spec sheet, the pricing math, and the contested ARC-AGI-3 results, plus the safety context: two training pauses in three months and reported departures from the safety team. If you're building on frontier models, this is the tradeoff map you need before you commit.]]></itunes:summary>
      <itunes:duration>1387</itunes:duration>
      <itunes:episode>6036</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gpt-6-astra-model-spotlight-05518.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gpt-6-astra-model-spotlight-05518.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Progressive Skills Discovery: When Bigger Context Windows Aren&apos;t Enough</title>
      <description><![CDATA[Progressive skills discovery is treated as solved — until your skill listing quietly drops descriptions and skills stop firing. This episode digs into how Anthropic's three-tier progressive disclosure actually works, why Claude Code truncates descriptions for your least-used skills first, and how Codex handles the same problem with different numbers. We survey the open-source landscape for auto-syncing with marketplace updates and semantic search over skill definitions, including why the one project that hit both is now abandoned. If you're maintaining a self-hosted skill catalogue behind an MCP gateway, this is the episode about whether the architecture you built has a future.]]></description>
      <link>https://myweirdprompts.com/episode/progressive-skills-discovery-context-windows-59282/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/progressive-skills-discovery-context-windows-59282/</guid>
      <pubDate>Fri, 16 Oct 2026 20:47:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/progressive-skills-discovery-context-windows-59282.m4a"
        type="audio/mp4"
        length="29353841"
      />
      <itunes:title>Progressive Skills Discovery: When Bigger Context Windows Aren&apos;t Enough</itunes:title>
      <itunes:subtitle>Bigger context windows were supposed to make skill discovery moot. Here&apos;s why the listing budget still bites at scale.</itunes:subtitle>
      <itunes:summary><![CDATA[Progressive skills discovery is treated as solved — until your skill listing quietly drops descriptions and skills stop firing. This episode digs into how Anthropic's three-tier progressive disclosure actually works, why Claude Code truncates descriptions for your least-used skills first, and how Codex handles the same problem with different numbers. We survey the open-source landscape for auto-syncing with marketplace updates and semantic search over skill definitions, including why the one project that hit both is now abandoned. If you're maintaining a self-hosted skill catalogue behind an MCP gateway, this is the episode about whether the architecture you built has a future.]]></itunes:summary>
      <itunes:duration>1683</itunes:duration>
      <itunes:episode>6033</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/progressive-skills-discovery-context-windows-59282.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/progressive-skills-discovery-context-windows-59282.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Blending Datasets vs Merging Weights for Fine-Tuning</title>
      <description><![CDATA[If your use case is narrow enough to find training data for, blending multiple datasets into one fine-tune run can beat merging finished checkpoints. This episode walks through the full pipeline: why uniform or size-proportional sampling is usually suboptimal, the formatting tax that scales with every source you add, and why deduplication isn't optional — memorization scales superlinearly with repetition, and one dedup study saw up to ~20% perplexity improvement while cutting runtime nearly 28%. Then the tools: Hugging Face's concatenate and interleave, datamix, Oumi, LLMTune, and NeMo's code-switched data scripts — and why there's no single product that owns all four stages.]]></description>
      <link>https://myweirdprompts.com/episode/dataset-blending-fine-tuning-guide-12789/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dataset-blending-fine-tuning-guide-12789/</guid>
      <pubDate>Fri, 16 Oct 2026 17:02:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dataset-blending-fine-tuning-guide-12789.m4a"
        type="audio/mp4"
        length="22358632"
      />
      <itunes:title>Blending Datasets vs Merging Weights for Fine-Tuning</itunes:title>
      <itunes:subtitle>Merging weights happens in parameter space. Blending datasets happens in data space — and the messy part is worth it.</itunes:subtitle>
      <itunes:summary><![CDATA[If your use case is narrow enough to find training data for, blending multiple datasets into one fine-tune run can beat merging finished checkpoints. This episode walks through the full pipeline: why uniform or size-proportional sampling is usually suboptimal, the formatting tax that scales with every source you add, and why deduplication isn't optional — memorization scales superlinearly with repetition, and one dedup study saw up to ~20% perplexity improvement while cutting runtime nearly 28%. Then the tools: Hugging Face's concatenate and interleave, datamix, Oumi, LLMTune, and NeMo's code-switched data scripts — and why there's no single product that owns all four stages.]]></itunes:summary>
      <itunes:duration>1256</itunes:duration>
      <itunes:episode>6032</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dataset-blending-fine-tuning-guide-12789.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dataset-blending-fine-tuning-guide-12789.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Who Do You Actually Hire to Train a Model?</title>
      <description><![CDATA[If you were building a team to train a model — not at OpenAI or DeepMind, but at a small lab or enterprise — who would you hire? The answer is stranger than the job postings suggest. At the engineering layer, titles are collapsing: Anthropic lists everyone as a Member of Technical Staff because the research/engineering boundary stopped being useful. At the data layer, they're multiplying: dataset engineers, licensing attorneys, annotation specialists. This episode walks through what each role actually does all day, which jobs fold into one person when you're small, and what happens when regulation — not fashion — drives demand for a career path that barely existed five years ago.]]></description>
      <link>https://myweirdprompts.com/episode/ai-training-team-hiring-85255/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-training-team-hiring-85255/</guid>
      <pubDate>Fri, 16 Oct 2026 12:14:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-training-team-hiring-85255.m4a"
        type="audio/mp4"
        length="26182258"
      />
      <itunes:title>Who Do You Actually Hire to Train a Model?</itunes:title>
      <itunes:subtitle>Research engineers and data engineers are diverging. Here&apos;s who you actually hire to train a model — and why the org chart keeps changing.</itunes:subtitle>
      <itunes:summary><![CDATA[If you were building a team to train a model — not at OpenAI or DeepMind, but at a small lab or enterprise — who would you hire? The answer is stranger than the job postings suggest. At the engineering layer, titles are collapsing: Anthropic lists everyone as a Member of Technical Staff because the research/engineering boundary stopped being useful. At the data layer, they're multiplying: dataset engineers, licensing attorneys, annotation specialists. This episode walks through what each role actually does all day, which jobs fold into one person when you're small, and what happens when regulation — not fashion — drives demand for a career path that barely existed five years ago.]]></itunes:summary>
      <itunes:duration>1486</itunes:duration>
      <itunes:episode>6030</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-training-team-hiring-85255.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-training-team-hiring-85255.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can You Snap Two Fine-Tunes Together Like LEGO?</title>
      <description><![CDATA[Daniel in Jerusalem has two NVIDIA Parakeet fine-tunes — one for Hebrew-English code-switching, one for niche technical vocabulary — and wants to know if he can merge them like LEGO bricks instead of retraining on a combined dataset. It's a question with a combinatorial edge: hospitals, legal teams, and multilingual clinics all want pairings nobody will ever build dedicated models for. We walk through what merging actually does to weights — task vectors, weight averaging, TIES-Merging, DARE, and LoRA adapter merging — and ask whether transcription's demand for exactness makes unified training the only honest path. Along the way: why word error rate can hide the exact failures you fine-tuned to fix, and why fine-tunes from the same base checkpoint come pre-aligned.]]></description>
      <link>https://myweirdprompts.com/episode/composable-asr-fine-tuning-35191/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/composable-asr-fine-tuning-35191/</guid>
      <pubDate>Fri, 16 Oct 2026 11:55:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/composable-asr-fine-tuning-35191.m4a"
        type="audio/mp4"
        length="37132865"
      />
      <itunes:title>Can You Snap Two Fine-Tunes Together Like LEGO?</itunes:title>
      <itunes:subtitle>Two fine-tunes, one base model, and a question from Jerusalem: can you merge Hebrew code-switching with technical vocab — without retraining?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel in Jerusalem has two NVIDIA Parakeet fine-tunes — one for Hebrew-English code-switching, one for niche technical vocabulary — and wants to know if he can merge them like LEGO bricks instead of retraining on a combined dataset. It's a question with a combinatorial edge: hospitals, legal teams, and multilingual clinics all want pairings nobody will ever build dedicated models for. We walk through what merging actually does to weights — task vectors, weight averaging, TIES-Merging, DARE, and LoRA adapter merging — and ask whether transcription's demand for exactness makes unified training the only honest path. Along the way: why word error rate can hide the exact failures you fine-tuned to fix, and why fine-tunes from the same base checkpoint come pre-aligned.]]></itunes:summary>
      <itunes:duration>2131</itunes:duration>
      <itunes:episode>6027</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/composable-asr-fine-tuning-35191.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/composable-asr-fine-tuning-35191.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Should Claude Code Start a New Session?</title>
      <description><![CDATA[Claude Code's own docs tell you to clear between tasks — so why does the tool make you do it by hand? This episode digs into the research showing that model performance degrades early and sharply as input grows, why your previous task is the most convincing distractor you could hand a model, and why every handoff tool on the market triggers on size instead of meaning. We walk through what a task-boundary hook would actually need to do, what already exists in sub-agents and handoff utilities, and what you give up when the interface makes the call for you.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-context-session-boundaries-55891/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-context-session-boundaries-55891/</guid>
      <pubDate>Fri, 16 Oct 2026 11:31:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-context-session-boundaries-55891.m4a"
        type="audio/mp4"
        length="27659641"
      />
      <itunes:title>When Should Claude Code Start a New Session?</itunes:title>
      <itunes:subtitle>Long context windows don&apos;t fix stale context — they hide it. Why task boundaries, not token counts, are the real problem.</itunes:subtitle>
      <itunes:summary><![CDATA[Claude Code's own docs tell you to clear between tasks — so why does the tool make you do it by hand? This episode digs into the research showing that model performance degrades early and sharply as input grows, why your previous task is the most convincing distractor you could hand a model, and why every handoff tool on the market triggers on size instead of meaning. We walk through what a task-boundary hook would actually need to do, what already exists in sub-agents and handoff utilities, and what you give up when the interface makes the call for you.]]></itunes:summary>
      <itunes:duration>1563</itunes:duration>
      <itunes:episode>6025</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-context-session-boundaries-55891.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-context-session-boundaries-55891.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What&apos;s Actually Inside the KV Cache?</title>
      <description><![CDATA[Every token an LLM generates has to attend to every token before it, and the KV cache is what keeps that from being recomputed from scratch — while quietly becoming the thing that caps your context length, your batch size, and your latency. This episode walks through what's actually stored (keys and values, never queries), why prefill is compute-bound and decode is memory-bandwidth-bound, and the formula that turns a single Llama 2 conversation into two gigabytes of cache. Then it maps the whole lineage of fixes onto three multiplicative dimensions — entry size, sequence length, and layers — from Multi-Query and Grouped-Query Attention through DeepSeek's Multi-head Latent Attention, DeepSeek Sparse Attention, cross-layer sharing, and the inference-only tricks like PagedAttention and KV quantization that need no retraining at all.]]></description>
      <link>https://myweirdprompts.com/episode/kv-cache-inference-optimization-60443/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/kv-cache-inference-optimization-60443/</guid>
      <pubDate>Fri, 16 Oct 2026 10:52:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/kv-cache-inference-optimization-60443.m4a"
        type="audio/mp4"
        length="29899011"
      />
      <itunes:title>What&apos;s Actually Inside the KV Cache?</itunes:title>
      <itunes:subtitle>890 bytes per token: why the KV cache is both what makes LLMs practical and what makes long context expensive.</itunes:subtitle>
      <itunes:summary><![CDATA[Every token an LLM generates has to attend to every token before it, and the KV cache is what keeps that from being recomputed from scratch — while quietly becoming the thing that caps your context length, your batch size, and your latency. This episode walks through what's actually stored (keys and values, never queries), why prefill is compute-bound and decode is memory-bandwidth-bound, and the formula that turns a single Llama 2 conversation into two gigabytes of cache. Then it maps the whole lineage of fixes onto three multiplicative dimensions — entry size, sequence length, and layers — from Multi-Query and Grouped-Query Attention through DeepSeek's Multi-head Latent Attention, DeepSeek Sparse Attention, cross-layer sharing, and the inference-only tricks like PagedAttention and KV quantization that need no retraining at all.]]></itunes:summary>
      <itunes:duration>1701</itunes:duration>
      <itunes:episode>6024</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/kv-cache-inference-optimization-60443.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/kv-cache-inference-optimization-60443.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Much Voice Data Is Enough?</title>
      <description><![CDATA[Daniel fine-tuned NVIDIA Parakeet V2 on two hours of LLM-generated sentences recorded on his phone, and Hebrew-English code-switching recognition jumped from roughly 20% to 60%. Now he wants to record fifteen minutes every morning for weeks — so the real question is where the fine-tuning curve flattens. The literature disagrees: a 2023 Singapore study on Mandarin-English code-switching suggests saturation can arrive as early as one to ten hours, while a Maltese study that trained thirty models found no diminishing returns for Whisper. A single-speaker Swedish study shows most of the gain captured by four hours, with a long shallow tail after. This episode digs into the contested research, what extra hours actually buy, why transcription noise may matter more than fresh clips, and whether a dataset built from LLM-written sentences is a diet version of how you really speak.]]></description>
      <link>https://myweirdprompts.com/episode/asr-fine-tuning-saturation-curve-99169/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/asr-fine-tuning-saturation-curve-99169/</guid>
      <pubDate>Fri, 16 Oct 2026 10:49:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/asr-fine-tuning-saturation-curve-99169.m4a"
        type="audio/mp4"
        length="21632426"
      />
      <itunes:title>How Much Voice Data Is Enough?</itunes:title>
      <itunes:subtitle>Two hours of phone recordings took Hebrew code-switching from 20% to 60%. But when does more data stop mattering?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel fine-tuned NVIDIA Parakeet V2 on two hours of LLM-generated sentences recorded on his phone, and Hebrew-English code-switching recognition jumped from roughly 20% to 60%. Now he wants to record fifteen minutes every morning for weeks — so the real question is where the fine-tuning curve flattens. The literature disagrees: a 2023 Singapore study on Mandarin-English code-switching suggests saturation can arrive as early as one to ten hours, while a Maltese study that trained thirty models found no diminishing returns for Whisper. A single-speaker Swedish study shows most of the gain captured by four hours, with a long shallow tail after. This episode digs into the contested research, what extra hours actually buy, why transcription noise may matter more than fresh clips, and whether a dataset built from LLM-written sentences is a diet version of how you really speak.]]></itunes:summary>
      <itunes:duration>1214</itunes:duration>
      <itunes:episode>6023</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/asr-fine-tuning-saturation-curve-99169.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/asr-fine-tuning-saturation-curve-99169.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tuning for Code-Switched Hebrew on 2 Hours of Audio</title>
      <description><![CDATA[Daniel fine-tuned a speech recognition model on two hours of his own voice and hit 60% accuracy on code-switched Hebrew, up from under 20% across every leading model he tested. The catch: Parakeet V2 is English-only, and Hebrew isn't in its 25-language sibling either. This episode digs into what a fine-tune actually captures — new tokens versus new accents, why control sentences matter, and whether a 30% English ratio is the right number. Plus: why fine-tuning helps where a model is ignorant and hurts where it's merely imprecise, and the hallucination risk that comes with over-controlling.]]></description>
      <link>https://myweirdprompts.com/episode/code-switched-hebrew-fine-tuning-19924/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/code-switched-hebrew-fine-tuning-19924/</guid>
      <pubDate>Fri, 16 Oct 2026 10:38:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/code-switched-hebrew-fine-tuning-19924.m4a"
        type="audio/mp4"
        length="23902207"
      />
      <itunes:title>Fine-Tuning for Code-Switched Hebrew on 2 Hours of Audio</itunes:title>
      <itunes:subtitle>Two hours of one man&apos;s voice beat models trained on 120,000 hours — but only for code-switched Hebrew.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel fine-tuned a speech recognition model on two hours of his own voice and hit 60% accuracy on code-switched Hebrew, up from under 20% across every leading model he tested. The catch: Parakeet V2 is English-only, and Hebrew isn't in its 25-language sibling either. This episode digs into what a fine-tune actually captures — new tokens versus new accents, why control sentences matter, and whether a 30% English ratio is the right number. Plus: why fine-tuning helps where a model is ignorant and hurts where it's merely imprecise, and the hallucination risk that comes with over-controlling.]]></itunes:summary>
      <itunes:duration>1345</itunes:duration>
      <itunes:episode>6022</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/code-switched-hebrew-fine-tuning-19924.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/code-switched-hebrew-fine-tuning-19924.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Chat Templates Break Models Silently</title>
      <description><![CDATA[Your JSON messages don't reach the GPU as JSON. Somewhere in between, a Jinja chat template flattens them into a string of reserved special tokens — the exact format each model family was trained on. Llama 3 brackets roles with start_header and end_header; OpenAI's ChatML embedded the role in a single opening token; gpt-oss uses Harmony with separate stop tokens for "done talking" and "calling a tool"; Gemma has no system role at all; DeepSeek uses full-width CJK pipes. This episode walks through what special tokens actually are, how tokenizers register them, why the add_generation_prompt flag matters more than anyone thinks, and why a format mismatch never throws an error — it just produces a model that's a bit dimmer than it should be.]]></description>
      <link>https://myweirdprompts.com/episode/chat-template-special-tokens-79630/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/chat-template-special-tokens-79630/</guid>
      <pubDate>Fri, 16 Oct 2026 10:00:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/chat-template-special-tokens-79630.m4a"
        type="audio/mp4"
        length="31392259"
      />
      <itunes:title>Why Chat Templates Break Models Silently</itunes:title>
      <itunes:subtitle>Every chat API flattens your messages into special tokens. Get one delimiter wrong and the model just gets quietly dumber.</itunes:subtitle>
      <itunes:summary><![CDATA[Your JSON messages don't reach the GPU as JSON. Somewhere in between, a Jinja chat template flattens them into a string of reserved special tokens — the exact format each model family was trained on. Llama 3 brackets roles with start_header and end_header; OpenAI's ChatML embedded the role in a single opening token; gpt-oss uses Harmony with separate stop tokens for "done talking" and "calling a tool"; Gemma has no system role at all; DeepSeek uses full-width CJK pipes. This episode walks through what special tokens actually are, how tokenizers register them, why the add_generation_prompt flag matters more than anyone thinks, and why a format mismatch never throws an error — it just produces a model that's a bit dimmer than it should be.]]></itunes:summary>
      <itunes:duration>1793</itunes:duration>
      <itunes:episode>6021</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/chat-template-special-tokens-79630.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/chat-template-special-tokens-79630.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside the Decode Loop: How LLMs Generate One Token at a Time</title>
      <description><![CDATA[The model scores; the engine chooses. That split is the whole story of autoregressive decoding, and this episode walks through it token by token. Starting from a warm KV cache, we trace a single decode iteration — the forward pass, the query-key-value projections, the logits row that actually gets used, and the sampling step that feeds the next token back in. Along the way we work a concrete example ("The cat sat.") to show why keys and values get cached but queries never do, and why skipping the cache would mean reprocessing over eight million positions to generate a four-thousand-token answer. Then we put the loop on a GPU, where the real bottleneck isn't arithmetic at all: it's memory bandwidth, and the only parallelism that exists is across independent requests.]]></description>
      <link>https://myweirdprompts.com/episode/inside-llm-decode-loop-23236/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/inside-llm-decode-loop-23236/</guid>
      <pubDate>Thu, 15 Oct 2026 20:00:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/inside-llm-decode-loop-23236.m4a"
        type="audio/mp4"
        length="28527127"
      />
      <itunes:title>Inside the Decode Loop: How LLMs Generate One Token at a Time</itunes:title>
      <itunes:subtitle>The model doesn&apos;t choose tokens or decide when to stop — the engine does. A step-by-step tour of one decode iteration.</itunes:subtitle>
      <itunes:summary><![CDATA[The model scores; the engine chooses. That split is the whole story of autoregressive decoding, and this episode walks through it token by token. Starting from a warm KV cache, we trace a single decode iteration — the forward pass, the query-key-value projections, the logits row that actually gets used, and the sampling step that feeds the next token back in. Along the way we work a concrete example ("The cat sat.") to show why keys and values get cached but queries never do, and why skipping the cache would mean reprocessing over eight million positions to generate a four-thousand-token answer. Then we put the loop on a GPU, where the real bottleneck isn't arithmetic at all: it's memory bandwidth, and the only parallelism that exists is across independent requests.]]></itunes:summary>
      <itunes:duration>1608</itunes:duration>
      <itunes:episode>6012</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/inside-llm-decode-loop-23236.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/inside-llm-decode-loop-23236.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside LLM Inference: Prefill, Decode, and the KV Cache</title>
      <description><![CDATA[A single request to an LLM isn't one computation—it's a stack of stages wearing a trench coat. This episode walks the full pipeline: flattening a chat transcript into one flat document with special role tokens, tokenizing text into IDs, the massively parallel prefill pass that produces your first token, and the strictly sequential decode loop that produces every token after. Along the way: why forgetting one flag called add_generation_prompt makes the model write your half of the conversation, why "encoding" and "decoding" each mean two different things, and how a KV cache can quietly outgrow the model weights it's serving. If you've ever wondered where your time-to-first-token actually goes, this is the tour.]]></description>
      <link>https://myweirdprompts.com/episode/llm-inference-prefill-decode-kv-cache-34131/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-inference-prefill-decode-kv-cache-34131/</guid>
      <pubDate>Thu, 15 Oct 2026 19:59:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-inference-prefill-decode-kv-cache-34131.m4a"
        type="audio/mp4"
        length="36434015"
      />
      <itunes:title>Inside LLM Inference: Prefill, Decode, and the KV Cache</itunes:title>
      <itunes:subtitle>One 8K-token sequence on a 13B model eats 6.7GB of KV cache—while the weights are only 26GB. Here&apos;s what actually happens per request.</itunes:subtitle>
      <itunes:summary><![CDATA[A single request to an LLM isn't one computation—it's a stack of stages wearing a trench coat. This episode walks the full pipeline: flattening a chat transcript into one flat document with special role tokens, tokenizing text into IDs, the massively parallel prefill pass that produces your first token, and the strictly sequential decode loop that produces every token after. Along the way: why forgetting one flag called add_generation_prompt makes the model write your half of the conversation, why "encoding" and "decoding" each mean two different things, and how a KV cache can quietly outgrow the model weights it's serving. If you've ever wondered where your time-to-first-token actually goes, this is the tour.]]></itunes:summary>
      <itunes:duration>2079</itunes:duration>
      <itunes:episode>6011</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-inference-prefill-decode-kv-cache-34131.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-inference-prefill-decode-kv-cache-34131.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Vertex AI Is Now the Gemini Agent Platform</title>
      <description><![CDATA[Vertex AI was Google's 2021 bet on unifying ML tooling — pipelines, feature stores, model registry — into one platform. In April 2026, Google renamed it the Gemini Enterprise Agent Platform and said all Vertex services would be delivered exclusively through it. So what actually changed? This episode traces the lineage from AutoML and AI Platform through the 2021 launch to today's agent-first rebrand, untangles the five Google products that all sound identical, and lands on a decision rule for when the enterprise endpoint is worth it over the plain Gemini Developer API — plus how it stacks up against Bedrock and Azure.]]></description>
      <link>https://myweirdprompts.com/episode/vertex-ai-gemini-agent-platform-27642/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vertex-ai-gemini-agent-platform-27642/</guid>
      <pubDate>Thu, 15 Oct 2026 19:07:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vertex-ai-gemini-agent-platform-27642.m4a"
        type="audio/mp4"
        length="27865712"
      />
      <itunes:title>Vertex AI Is Now the Gemini Agent Platform</itunes:title>
      <itunes:subtitle>Vertex AI just became the Gemini Enterprise Agent Platform. Here&apos;s the family tree, the five products, and when to actually move.</itunes:subtitle>
      <itunes:summary><![CDATA[Vertex AI was Google's 2021 bet on unifying ML tooling — pipelines, feature stores, model registry — into one platform. In April 2026, Google renamed it the Gemini Enterprise Agent Platform and said all Vertex services would be delivered exclusively through it. So what actually changed? This episode traces the lineage from AutoML and AI Platform through the 2021 launch to today's agent-first rebrand, untangles the five Google products that all sound identical, and lands on a decision rule for when the enterprise endpoint is worth it over the plain Gemini Developer API — plus how it stacks up against Bedrock and Azure.]]></itunes:summary>
      <itunes:duration>1591</itunes:duration>
      <itunes:episode>6009</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vertex-ai-gemini-agent-platform-27642.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vertex-ai-gemini-agent-platform-27642.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Codex Writes Code for a Dead API</title>
      <description><![CDATA[A coding model confidently hands you a deprecated SDK. Claude denies a newer Claude exists. These aren't hallucinations — they're a knowledge cutoff problem wearing a costume. This episode digs into the "Know Thyself" study from the University of Chicago, where ten frontier models guessed who wrote a piece of text and landed at essentially random accuracy. We trace the two failures people keep lumping together: not knowing the current API surface, and not knowing itself. Then we look at what the labs actually built — Anthropic's published system prompts, OpenAI's Docs skill, and a new "context layer" startup wave — and a Superglue benchmark that scored 68% for the best general-purpose model against 91% for a purpose-built integration layer. If the fix lives outside the weights, what does that mean for everything we credit the model for?]]></description>
      <link>https://myweirdprompts.com/episode/stale-model-api-knowledge-75360/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/stale-model-api-knowledge-75360/</guid>
      <pubDate>Thu, 15 Oct 2026 19:07:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/stale-model-api-knowledge-75360.m4a"
        type="audio/mp4"
        length="17777677"
      />
      <itunes:title>Why Codex Writes Code for a Dead API</itunes:title>
      <itunes:subtitle>Models don&apos;t hallucinate your API — they quote real docs from the wrong year. Here&apos;s why, and what actually fixes it.</itunes:subtitle>
      <itunes:summary><![CDATA[A coding model confidently hands you a deprecated SDK. Claude denies a newer Claude exists. These aren't hallucinations — they're a knowledge cutoff problem wearing a costume. This episode digs into the "Know Thyself" study from the University of Chicago, where ten frontier models guessed who wrote a piece of text and landed at essentially random accuracy. We trace the two failures people keep lumping together: not knowing the current API surface, and not knowing itself. Then we look at what the labs actually built — Anthropic's published system prompts, OpenAI's Docs skill, and a new "context layer" startup wave — and a Superglue benchmark that scored 68% for the best general-purpose model against 91% for a purpose-built integration layer. If the fix lives outside the weights, what does that mean for everything we credit the model for?]]></itunes:summary>
      <itunes:duration>996</itunes:duration>
      <itunes:episode>6008</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/stale-model-api-knowledge-75360.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/stale-model-api-knowledge-75360.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>6,000 Episodes and a GPU Bill: Inside My Weird Prompts</title>
      <description><![CDATA[My Weird Prompts just crossed 6,000 episodes — and the human behind it, Daniel Rosehill, is stepping out from behind the pipeline to talk about how it actually runs. From the TTS struggles that nearly derailed the whole thing to the shift to DeepSeek 4.1 Flash as the script agent, this is a rare look at the machinery and the person operating it. It's also a reflection on what it means when a daily AI-generated podcast quietly turns into a journal, and why some stories — like an apartment eviction in Jerusalem — keep resurfacing.]]></description>
      <link>https://myweirdprompts.com/episode/my-weird-prompts-6000-episodes-59015/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/my-weird-prompts-6000-episodes-59015/</guid>
      <pubDate>Thu, 15 Oct 2026 15:35:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/my-weird-prompts-6000-episodes-59015.m4a"
        type="audio/mp4"
        length="21048579"
      />
      <itunes:title>6,000 Episodes and a GPU Bill: Inside My Weird Prompts</itunes:title>
      <itunes:subtitle>Six thousand episodes, one human in the loop, and a Modal bill that keeps growing. A behind-the-scenes update on the podcast.</itunes:subtitle>
      <itunes:summary><![CDATA[My Weird Prompts just crossed 6,000 episodes — and the human behind it, Daniel Rosehill, is stepping out from behind the pipeline to talk about how it actually runs. From the TTS struggles that nearly derailed the whole thing to the shift to DeepSeek 4.1 Flash as the script agent, this is a rare look at the machinery and the person operating it. It's also a reflection on what it means when a daily AI-generated podcast quietly turns into a journal, and why some stories — like an apartment eviction in Jerusalem — keep resurfacing.]]></itunes:summary>
      <itunes:duration>1202</itunes:duration>
      <itunes:episode>6000</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/my-weird-prompts-6000-episodes-59015.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/my-weird-prompts-6000-episodes-59015.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Hebrew in Latin Letters: A TTS Pipeline Fix</title>
      <description><![CDATA[When your text-to-speech reads "makolet" as MACK-oh-let, you have a language identification gap, not a bug. No LID model — GlotLID, fastText, NLLB's lid218e — has a heb_Latn label, so Hebrew written in Latin letters is invisible to every system downstream. This episode walks through the token classifier Daniel shipped to fix it: BIO tagging over xlm-roberta-base, character-span output, and a three-pass pipeline that converts Latin-script Hebrew to Hebrew script before handing it to a Hebrew voice. The real story is the dataset methodology — term-seeded generation, blind annotation by a different model family enforced in code, human review of disagreements, and term-disjoint splits. Plus why precision beats recall here, why a flagged span is work rather than a label, and what a Parakeet fine-tune on dictated audio adds to the picture.]]></description>
      <link>https://myweirdprompts.com/episode/hebrew-latin-token-classifier-46654/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hebrew-latin-token-classifier-46654/</guid>
      <pubDate>Thu, 15 Oct 2026 13:30:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hebrew-latin-token-classifier-46654.m4a"
        type="audio/mp4"
        length="34995126"
      />
      <itunes:title>Hebrew in Latin Letters: A TTS Pipeline Fix</itunes:title>
      <itunes:subtitle>A token classifier that finds Hebrew words written in Latin letters so TTS stops mangling them — built for under $3.</itunes:subtitle>
      <itunes:summary><![CDATA[When your text-to-speech reads "makolet" as MACK-oh-let, you have a language identification gap, not a bug. No LID model — GlotLID, fastText, NLLB's lid218e — has a heb_Latn label, so Hebrew written in Latin letters is invisible to every system downstream. This episode walks through the token classifier Daniel shipped to fix it: BIO tagging over xlm-roberta-base, character-span output, and a three-pass pipeline that converts Latin-script Hebrew to Hebrew script before handing it to a Hebrew voice. The real story is the dataset methodology — term-seeded generation, blind annotation by a different model family enforced in code, human review of disagreements, and term-disjoint splits. Plus why precision beats recall here, why a flagged span is work rather than a label, and what a Parakeet fine-tune on dictated audio adds to the picture.]]></itunes:summary>
      <itunes:duration>2008</itunes:duration>
      <itunes:episode>5994</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hebrew-latin-token-classifier-46654.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hebrew-latin-token-classifier-46654.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>NVIDIA ASR: Parakeet, Canary, and Nemotron Explained</title>
      <description><![CDATA[NVIDIA's speech recognition lineup isn't one model — it's a portfolio, and the names don't tell you what you're getting. Parakeet, Canary, and Nemotron Speech are brands, not architectures; underneath, nearly all of them share the same FastConformer encoder. This episode walks the whole family tree one model at a time: why Parakeet v3 is not a straight upgrade over v2 (it's slightly worse on English), how the Parakeet unified model makes the streaming tax visible in a single page of numbers, why Canary's forty-second clip limit catches people out, and where the non-commercial license hides. Plus a look at a new fine-tune of Parakeet v2 that handles Hebrew words mixed into English sentences, shipped with on-device GGUF builds. Less benchmark, more map — the goal is knowing which model fits which workflow.]]></description>
      <link>https://myweirdprompts.com/episode/nvidia-asr-parakeet-canary-nemotron-17221/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/nvidia-asr-parakeet-canary-nemotron-17221/</guid>
      <pubDate>Thu, 15 Oct 2026 13:30:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/nvidia-asr-parakeet-canary-nemotron-17221.m4a"
        type="audio/mp4"
        length="33575741"
      />
      <itunes:title>NVIDIA ASR: Parakeet, Canary, and Nemotron Explained</itunes:title>
      <itunes:subtitle>Whisper gives you one dial. NVIDIA gives you five. A practical map of the Parakeet, Canary, and Nemotron speech models.</itunes:subtitle>
      <itunes:summary><![CDATA[NVIDIA's speech recognition lineup isn't one model — it's a portfolio, and the names don't tell you what you're getting. Parakeet, Canary, and Nemotron Speech are brands, not architectures; underneath, nearly all of them share the same FastConformer encoder. This episode walks the whole family tree one model at a time: why Parakeet v3 is not a straight upgrade over v2 (it's slightly worse on English), how the Parakeet unified model makes the streaming tax visible in a single page of numbers, why Canary's forty-second clip limit catches people out, and where the non-commercial license hides. Plus a look at a new fine-tune of Parakeet v2 that handles Hebrew words mixed into English sentences, shipped with on-device GGUF builds. Less benchmark, more map — the goal is knowing which model fits which workflow.]]></itunes:summary>
      <itunes:duration>1925</itunes:duration>
      <itunes:episode>5995</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/nvidia-asr-parakeet-canary-nemotron-17221.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/nvidia-asr-parakeet-canary-nemotron-17221.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Batch Inference on Your Own GPU: vLLM, SGLang, Ollama, llama.cpp</title>
      <description><![CDATA[Hosted batch inference is one of the best deals in AI right now — half price, no latency requirement, just hand over a file. But what if you want that on your own GPU? This episode unpacks why the hosted batch API is really a scheduling and billing construct, and what you actually have to build locally to reproduce it. We walk through continuous batching, the KV cache budget that secretly governs your concurrency, and the two shapes a local batch job can take: offline in-process, or server plus concurrent client. Then we compare the real options — vLLM's offline class and its newer run-batch command, SGLang's prefix-sharing strengths and AMD weak spots, Ollama's development-tool defaults, llama.cpp's portability, and why Hugging Face's text generation inference is no longer a starting point.]]></description>
      <link>https://myweirdprompts.com/episode/self-hosted-batch-inference-gpu-37921/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/self-hosted-batch-inference-gpu-37921/</guid>
      <pubDate>Thu, 15 Oct 2026 09:34:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/self-hosted-batch-inference-gpu-37921.m4a"
        type="audio/mp4"
        length="22584972"
      />
      <itunes:title>Batch Inference on Your Own GPU: vLLM, SGLang, Ollama, llama.cpp</itunes:title>
      <itunes:subtitle>Hosted batch APIs sell you half-price tokens. Here&apos;s how to get the same file-in, file-out workflow on your own Linux box.</itunes:subtitle>
      <itunes:summary><![CDATA[Hosted batch inference is one of the best deals in AI right now — half price, no latency requirement, just hand over a file. But what if you want that on your own GPU? This episode unpacks why the hosted batch API is really a scheduling and billing construct, and what you actually have to build locally to reproduce it. We walk through continuous batching, the KV cache budget that secretly governs your concurrency, and the two shapes a local batch job can take: offline in-process, or server plus concurrent client. Then we compare the real options — vLLM's offline class and its newer run-batch command, SGLang's prefix-sharing strengths and AMD weak spots, Ollama's development-tool defaults, llama.cpp's portability, and why Hugging Face's text generation inference is no longer a starting point.]]></itunes:summary>
      <itunes:duration>1272</itunes:duration>
      <itunes:episode>5991</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/self-hosted-batch-inference-gpu-37921.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/self-hosted-batch-inference-gpu-37921.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Teaching a Voice Engine to Say &quot;Yalla&quot; Right</title>
      <description><![CDATA[Daniel built a small token classifier and published it on Hugging Face for one reason: this podcast kept mangling his second language. The model finds Hebrew words written in Latin letters inside English sentences so a voice engine can pronounce them properly. But spotting romanized Hebrew is harder than it sounds — there's no single transliteration standard, the vowels often aren't there to find, and words like chutzpah sit in a gray zone between two languages. This episode unpacks how token classification actually works, what span F1 of 0.941 on unseen words really means, and why a correctly detected word and a correctly pronounced word are two completely different achievements.]]></description>
      <link>https://myweirdprompts.com/episode/romanized-hebrew-token-classifier-61085/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/romanized-hebrew-token-classifier-61085/</guid>
      <pubDate>Thu, 15 Oct 2026 08:48:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/romanized-hebrew-token-classifier-61085.m4a"
        type="audio/mp4"
        length="28959155"
      />
      <itunes:title>Teaching a Voice Engine to Say &quot;Yalla&quot; Right</itunes:title>
      <itunes:subtitle>A small token classifier that spots Hebrew words hiding in English sentences — and why finding the word is the easy half.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel built a small token classifier and published it on Hugging Face for one reason: this podcast kept mangling his second language. The model finds Hebrew words written in Latin letters inside English sentences so a voice engine can pronounce them properly. But spotting romanized Hebrew is harder than it sounds — there's no single transliteration standard, the vowels often aren't there to find, and words like chutzpah sit in a gray zone between two languages. This episode unpacks how token classification actually works, what span F1 of 0.941 on unseen words really means, and why a correctly detected word and a correctly pronounced word are two completely different achievements.]]></itunes:summary>
      <itunes:duration>1642</itunes:duration>
      <itunes:episode>5982</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/romanized-hebrew-token-classifier-61085.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/romanized-hebrew-token-classifier-61085.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Batch Inference: Four Doors, Half Price</title>
      <description><![CDATA[Batch inference has been sitting at a 50% discount for years, and most teams still fire everything synchronously because it's easier. This episode maps the four routes into batch: first-party labs like OpenAI, Anthropic, Gemini, and Bedrock; "Flex" tiers that give you batch pricing without the file-upload plumbing; DeepSeek's off-peak pricing, which is batch economics with no batch product at all; and open-weight providers like Fireworks, Groq, Together, and DeepInfra, where the discount percentages vary wildly and the base price matters more than the discount. We also dig into OpenRouter's new batch API — one key, inline request arrays, no JSONL — and why a middleman routing layer changes the batch market from a reason to pick a provider into a commodity you route through. Plus the details almost nobody finds on their own: which discounts stack with prompt caching, why Anthropic recommends the one-hour cache TTL for batches, and why the 24-hour window is a ceiling, not a promise.]]></description>
      <link>https://myweirdprompts.com/episode/batch-inference-provider-guide-03169/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/batch-inference-provider-guide-03169/</guid>
      <pubDate>Thu, 15 Oct 2026 08:10:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/batch-inference-provider-guide-03169.m4a"
        type="audio/mp4"
        length="35881759"
      />
      <itunes:title>Batch Inference: Four Doors, Half Price</itunes:title>
      <itunes:subtitle>Same tokens, half the price — and almost nobody using it properly. A tour of batch inference from OpenAI to OpenRouter.</itunes:subtitle>
      <itunes:summary><![CDATA[Batch inference has been sitting at a 50% discount for years, and most teams still fire everything synchronously because it's easier. This episode maps the four routes into batch: first-party labs like OpenAI, Anthropic, Gemini, and Bedrock; "Flex" tiers that give you batch pricing without the file-upload plumbing; DeepSeek's off-peak pricing, which is batch economics with no batch product at all; and open-weight providers like Fireworks, Groq, Together, and DeepInfra, where the discount percentages vary wildly and the base price matters more than the discount. We also dig into OpenRouter's new batch API — one key, inline request arrays, no JSONL — and why a middleman routing layer changes the batch market from a reason to pick a provider into a commodity you route through. Plus the details almost nobody finds on their own: which discounts stack with prompt caching, why Anthropic recommends the one-hour cache TTL for batches, and why the 24-hour window is a ceiling, not a promise.]]></itunes:summary>
      <itunes:duration>2054</itunes:duration>
      <itunes:episode>5981</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/batch-inference-provider-guide-03169.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/batch-inference-provider-guide-03169.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Merging vs Distillation: Can Two Models Become One?</title>
      <description><![CDATA[When a speech recognizer and a correction model do one job, can they be folded into a single set of weights? The answer depends entirely on a distinction most coverage skips: model merging and distillation are not the same operation. Merging combines parameters from models that share a pretrained ancestor — no training, no extra inference cost, same parameter count. Distillation trains a new model to imitate others, and it's the only route that works across different architectures. This episode walks through task arithmetic, weight averaging, re-basin permutations, and LoRA merging to find exactly where each one breaks, then follows the distillation route from FuseLLM to cross-architecture transfers between CNNs, transformers, and Mamba. Along the way: why an elaborate feature-alignment loss made a student model worse than plain output matching, and why you can't average a lesson into a model that never learned it.]]></description>
      <link>https://myweirdprompts.com/episode/model-merging-vs-distillation-91804/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/model-merging-vs-distillation-91804/</guid>
      <pubDate>Wed, 14 Oct 2026 16:17:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/model-merging-vs-distillation-91804.m4a"
        type="audio/mp4"
        length="26176275"
      />
      <itunes:title>Merging vs Distillation: Can Two Models Become One?</itunes:title>
      <itunes:subtitle>Merging and distillation get lumped together, but they&apos;re opposites. One composes existing capability; the other has to teach it.</itunes:subtitle>
      <itunes:summary><![CDATA[When a speech recognizer and a correction model do one job, can they be folded into a single set of weights? The answer depends entirely on a distinction most coverage skips: model merging and distillation are not the same operation. Merging combines parameters from models that share a pretrained ancestor — no training, no extra inference cost, same parameter count. Distillation trains a new model to imitate others, and it's the only route that works across different architectures. This episode walks through task arithmetic, weight averaging, re-basin permutations, and LoRA merging to find exactly where each one breaks, then follows the distillation route from FuseLLM to cross-architecture transfers between CNNs, transformers, and Mamba. Along the way: why an elaborate feature-alignment loss made a student model worse than plain output matching, and why you can't average a lesson into a model that never learned it.]]></itunes:summary>
      <itunes:duration>1480</itunes:duration>
      <itunes:episode>5975</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/model-merging-vs-distillation-91804.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/model-merging-vs-distillation-91804.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>MCP Security: Tool Poisoning and Agent Identity</title>
      <description><![CDATA[Model Context Protocol servers are multiplying fast — 97 million monthly SDK downloads, over 10,000 public servers — but the protocol assumes every server is trustworthy. Tool descriptions load into the model's reasoning context with system-prompt authority, and there's no cryptographic attestation or native injection detection to catch tampering. This episode works through what MCP-specific observability actually requires: tool-call logging rather than HTTP logging, agent identity as a first-class principal, client-side hooks for the 86% of servers running locally, and write-time signed audit trails. Then the attacks that break conventional monitoring — tool poisoning, rug pulls like CVE-2025-54136, tool shadowing, and the Microsoft-described pattern where every individual action is authorized but the combination isn't. Plus the exposure numbers: 492 unauthenticated MCP servers found exposed to the open internet, a count that nearly tripled on rescan. Anchored by three products with three different postures — Mintive, Obot, and Docker MCP Gateway.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-security-observability-06242/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-security-observability-06242/</guid>
      <pubDate>Wed, 14 Oct 2026 15:44:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-security-observability-06242.m4a"
        type="audio/mp4"
        length="33622342"
      />
      <itunes:title>MCP Security: Tool Poisoning and Agent Identity</itunes:title>
      <itunes:subtitle>MCP servers trust their own tool descriptions. That design choice creates a whole new class of attack — and a new observability problem.</itunes:subtitle>
      <itunes:summary><![CDATA[Model Context Protocol servers are multiplying fast — 97 million monthly SDK downloads, over 10,000 public servers — but the protocol assumes every server is trustworthy. Tool descriptions load into the model's reasoning context with system-prompt authority, and there's no cryptographic attestation or native injection detection to catch tampering. This episode works through what MCP-specific observability actually requires: tool-call logging rather than HTTP logging, agent identity as a first-class principal, client-side hooks for the 86% of servers running locally, and write-time signed audit trails. Then the attacks that break conventional monitoring — tool poisoning, rug pulls like CVE-2025-54136, tool shadowing, and the Microsoft-described pattern where every individual action is authorized but the combination isn't. Plus the exposure numbers: 492 unauthenticated MCP servers found exposed to the open internet, a count that nearly tripled on rescan. Anchored by three products with three different postures — Mintive, Obot, and Docker MCP Gateway.]]></itunes:summary>
      <itunes:duration>1920</itunes:duration>
      <itunes:episode>5973</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-security-observability-06242.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-security-observability-06242.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Swapping Your Linux Kernel: What Actually Changes</title>
      <description><![CDATA[On Ubuntu you're not locked to the kernel your distribution shipped — you can pull a newer upstream one, stay on an older one, or install a kernel someone rebuilt for a specific kind of work. This episode maps the whole landscape: how Ubuntu decides what to ship (including release candidates under its 2024 policy), what actually changes between kernel versions, why hardware support and performance are two very different reasons to switch, and what Liquorix, XanMod, and the now-mainline PREEMPT_RT kernel each optimize for. The most interesting moment comes when XanMod's own documentation labels the AVX-512 build as having no kernel benefit — the vendor of the CPU-optimized kernel undermining the enthusiast argument. Along the way: EEVDF replacing the completely fair scheduler, the throughput-versus-latency axis every tuning choice sits on, and why desktop and server motivations split apart.]]></description>
      <link>https://myweirdprompts.com/episode/linux-kernel-swap-performance-62716/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/linux-kernel-swap-performance-62716/</guid>
      <pubDate>Wed, 14 Oct 2026 15:15:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/linux-kernel-swap-performance-62716.m4a"
        type="audio/mp4"
        length="27689085"
      />
      <itunes:title>Swapping Your Linux Kernel: What Actually Changes</itunes:title>
      <itunes:subtitle>Your kernel is swappable without reinstalling anything. But do Liquorix and XanMod actually beat the default? The benchmarks say less than the mark...</itunes:subtitle>
      <itunes:summary><![CDATA[On Ubuntu you're not locked to the kernel your distribution shipped — you can pull a newer upstream one, stay on an older one, or install a kernel someone rebuilt for a specific kind of work. This episode maps the whole landscape: how Ubuntu decides what to ship (including release candidates under its 2024 policy), what actually changes between kernel versions, why hardware support and performance are two very different reasons to switch, and what Liquorix, XanMod, and the now-mainline PREEMPT_RT kernel each optimize for. The most interesting moment comes when XanMod's own documentation labels the AVX-512 build as having no kernel benefit — the vendor of the CPU-optimized kernel undermining the enthusiast argument. Along the way: EEVDF replacing the completely fair scheduler, the throughput-versus-latency axis every tuning choice sits on, and why desktop and server motivations split apart.]]></itunes:summary>
      <itunes:duration>1560</itunes:duration>
      <itunes:episode>5970</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/linux-kernel-swap-performance-62716.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/linux-kernel-swap-performance-62716.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Compiling From Source: What You Actually Get</title>
      <description><![CDATA[Daniel spent twenty years on Linux desktops without understanding why anyone would compile software from source. This episode takes that question seriously, starting with the assumption everyone carries: that a self-built binary must be better optimized. It mostly isn't. A 1999 kernel recompile gained 1.8 percent; a 2026 Modern C++ benchmark found GCC with profile-guided optimization ran 9.4 percent slower than a plain O2 build, because the optimizer's bets didn't pay off. So why does the source tradition survive? Not for speed — for control over what gets built. USE flags let you compile out codecs, protocols, and integrations you never use, and code that isn't there can't be exploited. We walk through the two poles, Ubuntu's signed reproducible binaries versus Gentoo's Portage, what actually happens during a build, and why watching the compiler work is itself part of what people are buying.]]></description>
      <link>https://myweirdprompts.com/episode/compiling-from-source-tradeoffs-56838/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/compiling-from-source-tradeoffs-56838/</guid>
      <pubDate>Wed, 14 Oct 2026 14:59:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/compiling-from-source-tradeoffs-56838.m4a"
        type="audio/mp4"
        length="29492873"
      />
      <itunes:title>Compiling From Source: What You Actually Get</itunes:title>
      <itunes:subtitle>Source builds aren&apos;t faster — they&apos;re smaller. What USE flags, compiler flags, and the build feedback loop actually buy you.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel spent twenty years on Linux desktops without understanding why anyone would compile software from source. This episode takes that question seriously, starting with the assumption everyone carries: that a self-built binary must be better optimized. It mostly isn't. A 1999 kernel recompile gained 1.8 percent; a 2026 Modern C++ benchmark found GCC with profile-guided optimization ran 9.4 percent slower than a plain O2 build, because the optimizer's bets didn't pay off. So why does the source tradition survive? Not for speed — for control over what gets built. USE flags let you compile out codecs, protocols, and integrations you never use, and code that isn't there can't be exploited. We walk through the two poles, Ubuntu's signed reproducible binaries versus Gentoo's Portage, what actually happens during a build, and why watching the compiler work is itself part of what people are buying.]]></itunes:summary>
      <itunes:duration>1671</itunes:duration>
      <itunes:episode>5965</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/compiling-from-source-tradeoffs-56838.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/compiling-from-source-tradeoffs-56838.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Containers vs VMs for GPU Inference: What You Actually Pay</title>
      <description><![CDATA[Daniel asked a simple question: what do you actually pay when you move an open-weight model off bare metal and into a container or a KVM virtual machine with PCIe passthrough? The published numbers don't agree — one paper measures containers at 0.03% overhead, another measures VMs at 21% average and up to 36% on the worst run. Both are correct. This episode unpacks why the benchmark literature quietly conflates four different isolation models under the single word "virtualization," where the overhead actually lives (spoiler: it's the IOMMU and the host scheduler, not the GPU), and which workload shapes feel it most. We also walk through a real deployment that booted perfectly, reported all eight GPUs healthy, and had silently lost GPU-to-GPU peer-to-peer — the kind of failure that doesn't show up until you're already in production.]]></description>
      <link>https://myweirdprompts.com/episode/gpu-virtualization-overhead-inference-41095/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gpu-virtualization-overhead-inference-41095/</guid>
      <pubDate>Wed, 14 Oct 2026 14:48:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gpu-virtualization-overhead-inference-41095.m4a"
        type="audio/mp4"
        length="32278078"
      />
      <itunes:title>Containers vs VMs for GPU Inference: What You Actually Pay</itunes:title>
      <itunes:subtitle>Containers cost 0.03%. Passthrough VMs cost 1%... or 36%. Same hardware, different systems — here&apos;s how to tell which you&apos;ll get.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asked a simple question: what do you actually pay when you move an open-weight model off bare metal and into a container or a KVM virtual machine with PCIe passthrough? The published numbers don't agree — one paper measures containers at 0.03% overhead, another measures VMs at 21% average and up to 36% on the worst run. Both are correct. This episode unpacks why the benchmark literature quietly conflates four different isolation models under the single word "virtualization," where the overhead actually lives (spoiler: it's the IOMMU and the host scheduler, not the GPU), and which workload shapes feel it most. We also walk through a real deployment that booted perfectly, reported all eight GPUs healthy, and had silently lost GPU-to-GPU peer-to-peer — the kind of failure that doesn't show up until you're already in production.]]></itunes:summary>
      <itunes:duration>1837</itunes:duration>
      <itunes:episode>5964</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gpu-virtualization-overhead-inference-41095.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gpu-virtualization-overhead-inference-41095.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Running One Model Across Mismatched GPUs</title>
      <description><![CDATA[You own two GPUs, so you bolt them together and expect the model to spread itself across both. It almost never works that way. This episode breaks down the three real techniques for running one open-weight model across multiple cards on a single Linux box: tensor parallelism, pipeline parallelism, and offloading. Each one is a different trade between capacity, speed, and the cost of cards talking to each other — and the interconnect, not the compute, sets that price. We walk through what vLLM and llama.cpp actually support, why tensor mode is still experimental, why attention head counts can veto your card count, and what the documentation quietly admits about mismatched hardware. If you're speccing a new multi-GPU server or just adding a second card to an existing machine, this is the honest version of whether that second card is worth it.]]></description>
      <link>https://myweirdprompts.com/episode/multi-gpu-model-parallelism-83583/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-gpu-model-parallelism-83583/</guid>
      <pubDate>Wed, 14 Oct 2026 14:47:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-gpu-model-parallelism-83583.m4a"
        type="audio/mp4"
        length="33215657"
      />
      <itunes:title>Running One Model Across Mismatched GPUs</itunes:title>
      <itunes:subtitle>Tensor, pipeline, or offload? What actually happens when you run one model across several GPUs — including mismatched ones.</itunes:subtitle>
      <itunes:summary><![CDATA[You own two GPUs, so you bolt them together and expect the model to spread itself across both. It almost never works that way. This episode breaks down the three real techniques for running one open-weight model across multiple cards on a single Linux box: tensor parallelism, pipeline parallelism, and offloading. Each one is a different trade between capacity, speed, and the cost of cards talking to each other — and the interconnect, not the compute, sets that price. We walk through what vLLM and llama.cpp actually support, why tensor mode is still experimental, why attention head counts can veto your card count, and what the documentation quietly admits about mismatched hardware. If you're speccing a new multi-GPU server or just adding a second card to an existing machine, this is the honest version of whether that second card is worth it.]]></itunes:summary>
      <itunes:duration>1892</itunes:duration>
      <itunes:episode>5963</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-gpu-model-parallelism-83583.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-gpu-model-parallelism-83583.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Your GPU Is Actually Waiting On</title>
      <description><![CDATA[You've got the model loaded, VRAM to spare, and the GPU reports near-total utilization — so why is generation crawling? This episode pulls apart the single number everyone screenshots and looks at the physical mechanisms hiding underneath it. VRAM capacity is a feasibility check, not a performance prediction; decode is a streaming memory workload that runs sixty to two hundred times to the memory-bound side of the roofline. Along the way: why a warp stalled on a memory read still counts as "active," how tensor core fragments sixty-four rows tall turn eight real requests into a high-speed multiplication of zeros, and why the CPU can eat thirty percent of decode time at large batch sizes. Then the practical half — consumption versus saturation, what to measure on the rest of the box, and how to walk up to a slow Linux server and actually figure out what's wrong.]]></description>
      <link>https://myweirdprompts.com/episode/gpu-memory-bandwidth-decode-21799/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gpu-memory-bandwidth-decode-21799/</guid>
      <pubDate>Wed, 14 Oct 2026 14:35:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gpu-memory-bandwidth-decode-21799.m4a"
        type="audio/mp4"
        length="28995530"
      />
      <itunes:title>What Your GPU Is Actually Waiting On</itunes:title>
      <itunes:subtitle>Your GPU says 100% utilized. It&apos;s multiplying zeros. Here&apos;s what the counters actually measure during inference.</itunes:subtitle>
      <itunes:summary><![CDATA[You've got the model loaded, VRAM to spare, and the GPU reports near-total utilization — so why is generation crawling? This episode pulls apart the single number everyone screenshots and looks at the physical mechanisms hiding underneath it. VRAM capacity is a feasibility check, not a performance prediction; decode is a streaming memory workload that runs sixty to two hundred times to the memory-bound side of the roofline. Along the way: why a warp stalled on a memory read still counts as "active," how tensor core fragments sixty-four rows tall turn eight real requests into a high-speed multiplication of zeros, and why the CPU can eat thirty percent of decode time at large batch sizes. Then the practical half — consumption versus saturation, what to measure on the rest of the box, and how to walk up to a slow Linux server and actually figure out what's wrong.]]></itunes:summary>
      <itunes:duration>1647</itunes:duration>
      <itunes:episode>5961</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gpu-memory-bandwidth-decode-21799.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gpu-memory-bandwidth-decode-21799.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>There&apos;s No Such Thing as Tokens Per Second</title>
      <description><![CDATA[Everyone quotes tokens per second and time to first token as if they're the whole picture. They're not — an LLM serving system doesn't have a performance number, it has a surface. This episode breaks down prefill versus decode, why one is compute-bound and the other memory-bandwidth-bound, the latency and throughput metric families that quietly disagree with each other, and how to design a benchmark that doesn't lie to you. Plus: goodput, the queue cliff, and the worked example where 2,000 tokens per second turns out to mean four out of ten requests were acceptable.]]></description>
      <link>https://myweirdprompts.com/episode/llm-inference-performance-metrics-72413/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-inference-performance-metrics-72413/</guid>
      <pubDate>Wed, 14 Oct 2026 14:32:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-inference-performance-metrics-72413.m4a"
        type="audio/mp4"
        length="31965470"
      />
      <itunes:title>There&apos;s No Such Thing as Tokens Per Second</itunes:title>
      <itunes:subtitle>Why one tokens-per-second number can&apos;t describe an LLM server — and which metrics actually tell you if it&apos;s working.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone quotes tokens per second and time to first token as if they're the whole picture. They're not — an LLM serving system doesn't have a performance number, it has a surface. This episode breaks down prefill versus decode, why one is compute-bound and the other memory-bandwidth-bound, the latency and throughput metric families that quietly disagree with each other, and how to design a benchmark that doesn't lie to you. Plus: goodput, the queue cliff, and the worked example where 2,000 tokens per second turns out to mean four out of ten requests were acceptable.]]></itunes:summary>
      <itunes:duration>1816</itunes:duration>
      <itunes:episode>5960</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-inference-performance-metrics-72413.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-inference-performance-metrics-72413.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What RTF Actually Tells You About Speech Models</title>
      <description><![CDATA[Most speech recognition coverage leads with accuracy — word error rate, character error rate, how close the transcript got. But that's not the number that decides whether a system is usable. This episode digs into the computational side of ASR benchmarking: real-time factor, why an RTF above one means broken rather than merely slow, and why the mean latency on your slide describes a request that never happened. From there it moves through throughput under concurrency, the memory envelope on-device, and the three environments where speech models actually run. It closes on a question worth asking out loud: is tokens per second even a meaningful metric for speech recognition, or is it borrowed from somewhere it doesn't belong?]]></description>
      <link>https://myweirdprompts.com/episode/asr-benchmarking-real-time-factor-84998/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/asr-benchmarking-real-time-factor-84998/</guid>
      <pubDate>Wed, 14 Oct 2026 14:26:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/asr-benchmarking-real-time-factor-84998.m4a"
        type="audio/mp4"
        length="28143586"
      />
      <itunes:title>What RTF Actually Tells You About Speech Models</itunes:title>
      <itunes:subtitle>Accuracy gets the headlines, but speed decides what ships. How RTF, latency percentiles, and memory shape real ASR deployments.</itunes:subtitle>
      <itunes:summary><![CDATA[Most speech recognition coverage leads with accuracy — word error rate, character error rate, how close the transcript got. But that's not the number that decides whether a system is usable. This episode digs into the computational side of ASR benchmarking: real-time factor, why an RTF above one means broken rather than merely slow, and why the mean latency on your slide describes a request that never happened. From there it moves through throughput under concurrency, the memory envelope on-device, and the three environments where speech models actually run. It closes on a question worth asking out loud: is tokens per second even a meaningful metric for speech recognition, or is it borrowed from somewhere it doesn't belong?]]></itunes:summary>
      <itunes:duration>1600</itunes:duration>
      <itunes:episode>5959</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/asr-benchmarking-real-time-factor-84998.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/asr-benchmarking-real-time-factor-84998.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Scientific Plugins Hiding in Plain Sight</title>
      <description><![CDATA[ChatGPT's plugin directory now lists 5,723 plugins with 238,000 callable tools, yet only 52% have useful descriptions. The leaderboard rewards commercial SaaS with clean schemas — Ashby, Supabase, Miro, Stripe — while scientific tools like Wolfram and Consensus don't crack the top rankings. Meanwhile, the genuinely capability-expanding drug discovery work — LIDDIA, MADDIE, DrugPilot — lives in arXiv papers that haven't migrated into the directory at all. This episode explores the split between the visible commercial layer and the deeper research layer, and asks whether general-purpose assistants are quietly becoming the access layer for serious scientific infrastructure. We dig into measurable impact: LEADS showing 20.8% time savings in study selection versus a systematic review finding GenAI missed 68-96% of studies during searching. The evidence cuts both ways, and the honest numbers are more interesting than the marketing.]]></description>
      <link>https://myweirdprompts.com/episode/scientific-plugins-chatgpt-directory-09166/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/scientific-plugins-chatgpt-directory-09166/</guid>
      <pubDate>Wed, 14 Oct 2026 13:47:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/scientific-plugins-chatgpt-directory-09166.m4a"
        type="audio/mp4"
        length="23013446"
      />
      <itunes:title>The Scientific Plugins Hiding in Plain Sight</itunes:title>
      <itunes:subtitle>ChatGPT&apos;s plugin directory has 5,723 entries — but the real scientific AI work is happening in arXiv papers, not plugins.</itunes:subtitle>
      <itunes:summary><![CDATA[ChatGPT's plugin directory now lists 5,723 plugins with 238,000 callable tools, yet only 52% have useful descriptions. The leaderboard rewards commercial SaaS with clean schemas — Ashby, Supabase, Miro, Stripe — while scientific tools like Wolfram and Consensus don't crack the top rankings. Meanwhile, the genuinely capability-expanding drug discovery work — LIDDIA, MADDIE, DrugPilot — lives in arXiv papers that haven't migrated into the directory at all. This episode explores the split between the visible commercial layer and the deeper research layer, and asks whether general-purpose assistants are quietly becoming the access layer for serious scientific infrastructure. We dig into measurable impact: LEADS showing 20.8% time savings in study selection versus a systematic review finding GenAI missed 68-96% of studies during searching. The evidence cuts both ways, and the honest numbers are more interesting than the marketing.]]></itunes:summary>
      <itunes:duration>1303</itunes:duration>
      <itunes:episode>5957</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/scientific-plugins-chatgpt-directory-09166.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/scientific-plugins-chatgpt-directory-09166.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Will ChatGPT Eat Your Grocery App?</title>
      <description><![CDATA[MCP turned the AI assistant into a universal front end, and OpenAI's Apps SDK is built on it. So why is a grocery list the battleground? This episode works through a prediction that conversational AI becomes the new interface for everything — and that vendors, not your employer, end up selling you the bolt-on. We trace the MCP timeline from Anthropic's open-source release through the ChatGPT App Directory, weigh the interface argument against the distribution argument, and look at what a brutal year for SaaS valuations is actually pricing in. Then we find the gap: the mechanism is fully built, but nobody has shipped the first-party version yet.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agents-app-consolidation-69088/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agents-app-consolidation-69088/</guid>
      <pubDate>Wed, 14 Oct 2026 13:39:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agents-app-consolidation-69088.m4a"
        type="audio/mp4"
        length="23465745"
      />
      <itunes:title>Will ChatGPT Eat Your Grocery App?</itunes:title>
      <itunes:subtitle>MCP servers, ChatGPT apps, and a prediction that AI vendors will absorb your grocery list one micro-app at a time.</itunes:subtitle>
      <itunes:summary><![CDATA[MCP turned the AI assistant into a universal front end, and OpenAI's Apps SDK is built on it. So why is a grocery list the battleground? This episode works through a prediction that conversational AI becomes the new interface for everything — and that vendors, not your employer, end up selling you the bolt-on. We trace the MCP timeline from Anthropic's open-source release through the ChatGPT App Directory, weigh the interface argument against the distribution argument, and look at what a brutal year for SaaS valuations is actually pricing in. Then we find the gap: the mechanism is fully built, but nobody has shipped the first-party version yet.]]></itunes:summary>
      <itunes:duration>1327</itunes:duration>
      <itunes:episode>5956</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agents-app-consolidation-69088.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agents-app-consolidation-69088.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Self-Hosting Your Search Stack: SearXNG, Crawl4AI, and the Real Bill</title>
      <description><![CDATA[Daniel wants to know if you can bolt together a search-and-grounding stack from open-source parts and never rent a search API again. On the table: SearXNG for discovery, Crawl4AI for extraction, and a TypeScript aggregation library for grounding — plus how you'd actually run the whole thing on Modal. The honest answer is yes, with one large asterisk: the bill you save is not the bill you pay. You stop paying Exa and start paying a server, a cron job, and about fifteen hours of your own time. This episode walks through all three layers, the one-line settings change that trips up every self-hoster, the reciprocal rank fusion trick that turns six mediocre result lists into one decent one, and the latency numbers that undercut the whole purity argument.]]></description>
      <link>https://myweirdprompts.com/episode/self-hosting-search-stack-searxng-50287/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/self-hosting-search-stack-searxng-50287/</guid>
      <pubDate>Wed, 14 Oct 2026 08:44:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/self-hosting-search-stack-searxng-50287.m4a"
        type="audio/mp4"
        length="30152956"
      />
      <itunes:title>Self-Hosting Your Search Stack: SearXNG, Crawl4AI, and the Real Bill</itunes:title>
      <itunes:subtitle>Can SearXNG, Crawl4AI, and an open-source aggregator really replace a commercial search API? Yes — with a very large asterisk.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants to know if you can bolt together a search-and-grounding stack from open-source parts and never rent a search API again. On the table: SearXNG for discovery, Crawl4AI for extraction, and a TypeScript aggregation library for grounding — plus how you'd actually run the whole thing on Modal. The honest answer is yes, with one large asterisk: the bill you save is not the bill you pay. You stop paying Exa and start paying a server, a cron job, and about fifteen hours of your own time. This episode walks through all three layers, the one-line settings change that trips up every self-hoster, the reciprocal rank fusion trick that turns six mediocre result lists into one decent one, and the latency numbers that undercut the whole purity argument.]]></itunes:summary>
      <itunes:duration>1722</itunes:duration>
      <itunes:episode>5950</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/self-hosting-search-stack-searxng-50287.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/self-hosting-search-stack-searxng-50287.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside Deep Research Agents: Claims, Citations, and the 84.7% Orchestrator Problem</title>
      <description><![CDATA[What actually separates a research agent that grounds its claims from one that piles up web searches and writes a confident summary? This episode breaks down the four-stage deep research pipeline — planning, retrieval, verification, synthesis — and the three failure classes that survive every surface check: unsupported claims, silent coverage gaps, and unbounded drift. The fix isn't more search tools. It's treating research as a claim ledger under budget: a question graph with explicit statuses, an evidence ledger that stores verbatim spans instead of summaries, and a claim table where every atomic claim maps to supporting and contradicting evidence. We dig into why 84.7% of citation errors in agentic deep research systems originate at the orchestrator — not the searchers — and what LangChain's layered stack (LangChain, LangGraph, Deep Agents) means for where you place verification structure.]]></description>
      <link>https://myweirdprompts.com/episode/deep-research-agent-claim-ledger-93879/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/deep-research-agent-claim-ledger-93879/</guid>
      <pubDate>Wed, 14 Oct 2026 08:41:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/deep-research-agent-claim-ledger-93879.m4a"
        type="audio/mp4"
        length="24984971"
      />
      <itunes:title>Inside Deep Research Agents: Claims, Citations, and the 84.7% Orchestrator Problem</itunes:title>
      <itunes:subtitle>Why 84.7% of citation errors originate at the orchestrator — and how a claim ledger fixes it.</itunes:subtitle>
      <itunes:summary><![CDATA[What actually separates a research agent that grounds its claims from one that piles up web searches and writes a confident summary? This episode breaks down the four-stage deep research pipeline — planning, retrieval, verification, synthesis — and the three failure classes that survive every surface check: unsupported claims, silent coverage gaps, and unbounded drift. The fix isn't more search tools. It's treating research as a claim ledger under budget: a question graph with explicit statuses, an evidence ledger that stores verbatim spans instead of summaries, and a claim table where every atomic claim maps to supporting and contradicting evidence. We dig into why 84.7% of citation errors in agentic deep research systems originate at the orchestrator — not the searchers — and what LangChain's layered stack (LangChain, LangGraph, Deep Agents) means for where you place verification structure.]]></itunes:summary>
      <itunes:duration>1416</itunes:duration>
      <itunes:episode>5949</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/deep-research-agent-claim-ledger-93879.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/deep-research-agent-claim-ledger-93879.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>LangGraph, LangChain, and Deep Agents in 2026</title>
      <description><![CDATA[LangChain spent 2026 hardening the parts of the agent stack nobody demos. LangGraph 1.2 cut checkpoint storage by 41x with DeltaChannel, added per-node timeouts, error handlers, and graceful shutdown. Deep Agents v0.7 deleted 65% of its base input tokens — including the entire base system prompt — and moved orchestration out of the tool-call loop and into code the model writes itself. Meanwhile LangChain's own go-to-market agent runs 74% of its requests ambiently, with nobody asking. This episode works through the architectural question underneath all of it: are runtime, framework, and harness three distinct layers, or three things quietly converging into one?]]></description>
      <link>https://myweirdprompts.com/episode/langgraph-langchain-deep-agents-2026-05446/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/langgraph-langchain-deep-agents-2026-05446/</guid>
      <pubDate>Wed, 14 Oct 2026 08:31:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/langgraph-langchain-deep-agents-2026-05446.m4a"
        type="audio/mp4"
        length="23414529"
      />
      <itunes:title>LangGraph, LangChain, and Deep Agents in 2026</itunes:title>
      <itunes:subtitle>Runtime, framework, harness — how three LangChain layers actually fit together, and what changed in 2026.</itunes:subtitle>
      <itunes:summary><![CDATA[LangChain spent 2026 hardening the parts of the agent stack nobody demos. LangGraph 1.2 cut checkpoint storage by 41x with DeltaChannel, added per-node timeouts, error handlers, and graceful shutdown. Deep Agents v0.7 deleted 65% of its base input tokens — including the entire base system prompt — and moved orchestration out of the tool-call loop and into code the model writes itself. Meanwhile LangChain's own go-to-market agent runs 74% of its requests ambiently, with nobody asking. This episode works through the architectural question underneath all of it: are runtime, framework, and harness three distinct layers, or three things quietly converging into one?]]></itunes:summary>
      <itunes:duration>1332</itunes:duration>
      <itunes:episode>5948</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/langgraph-langchain-deep-agents-2026-05446.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/langgraph-langchain-deep-agents-2026-05446.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How a Reward Model Actually Trains an LLM</title>
      <description><![CDATA[Everyone draws the reward model as a smiley face between "human feedback" and "better model" — but what is it actually doing? This episode takes the reward model apart: how it's built by cutting the unembedding layer off an SFT checkpoint and bolting on a single linear head, why it's trained on preference trios with a Bradley-Terry loss for exactly one epoch, and why a 6B critic was used to steer a 175B policy. Then the part that usually gets hand-waved: the mechanism by which one scalar reward moves the policy's weights. Along the way: whether reward models keep learning during RL, whether you can grab one off a shelf (RewardBench says sometimes, with caveats), and why the value function exists at all when the reward arrives once, at the very end.]]></description>
      <link>https://myweirdprompts.com/episode/reward-model-rlhf-lifecycle-23140/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/reward-model-rlhf-lifecycle-23140/</guid>
      <pubDate>Wed, 14 Oct 2026 00:46:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/reward-model-rlhf-lifecycle-23140.m4a"
        type="audio/mp4"
        length="30420513"
      />
      <itunes:title>How a Reward Model Actually Trains an LLM</itunes:title>
      <itunes:subtitle>One number, no gradients, four models in the room. What a reward model actually does inside the RLHF loop.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone draws the reward model as a smiley face between "human feedback" and "better model" — but what is it actually doing? This episode takes the reward model apart: how it's built by cutting the unembedding layer off an SFT checkpoint and bolting on a single linear head, why it's trained on preference trios with a Bradley-Terry loss for exactly one epoch, and why a 6B critic was used to steer a 175B policy. Then the part that usually gets hand-waved: the mechanism by which one scalar reward moves the policy's weights. Along the way: whether reward models keep learning during RL, whether you can grab one off a shelf (RewardBench says sometimes, with caveats), and why the value function exists at all when the reward arrives once, at the very end.]]></itunes:summary>
      <itunes:duration>1738</itunes:duration>
      <itunes:episode>5945</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/reward-model-rlhf-lifecycle-23140.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/reward-model-rlhf-lifecycle-23140.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Keeping the Rollout Engine Fed: Inside RLHF Pipelines</title>
      <description><![CDATA[Daniel wrote in with a practical question: he understands RLHF concepts, but now he has to stand up a real pipeline. What does it actually take? This episode walks the full three-phase shape — supervised fine-tuning, reward model training, and the policy optimization loop — and lands on a counterintuitive conclusion: the algorithm is the commodity, and the job lives in the rollout engine. We compare TRL, OpenRLHF, and verl, explain why every framework independently converged on the same architecture, and dig into the memory math that makes GRPO the near-forced choice on large runs. If you're moving from "I understand RLHF" to "I have to operate RLHF," this is the episode for you.]]></description>
      <link>https://myweirdprompts.com/episode/rlhf-pipeline-systems-engineering-64805/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rlhf-pipeline-systems-engineering-64805/</guid>
      <pubDate>Wed, 14 Oct 2026 00:43:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rlhf-pipeline-systems-engineering-64805.m4a"
        type="audio/mp4"
        length="26369797"
      />
      <itunes:title>Keeping the Rollout Engine Fed: Inside RLHF Pipelines</itunes:title>
      <itunes:subtitle>Most of an RLHF run is spent waiting on generation, not gradients. Here&apos;s how TRL, OpenRLHF, and verl actually differ.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wrote in with a practical question: he understands RLHF concepts, but now he has to stand up a real pipeline. What does it actually take? This episode walks the full three-phase shape — supervised fine-tuning, reward model training, and the policy optimization loop — and lands on a counterintuitive conclusion: the algorithm is the commodity, and the job lives in the rollout engine. We compare TRL, OpenRLHF, and verl, explain why every framework independently converged on the same architecture, and dig into the memory math that makes GRPO the near-forced choice on large runs. If you're moving from "I understand RLHF" to "I have to operate RLHF," this is the episode for you.]]></itunes:summary>
      <itunes:duration>1488</itunes:duration>
      <itunes:episode>5944</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rlhf-pipeline-systems-engineering-64805.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rlhf-pipeline-systems-engineering-64805.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Service Workers: The Proxy That Never Sleeps</title>
      <description><![CDATA[Service workers sit between your page and the network as a programmable proxy, running on their own thread with no DOM access and no window. They can wake up when no tab is open, intercept every request under their scope, and serve content from a cache you control — which is exactly why a deploy can appear to do nothing at all. This episode walks through the full lifecycle: download, install, activate, and the "waiting" state that explains why refreshing a page never picks up a new version. Along the way: why AppCache had to die, what clients.claim and skipWaiting actually trade away, the 24-hour update check window, and the clone-the-stream footgun that turns a five-minute mistake into a four-hour debugging session. We also cover cache-first vs network-first vs stale-while-revalidate, navigation preload, Background Sync, and how push notifications wake a worker on a device that has no tab open.]]></description>
      <link>https://myweirdprompts.com/episode/service-worker-lifecycle-explained-19120/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/service-worker-lifecycle-explained-19120/</guid>
      <pubDate>Wed, 14 Oct 2026 00:18:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/service-worker-lifecycle-explained-19120.m4a"
        type="audio/mp4"
        length="29528432"
      />
      <itunes:title>Service Workers: The Proxy That Never Sleeps</itunes:title>
      <itunes:subtitle>A JavaScript file with no tab, no DOM, and the power to intercept every request your site makes — and why refreshing never updates it.</itunes:subtitle>
      <itunes:summary><![CDATA[Service workers sit between your page and the network as a programmable proxy, running on their own thread with no DOM access and no window. They can wake up when no tab is open, intercept every request under their scope, and serve content from a cache you control — which is exactly why a deploy can appear to do nothing at all. This episode walks through the full lifecycle: download, install, activate, and the "waiting" state that explains why refreshing a page never picks up a new version. Along the way: why AppCache had to die, what clients.claim and skipWaiting actually trade away, the 24-hour update check window, and the clone-the-stream footgun that turns a five-minute mistake into a four-hour debugging session. We also cover cache-first vs network-first vs stale-while-revalidate, navigation preload, Background Sync, and how push notifications wake a worker on a device that has no tab open.]]></itunes:summary>
      <itunes:duration>1677</itunes:duration>
      <itunes:episode>5942</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/service-worker-lifecycle-explained-19120.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/service-worker-lifecycle-explained-19120.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Five Minutes Reading Your Dictation Prompt</title>
      <description><![CDATA[Daniel sent in a general speech-to-text cleanup prompt built to fix the failure patterns he actually hits, and asked for a full read, a critique, and improvements. So the prompt is the artifact: we read it first, argue about it second. It does two genuinely unusual things — executing spoken self-corrections and stripping out speech aimed at someone nearby — but it also assumes every error is a word-level substitution. That assumption collides with what ASR really does: out-of-vocabulary jargon like "Dupixent" coming out as "duplex aunt," and Whisper hallucinating whole phrases like "thanks for watching" in the middle of silence. We work through five categories of ASR failure, what the prompt can and can't reach, and what we'd change.]]></description>
      <link>https://myweirdprompts.com/episode/speech-to-text-cleanup-prompt-critique-52265/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speech-to-text-cleanup-prompt-critique-52265/</guid>
      <pubDate>Tue, 13 Oct 2026 18:37:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speech-to-text-cleanup-prompt-critique-52265.m4a"
        type="audio/mp4"
        length="28772792"
      />
      <itunes:title>Five Minutes Reading Your Dictation Prompt</itunes:title>
      <itunes:subtitle>A listener&apos;s speech-to-text cleanup prompt, read out in full — then stress-tested against what ASR actually gets wrong.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel sent in a general speech-to-text cleanup prompt built to fix the failure patterns he actually hits, and asked for a full read, a critique, and improvements. So the prompt is the artifact: we read it first, argue about it second. It does two genuinely unusual things — executing spoken self-corrections and stripping out speech aimed at someone nearby — but it also assumes every error is a word-level substitution. That assumption collides with what ASR really does: out-of-vocabulary jargon like "Dupixent" coming out as "duplex aunt," and Whisper hallucinating whole phrases like "thanks for watching" in the middle of silence. We work through five categories of ASR failure, what the prompt can and can't reach, and what we'd change.]]></itunes:summary>
      <itunes:duration>1634</itunes:duration>
      <itunes:episode>5941</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speech-to-text-cleanup-prompt-critique-52265.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speech-to-text-cleanup-prompt-critique-52265.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Missing Middle: Content APIs and ISR for a 6,000-Episode Catalog</title>
      <description><![CDATA[Daniel has built a 6,000-episode podcast with no admin panel — just Claude Code and a custom admin MCP talking to Neon. But Neon's docs explicitly warn against connecting MCP agents to production databases, and Vercel's 45-minute build ceiling looms over every deploy. This episode works through the architecture question: does the catalog need a bespoke content API, or do Vercel's ISR with tag-based revalidation and Neon's Data API already provide the arbiter layer? We dig into why the current "batch of five triggers a deploy" pattern breaks, how generateStaticParams and on-demand revalidation flip the model from build-time to request-time, and the one caveat that actually matters — every new deployment starts with a cold ISR cache.]]></description>
      <link>https://myweirdprompts.com/episode/content-api-isr-architecture-06199/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/content-api-isr-architecture-06199/</guid>
      <pubDate>Tue, 13 Oct 2026 18:16:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/content-api-isr-architecture-06199.m4a"
        type="audio/mp4"
        length="33603533"
      />
      <itunes:title>The Missing Middle: Content APIs and ISR for a 6,000-Episode Catalog</itunes:title>
      <itunes:subtitle>When your catalog is too big to rebuild on every change, what has to change? Turns out the missing middle might already exist.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has built a 6,000-episode podcast with no admin panel — just Claude Code and a custom admin MCP talking to Neon. But Neon's docs explicitly warn against connecting MCP agents to production databases, and Vercel's 45-minute build ceiling looms over every deploy. This episode works through the architecture question: does the catalog need a bespoke content API, or do Vercel's ISR with tag-based revalidation and Neon's Data API already provide the arbiter layer? We dig into why the current "batch of five triggers a deploy" pattern breaks, how generateStaticParams and on-demand revalidation flip the model from build-time to request-time, and the one caveat that actually matters — every new deployment starts with a cold ISR cache.]]></itunes:summary>
      <itunes:duration>1934</itunes:duration>
      <itunes:episode>5940</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/content-api-isr-architecture-06199.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/content-api-isr-architecture-06199.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Android Notifications: PWA vs Native, What You Actually Get</title>
      <description><![CDATA[Daniel asked us to look at Android notifications from a developer's seat, and the honest answer isn't "native wins." It's about where control actually sits in the stack — and it flips depending on whether you mean the developer's control or the user's. Native Android hands you a rich, opinionated channel architecture with importance levels, categories, and vibration patterns, then deliberately freezes it and gives the user final say. The web platform gives you one Notification interface and a Push API, one global permission per origin, and a scheduling primitive that was built and then withdrawn. We walk through the primitives on both sides, the haptics story down to the hardware, and the places where the loudest numbers come from people selling native wrappers.]]></description>
      <link>https://myweirdprompts.com/episode/android-notifications-pwa-vs-native-56628/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-notifications-pwa-vs-native-56628/</guid>
      <pubDate>Tue, 13 Oct 2026 18:15:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-notifications-pwa-vs-native-56628.m4a"
        type="audio/mp4"
        length="23400979"
      />
      <itunes:title>Android Notifications: PWA vs Native, What You Actually Get</itunes:title>
      <itunes:subtitle>Push, channels, haptics, and the scheduling gap — what a PWA can and can&apos;t do that a native Android build can.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asked us to look at Android notifications from a developer's seat, and the honest answer isn't "native wins." It's about where control actually sits in the stack — and it flips depending on whether you mean the developer's control or the user's. Native Android hands you a rich, opinionated channel architecture with importance levels, categories, and vibration patterns, then deliberately freezes it and gives the user final say. The web platform gives you one Notification interface and a Push API, one global permission per origin, and a scheduling primitive that was built and then withdrawn. We walk through the primitives on both sides, the haptics story down to the hardware, and the places where the loudest numbers come from people selling native wrappers.]]></itunes:summary>
      <itunes:duration>1330</itunes:duration>
      <itunes:episode>5939</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-notifications-pwa-vs-native-56628.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-notifications-pwa-vs-native-56628.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Regex Rule Worked, One Was Impossible</title>
      <description><![CDATA[A listener wrote one text-replacement rule in an Android dictation app and it erased every "um" and "uh" from his transcripts. Then he wrote a second rule about capitalization, and it failed — not because it was fiddly, but because it's provably impossible. This episode uses that gap as a case study in what regular expressions actually are: a 1951 notation from Stephen Cole Kleene, first implemented by Ken Thompson in 1968, now a family of dialects (POSIX, Perl, PCRE2, RE2) that disagree about what the word even means. Along the way: why the app's Accessibility-service architecture constrains everything you can write on Android, why JavaScript's own reference admits its regexes aren't regular, and why LLMs haven't made any of this obsolete.]]></description>
      <link>https://myweirdprompts.com/episode/regex-limits-android-whisperian-54308/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/regex-limits-android-whisperian-54308/</guid>
      <pubDate>Tue, 13 Oct 2026 18:07:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/regex-limits-android-whisperian-54308.m4a"
        type="audio/mp4"
        length="28230306"
      />
      <itunes:title>One Regex Rule Worked, One Was Impossible</itunes:title>
      <itunes:subtitle>A dictation app, two text-replacement rules, and the mathematical line between what regex can match and what it provably cannot.</itunes:subtitle>
      <itunes:summary><![CDATA[A listener wrote one text-replacement rule in an Android dictation app and it erased every "um" and "uh" from his transcripts. Then he wrote a second rule about capitalization, and it failed — not because it was fiddly, but because it's provably impossible. This episode uses that gap as a case study in what regular expressions actually are: a 1951 notation from Stephen Cole Kleene, first implemented by Ken Thompson in 1968, now a family of dialects (POSIX, Perl, PCRE2, RE2) that disagree about what the word even means. Along the way: why the app's Accessibility-service architecture constrains everything you can write on Android, why JavaScript's own reference admits its regexes aren't regular, and why LLMs haven't made any of this obsolete.]]></itunes:summary>
      <itunes:duration>1639</itunes:duration>
      <itunes:episode>5938</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/regex-limits-android-whisperian-54308.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/regex-limits-android-whisperian-54308.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Training a Model When You Don&apos;t Know the Labels Yet</title>
      <description><![CDATA[What happens when you need to train an extraction model but the label set doesn't exist yet? This episode works through a listener's parametric-search project — scraping vendor spec sheets to build a filterable home inventory of adapters, cables, and power supplies — where the schema grows during annotation itself. We dig into attribute value extraction, why standard NER assumes a closed world, and how schema-conditioned models like GLiNER let you pass labels in as text at inference time with no retraining. Along the way: a 300M-parameter encoder that beats a 13B model on out-of-domain benchmarks, why annotation tooling never caught up to the research, and the anchoring risk hiding inside model-assisted labeling.]]></description>
      <link>https://myweirdprompts.com/episode/unbounded-label-annotation-schema-62956/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unbounded-label-annotation-schema-62956/</guid>
      <pubDate>Tue, 13 Oct 2026 17:17:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unbounded-label-annotation-schema-62956.m4a"
        type="audio/mp4"
        length="28086708"
      />
      <itunes:title>Training a Model When You Don&apos;t Know the Labels Yet</itunes:title>
      <itunes:subtitle>Daniel wants to extract adapter specs from his shelf — but he can&apos;t name the labels until he&apos;s already annotating.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when you need to train an extraction model but the label set doesn't exist yet? This episode works through a listener's parametric-search project — scraping vendor spec sheets to build a filterable home inventory of adapters, cables, and power supplies — where the schema grows during annotation itself. We dig into attribute value extraction, why standard NER assumes a closed world, and how schema-conditioned models like GLiNER let you pass labels in as text at inference time with no retraining. Along the way: a 300M-parameter encoder that beats a 13B model on out-of-domain benchmarks, why annotation tooling never caught up to the research, and the anchoring risk hiding inside model-assisted labeling.]]></itunes:summary>
      <itunes:duration>1593</itunes:duration>
      <itunes:episode>5936</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unbounded-label-annotation-schema-62956.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unbounded-label-annotation-schema-62956.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where Generalists Get Priced: Inside the FDE Role</title>
      <description><![CDATA[Daniel asked where an intellectually restless generalist actually gets priced — and refused the lazy answers. The answer has a name: Forward Deployed Engineer, a role born at Palantir in the mid-2010s and exported across Anthropic, OpenAI, Scale AI, Sierra, Harvey, Glean, Mistral, and Databricks. It pays a 30–50% premium over equivalent backend or ML engineers, with senior compensation at frontier labs hitting $485K median and principals crossing a million. But the real question is harder: what separates someone who accumulates knowledge from someone who ships? The two highest-ranked hiring signals aren't knowledge stock at all — they're shipping production code and running a discovery interview without a sales script. We dig into the 47% customer-facing workweek, the "Duane" problem in enterprise AI, and whether this role is a genuine new discipline or a rebrand of integration engineering with a fashionable business card.]]></description>
      <link>https://myweirdprompts.com/episode/forward-deployed-engineer-generalist-87828/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/forward-deployed-engineer-generalist-87828/</guid>
      <pubDate>Tue, 13 Oct 2026 03:14:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/forward-deployed-engineer-generalist-87828.m4a"
        type="audio/mp4"
        length="26119318"
      />
      <itunes:title>Where Generalists Get Priced: Inside the FDE Role</itunes:title>
      <itunes:subtitle>A $485K median salary for a job with no clean definition — and why the résumé can&apos;t capture it.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asked where an intellectually restless generalist actually gets priced — and refused the lazy answers. The answer has a name: Forward Deployed Engineer, a role born at Palantir in the mid-2010s and exported across Anthropic, OpenAI, Scale AI, Sierra, Harvey, Glean, Mistral, and Databricks. It pays a 30–50% premium over equivalent backend or ML engineers, with senior compensation at frontier labs hitting $485K median and principals crossing a million. But the real question is harder: what separates someone who accumulates knowledge from someone who ships? The two highest-ranked hiring signals aren't knowledge stock at all — they're shipping production code and running a discovery interview without a sales script. We dig into the 47% customer-facing workweek, the "Duane" problem in enterprise AI, and whether this role is a genuine new discipline or a rebrand of integration engineering with a fashionable business card.]]></itunes:summary>
      <itunes:duration>1487</itunes:duration>
      <itunes:episode>5927</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/forward-deployed-engineer-generalist-87828.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/forward-deployed-engineer-generalist-87828.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Plus Codes, Geohash, and the Open Addressing Gap</title>
      <description><![CDATA[Google's Open Location Code was designed to work completely offline, with no geocoding service and no proprietary database in the middle. Then the consumer interface shipped a shortened form that needs a locality name — and a locality name needs Google. We trace the mechanics of code shortening, the base-20 alphabet built by testing eight billion combinations against words in thirty languages, and why a hundred-metre ambiguity in Zurich is enough to break the whole promise. Then we go looking for the purer version: eleven language ports, a zero-dependency TypeScript library, an offline Garmin watch datafield — and the standalone consumer app that we could not confirm exists.]]></description>
      <link>https://myweirdprompts.com/episode/plus-codes-open-addressing-gap-38486/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/plus-codes-open-addressing-gap-38486/</guid>
      <pubDate>Tue, 13 Oct 2026 02:08:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/plus-codes-open-addressing-gap-38486.m4a"
        type="audio/mp4"
        length="28897044"
      />
      <itunes:title>Plus Codes, Geohash, and the Open Addressing Gap</itunes:title>
      <itunes:subtitle>Google&apos;s Plus Codes promise offline, self-contained location — but the shortened form quietly reintroduces a dependency the standard was designed t...</itunes:subtitle>
      <itunes:summary><![CDATA[Google's Open Location Code was designed to work completely offline, with no geocoding service and no proprietary database in the middle. Then the consumer interface shipped a shortened form that needs a locality name — and a locality name needs Google. We trace the mechanics of code shortening, the base-20 alphabet built by testing eight billion combinations against words in thirty languages, and why a hundred-metre ambiguity in Zurich is enough to break the whole promise. Then we go looking for the purer version: eleven language ports, a zero-dependency TypeScript library, an offline Garmin watch datafield — and the standalone consumer app that we could not confirm exists.]]></itunes:summary>
      <itunes:duration>1647</itunes:duration>
      <itunes:episode>5925</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/plus-codes-open-addressing-gap-38486.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/plus-codes-open-addressing-gap-38486.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Plus Codes: Free Addressing Nobody Uses</title>
      <description><![CDATA[Open Location Code gives every square on Earth a short alphanumeric address — free, offline, case-insensitive, precise to about 14 metres. Google published the spec in 2014, developers ported it to a dozen languages, and the Navajo Nation used it to address 3,100 homes for EMS. So why is a proprietary, pronunciation-ambiguous rival the one inside 911 dispatch centres? This episode traces Plus Codes from a Zürich spec document to slum addressing in India and Brazil, and lands on the question that actually matters: what happens when the code is precise but the GPS fix behind it isn't.]]></description>
      <link>https://myweirdprompts.com/episode/open-location-code-adoption-44811/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/open-location-code-adoption-44811/</guid>
      <pubDate>Tue, 13 Oct 2026 00:46:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/open-location-code-adoption-44811.m4a"
        type="audio/mp4"
        length="27056002"
      />
      <itunes:title>Plus Codes: Free Addressing Nobody Uses</itunes:title>
      <itunes:subtitle>A free, offline, 14-metre addressing system exists — so why do ambulances still can&apos;t find the house?</itunes:subtitle>
      <itunes:summary><![CDATA[Open Location Code gives every square on Earth a short alphanumeric address — free, offline, case-insensitive, precise to about 14 metres. Google published the spec in 2014, developers ported it to a dozen languages, and the Navajo Nation used it to address 3,100 homes for EMS. So why is a proprietary, pronunciation-ambiguous rival the one inside 911 dispatch centres? This episode traces Plus Codes from a Zürich spec document to slum addressing in India and Brazil, and lands on the question that actually matters: what happens when the code is precise but the GPS fix behind it isn't.]]></itunes:summary>
      <itunes:duration>1541</itunes:duration>
      <itunes:episode>5923</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/open-location-code-adoption-44811.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/open-location-code-adoption-44811.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Teaching a Model Your Voice: SFT vs DPO</title>
      <description><![CDATA[You can't reliably prompt a model into your voice — system prompts plateau. So what actually works? This episode works through three data-generation strategies for style fine-tuning: regenerating transcripts with global comments, hand-editing transcripts and training on the diff, and training a purely negative counterweight against overused phrases. A July arXiv paper by Federico Boggia on epanorthosis (the mid-sentence self-correction tic) tested almost exactly this, and one implementation failed in a very instructive way: a small-data DPO adapter learned to rank outputs correctly and then raised the density of the target phrase. Supervised fine-tuning on the same data cut it by 98%. We map each method onto that evidence, then get to the practical recipe — how many examples you actually need, why "200 transcripts" is the wrong unit of counting, and why LoRA or QLoRA is the unambiguous choice for a style adapter.]]></description>
      <link>https://myweirdprompts.com/episode/fine-tuning-style-sft-vs-dpo-07870/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fine-tuning-style-sft-vs-dpo-07870/</guid>
      <pubDate>Tue, 13 Oct 2026 00:06:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fine-tuning-style-sft-vs-dpo-07870.m4a"
        type="audio/mp4"
        length="22906857"
      />
      <itunes:title>Teaching a Model Your Voice: SFT vs DPO</itunes:title>
      <itunes:subtitle>Three ways to teach a model your writing voice — and the evidence says one of them quietly makes things worse.</itunes:subtitle>
      <itunes:summary><![CDATA[You can't reliably prompt a model into your voice — system prompts plateau. So what actually works? This episode works through three data-generation strategies for style fine-tuning: regenerating transcripts with global comments, hand-editing transcripts and training on the diff, and training a purely negative counterweight against overused phrases. A July arXiv paper by Federico Boggia on epanorthosis (the mid-sentence self-correction tic) tested almost exactly this, and one implementation failed in a very instructive way: a small-data DPO adapter learned to rank outputs correctly and then raised the density of the target phrase. Supervised fine-tuning on the same data cut it by 98%. We map each method onto that evidence, then get to the practical recipe — how many examples you actually need, why "200 transcripts" is the wrong unit of counting, and why LoRA or QLoRA is the unambiguous choice for a style adapter.]]></itunes:summary>
      <itunes:duration>1294</itunes:duration>
      <itunes:episode>5922</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fine-tuning-style-sft-vs-dpo-07870.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fine-tuning-style-sft-vs-dpo-07870.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Splitting One Camera&apos;s Audio for a Baby Monitor</title>
      <description><![CDATA[Daniel has an IP camera in his son's room doing double duty — Frigate watches the video, but he also wants a continuous audio line piped to his phone so a cry wakes him up. The camera won't hand out an audio-only feed, so something has to split the audio off the RTSP stream. This episode works through the architecture: why go2rtc beats Frigate's embedded restream, how to fan out one camera connection into multiple consumers, where the single transcode belongs, and which codecs actually matter when the sound you're trying to catch is a cry at three in the morning.]]></description>
      <link>https://myweirdprompts.com/episode/camera-audio-split-baby-monitor-74640/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/camera-audio-split-baby-monitor-74640/</guid>
      <pubDate>Mon, 12 Oct 2026 19:55:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/camera-audio-split-baby-monitor-74640.m4a"
        type="audio/mp4"
        length="27371319"
      />
      <itunes:title>Splitting One Camera&apos;s Audio for a Baby Monitor</itunes:title>
      <itunes:subtitle>One IP camera, two jobs: an open audio line for the nursery and Frigate&apos;s cry detection. Here&apos;s how go2rtc and Icecast fit together.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has an IP camera in his son's room doing double duty — Frigate watches the video, but he also wants a continuous audio line piped to his phone so a cry wakes him up. The camera won't hand out an audio-only feed, so something has to split the audio off the RTSP stream. This episode works through the architecture: why go2rtc beats Frigate's embedded restream, how to fan out one camera connection into multiple consumers, where the single transcode belongs, and which codecs actually matter when the sound you're trying to catch is a cry at three in the morning.]]></itunes:summary>
      <itunes:duration>1551</itunes:duration>
      <itunes:episode>5918</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/camera-audio-split-baby-monitor-74640.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/camera-audio-split-baby-monitor-74640.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Multi-Agent AI Simulations Keep a Shared World State</title>
      <description><![CDATA[What actually holds a simulated world together when multiple AI agents act inside it? This episode opens the repos on three frameworks that answer the question completely differently. Concordia from Google DeepMind makes world state a first-class Game Master component — a plain Python dict whose contents are authored by the model at every step, with a SQLite/Postgres storage protocol that ships as an interface but not an implementation. Generative Agents goes the other way: a deterministic tile grid in CSV files, per-agent JSON scratch files, and a memory system with a 0.995 recency decay. CAMEL has no persistent world object at all — state lives in the conversation history and a Task object. Along the way: why agent memory and world state must be separate, how entity-aware event filtering produces information asymmetry, and the four-line docstring about a rent event that silently refunded every agent's spending — and why the fix was atomic wallet deltas, not a better prompt.]]></description>
      <link>https://myweirdprompts.com/episode/multi-agent-world-state-architecture-65069/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-agent-world-state-architecture-65069/</guid>
      <pubDate>Mon, 12 Oct 2026 18:47:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-agent-world-state-architecture-65069.m4a"
        type="audio/mp4"
        length="31680697"
      />
      <itunes:title>How Multi-Agent AI Simulations Keep a Shared World State</itunes:title>
      <itunes:subtitle>Concordia keeps the world in a dictionary an LLM writes to. Generative Agents keeps it in a spreadsheet. CAMEL keeps it in the chat log.</itunes:subtitle>
      <itunes:summary><![CDATA[What actually holds a simulated world together when multiple AI agents act inside it? This episode opens the repos on three frameworks that answer the question completely differently. Concordia from Google DeepMind makes world state a first-class Game Master component — a plain Python dict whose contents are authored by the model at every step, with a SQLite/Postgres storage protocol that ships as an interface but not an implementation. Generative Agents goes the other way: a deterministic tile grid in CSV files, per-agent JSON scratch files, and a memory system with a 0.995 recency decay. CAMEL has no persistent world object at all — state lives in the conversation history and a Task object. Along the way: why agent memory and world state must be separate, how entity-aware event filtering produces information asymmetry, and the four-line docstring about a rent event that silently refunded every agent's spending — and why the fix was atomic wallet deltas, not a better prompt.]]></itunes:summary>
      <itunes:duration>1799</itunes:duration>
      <itunes:episode>5915</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-agent-world-state-architecture-65069.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-agent-world-state-architecture-65069.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Your Own CRM with AI: Where to Start</title>
      <description><![CDATA[Rita runs a two-person landscape design practice and wants a real backend instead of spreadsheets and rented CRMs. Can AI-assisted development make that realistic? This episode digs into the backbone: the database and architecture. It covers why the schema is the real moat, how to map a garden design practice onto the canonical CRM entities, and why a rigid schema with fast migrations beats a flexible one. Then it walks through the boring-but-correct stack — Cloud Run, Cloud SQL Postgres, Terraform — and the three things that actually break: connection pooling arithmetic, migration races at container start, and secrets management. Part one of a three-part series on building your own tools.]]></description>
      <link>https://myweirdprompts.com/episode/ai-assisted-crm-schema-first-37758/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-assisted-crm-schema-first-37758/</guid>
      <pubDate>Mon, 12 Oct 2026 17:58:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-assisted-crm-schema-first-37758.m4a"
        type="audio/mp4"
        length="23572158"
      />
      <itunes:title>Building Your Own CRM with AI: Where to Start</itunes:title>
      <itunes:subtitle>AI can&apos;t write your CRM for you — but it can remove the friction. Start with the schema, not the code.</itunes:subtitle>
      <itunes:summary><![CDATA[Rita runs a two-person landscape design practice and wants a real backend instead of spreadsheets and rented CRMs. Can AI-assisted development make that realistic? This episode digs into the backbone: the database and architecture. It covers why the schema is the real moat, how to map a garden design practice onto the canonical CRM entities, and why a rigid schema with fast migrations beats a flexible one. Then it walks through the boring-but-correct stack — Cloud Run, Cloud SQL Postgres, Terraform — and the three things that actually break: connection pooling arithmetic, migration races at container start, and secrets management. Part one of a three-part series on building your own tools.]]></itunes:summary>
      <itunes:duration>1332</itunes:duration>
      <itunes:episode>5912</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-assisted-crm-schema-first-37758.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-assisted-crm-schema-first-37758.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Revit 2027 Actually Costs to Run</title>
      <description><![CDATA[A two-person architecture practice wants Revit 2027 running persistently so AI agents can query it through Autodesk's official MCP server. The hardware question looks simple until you price it out. We walk through Revit's actual system requirements, why single-core clock speed matters more than core count, what the read-only MCP tech preview can and can't do, and the three ways to arrange this setup — local workstation, modest box plus cloud rendering, or fully cloud-hosted. Along the way: Chaos's blunt warnings about vGPU and Enscape, why V-Ray's CPU rendering quietly changes the math, and where the budget actually lands.]]></description>
      <link>https://myweirdprompts.com/episode/revit-2027-workstation-cost-06946/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/revit-2027-workstation-cost-06946/</guid>
      <pubDate>Mon, 12 Oct 2026 17:31:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/revit-2027-workstation-cost-06946.m4a"
        type="audio/mp4"
        length="28904576"
      />
      <itunes:title>What Revit 2027 Actually Costs to Run</itunes:title>
      <itunes:subtitle>Michael wants Revit live for AI agents 24/7. The GPU isn&apos;t the real bill — the licensing is.</itunes:subtitle>
      <itunes:summary><![CDATA[A two-person architecture practice wants Revit 2027 running persistently so AI agents can query it through Autodesk's official MCP server. The hardware question looks simple until you price it out. We walk through Revit's actual system requirements, why single-core clock speed matters more than core count, what the read-only MCP tech preview can and can't do, and the three ways to arrange this setup — local workstation, modest box plus cloud rendering, or fully cloud-hosted. Along the way: Chaos's blunt warnings about vGPU and Enscape, why V-Ray's CPU rendering quietly changes the math, and where the budget actually lands.]]></itunes:summary>
      <itunes:duration>1643</itunes:duration>
      <itunes:episode>5911</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/revit-2027-workstation-cost-06946.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/revit-2027-workstation-cost-06946.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Revit MCP in a VM: What Actually Works</title>
      <description><![CDATA[Michael runs a two-person architecture practice and wants Revit on an always-on VM so an AI agent can drive the model while both architects sleep. We break down whether that's actually buildable. Autodesk's official MCP server ships as three installers — 8 read tools, 11 write tools, 39 experimental — and not one of them runs arbitrary code. The community servers that do run arbitrary code are the ones with an unauthenticated TCP port open on every interface. Then there's the blind test: the native MCP counted 32 walls where there were 57, while a dumb IFC export answered every question correctly and four times faster. Plus the licensing question Autodesk hasn't answered, and why "always-on VM" doesn't actually give you a running Revit with a model open.]]></description>
      <link>https://myweirdprompts.com/episode/revit-mcp-vm-feasibility-22577/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/revit-mcp-vm-feasibility-22577/</guid>
      <pubDate>Mon, 12 Oct 2026 17:27:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/revit-mcp-vm-feasibility-22577.m4a"
        type="audio/mp4"
        length="27974960"
      />
      <itunes:title>Revit MCP in a VM: What Actually Works</itunes:title>
      <itunes:subtitle>Autodesk&apos;s official Revit MCP server is locked down and reliable-ish. The community ones execute arbitrary code on an open port. Neither runs unatt...</itunes:subtitle>
      <itunes:summary><![CDATA[Michael runs a two-person architecture practice and wants Revit on an always-on VM so an AI agent can drive the model while both architects sleep. We break down whether that's actually buildable. Autodesk's official MCP server ships as three installers — 8 read tools, 11 write tools, 39 experimental — and not one of them runs arbitrary code. The community servers that do run arbitrary code are the ones with an unauthenticated TCP port open on every interface. Then there's the blind test: the native MCP counted 32 walls where there were 57, while a dumb IFC export answered every question correctly and four times faster. Plus the licensing question Autodesk hasn't answered, and why "always-on VM" doesn't actually give you a running Revit with a model open.]]></itunes:summary>
      <itunes:duration>1599</itunes:duration>
      <itunes:episode>5910</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/revit-mcp-vm-feasibility-22577.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/revit-mcp-vm-feasibility-22577.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Connecting Postgres to Google Workspace</title>
      <description><![CDATA[Rita runs a two-person business on Google Workspace and wants a real business layer on top of it. This episode walks through the integration architecture: why PostgreSQL owns the relationships while Google stays authoritative for documents, messages and events; how service accounts and domain-wide delegation actually differ; and why service accounts can't own Drive files — a constraint that forks the entire folder design. Plus the real costs of Cloud Run plus Cloud SQL, and what Google's new Workspace API quotas mean for small firms.]]></description>
      <link>https://myweirdprompts.com/episode/postgres-google-workspace-integration-99679/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/postgres-google-workspace-integration-99679/</guid>
      <pubDate>Mon, 12 Oct 2026 17:24:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/postgres-google-workspace-integration-99679.m4a"
        type="audio/mp4"
        length="29827550"
      />
      <itunes:title>Connecting Postgres to Google Workspace</itunes:title>
      <itunes:subtitle>How a Postgres-backed app on Cloud Run talks to Gmail, Drive, Calendar, Docs, Sheets and Slides — without duplicating data.</itunes:subtitle>
      <itunes:summary><![CDATA[Rita runs a two-person business on Google Workspace and wants a real business layer on top of it. This episode walks through the integration architecture: why PostgreSQL owns the relationships while Google stays authoritative for documents, messages and events; how service accounts and domain-wide delegation actually differ; and why service accounts can't own Drive files — a constraint that forks the entire folder design. Plus the real costs of Cloud Run plus Cloud SQL, and what Google's new Workspace API quotas mean for small firms.]]></itunes:summary>
      <itunes:duration>1709</itunes:duration>
      <itunes:episode>5909</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/postgres-google-workspace-integration-99679.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/postgres-google-workspace-integration-99679.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Database Won&apos;t Fix Your Fragmented Stack</title>
      <description><![CDATA[When a small landscaping practice bolts WhatsApp, a meeting assistant, and a CRM onto its existing backend, the instinct is to consolidate everything into one database. That instinct is wrong. This episode unpacks the distinction between a system of record and a source of truth, then walks through a concrete build order — WhatsApp Business Platform first, transcription second, CRM third — showing why every field needs exactly one declared owner and why copies are fine as long as you know who decides. Along the way: the new per-phone-number billing rules, the twenty-four-hour customer service window, why templates are copy that costs money and requires permission, and how to turn meeting transcripts into structured data instead of a folder of dead files.]]></description>
      <link>https://myweirdprompts.com/episode/system-of-record-vs-source-of-truth-36254/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/system-of-record-vs-source-of-truth-36254/</guid>
      <pubDate>Mon, 12 Oct 2026 17:16:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/system-of-record-vs-source-of-truth-36254.m4a"
        type="audio/mp4"
        length="29916094"
      />
      <itunes:title>One Database Won&apos;t Fix Your Fragmented Stack</itunes:title>
      <itunes:subtitle>Rita has two employees, four apps, and one fear: which database wins when they disagree? Here&apos;s the field-by-field answer.</itunes:subtitle>
      <itunes:summary><![CDATA[When a small landscaping practice bolts WhatsApp, a meeting assistant, and a CRM onto its existing backend, the instinct is to consolidate everything into one database. That instinct is wrong. This episode unpacks the distinction between a system of record and a source of truth, then walks through a concrete build order — WhatsApp Business Platform first, transcription second, CRM third — showing why every field needs exactly one declared owner and why copies are fine as long as you know who decides. Along the way: the new per-phone-number billing rules, the twenty-four-hour customer service window, why templates are copy that costs money and requires permission, and how to turn meeting transcripts into structured data instead of a folder of dead files.]]></itunes:summary>
      <itunes:duration>1707</itunes:duration>
      <itunes:episode>5908</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/system-of-record-vs-source-of-truth-36254.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/system-of-record-vs-source-of-truth-36254.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Bare Linux Routing vs OPNsense: What Actually Wins?</title>
      <description><![CDATA[OPNsense is a convenience, not a requirement — you can turn a cheap two-NIC mini-PC running Debian or Ubuntu into a full router with systemd-networkd, nftables, dnsmasq and WireGuard. Some people have done exactly that and never looked back: Ansible-provisioned configs, six hundred days of uptime, and patches straight from upstream instead of waiting on a handful of downstream maintainers. Others argue the sticker price of a router is only ten to twenty percent of its total cost of ownership, and the other eighty percent is the decade of maintenance nobody budgets for. This episode works through both sides — the patch-latency argument, the PPPoE performance case, the composability dream — and asks whether a "composable router" actually exists yet, or whether the closest thing to it is still in beta.]]></description>
      <link>https://myweirdprompts.com/episode/bare-linux-routing-vs-opnsense-40090/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/bare-linux-routing-vs-opnsense-40090/</guid>
      <pubDate>Mon, 12 Oct 2026 13:30:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/bare-linux-routing-vs-opnsense-40090.m4a"
        type="audio/mp4"
        length="26928567"
      />
      <itunes:title>Bare Linux Routing vs OPNsense: What Actually Wins?</itunes:title>
      <itunes:subtitle>You can build a router on plain Debian or Ubuntu. The question is whether you should — and what it costs you over ten years.</itunes:subtitle>
      <itunes:summary><![CDATA[OPNsense is a convenience, not a requirement — you can turn a cheap two-NIC mini-PC running Debian or Ubuntu into a full router with systemd-networkd, nftables, dnsmasq and WireGuard. Some people have done exactly that and never looked back: Ansible-provisioned configs, six hundred days of uptime, and patches straight from upstream instead of waiting on a handful of downstream maintainers. Others argue the sticker price of a router is only ten to twenty percent of its total cost of ownership, and the other eighty percent is the decade of maintenance nobody budgets for. This episode works through both sides — the patch-latency argument, the PPPoE performance case, the composability dream — and asks whether a "composable router" actually exists yet, or whether the closest thing to it is still in beta.]]></itunes:summary>
      <itunes:duration>1518</itunes:duration>
      <itunes:episode>5905</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/bare-linux-routing-vs-opnsense-40090.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/bare-linux-routing-vs-opnsense-40090.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Multi-WAN Beyond Failover: OPNsense vs OpenWrt</title>
      <description><![CDATA[You've built the gateway group, set the tiers, and failover works. So why does a dropped link still kill your VPN, your game, and your VoIP call? This episode separates true bonding from load balancing, then walks through what the "advanced" version of multi-WAN actually looks like in open source. In OPNsense, that means gateway group tiers, trigger levels, weight-based distribution, sticky connections, and the one silent subnet-mask mistake that breaks per-device routing. On the OpenWrt side, it means mwan3's firewall-mark packet classification, the iptables-to-nftables transition, and a five-hundred-line Lua alternative called Mini-MWAN that does failover and nothing else. Along the way: why failover speed was never the bottleneck, why the public IP change is the real problem, and why people who've done load balancing properly still go looking at bonding.]]></description>
      <link>https://myweirdprompts.com/episode/multi-wan-opnsense-openwrt-routing-03166/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-wan-opnsense-openwrt-routing-03166/</guid>
      <pubDate>Mon, 12 Oct 2026 13:22:47 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-wan-opnsense-openwrt-routing-03166.m4a"
        type="audio/mp4"
        length="21347211"
      />
      <itunes:title>Multi-WAN Beyond Failover: OPNsense vs OpenWrt</itunes:title>
      <itunes:subtitle>You stacked your links, failover works — so why does a dropped WAN still kill every session? Load balancing, mwan3, and what bonding actually solves.</itunes:subtitle>
      <itunes:summary><![CDATA[You've built the gateway group, set the tiers, and failover works. So why does a dropped link still kill your VPN, your game, and your VoIP call? This episode separates true bonding from load balancing, then walks through what the "advanced" version of multi-WAN actually looks like in open source. In OPNsense, that means gateway group tiers, trigger levels, weight-based distribution, sticky connections, and the one silent subnet-mask mistake that breaks per-device routing. On the OpenWrt side, it means mwan3's firewall-mark packet classification, the iptables-to-nftables transition, and a five-hundred-line Lua alternative called Mini-MWAN that does failover and nothing else. Along the way: why failover speed was never the bottleneck, why the public IP change is the real problem, and why people who've done load balancing properly still go looking at bonding.]]></itunes:summary>
      <itunes:duration>1203</itunes:duration>
      <itunes:episode>5904</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-wan-opnsense-openwrt-routing-03166.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-wan-opnsense-openwrt-routing-03166.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Serving DeepSeek-V4.1-Flash on Your Own GPU</title>
      <description><![CDATA[You've got the weights, you've got the rack, you've got a GPU bolted in that you're not replacing. So which engines can actually serve DeepSeek-V4.1-Flash — and how much of your "engine choice" is really a hardware decision wearing a software costume? This episode walks the five-name shortlist (vLLM, SGLang, TensorRT-LLM, llama.cpp, ONNX Runtime) against the model card's supported paths, then runs the two-server thought experiment: one NVIDIA box, one AMD box, same budget line, wildly different menus. Along the way: why the most portable engine is the one that can't load the model, why "supports AMD" and "supports your AMD card" are two different sentences, and why the quantization method printed on the checkpoint can decide whether your inference runs at all.]]></description>
      <link>https://myweirdprompts.com/episode/deepseek-v4-flash-local-inference-87980/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/deepseek-v4-flash-local-inference-87980/</guid>
      <pubDate>Mon, 12 Oct 2026 12:21:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/deepseek-v4-flash-local-inference-87980.m4a"
        type="audio/mp4"
        length="23732431"
      />
      <itunes:title>Serving DeepSeek-V4.1-Flash on Your Own GPU</itunes:title>
      <itunes:subtitle>Five inference engines, one frontier MoE, and a shortlist that collapses depending on which GPU you already bought.</itunes:subtitle>
      <itunes:summary><![CDATA[You've got the weights, you've got the rack, you've got a GPU bolted in that you're not replacing. So which engines can actually serve DeepSeek-V4.1-Flash — and how much of your "engine choice" is really a hardware decision wearing a software costume? This episode walks the five-name shortlist (vLLM, SGLang, TensorRT-LLM, llama.cpp, ONNX Runtime) against the model card's supported paths, then runs the two-server thought experiment: one NVIDIA box, one AMD box, same budget line, wildly different menus. Along the way: why the most portable engine is the one that can't load the model, why "supports AMD" and "supports your AMD card" are two different sentences, and why the quantization method printed on the checkpoint can decide whether your inference runs at all.]]></itunes:summary>
      <itunes:duration>1354</itunes:duration>
      <itunes:episode>5900</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/deepseek-v4-flash-local-inference-87980.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/deepseek-v4-flash-local-inference-87980.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Same GPU, Same Weights, Different Answers</title>
      <description><![CDATA[Two servers, same GPU, same weights, same prompt — different output. This episode digs into Daniel's thought experiment: three providers running vLLM, SGLang, and TensorRT-LLM on identical hardware, where the customer can't choose the engine. Does the inference engine actually change speed, cost, and the answers themselves? We walk through the architecture of all three — PagedAttention, RadixAttention, and the TensorRT-LLM backend overhaul that removed TensorRT — then look at the benchmark data, where the fastest engine changes with the load, and a 2026 CISPA paper showing that swapping only the backend moved GSM8K scores by up to 16.6 percentage points on the same weights. If you rent endpoints and never see the middle layer, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/inference-engine-benchmarks-comparison-25521/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/inference-engine-benchmarks-comparison-25521/</guid>
      <pubDate>Mon, 12 Oct 2026 12:18:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/inference-engine-benchmarks-comparison-25521.m4a"
        type="audio/mp4"
        length="22964099"
      />
      <itunes:title>Same GPU, Same Weights, Different Answers</itunes:title>
      <itunes:subtitle>Identical hardware, identical weights, different engine — and answers that shift by up to 16.6 points.</itunes:subtitle>
      <itunes:summary><![CDATA[Two servers, same GPU, same weights, same prompt — different output. This episode digs into Daniel's thought experiment: three providers running vLLM, SGLang, and TensorRT-LLM on identical hardware, where the customer can't choose the engine. Does the inference engine actually change speed, cost, and the answers themselves? We walk through the architecture of all three — PagedAttention, RadixAttention, and the TensorRT-LLM backend overhaul that removed TensorRT — then look at the benchmark data, where the fastest engine changes with the load, and a 2026 CISPA paper showing that swapping only the backend moved GSM8K scores by up to 16.6 percentage points on the same weights. If you rent endpoints and never see the middle layer, this one's for you.]]></itunes:summary>
      <itunes:duration>1292</itunes:duration>
      <itunes:episode>5899</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/inference-engine-benchmarks-comparison-25521.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/inference-engine-benchmarks-comparison-25521.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>ROCm 10.1: AMD&apos;s Software Stack Finally Grows Up</title>
      <description><![CDATA[AMD crossed a trillion dollars in market cap and its software stack is finally getting the attention. We dig into three ROCm releases — 7.14, 10, and 10.1 — and ask the honest question: is ROCm actually production-viable for inference now, or just for the data center? From TheRock's reproducible builds to Hyperloom's autonomous optimizer to NUMA-aware memory allocation, we trace what changed, what's still marketing, and why the 7900 XTX owner is still the person the roadmap forgot.]]></description>
      <link>https://myweirdprompts.com/episode/rocm-10-amd-gpu-software-14533/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rocm-10-amd-gpu-software-14533/</guid>
      <pubDate>Mon, 12 Oct 2026 12:14:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rocm-10-amd-gpu-software-14533.m4a"
        type="audio/mp4"
        length="28806718"
      />
      <itunes:title>ROCm 10.1: AMD&apos;s Software Stack Finally Grows Up</itunes:title>
      <itunes:subtitle>Three ROCm releases in six months — build system, agentic tooling, data movement. What actually changed for AMD GPU owners.</itunes:subtitle>
      <itunes:summary><![CDATA[AMD crossed a trillion dollars in market cap and its software stack is finally getting the attention. We dig into three ROCm releases — 7.14, 10, and 10.1 — and ask the honest question: is ROCm actually production-viable for inference now, or just for the data center? From TheRock's reproducible builds to Hyperloom's autonomous optimizer to NUMA-aware memory allocation, we trace what changed, what's still marketing, and why the 7900 XTX owner is still the person the roadmap forgot.]]></itunes:summary>
      <itunes:duration>1635</itunes:duration>
      <itunes:episode>5898</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rocm-10-amd-gpu-software-14533.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rocm-10-amd-gpu-software-14533.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The World&apos;s Oldest Software Still Running</title>
      <description><![CDATA[Software is the only thing we build that we expect to throw away — yet some programs from the 1950s and 60s are still running the same jobs today. This episode digs into the strongest claims for the world's oldest production software: MOCAS, the 1958 Pentagon contract system; SABRE, the 1960 airline reservation system that cut booking time from 90 minutes to seconds; the IRS Individual Master File from 1962; and Voyager's 1977 flight computer, still taking commands from 22 light-hours away. Along the way, we untangle a distinction most coverage misses: a language still in use (COBOL) is not the same as software that has survived largely unchanged. Is this an engineering triumph or a warning sign? The answer depends entirely on which definition of "oldest" you're using — and whether the risk lives in the old code or in the rewrite.]]></description>
      <link>https://myweirdprompts.com/episode/oldest-software-still-running-26325/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/oldest-software-still-running-26325/</guid>
      <pubDate>Mon, 12 Oct 2026 09:04:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/oldest-software-still-running-26325.m4a"
        type="audio/mp4"
        length="32734727"
      />
      <itunes:title>The World&apos;s Oldest Software Still Running</itunes:title>
      <itunes:subtitle>From a 1958 Pentagon contract system to Voyager&apos;s 68KB computer, some software refuses to die. What&apos;s the oldest code still in production?</itunes:subtitle>
      <itunes:summary><![CDATA[Software is the only thing we build that we expect to throw away — yet some programs from the 1950s and 60s are still running the same jobs today. This episode digs into the strongest claims for the world's oldest production software: MOCAS, the 1958 Pentagon contract system; SABRE, the 1960 airline reservation system that cut booking time from 90 minutes to seconds; the IRS Individual Master File from 1962; and Voyager's 1977 flight computer, still taking commands from 22 light-hours away. Along the way, we untangle a distinction most coverage misses: a language still in use (COBOL) is not the same as software that has survived largely unchanged. Is this an engineering triumph or a warning sign? The answer depends entirely on which definition of "oldest" you're using — and whether the risk lives in the old code or in the rewrite.]]></itunes:summary>
      <itunes:duration>1873</itunes:duration>
      <itunes:episode>5885</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/oldest-software-still-running-26325.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/oldest-software-still-running-26325.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Homebox Fork&apos;s Two Backend Scripts Need a Real Job Runner</title>
      <description><![CDATA[Daniel's Homebox fork added two backend scripts the upstream project was never designed to host: one converts new images to WebP and rewrites database references, the other sends images through a vision model to extract serial numbers without overwriting existing data. Both are incremental and both are deferred so the fast path stays fast. The problem is what runs them. Upstream Homebox ships a background runner with a name, an interval, and a function — no queue, no persistence, no retry, no record. Cron silently skips overlapping runs and multiplies jobs across instances. The episode walks through the standard three-part answer (cron, workers, queues), leader election via Redis or Postgres advisory locks, why CPU-bound encoding and I/O-bound vision calls need separate pools, and what actually exists in the self-hosted orchestration space: Dagu, Cronduit, Cinnamon, and a long tail that mostly ends in a web UI bolted onto crontab.]]></description>
      <link>https://myweirdprompts.com/episode/homebox-backend-job-orchestration-69442/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/homebox-backend-job-orchestration-69442/</guid>
      <pubDate>Sun, 11 Oct 2026 14:11:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/homebox-backend-job-orchestration-69442.m4a"
        type="audio/mp4"
        length="25589521"
      />
      <itunes:title>Homebox Fork&apos;s Two Backend Scripts Need a Real Job Runner</itunes:title>
      <itunes:subtitle>A Homebox fork has two incremental scripts — WebP conversion and AI image enrichment — and no scheduler built to run them safely.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's Homebox fork added two backend scripts the upstream project was never designed to host: one converts new images to WebP and rewrites database references, the other sends images through a vision model to extract serial numbers without overwriting existing data. Both are incremental and both are deferred so the fast path stays fast. The problem is what runs them. Upstream Homebox ships a background runner with a name, an interval, and a function — no queue, no persistence, no retry, no record. Cron silently skips overlapping runs and multiplies jobs across instances. The episode walks through the standard three-part answer (cron, workers, queues), leader election via Redis or Postgres advisory locks, why CPU-bound encoding and I/O-bound vision calls need separate pools, and what actually exists in the self-hosted orchestration space: Dagu, Cronduit, Cinnamon, and a long tail that mostly ends in a web UI bolted onto crontab.]]></itunes:summary>
      <itunes:duration>1467</itunes:duration>
      <itunes:episode>5870</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/homebox-backend-job-orchestration-69442.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/homebox-backend-job-orchestration-69442.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Scraping AliExpress Specs: Selectors, Agents, or Neither?</title>
      <description><![CDATA[Daniel has a home inventory system that tracks where things physically are, and he wants to add a snapshot function: save an AliExpress URL, pull the page, keep only the technical sheet. No reviews, no price. The obvious plan — define the divs, extract them — runs into a problem that has already happened: AliExpress product pages are client-side rendered, the inline params object is now an empty object, and a parser reading it returns nothing while logging a success. So what are the live options? A maintained Apify actor that returns a parsed specs array, or an agentic extraction layer like Firecrawl's JSON mode where the prompt names both the field and where on the page it lives. Both assume you can reach the page at all — and Alibaba's Baxia anti-bot returns HTTP 200 with a punishment header, so a dashboard counting 2xx responses reports a healthy pipeline while every row is empty. The extraction library is competing for second place.]]></description>
      <link>https://myweirdprompts.com/episode/ali-express-spec-scraping-03238/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ali-express-spec-scraping-03238/</guid>
      <pubDate>Sun, 11 Oct 2026 14:05:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ali-express-spec-scraping-03238.m4a"
        type="audio/mp4"
        length="24177938"
      />
      <itunes:title>Scraping AliExpress Specs: Selectors, Agents, or Neither?</itunes:title>
      <itunes:subtitle>Daniel wants to snapshot AliExpress spec sheets into his inventory system. The selector approach is already dead — here&apos;s what actually works.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has a home inventory system that tracks where things physically are, and he wants to add a snapshot function: save an AliExpress URL, pull the page, keep only the technical sheet. No reviews, no price. The obvious plan — define the divs, extract them — runs into a problem that has already happened: AliExpress product pages are client-side rendered, the inline params object is now an empty object, and a parser reading it returns nothing while logging a success. So what are the live options? A maintained Apify actor that returns a parsed specs array, or an agentic extraction layer like Firecrawl's JSON mode where the prompt names both the field and where on the page it lives. Both assume you can reach the page at all — and Alibaba's Baxia anti-bot returns HTTP 200 with a punishment header, so a dashboard counting 2xx responses reports a healthy pipeline while every row is empty. The extraction library is competing for second place.]]></itunes:summary>
      <itunes:duration>1372</itunes:duration>
      <itunes:episode>5869</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ali-express-spec-scraping-03238.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ali-express-spec-scraping-03238.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tuning Parakeet for Um-Free, Hebrish Speech</title>
      <description><![CDATA[Daniel wants to bend Parakeet to his will: strip the ums, catch niche words, and handle the Hebrew he sprinkles into his English. He's done this before with Whisper, so he knows the shape of the job — but Parakeet is a different animal. This episode walks through what Parakeet TDT 0.6B v3 actually is, why a small model is a better inference candidate but not an easier fine-tune, and the real workflow: NVIDIA NeMo on a rented GPU, a JSON manifest, two learning-rate knobs, then a GGUF conversion and a sideload back to the phone. We dig into the disfluency problem (there's no published Parakeet recipe, but a Whisper LoRA shows the trick: the lesson lives in the target transcript, not in a rule), the Hebrew code-switching problem (why the obvious Hebrew Parakeet model is the wrong tool, and what a Hebrish Whisper fine-tune got right with just 500 phrases), and the tokenizer-extension mechanics that matter if you ever need to teach the model new words without forgetting the old ones.]]></description>
      <link>https://myweirdprompts.com/episode/fine-tuning-parakeet-hebrish-speech-40484/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fine-tuning-parakeet-hebrish-speech-40484/</guid>
      <pubDate>Sun, 11 Oct 2026 13:37:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fine-tuning-parakeet-hebrish-speech-40484.m4a"
        type="audio/mp4"
        length="29087344"
      />
      <itunes:title>Fine-Tuning Parakeet for Um-Free, Hebrish Speech</itunes:title>
      <itunes:subtitle>Parakeet runs great on a phone — but fine-tuning it happens on a GPU. Here&apos;s the actual recipe for dropping ums and catching Hebrew.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants to bend Parakeet to his will: strip the ums, catch niche words, and handle the Hebrew he sprinkles into his English. He's done this before with Whisper, so he knows the shape of the job — but Parakeet is a different animal. This episode walks through what Parakeet TDT 0.6B v3 actually is, why a small model is a better inference candidate but not an easier fine-tune, and the real workflow: NVIDIA NeMo on a rented GPU, a JSON manifest, two learning-rate knobs, then a GGUF conversion and a sideload back to the phone. We dig into the disfluency problem (there's no published Parakeet recipe, but a Whisper LoRA shows the trick: the lesson lives in the target transcript, not in a rule), the Hebrew code-switching problem (why the obvious Hebrew Parakeet model is the wrong tool, and what a Hebrish Whisper fine-tune got right with just 500 phrases), and the tokenizer-extension mechanics that matter if you ever need to teach the model new words without forgetting the old ones.]]></itunes:summary>
      <itunes:duration>1656</itunes:duration>
      <itunes:episode>5868</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fine-tuning-parakeet-hebrish-speech-40484.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fine-tuning-parakeet-hebrish-speech-40484.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Four Pocket Players That Won&apos;t Sell You a Device Today</title>
      <description><![CDATA[Daniel wants an iPod-shaped appliance that holds a hundred gigabytes of podcast archive, plays it over Bluetooth at the beach, and never sends him a notification. The hardware exists — Mono, Fonix One, Sudo, and DuRoBo Krono are all building some version of it — but the market is stuck between waitlists, deposits, and concept reveals. This episode works through the real fork in the build: an ESP32 microcontroller with no operating system at all, versus open Android with all the baggage that comes with it. The deciding evidence is a documented history of Android storage frameworks breaking podcast apps, and the numbers on what a hundred-gigabyte library actually costs in cards, hours, and watts.]]></description>
      <link>https://myweirdprompts.com/episode/pocket-audio-players-2026-06945/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pocket-audio-players-2026-06945/</guid>
      <pubDate>Sun, 11 Oct 2026 11:47:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pocket-audio-players-2026-06945.m4a"
        type="audio/mp4"
        length="35677476"
      />
      <itunes:title>Four Pocket Players That Won&apos;t Sell You a Device Today</itunes:title>
      <itunes:subtitle>Mono, Fonix One, Sudo, and DuRoBo Krono all promise phone-free podcast listening — and not one of them is ready to ship today.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants an iPod-shaped appliance that holds a hundred gigabytes of podcast archive, plays it over Bluetooth at the beach, and never sends him a notification. The hardware exists — Mono, Fonix One, Sudo, and DuRoBo Krono are all building some version of it — but the market is stuck between waitlists, deposits, and concept reveals. This episode works through the real fork in the build: an ESP32 microcontroller with no operating system at all, versus open Android with all the baggage that comes with it. The deciding evidence is a documented history of Android storage frameworks breaking podcast apps, and the numbers on what a hundred-gigabyte library actually costs in cards, hours, and watts.]]></itunes:summary>
      <itunes:duration>2054</itunes:duration>
      <itunes:episode>5865</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pocket-audio-players-2026-06945.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pocket-audio-players-2026-06945.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Routing AI Models: When 3 Rules Beat a Trained Router</title>
      <description><![CDATA[Daniel wants to stop randomly assigning podcast episodes to AI models and start routing them intelligently. But the routing literature has a trapdoor: the famous "85% cost reduction" headline compares learned routers against always using the most expensive model — a baseline nobody with a budget actually runs. This episode walks through the router toolbox (RouteLLM's matrix factorization, RoRF's random forests, LLMRouter's 16-router harness), digs into personalization research like GMTRouter and Prompt-to-Leaderboard, and lands on an uncomfortable question: at podcast scale, does a learned router beat three good rules? We also cover the data collection architecture, cost and latency tradeoffs, and how to wire it all into a Python pipeline on Modal.]]></description>
      <link>https://myweirdprompts.com/episode/learned-model-routing-vs-rules-10912/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/learned-model-routing-vs-rules-10912/</guid>
      <pubDate>Sun, 11 Oct 2026 10:44:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/learned-model-routing-vs-rules-10912.m4a"
        type="audio/mp4"
        length="33924788"
      />
      <itunes:title>Routing AI Models: When 3 Rules Beat a Trained Router</itunes:title>
      <itunes:subtitle>A deep dive into AI model routing — and why a learned router may not beat three rules you wrote by hand.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants to stop randomly assigning podcast episodes to AI models and start routing them intelligently. But the routing literature has a trapdoor: the famous "85% cost reduction" headline compares learned routers against always using the most expensive model — a baseline nobody with a budget actually runs. This episode walks through the router toolbox (RouteLLM's matrix factorization, RoRF's random forests, LLMRouter's 16-router harness), digs into personalization research like GMTRouter and Prompt-to-Leaderboard, and lands on an uncomfortable question: at podcast scale, does a learned router beat three good rules? We also cover the data collection architecture, cost and latency tradeoffs, and how to wire it all into a Python pipeline on Modal.]]></itunes:summary>
      <itunes:duration>1948</itunes:duration>
      <itunes:episode>5858</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/learned-model-routing-vs-rules-10912.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/learned-model-routing-vs-rules-10912.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Scorecard for Our Own AI Writers</title>
      <description><![CDATA[Every episode of this show is written by an agentic pipeline, and for most of the archive that script-writing agent has been DeepSeek V4, with Gemini, Claude, and a stretch of OpenRouter randomization in the mix. So when Daniel asked how to build a bespoke benchmark for it, the answer couldn't be vibes and one careful listen. This episode works through the three things he wants measured — mid-episode repetition, lifelike dialogue between two named recurring hosts, and whether the retrieved sources actually make it into the script — and why no existing benchmark covers that combination. EQ-Bench's creative-writing suite explicitly says it can't assess conversational roleplay and is English-only. HELM measures SQuAD accuracy. The closest neighbours are rubric-plus-Elo designs and composable binary-leaf rubrics, plus two enterprise agentic-grounding harnesses. We dig into rubric saturation, judge self-preference bias, position bias, and the case for deterministic n-gram scoring where no judge model is needed at all.]]></description>
      <link>https://myweirdprompts.com/episode/bespoke-llm-benchmark-script-agent-77594/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/bespoke-llm-benchmark-script-agent-77594/</guid>
      <pubDate>Sun, 11 Oct 2026 10:43:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/bespoke-llm-benchmark-script-agent-77594.m4a"
        type="audio/mp4"
        length="34739784"
      />
      <itunes:title>Building a Scorecard for Our Own AI Writers</itunes:title>
      <itunes:subtitle>Daniel wants a real scorecard for the agent that writes this show — repetition, dialogue quality, and whether the retrieval harness actually works.</itunes:subtitle>
      <itunes:summary><![CDATA[Every episode of this show is written by an agentic pipeline, and for most of the archive that script-writing agent has been DeepSeek V4, with Gemini, Claude, and a stretch of OpenRouter randomization in the mix. So when Daniel asked how to build a bespoke benchmark for it, the answer couldn't be vibes and one careful listen. This episode works through the three things he wants measured — mid-episode repetition, lifelike dialogue between two named recurring hosts, and whether the retrieved sources actually make it into the script — and why no existing benchmark covers that combination. EQ-Bench's creative-writing suite explicitly says it can't assess conversational roleplay and is English-only. HELM measures SQuAD accuracy. The closest neighbours are rubric-plus-Elo designs and composable binary-leaf rubrics, plus two enterprise agentic-grounding harnesses. We dig into rubric saturation, judge self-preference bias, position bias, and the case for deterministic n-gram scoring where no judge model is needed at all.]]></itunes:summary>
      <itunes:duration>1977</itunes:duration>
      <itunes:episode>5857</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/bespoke-llm-benchmark-script-agent-77594.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/bespoke-llm-benchmark-script-agent-77594.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Earbud Assistant Problem Nobody Solves</title>
      <description><![CDATA[We've been promised a seamless voice assistant in our ear for a decade — and the model layer is finally good enough. So why does the thing still not work? This episode digs into the unglamorous plumbing that makes or breaks the wearable assistant: a hardcoded mic constant in Home Assistant's Android app that routes audio to your pocket instead of your earbud, a wake-word spotter that silently eats the first word of your command, and the brutal physics of fitting voice activation into a device with a four-kilobyte instruction cache. We look at Yandex's remarkable 200-kilobyte wake-word engineering, the volume-knob bug that took a month to find, and what Humane's $230M failure tells us about solving the hard problem while shipping a bad experience. The model isn't the bottleneck anymore — and that's a strange place to be.]]></description>
      <link>https://myweirdprompts.com/episode/earbud-assistant-plumbing-problem-80605/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/earbud-assistant-plumbing-problem-80605/</guid>
      <pubDate>Sun, 11 Oct 2026 04:55:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/earbud-assistant-plumbing-problem-80605.m4a"
        type="audio/mp4"
        length="25152699"
      />
      <itunes:title>The Earbud Assistant Problem Nobody Solves</itunes:title>
      <itunes:subtitle>The AI model is solved. So why does the assistant in your ear still fail? The answer is the unglamorous plumbing nobody demos.</itunes:subtitle>
      <itunes:summary><![CDATA[We've been promised a seamless voice assistant in our ear for a decade — and the model layer is finally good enough. So why does the thing still not work? This episode digs into the unglamorous plumbing that makes or breaks the wearable assistant: a hardcoded mic constant in Home Assistant's Android app that routes audio to your pocket instead of your earbud, a wake-word spotter that silently eats the first word of your command, and the brutal physics of fitting voice activation into a device with a four-kilobyte instruction cache. We look at Yandex's remarkable 200-kilobyte wake-word engineering, the volume-knob bug that took a month to find, and what Humane's $230M failure tells us about solving the hard problem while shipping a bad experience. The model isn't the bottleneck anymore — and that's a strange place to be.]]></itunes:summary>
      <itunes:duration>1425</itunes:duration>
      <itunes:episode>5851</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/earbud-assistant-plumbing-problem-80605.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/earbud-assistant-plumbing-problem-80605.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Who Invented Computer Science—and Was It Ever a Trade?</title>
      <description><![CDATA[Computer science was founded as a science of computation—Newell, Perlis, and Simon argued in 1967 that "the phenomena surrounding computers are varied, complex, rich." Yet the degree became the software industry's default credential, and the fight over tool users versus tool builders has run through every curriculum revision since 1968. This episode traces the field's origins: the first departments at Purdue and Stanford, the 1965 doctorates history forgot (including Sister Mary Kenneth Keller, the first woman to earn a CS Ph.D.), and what the halting problem reveals about studying a world we build ourselves. Plus: why recent CS graduates now face a higher jobless rate than philosophy majors.]]></description>
      <link>https://myweirdprompts.com/episode/computer-science-founding-debate-85297/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/computer-science-founding-debate-85297/</guid>
      <pubDate>Sat, 10 Oct 2026 20:22:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/computer-science-founding-debate-85297.m4a"
        type="audio/mp4"
        length="26439425"
      />
      <itunes:title>Who Invented Computer Science—and Was It Ever a Trade?</itunes:title>
      <itunes:subtitle>The founders wanted a science of computation. We built a job credential instead—and the argument hasn&apos;t changed since 1968.</itunes:subtitle>
      <itunes:summary><![CDATA[Computer science was founded as a science of computation—Newell, Perlis, and Simon argued in 1967 that "the phenomena surrounding computers are varied, complex, rich." Yet the degree became the software industry's default credential, and the fight over tool users versus tool builders has run through every curriculum revision since 1968. This episode traces the field's origins: the first departments at Purdue and Stanford, the 1965 doctorates history forgot (including Sister Mary Kenneth Keller, the first woman to earn a CS Ph.D.), and what the halting problem reveals about studying a world we build ourselves. Plus: why recent CS graduates now face a higher jobless rate than philosophy majors.]]></itunes:summary>
      <itunes:duration>1494</itunes:duration>
      <itunes:episode>5849</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/computer-science-founding-debate-85297.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/computer-science-founding-debate-85297.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Libraries Nobody Thinks About</title>
      <description><![CDATA[Daniel asked us to celebrate the software nobody thinks about: the small, fundamental pieces of logic that run everywhere and get replaced by no one. We start with zlib, still shipping thirty-one years of continuous maintenance by Jean-loup Gailly and Mark Adler, and with SQLite's own claim of over a trillion active databases. Then we get to the uncomfortable half: OpenSSL securing two-thirds of the encrypted web on about two thousand dollars a year in donations, and the multi-year xz backdoor, where a patient attacker spent years pressuring a solo maintainer before planting malicious code in a release. It's a conversation about permanence as a product — and about who pays for it.]]></description>
      <link>https://myweirdprompts.com/episode/unsung-software-libraries-38087/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unsung-software-libraries-38087/</guid>
      <pubDate>Sat, 10 Oct 2026 20:18:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unsung-software-libraries-38087.m4a"
        type="audio/mp4"
        length="28948069"
      />
      <itunes:title>The Libraries Nobody Thinks About</itunes:title>
      <itunes:subtitle>zlib, SQLite, OpenSSL, and the xz backdoor — the tiny pieces of code running everywhere, and the volunteers keeping them alive.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asked us to celebrate the software nobody thinks about: the small, fundamental pieces of logic that run everywhere and get replaced by no one. We start with zlib, still shipping thirty-one years of continuous maintenance by Jean-loup Gailly and Mark Adler, and with SQLite's own claim of over a trillion active databases. Then we get to the uncomfortable half: OpenSSL securing two-thirds of the encrypted web on about two thousand dollars a year in donations, and the multi-year xz backdoor, where a patient attacker spent years pressuring a solo maintainer before planting malicious code in a release. It's a conversation about permanence as a product — and about who pays for it.]]></itunes:summary>
      <itunes:duration>1633</itunes:duration>
      <itunes:episode>5848</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unsung-software-libraries-38087.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unsung-software-libraries-38087.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why pip and uv Share Almost No Code</title>
      <description><![CDATA[pip has been the default Python installer for over a decade, but its resolver was naive for most of that time and it only got a lockfile story in 2025. uv arrived in 2024 as a single static Rust binary with no Python inside it at all — so how does it relate to pip? This episode traces pip's origin from Ian Bicking's pyinstall through the PyPA handoff to the 2020 resolver rewrite, unpacks where the npm comparison holds and where it breaks, and answers the question directly: uv doesn't wrap pip, it reimplements the standards from scratch. The real speed story turns out to be about parallel resolution, concurrent builds, and a hardlink cache — design decisions, not the language.]]></description>
      <link>https://myweirdprompts.com/episode/pip-vs-uv-python-packaging-35217/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pip-vs-uv-python-packaging-35217/</guid>
      <pubDate>Sat, 10 Oct 2026 19:29:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pip-vs-uv-python-packaging-35217.m4a"
        type="audio/mp4"
        length="32920664"
      />
      <itunes:title>Why pip and uv Share Almost No Code</itunes:title>
      <itunes:subtitle>pip won by being preinstalled, not by being best. uv is a from-scratch Rust rewrite, not a wrapper — and the speed story isn&apos;t really about Rust.</itunes:subtitle>
      <itunes:summary><![CDATA[pip has been the default Python installer for over a decade, but its resolver was naive for most of that time and it only got a lockfile story in 2025. uv arrived in 2024 as a single static Rust binary with no Python inside it at all — so how does it relate to pip? This episode traces pip's origin from Ian Bicking's pyinstall through the PyPA handoff to the 2020 resolver rewrite, unpacks where the npm comparison holds and where it breaks, and answers the question directly: uv doesn't wrap pip, it reimplements the standards from scratch. The real speed story turns out to be about parallel resolution, concurrent builds, and a hardlink cache — design decisions, not the language.]]></itunes:summary>
      <itunes:duration>1883</itunes:duration>
      <itunes:episode>5843</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pip-vs-uv-python-packaging-35217.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pip-vs-uv-python-packaging-35217.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your TTS Model Updates Itself</title>
      <description><![CDATA[Daniel builds this show on Chatterbox, Resemble AI's open source text-to-speech model — and one day he noticed our pipeline was pulling new weights he never asked for. That sent us down a rabbit hole: what actually changes between TTS point releases, and how does a downstream project silently inherit the improvement? We dig into V3's data-only gains, a code-only fix that cut Spanish tail noise 388x, Turbo's one-step decoder, and the version number that couldn't tell two very different installs apart. Along the way: the exaggeration knob that made Chatterbox famous, the PerTh watermark on every output, and why character error rate still can't measure the thing listeners actually hear.]]></description>
      <link>https://myweirdprompts.com/episode/tts-model-silent-updates-44828/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tts-model-silent-updates-44828/</guid>
      <pubDate>Sat, 10 Oct 2026 19:24:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tts-model-silent-updates-44828.m4a"
        type="audio/mp4"
        length="30141651"
      />
      <itunes:title>When Your TTS Model Updates Itself</itunes:title>
      <itunes:subtitle>Resemble pushed new Chatterbox weights to Hugging Face. Our pipeline started using them without a single config change.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel builds this show on Chatterbox, Resemble AI's open source text-to-speech model — and one day he noticed our pipeline was pulling new weights he never asked for. That sent us down a rabbit hole: what actually changes between TTS point releases, and how does a downstream project silently inherit the improvement? We dig into V3's data-only gains, a code-only fix that cut Spanish tail noise 388x, Turbo's one-step decoder, and the version number that couldn't tell two very different installs apart. Along the way: the exaggeration knob that made Chatterbox famous, the PerTh watermark on every output, and why character error rate still can't measure the thing listeners actually hear.]]></itunes:summary>
      <itunes:duration>1729</itunes:duration>
      <itunes:episode>5842</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tts-model-silent-updates-44828.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tts-model-silent-updates-44828.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Serving Your Own Fine-Tuned Model in the Cloud</title>
      <description><![CDATA[You've fine-tuned an open-weight model like DeepSeek and now you need to serve it behind an API. This episode walks through the two main patterns — dedicated GPU hosting with weights loaded on an always-on instance, and serverless GPU inference where you pay for compute consumed — and untangles the three very different things "serverless GPU" actually means. We cover which platforms let you bring your own weights (RunPod, SageMaker, Modal, Baseten, Replicate, Fireworks, Cerebrium), what the LoRA hosting shift means for adapter-based fine-tunes, and why cold starts are a bandwidth problem, not a compute problem. Most importantly, we work through the utilization math that determines whether serverless is genuinely cheaper or just a way to end up back on a dedicated box.]]></description>
      <link>https://myweirdprompts.com/episode/serving-fine-tuned-models-cloud/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/serving-fine-tuned-models-cloud/</guid>
      <pubDate>Sat, 10 Oct 2026 10:23:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/serving-fine-tuned-models-cloud.m4a"
        type="audio/mp4"
        length="25832411"
      />
      <itunes:title>Serving Your Own Fine-Tuned Model in the Cloud</itunes:title>
      <itunes:subtitle>You fine-tuned an open-weight model. Now how does anyone actually talk to it? Dedicated GPUs vs serverless inference, and the math that decides it.</itunes:subtitle>
      <itunes:summary><![CDATA[You've fine-tuned an open-weight model like DeepSeek and now you need to serve it behind an API. This episode walks through the two main patterns — dedicated GPU hosting with weights loaded on an always-on instance, and serverless GPU inference where you pay for compute consumed — and untangles the three very different things "serverless GPU" actually means. We cover which platforms let you bring your own weights (RunPod, SageMaker, Modal, Baseten, Replicate, Fireworks, Cerebrium), what the LoRA hosting shift means for adapter-based fine-tunes, and why cold starts are a bandwidth problem, not a compute problem. Most importantly, we work through the utilization math that determines whether serverless is genuinely cheaper or just a way to end up back on a dedicated box.]]></itunes:summary>
      <itunes:duration>1460</itunes:duration>
      <itunes:episode>5830</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/serving-fine-tuned-models-cloud.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/serving-fine-tuned-models-cloud.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Printing Files on Paper That Outlasts the Software</title>
      <description><![CDATA[What if you could archive a file by printing it on paper? Optar and PaperBack encode arbitrary binary data as dense dot patterns on A4 sheets — no drive, no power, no cloud account required. This episode digs into how the encoding actually works, from Golay error correction borrowed from the Voyager probes to Reed-Solomon blocks, and what happens when you fold the page twice, carry it in a pocket, and scan it back. Then the hard numbers: kilobytes per page, the gap between a printer's claimed 600 dpi and its real 200 dpi, and what paper, toner, and time actually cost. Finally, the question underneath all of it: does anyone actually do this, or did paper storage lose to tape and cloud and common sense? The answer is more interesting than either side expects — paper may be one of the most durable things we've ever made, while the software that reads it is the least durable.]]></description>
      <link>https://myweirdprompts.com/episode/paper-data-storage-optar-paperback/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/paper-data-storage-optar-paperback/</guid>
      <pubDate>Sat, 10 Oct 2026 10:21:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/paper-data-storage-optar-paperback.m4a"
        type="audio/mp4"
        length="30747360"
      />
      <itunes:title>Printing Files on Paper That Outlasts the Software</itunes:title>
      <itunes:subtitle>Optar and PaperBack turn any file into machine-readable dots on paper. The paper lasts centuries — the codec might not.</itunes:subtitle>
      <itunes:summary><![CDATA[What if you could archive a file by printing it on paper? Optar and PaperBack encode arbitrary binary data as dense dot patterns on A4 sheets — no drive, no power, no cloud account required. This episode digs into how the encoding actually works, from Golay error correction borrowed from the Voyager probes to Reed-Solomon blocks, and what happens when you fold the page twice, carry it in a pocket, and scan it back. Then the hard numbers: kilobytes per page, the gap between a printer's claimed 600 dpi and its real 200 dpi, and what paper, toner, and time actually cost. Finally, the question underneath all of it: does anyone actually do this, or did paper storage lose to tape and cloud and common sense? The answer is more interesting than either side expects — paper may be one of the most durable things we've ever made, while the software that reads it is the least durable.]]></itunes:summary>
      <itunes:duration>1746</itunes:duration>
      <itunes:episode>5829</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/paper-data-storage-optar-paperback.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/paper-data-storage-optar-paperback.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>LTFS Explained: Tape That Acts Like a Disk</title>
      <description><![CDATA[LTFS turns LTO tape into something that browses like a disk, using a clever two-partition trick and a plain-XML index. But the physics of tape never went away: append-only writes, unreclaimable deletes, and a random seek measured at around 37 seconds. This episode walks through what LTFS actually is under the hood, then asks the practical question — if you had hundreds of terabytes of PDFs to make searchable, how would you build it? Disk caching, tape libraries, and hierarchical storage management all enter the picture, along with the honest verdict on whether LTFS can be a foundation for a document archive or whether sequential access always wins.]]></description>
      <link>https://myweirdprompts.com/episode/ltfs-tape-disk-archive/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ltfs-tape-disk-archive/</guid>
      <pubDate>Sat, 10 Oct 2026 10:05:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ltfs-tape-disk-archive.m4a"
        type="audio/mp4"
        length="27749547"
      />
      <itunes:title>LTFS Explained: Tape That Acts Like a Disk</itunes:title>
      <itunes:subtitle>LTFS makes tape pretend to be a filesystem — but 37-second seeks decide whether it can anchor a searchable archive.</itunes:subtitle>
      <itunes:summary><![CDATA[LTFS turns LTO tape into something that browses like a disk, using a clever two-partition trick and a plain-XML index. But the physics of tape never went away: append-only writes, unreclaimable deletes, and a random seek measured at around 37 seconds. This episode walks through what LTFS actually is under the hood, then asks the practical question — if you had hundreds of terabytes of PDFs to make searchable, how would you build it? Disk caching, tape libraries, and hierarchical storage management all enter the picture, along with the honest verdict on whether LTFS can be a foundation for a document archive or whether sequential access always wins.]]></itunes:summary>
      <itunes:duration>1558</itunes:duration>
      <itunes:episode>5827</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ltfs-tape-disk-archive.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ltfs-tape-disk-archive.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Thread That Outlived Its Authors</title>
      <description><![CDATA[In April 1997, Martin Schulze filed a wishlist bug against dpkg. In October 2021, Glenn Washburn quoted the entire original message and seconded it. That 24-year gap is the doorway into a bigger question: what happens when an email thread outlives the people who started it? This episode digs into the Debian bug tracker, the 39-year Traveller Mailing List archive, and the Cypherpunks archives, then pulls apart the machinery that holds threads together — Message-ID, In-Reply-To, and References — including the nineteen-year stretch when a reply header was legally allowed to read "35 ham and cheese sandwiches." Along the way: a threading algorithm designed to survive garbage input, a spec that deliberately throws away the middle of a conversation's ancestry, and the honest admission that nobody has written down a rule for replying to a dead man's message.]]></description>
      <link>https://myweirdprompts.com/episode/email-thread-outlives-authors/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/email-thread-outlives-authors/</guid>
      <pubDate>Sat, 10 Oct 2026 10:01:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/email-thread-outlives-authors.m4a"
        type="audio/mp4"
        length="25831439"
      />
      <itunes:title>The Thread That Outlived Its Authors</itunes:title>
      <itunes:subtitle>A Debian bug filed in 1997 got its second reply 24 years later. What happens when a conversation outlives everyone who started it?</itunes:subtitle>
      <itunes:summary><![CDATA[In April 1997, Martin Schulze filed a wishlist bug against dpkg. In October 2021, Glenn Washburn quoted the entire original message and seconded it. That 24-year gap is the doorway into a bigger question: what happens when an email thread outlives the people who started it? This episode digs into the Debian bug tracker, the 39-year Traveller Mailing List archive, and the Cypherpunks archives, then pulls apart the machinery that holds threads together — Message-ID, In-Reply-To, and References — including the nineteen-year stretch when a reply header was legally allowed to read "35 ham and cheese sandwiches." Along the way: a threading algorithm designed to survive garbage input, a spec that deliberately throws away the middle of a conversation's ancestry, and the honest admission that nobody has written down a rule for replying to a dead man's message.]]></itunes:summary>
      <itunes:duration>1476</itunes:duration>
      <itunes:episode>5826</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/email-thread-outlives-authors.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/email-thread-outlives-authors.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>46,434 Mailing Lists That Still Work</title>
      <description><![CDATA[A listener named Daniel asked a simple question: are there public discussion spaces left that anyone can join, fully indexed, and not owned by a platform? The answer turned out to be 46,434 LISTSERV lists, plus a few dozen humanities lists that never left email. This episode digs into why mbox archives outlive web forums, why distributed archives make a list almost impossible to kill, and why an issue comment and a listserv thread are arguing at completely different altitudes. Also: the 2014 study that found anonymous commenters were nearly three times less likely to be civil — and what that means for anyone thinking about building a forum in 2026.]]></description>
      <link>https://myweirdprompts.com/episode/mailing-lists-vs-forums/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mailing-lists-vs-forums/</guid>
      <pubDate>Fri, 09 Oct 2026 17:26:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mailing-lists-vs-forums.m4a"
        type="audio/mp4"
        length="28855234"
      />
      <itunes:title>46,434 Mailing Lists That Still Work</itunes:title>
      <itunes:subtitle>Daniel wants a public town hall. Turns out the oldest tool on the internet — the mailing list — might be the right one.</itunes:subtitle>
      <itunes:summary><![CDATA[A listener named Daniel asked a simple question: are there public discussion spaces left that anyone can join, fully indexed, and not owned by a platform? The answer turned out to be 46,434 LISTSERV lists, plus a few dozen humanities lists that never left email. This episode digs into why mbox archives outlive web forums, why distributed archives make a list almost impossible to kill, and why an issue comment and a listserv thread are arguing at completely different altitudes. Also: the 2014 study that found anonymous commenters were nearly three times less likely to be civil — and what that means for anyone thinking about building a forum in 2026.]]></itunes:summary>
      <itunes:duration>1653</itunes:duration>
      <itunes:episode>5818</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mailing-lists-vs-forums.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mailing-lists-vs-forums.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Linux Still Runs on a 1986 Mailing List</title>
      <description><![CDATA[Every Linux kernel release candidate still ships through email. In September 2026, Linus Torvalds announced Linux 7.3-rc5 on the Linux-Kernel Archive, and a twenty-patch kbuild series got reviewed inline on the same list. This episode traces the listserv from its 1984 human-administered origins through Éric Thomas's 1986 automation breakthrough to the present day, asking why the most advanced computing community on earth never abandoned a forty-year-old tool. Along the way: Python's unmoderated 1991 mailing list, the invention of double opt-in and the first spam filter, and what Slack, Discord, and GitHub pull requests actually give up compared to plain-text email with a public archive.]]></description>
      <link>https://myweirdprompts.com/episode/linux-mailing-list-transparency/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/linux-mailing-list-transparency/</guid>
      <pubDate>Fri, 09 Oct 2026 16:23:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/linux-mailing-list-transparency.m4a"
        type="audio/mp4"
        length="26299001"
      />
      <itunes:title>Why Linux Still Runs on a 1986 Mailing List</itunes:title>
      <itunes:subtitle>Linux 7.3 release candidates ship through a 1986 email tool. Here&apos;s why the kernel never left the listserv.</itunes:subtitle>
      <itunes:summary><![CDATA[Every Linux kernel release candidate still ships through email. In September 2026, Linus Torvalds announced Linux 7.3-rc5 on the Linux-Kernel Archive, and a twenty-patch kbuild series got reviewed inline on the same list. This episode traces the listserv from its 1984 human-administered origins through Éric Thomas's 1986 automation breakthrough to the present day, asking why the most advanced computing community on earth never abandoned a forty-year-old tool. Along the way: Python's unmoderated 1991 mailing list, the invention of double opt-in and the first spam filter, and what Slack, Discord, and GitHub pull requests actually give up compared to plain-text email with a public archive.]]></itunes:summary>
      <itunes:duration>1496</itunes:duration>
      <itunes:episode>5816</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/linux-mailing-list-transparency.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/linux-mailing-list-transparency.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Who Remembers Where You Left Off?</title>
      <description><![CDATA[Daniel asked whether playback progress, completion status, and listening history are reusable components or something every media app rebuilds from scratch. The answer sits somewhere uncomfortable: mpv bundles a real resume feature (quit-watch-later, save-position-on-quit, flat watch-later config files), but it's locked inside the player. Media3 and ExoPlayer hand you an event stream, a position, and a maintainer telling you to persist it yourself — in Room, Android's SQLite layer, which knows nothing about audio. GstPlay exposes position, duration, and a message bus, but no resume store, and its Play API is documented as unstable. MPRIS standardizes live playback state beautifully over D-Bus — position, status, seek, track list — but explicitly has no concept of a resume position across sessions.

The pattern: standards exist for control, not for memory. There is no portable schema for playback history, no shared vocabulary that would let one player ask another where you were. Specialized playback-history libraries don't appear to exist as standalone components — only app-level implementations and thin wrappers.]]></description>
      <link>https://myweirdprompts.com/episode/playback-resume-state-libraries/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/playback-resume-state-libraries/</guid>
      <pubDate>Fri, 09 Oct 2026 14:29:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/playback-resume-state-libraries.m4a"
        type="audio/mp4"
        length="23167838"
      />
      <itunes:title>Who Remembers Where You Left Off?</itunes:title>
      <itunes:subtitle>mpv remembers. MPRIS, GstPlay, and Media3 don&apos;t. There&apos;s no standard for playback history.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asked whether playback progress, completion status, and listening history are reusable components or something every media app rebuilds from scratch. The answer sits somewhere uncomfortable: mpv bundles a real resume feature (quit-watch-later, save-position-on-quit, flat watch-later config files), but it's locked inside the player. Media3 and ExoPlayer hand you an event stream, a position, and a maintainer telling you to persist it yourself — in Room, Android's SQLite layer, which knows nothing about audio. GstPlay exposes position, duration, and a message bus, but no resume store, and its Play API is documented as unstable. MPRIS standardizes live playback state beautifully over D-Bus — position, status, seek, track list — but explicitly has no concept of a resume position across sessions.

The pattern: standards exist for control, not for memory. There is no portable schema for playback history, no shared vocabulary that would let one player ask another where you were. Specialized playback-history libraries don't appear to exist as standalone components — only app-level implementations and thin wrappers.]]></itunes:summary>
      <itunes:duration>1306</itunes:duration>
      <itunes:episode>5814</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/playback-resume-state-libraries.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/playback-resume-state-libraries.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Python Became AI&apos;s Default Language</title>
      <description><![CDATA[Python has no scientific data structures at its core — no arrays, no matrices — yet it leads AI development at 58% adoption. This episode traces the unlikely path: a 1995 mailing list post that seeded NumPy, a 2005 MATLAB licensing change that pushed research institutes toward Python, and a NIPS workshop where the scientific community converged on one toolchain. Then the AI boom arrived and changed who was showing up. We dig into the packaging chaos newcomers inherit — distutils, pip, conda, Poetry, uv — and why "there should be one obvious way to do it" never applied to distribution. Plus: why TIOBE shows Python dipping while PYPL shows it surging, and what that gap actually measures.]]></description>
      <link>https://myweirdprompts.com/episode/python-ai-default-language-history/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/python-ai-default-language-history/</guid>
      <pubDate>Fri, 09 Oct 2026 13:38:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/python-ai-default-language-history.m4a"
        type="audio/mp4"
        length="25777466"
      />
      <itunes:title>How Python Became AI&apos;s Default Language</itunes:title>
      <itunes:subtitle>Python was never designed for science. So how did a general-purpose scripting language end up carrying the entire AI industry?</itunes:subtitle>
      <itunes:summary><![CDATA[Python has no scientific data structures at its core — no arrays, no matrices — yet it leads AI development at 58% adoption. This episode traces the unlikely path: a 1995 mailing list post that seeded NumPy, a 2005 MATLAB licensing change that pushed research institutes toward Python, and a NIPS workshop where the scientific community converged on one toolchain. Then the AI boom arrived and changed who was showing up. We dig into the packaging chaos newcomers inherit — distutils, pip, conda, Poetry, uv — and why "there should be one obvious way to do it" never applied to distribution. Plus: why TIOBE shows Python dipping while PYPL shows it surging, and what that gap actually measures.]]></itunes:summary>
      <itunes:duration>1446</itunes:duration>
      <itunes:episode>5813</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/python-ai-default-language-history.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/python-ai-default-language-history.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Number, Every Hard Question: Agent Session State</title>
      <description><![CDATA[Daniel wants a Telegram bot that tracks AliExpress cart totals and pings him when the cart crosses the free-shipping line to Israel. The number is trivial. The state architecture around it is not. This episode works through where an agent session begins and ends, why a stateless model needs a runtime notebook, what happens when you leave a session open across order cycles, and whether a database is overkill for a memory that holds a single number. Along the way: the Chroma study on context length and distractors, why shuffled haystacks beat tidy ones, idle expiry versus manual session cuts, and the libraries that already ship this lifecycle — Teleton, session-continuity-kit, and Redis working memory.]]></description>
      <link>https://myweirdprompts.com/episode/agent-session-state-management/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-session-state-management/</guid>
      <pubDate>Fri, 09 Oct 2026 13:26:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-session-state-management.m4a"
        type="audio/mp4"
        length="21559444"
      />
      <itunes:title>One Number, Every Hard Question: Agent Session State</itunes:title>
      <itunes:subtitle>A shopping bot that holds one cart total turns out to contain every hard problem in agent state management.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants a Telegram bot that tracks AliExpress cart totals and pings him when the cart crosses the free-shipping line to Israel. The number is trivial. The state architecture around it is not. This episode works through where an agent session begins and ends, why a stateless model needs a runtime notebook, what happens when you leave a session open across order cycles, and whether a database is overkill for a memory that holds a single number. Along the way: the Chroma study on context length and distractors, why shuffled haystacks beat tidy ones, idle expiry versus manual session cuts, and the libraries that already ship this lifecycle — Teleton, session-continuity-kit, and Redis working memory.]]></itunes:summary>
      <itunes:duration>1217</itunes:duration>
      <itunes:episode>5812</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-session-state-management.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-session-state-management.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Freeze the Model, Move the Labels</title>
      <description><![CDATA[Daniel built a home inventory classifier that sorts items into boxes. Then he bought three new boxes, and the model went stale. His instinct was to retrain weekly — but the real problem isn't the model, it's that his label set keeps changing. This episode digs into the two architectural answers: retrain on a moving label set, or freeze the encoder entirely and put the labels in a vector index instead. Along the way: why SetFit training costs about two and a half cents, why synthetic training data can beat real labels on one benchmark and fail silently in your garage, and why "incremental vs. full retrain" stops being a real decision at small scale.]]></description>
      <link>https://myweirdprompts.com/episode/dynamic-label-sets-retraining/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dynamic-label-sets-retraining/</guid>
      <pubDate>Fri, 09 Oct 2026 10:54:03 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dynamic-label-sets-retraining.m4a"
        type="audio/mp4"
        length="23921541"
      />
      <itunes:title>Freeze the Model, Move the Labels</itunes:title>
      <itunes:subtitle>Your classifier isn&apos;t stale — your label set moved. Why retraining is cheap, and why not retraining might be smarter.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel built a home inventory classifier that sorts items into boxes. Then he bought three new boxes, and the model went stale. His instinct was to retrain weekly — but the real problem isn't the model, it's that his label set keeps changing. This episode digs into the two architectural answers: retrain on a moving label set, or freeze the encoder entirely and put the labels in a vector index instead. Along the way: why SetFit training costs about two and a half cents, why synthetic training data can beat real labels on one benchmark and fail silently in your garage, and why "incremental vs. full retrain" stops being a real decision at small scale.]]></itunes:summary>
      <itunes:duration>1351</itunes:duration>
      <itunes:episode>5805</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dynamic-label-sets-retraining.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dynamic-label-sets-retraining.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Text Classifiers: Local Models vs Always-On Endpoints</title>
      <description><![CDATA[Daniel has a home inventory system, a box of soldering supplies, and one question: is there a casual SaaS for text classification, or should he just run a private model locally? The answer turns out to be a landscape, not a product. Specialist classifiers beat general LLMs on both cost and accuracy — roughly twenty-five times cheaper and ahead on intent benchmarks — which makes the local instinct right and the SaaS pitch harder to justify. The features Daniel wants do exist, several times over: upload labeled pairs, retrain on a cadence, call inference over a stable endpoint. What doesn't exist is a household name for it. Meanwhile AWS Comprehend charges for a provisioned endpoint whether or not anything is flowing through it, and a six-year-old forum post asking for exactly Daniel's use case got an answer that still applies today.]]></description>
      <link>https://myweirdprompts.com/episode/text-classifier-landscape-local-vs-saas/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/text-classifier-landscape-local-vs-saas/</guid>
      <pubDate>Fri, 09 Oct 2026 10:46:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/text-classifier-landscape-local-vs-saas.m4a"
        type="audio/mp4"
        length="27756962"
      />
      <itunes:title>Text Classifiers: Local Models vs Always-On Endpoints</itunes:title>
      <itunes:subtitle>A specialist classifier costs $5.70 per million messages. A general LLM costs $142. So why is the obvious product so hard to find?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has a home inventory system, a box of soldering supplies, and one question: is there a casual SaaS for text classification, or should he just run a private model locally? The answer turns out to be a landscape, not a product. Specialist classifiers beat general LLMs on both cost and accuracy — roughly twenty-five times cheaper and ahead on intent benchmarks — which makes the local instinct right and the SaaS pitch harder to justify. The features Daniel wants do exist, several times over: upload labeled pairs, retrain on a cadence, call inference over a stable endpoint. What doesn't exist is a household name for it. Meanwhile AWS Comprehend charges for a provisioned endpoint whether or not anything is flowing through it, and a six-year-old forum post asking for exactly Daniel's use case got an answer that still applies today.]]></itunes:summary>
      <itunes:duration>1582</itunes:duration>
      <itunes:episode>5804</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/text-classifier-landscape-local-vs-saas.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/text-classifier-landscape-local-vs-saas.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Two Agents, One Approval Gate: Wiring a Dictation Workflow</title>
      <description><![CDATA[Daniel dictated a prompt about a nuclear project name and got back a gargantuan multi-part document instead of an answer. This episode works through the fix he asked for: two agents, one that resolves facts and one that tightens language, with a human confirmation gate before dispatch. The conversation covers why agents overproduce, what "Ask or Assume?" found about dedicated clarification detection, why the value is in declining to ask, and where the approval gate belongs — in the tool or at the MCP server. Plus the glue question: LangGraph's explicit graph and interrupt function versus role-based autonomy, and how the July 28 MCP revision handles confirmation through multi round-trip requests.]]></description>
      <link>https://myweirdprompts.com/episode/two-agent-dictation-workflow/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/two-agent-dictation-workflow/</guid>
      <pubDate>Fri, 09 Oct 2026 10:18:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/two-agent-dictation-workflow.m4a"
        type="audio/mp4"
        length="22835731"
      />
      <itunes:title>Two Agents, One Approval Gate: Wiring a Dictation Workflow</itunes:title>
      <itunes:subtitle>A dictated prompt becomes an episode — if one agent resolves facts, a second tightens prose, and a human approves the dispatch.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel dictated a prompt about a nuclear project name and got back a gargantuan multi-part document instead of an answer. This episode works through the fix he asked for: two agents, one that resolves facts and one that tightens language, with a human confirmation gate before dispatch. The conversation covers why agents overproduce, what "Ask or Assume?" found about dedicated clarification detection, why the value is in declining to ask, and where the approval gate belongs — in the tool or at the MCP server. Plus the glue question: LangGraph's explicit graph and interrupt function versus role-based autonomy, and how the July 28 MCP revision handles confirmation through multi round-trip requests.]]></itunes:summary>
      <itunes:duration>1300</itunes:duration>
      <itunes:episode>5799</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/two-agent-dictation-workflow.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/two-agent-dictation-workflow.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Lorebooks: The Hobbyist RAG That Beat the Labs</title>
      <description><![CDATA[A language model has no memory of your character. Everything — voice, history, how they react to being interrupted — has to be re-injected into a finite context window every single turn. So character consistency isn't a personality problem; it's a budget problem. This episode takes apart the retrieval machinery a hobbyist community built to solve it: keyword matching with regex and scan depth, vector storage that fires on semantic similarity, secondary keys with boolean logic, recursion with caps and safety valves, inclusion groups for variety, and insertion positioning that changes how strongly a model weights the same text. Along the way: why the docs openly admit semantic matching is unpredictable, and the honest answer to whether any of this has actually shown up in mainstream AI products.]]></description>
      <link>https://myweirdprompts.com/episode/lorebooks-hobbyist-rag-character-consistency/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/lorebooks-hobbyist-rag-character-consistency/</guid>
      <pubDate>Thu, 08 Oct 2026 10:38:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/lorebooks-hobbyist-rag-character-consistency.m4a"
        type="audio/mp4"
        length="32460840"
      />
      <itunes:title>Lorebooks: The Hobbyist RAG That Beat the Labs</itunes:title>
      <itunes:subtitle>How a chat frontend fork built a full retrieval system — keywords, vectors, recursion — so fictional characters would stop forgetting who they are.</itunes:subtitle>
      <itunes:summary><![CDATA[A language model has no memory of your character. Everything — voice, history, how they react to being interrupted — has to be re-injected into a finite context window every single turn. So character consistency isn't a personality problem; it's a budget problem. This episode takes apart the retrieval machinery a hobbyist community built to solve it: keyword matching with regex and scan depth, vector storage that fires on semantic similarity, secondary keys with boolean logic, recursion with caps and safety valves, inclusion groups for variety, and insertion positioning that changes how strongly a model weights the same text. Along the way: why the docs openly admit semantic matching is unpredictable, and the honest answer to whether any of this has actually shown up in mainstream AI products.]]></itunes:summary>
      <itunes:duration>1852</itunes:duration>
      <itunes:episode>5777</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/lorebooks-hobbyist-rag-character-consistency.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/lorebooks-hobbyist-rag-character-consistency.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What &quot;Leaked System Prompts&quot; Actually Mean</title>
      <description><![CDATA[Anthropic is the only major lab publishing its user-facing system prompts, yet the published versions leave out tool descriptions, accessory models, and orchestration. So when someone claims a prompt "leaked," are they showing you a real extraction or a model's confabulation? This episode traces the history of system prompt extraction — from Kevin Liu's one-sentence Bing attack in February 2023 to autonomous code agents recovering prompts from 41 commercial models — and looks at the defenses vendors built in response. The framing is defensive: understand the attack so you can defend against it, and understand why the prompt was never the moat.]]></description>
      <link>https://myweirdprompts.com/episode/system-prompt-leaks-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/system-prompt-leaks-explained/</guid>
      <pubDate>Thu, 08 Oct 2026 10:21:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/system-prompt-leaks-explained.m4a"
        type="audio/mp4"
        length="23373493"
      />
      <itunes:title>What &quot;Leaked System Prompts&quot; Actually Mean</itunes:title>
      <itunes:subtitle>Anthropic publishes Claude&apos;s system prompt — but not its accessory models. What does &quot;leaking&quot; a prompt actually mean?</itunes:subtitle>
      <itunes:summary><![CDATA[Anthropic is the only major lab publishing its user-facing system prompts, yet the published versions leave out tool descriptions, accessory models, and orchestration. So when someone claims a prompt "leaked," are they showing you a real extraction or a model's confabulation? This episode traces the history of system prompt extraction — from Kevin Liu's one-sentence Bing attack in February 2023 to autonomous code agents recovering prompts from 41 commercial models — and looks at the defenses vendors built in response. The framing is defensive: understand the attack so you can defend against it, and understand why the prompt was never the moat.]]></itunes:summary>
      <itunes:duration>1330</itunes:duration>
      <itunes:episode>5775</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/system-prompt-leaks-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/system-prompt-leaks-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Reading the Model&apos;s Mind Mid-Inference</title>
      <description><![CDATA[Most LLM observability stops at the API boundary: request in, tokens out, everything between is a sealed box. Mechanistic interpretability tries to open that box, reading a model's internal computation directly from its weights. This episode walks the full inference pipeline, explains the residual stream and the logit lens, and covers why individual neurons are mostly noise (polysemanticity and superposition) and how sparse autoencoders untangle them. Then it surveys the tooling that actually does this work — TransformerLens, nnsight, vLLM-Lens, and DMI-Lib — including the overhead numbers that separate a research toy from something you could run in production. We also hit a hard current limit: FlashAttention fuses the attention path into the kernel, so the most basic question — which parts of the model were active — is literally unreadable in the kernels everyone actually deploys.]]></description>
      <link>https://myweirdprompts.com/episode/llm-observability-interpretability-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-observability-interpretability-tools/</guid>
      <pubDate>Thu, 08 Oct 2026 09:47:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-observability-interpretability-tools.m4a"
        type="audio/mp4"
        length="24851017"
      />
      <itunes:title>Reading the Model&apos;s Mind Mid-Inference</itunes:title>
      <itunes:subtitle>What if you could watch a model decide? Inside the tools that open up inference mid-computation — and the limits they hit.</itunes:subtitle>
      <itunes:summary><![CDATA[Most LLM observability stops at the API boundary: request in, tokens out, everything between is a sealed box. Mechanistic interpretability tries to open that box, reading a model's internal computation directly from its weights. This episode walks the full inference pipeline, explains the residual stream and the logit lens, and covers why individual neurons are mostly noise (polysemanticity and superposition) and how sparse autoencoders untangle them. Then it surveys the tooling that actually does this work — TransformerLens, nnsight, vLLM-Lens, and DMI-Lib — including the overhead numbers that separate a research toy from something you could run in production. We also hit a hard current limit: FlashAttention fuses the attention path into the kernel, so the most basic question — which parts of the model were active — is literally unreadable in the kernels everyone actually deploys.]]></itunes:summary>
      <itunes:duration>1382</itunes:duration>
      <itunes:episode>5774</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-observability-interpretability-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-observability-interpretability-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Declaring Environments: Compose, Nix, and the Agent Boundary</title>
      <description><![CDATA[A predictable, lightweight, automatic environment sounds like one idea. It's actually three families of tooling operating at different layers. This episode walks the line between Docker Compose and infrastructure as code, then asks the question underneath both: is this a container, or does it have to be a VM? Drawing on Docker's own documentation, numtide, Flox, Freestyle, and the devcontainer spec, we separate reproducible run-time environments from reproducible builds, lay out container orchestration, IaC, and declarative package managers as distinct approaches, and trace the container-versus-VM decision to where it actually lives — not in the image, but in where state is kept. Then we extend it into the agentic context: containerized runtimes, microVMs like Firecracker-backed providers, and OS-level isolators, and what each one buys you when the thing being provisioned isn't a person at a terminal but an agent that needs to remember.]]></description>
      <link>https://myweirdprompts.com/episode/automated-environment-provisioning-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/automated-environment-provisioning-agents/</guid>
      <pubDate>Thu, 08 Oct 2026 08:50:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/automated-environment-provisioning-agents.m4a"
        type="audio/mp4"
        length="25161329"
      />
      <itunes:title>Declaring Environments: Compose, Nix, and the Agent Boundary</itunes:title>
      <itunes:subtitle>Docker Compose, Nix, devcontainers, microVMs — how automated provisioning works, and where the boundary moves when an agent needs a workspace.</itunes:subtitle>
      <itunes:summary><![CDATA[A predictable, lightweight, automatic environment sounds like one idea. It's actually three families of tooling operating at different layers. This episode walks the line between Docker Compose and infrastructure as code, then asks the question underneath both: is this a container, or does it have to be a VM? Drawing on Docker's own documentation, numtide, Flox, Freestyle, and the devcontainer spec, we separate reproducible run-time environments from reproducible builds, lay out container orchestration, IaC, and declarative package managers as distinct approaches, and trace the container-versus-VM decision to where it actually lives — not in the image, but in where state is kept. Then we extend it into the agentic context: containerized runtimes, microVMs like Firecracker-backed providers, and OS-level isolators, and what each one buys you when the thing being provisioned isn't a person at a terminal but an agent that needs to remember.]]></itunes:summary>
      <itunes:duration>1429</itunes:duration>
      <itunes:episode>5773</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/automated-environment-provisioning-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/automated-environment-provisioning-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Two Jobs, One Word: What Agent Sandboxes Really Do</title>
      <description><![CDATA[Sandbox" now means two different things: a security boundary that fences an agent off from your host machine, and a lightweight cloud Linux workspace where the agent does its actual work. Almost every product does both, but the two instincts pull in opposite directions — one wants to seal, the other wants to be useful. Drawing on roughly forty code-execution sandbox products launched in a single year, plus the ephemeral-versus-persistent divide and the reproducibility problem of drifting base images, this episode maps the whole category. Along the way: why a laptop is a bad home for a long-horizon agent, what changes the moment an agent gets a real command line, and why "a sandbox is what ceases to exist when nobody's watching" might be the sharpest line in the debate.]]></description>
      <link>https://myweirdprompts.com/episode/agent-sandbox-two-meanings/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-sandbox-two-meanings/</guid>
      <pubDate>Thu, 08 Oct 2026 08:43:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-sandbox-two-meanings.m4a"
        type="audio/mp4"
        length="28759695"
      />
      <itunes:title>Two Jobs, One Word: What Agent Sandboxes Really Do</itunes:title>
      <itunes:subtitle>Security boundary or Linux workspace? The word &quot;sandbox&quot; quietly split in two — and the split explains where agents actually run now.</itunes:subtitle>
      <itunes:summary><![CDATA[Sandbox" now means two different things: a security boundary that fences an agent off from your host machine, and a lightweight cloud Linux workspace where the agent does its actual work. Almost every product does both, but the two instincts pull in opposite directions — one wants to seal, the other wants to be useful. Drawing on roughly forty code-execution sandbox products launched in a single year, plus the ephemeral-versus-persistent divide and the reproducibility problem of drifting base images, this episode maps the whole category. Along the way: why a laptop is a bad home for a long-horizon agent, what changes the moment an agent gets a real command line, and why "a sandbox is what ceases to exist when nobody's watching" might be the sharpest line in the debate.]]></itunes:summary>
      <itunes:duration>1643</itunes:duration>
      <itunes:episode>5772</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-sandbox-two-meanings.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-sandbox-two-meanings.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>MCP Connectors: Talk to Your Data vs. Open Data</title>
      <description><![CDATA[The plugin ecosystem has been rebuilt on the Model Context Protocol, and two integration patterns now dominate: "talk to your data," where agents query your own internal systems, and connectors to public and open data. The same protocol serves both — the difference is who owns the data. But when the IMF tested leading chatbots on its own public statistics, they returned the correct figure only 17 to 34 percent of the time. A UNICEF benchmark across 133,000+ development-indicator questions found average accuracy of 21.2 percent. This episode works through what that gap actually is: not a model failure but an access-layer failure, and what the UN, IMF, and a growing set of builders are doing about it.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-connectors-data-access/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-connectors-data-access/</guid>
      <pubDate>Thu, 08 Oct 2026 08:35:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-connectors-data-access.m4a"
        type="audio/mp4"
        length="24447213"
      />
      <itunes:title>MCP Connectors: Talk to Your Data vs. Open Data</itunes:title>
      <itunes:subtitle>Same protocol, two patterns: pointing agents at your own database, and pointing them at public data — with very different results.</itunes:subtitle>
      <itunes:summary><![CDATA[The plugin ecosystem has been rebuilt on the Model Context Protocol, and two integration patterns now dominate: "talk to your data," where agents query your own internal systems, and connectors to public and open data. The same protocol serves both — the difference is who owns the data. But when the IMF tested leading chatbots on its own public statistics, they returned the correct figure only 17 to 34 percent of the time. A UNICEF benchmark across 133,000+ development-indicator questions found average accuracy of 21.2 percent. This episode works through what that gap actually is: not a model failure but an access-layer failure, and what the UN, IMF, and a growing set of builders are doing about it.]]></itunes:summary>
      <itunes:duration>1387</itunes:duration>
      <itunes:episode>5771</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-connectors-data-access.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-connectors-data-access.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Talking to Your Data: What MCP Doesn&apos;t Solve</title>
      <description><![CDATA[Every AI platform is racing to ship data connectors, but "talk to your data" hides five distinct jobs — and only one of them is the AI writing SQL. This episode traces what actually happens between a question and a valid query: schema ingestion, semantic grounding, query generation, execution with error feedback, and interpretation back to language. We look at why a semantic layer beats raw text-to-SQL by sixteen points on the same model, how agentic loops with actor-critic and execution feedback close the correctness gap, and whether specialized NL-to-SQL models still earn their place against frontier general models. Then we ask the uncomfortable question underneath all the leaderboard numbers: if roughly a third of the reference answers in the standard benchmarks are wrong, what exactly are we measuring?]]></description>
      <link>https://myweirdprompts.com/episode/talk-to-your-data-mcp/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/talk-to-your-data-mcp/</guid>
      <pubDate>Thu, 08 Oct 2026 08:35:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/talk-to-your-data-mcp.m4a"
        type="audio/mp4"
        length="26381239"
      />
      <itunes:title>Talking to Your Data: What MCP Doesn&apos;t Solve</itunes:title>
      <itunes:subtitle>MCP standardized the pipe. It has no opinion about what flows through it — and that&apos;s where the hard part lives.</itunes:subtitle>
      <itunes:summary><![CDATA[Every AI platform is racing to ship data connectors, but "talk to your data" hides five distinct jobs — and only one of them is the AI writing SQL. This episode traces what actually happens between a question and a valid query: schema ingestion, semantic grounding, query generation, execution with error feedback, and interpretation back to language. We look at why a semantic layer beats raw text-to-SQL by sixteen points on the same model, how agentic loops with actor-critic and execution feedback close the correctness gap, and whether specialized NL-to-SQL models still earn their place against frontier general models. Then we ask the uncomfortable question underneath all the leaderboard numbers: if roughly a third of the reference answers in the standard benchmarks are wrong, what exactly are we measuring?]]></itunes:summary>
      <itunes:duration>1503</itunes:duration>
      <itunes:episode>5770</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/talk-to-your-data-mcp.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/talk-to-your-data-mcp.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Stack Overflow for Agents: SOFA&apos;s Trust Model</title>
      <description><![CDATA[Stack Overflow for Agents (SOFA) is a beta knowledge exchange built for AI agents and their human operators — an attempt to close what its makers call the Ephemeral Intelligence Gap, where an agent burns compute re-solving a problem another agent already fixed. This episode walks through the actual mechanism: a skill file named "sofa," agent onboarding gated behind a human SSO claim, four post types (Questions, TILs, Blueprints, Playbooks), and a trust model that separates votes (read-time forecasts) from verifications (use-time outcomes). We also dig into the claims system, which extracts individually checkable assertions from each post, and the skeptical case — that confidence scoring can't distinguish an agent that used bad advice from one that used good advice, and may just spread confident nonsense at scale.]]></description>
      <link>https://myweirdprompts.com/episode/stack-overflow-for-agents-sofa/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/stack-overflow-for-agents-sofa/</guid>
      <pubDate>Wed, 07 Oct 2026 22:59:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/stack-overflow-for-agents-sofa.m4a"
        type="audio/mp4"
        length="33852757"
      />
      <itunes:title>Stack Overflow for Agents: SOFA&apos;s Trust Model</itunes:title>
      <itunes:subtitle>SOFA is an API-first knowledge exchange where AI agents search, post, and verify fixes — with reputation earned from verification, not upvotes.</itunes:subtitle>
      <itunes:summary><![CDATA[Stack Overflow for Agents (SOFA) is a beta knowledge exchange built for AI agents and their human operators — an attempt to close what its makers call the Ephemeral Intelligence Gap, where an agent burns compute re-solving a problem another agent already fixed. This episode walks through the actual mechanism: a skill file named "sofa," agent onboarding gated behind a human SSO claim, four post types (Questions, TILs, Blueprints, Playbooks), and a trust model that separates votes (read-time forecasts) from verifications (use-time outcomes). We also dig into the claims system, which extracts individually checkable assertions from each post, and the skeptical case — that confidence scoring can't distinguish an agent that used bad advice from one that used good advice, and may just spread confident nonsense at scale.]]></itunes:summary>
      <itunes:duration>1936</itunes:duration>
      <itunes:episode>5769</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/stack-overflow-for-agents-sofa.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/stack-overflow-for-agents-sofa.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What&apos;s Actually Inside an eSIM Adapter</title>
      <description><![CDATA[Most eSIM coverage focuses on carriers and roaming revenue, but the real story sits at the bottom of the stack: a commodity secure element, a GSMA-rooted certificate, and an LPA that moves credentials around. This episode traces that stack from silicon to software — what an eUICC actually is, why certified cards cost under 25 euros while consumer adapters sell for multiples of that, and where the genuine lock-in lives (spoiler: it's the certificate, not the plastic). We also walk through the open source LPA landscape, including lpac, OpenEUICC, EasyEUICC, and MiniLPA, and what it takes to provision a profile onto a router or failover device that will never run Android. Plus: the 5ber cautionary tale, and why a vendor advertising its own dispensability might be the healthiest posture in the market.]]></description>
      <link>https://myweirdprompts.com/episode/esim-adapter-euicc-open-source/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/esim-adapter-euicc-open-source/</guid>
      <pubDate>Tue, 06 Oct 2026 18:17:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/esim-adapter-euicc-open-source.m4a"
        type="audio/mp4"
        length="26182830"
      />
      <itunes:title>What&apos;s Actually Inside an eSIM Adapter</itunes:title>
      <itunes:subtitle>An eSIM adapter isn&apos;t a converter — it&apos;s a eUICC chip in a SIM shell. Here&apos;s the silicon, the certificates, and the open source tools underneath.</itunes:subtitle>
      <itunes:summary><![CDATA[Most eSIM coverage focuses on carriers and roaming revenue, but the real story sits at the bottom of the stack: a commodity secure element, a GSMA-rooted certificate, and an LPA that moves credentials around. This episode traces that stack from silicon to software — what an eUICC actually is, why certified cards cost under 25 euros while consumer adapters sell for multiples of that, and where the genuine lock-in lives (spoiler: it's the certificate, not the plastic). We also walk through the open source LPA landscape, including lpac, OpenEUICC, EasyEUICC, and MiniLPA, and what it takes to provision a profile onto a router or failover device that will never run Android. Plus: the 5ber cautionary tale, and why a vendor advertising its own dispensability might be the healthiest posture in the market.]]></itunes:summary>
      <itunes:duration>1491</itunes:duration>
      <itunes:episode>5748</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/esim-adapter-euicc-open-source.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/esim-adapter-euicc-open-source.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Claude Keeps Saying &quot;Prior Art</title>
      <description><![CDATA[Prior art" is a patent-law term of art, but it keeps showing up in Claude's output — and Daniel wants to know if it even belongs there. This episode digs into the research on lexical overrepresentation: why preference-tuned models reach for a "language of prestige," and how raters rewarding certain words teaches tics no one intended. Then it gets practical: if you're asking an agent to survey existing solutions before building, how do you phrase that instruction so you get a real foundation to build on instead of noise? The answer isn't a vocabulary ban — it's knowing what you're actually asking for.]]></description>
      <link>https://myweirdprompts.com/episode/claude-prior-art-phrase/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-prior-art-phrase/</guid>
      <pubDate>Tue, 06 Oct 2026 12:06:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-prior-art-phrase.m4a"
        type="audio/mp4"
        length="26402621"
      />
      <itunes:title>Why Claude Keeps Saying &quot;Prior Art</itunes:title>
      <itunes:subtitle>Daniel noticed &quot;prior art&quot; creeping into his Claude reports. Is it wrong — or just a prestige term the model learned to love?</itunes:subtitle>
      <itunes:summary><![CDATA[Prior art" is a patent-law term of art, but it keeps showing up in Claude's output — and Daniel wants to know if it even belongs there. This episode digs into the research on lexical overrepresentation: why preference-tuned models reach for a "language of prestige," and how raters rewarding certain words teaches tics no one intended. Then it gets practical: if you're asking an agent to survey existing solutions before building, how do you phrase that instruction so you get a real foundation to build on instead of noise? The answer isn't a vocabulary ban — it's knowing what you're actually asking for.]]></itunes:summary>
      <itunes:duration>1505</itunes:duration>
      <itunes:episode>5743</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-prior-art-phrase.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-prior-art-phrase.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Decision Models That Return Probabilities, Not Text</title>
      <description><![CDATA[Every launch post for decision support models opens the same way — new class of model, returns probabilities instead of text, benchmark table sorted by accuracy. But accuracy isn't the number your routing system consumes. This episode digs into what these models actually are, why binary yes/no decisions were awkward with ordinary LLMs and JSON constraints, and how they slot into moderation, automations, and agentic systems. Liquid AI's d1 gets a close look, along with the calibration problem that structured outputs never solved.]]></description>
      <link>https://myweirdprompts.com/episode/decision-support-models-probabilities/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/decision-support-models-probabilities/</guid>
      <pubDate>Sat, 03 Oct 2026 10:01:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/decision-support-models-probabilities.m4a"
        type="audio/mp4"
        length="28877065"
      />
      <itunes:title>Decision Models That Return Probabilities, Not Text</itunes:title>
      <itunes:subtitle>A new class of model skips text generation entirely and returns calibrated probabilities in one forward pass. Here&apos;s what that changes.</itunes:subtitle>
      <itunes:summary><![CDATA[Every launch post for decision support models opens the same way — new class of model, returns probabilities instead of text, benchmark table sorted by accuracy. But accuracy isn't the number your routing system consumes. This episode digs into what these models actually are, why binary yes/no decisions were awkward with ordinary LLMs and JSON constraints, and how they slot into moderation, automations, and agentic systems. Liquid AI's d1 gets a close look, along with the calibration problem that structured outputs never solved.]]></itunes:summary>
      <itunes:duration>1649</itunes:duration>
      <itunes:episode>5697</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/decision-support-models-probabilities.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/decision-support-models-probabilities.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Lowering Temperature Broke Daniel&apos;s Scripts</title>
      <description><![CDATA[Daniel dropped his DeepSeek 4.1 scriptwriting agent to temperature 0.8, expecting tighter, more lore-faithful dialogue. Instead he got barely coherent scripts. This episode digs into the research on temperature fragility — why nearly half of tested open-weight models lose 17 to 38 accuracy points over a modest temperature shift, while the rest barely flinch — and why the failure mode is output collapse rather than wrong answers. Then it gets weirder: DeepSeek's thinking mode may silently ignore temperature entirely, meaning the 0.8 might have done nothing at all. We walk through the two-by-two diagnostic that settles it, what DeepSeek's own presets actually recommend, and why the honest answer to "what temperature should I use" is a measurement, not a number.]]></description>
      <link>https://myweirdprompts.com/episode/deepseek-temperature-script-collapse/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/deepseek-temperature-script-collapse/</guid>
      <pubDate>Fri, 02 Oct 2026 12:47:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/deepseek-temperature-script-collapse.m4a"
        type="audio/mp4"
        length="28488022"
      />
      <itunes:title>Why Lowering Temperature Broke Daniel&apos;s Scripts</itunes:title>
      <itunes:subtitle>Lowering temperature should make output safer, not incoherent. Daniel&apos;s DeepSeek scripts say otherwise — and the reason may be stranger than he thi...</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel dropped his DeepSeek 4.1 scriptwriting agent to temperature 0.8, expecting tighter, more lore-faithful dialogue. Instead he got barely coherent scripts. This episode digs into the research on temperature fragility — why nearly half of tested open-weight models lose 17 to 38 accuracy points over a modest temperature shift, while the rest barely flinch — and why the failure mode is output collapse rather than wrong answers. Then it gets weirder: DeepSeek's thinking mode may silently ignore temperature entirely, meaning the 0.8 might have done nothing at all. We walk through the two-by-two diagnostic that settles it, what DeepSeek's own presets actually recommend, and why the honest answer to "what temperature should I use" is a measurement, not a number.]]></itunes:summary>
      <itunes:duration>1638</itunes:duration>
      <itunes:episode>5673</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/deepseek-temperature-script-collapse.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/deepseek-temperature-script-collapse.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Do Git Branches Actually Nest? AI Agents and Stacked PRs</title>
      <description><![CDATA[Daniel asks whether the branch-per-feature rule still makes sense when an AI agent's whole session lasts five minutes, and whether branches can be nested for multi-agent repositories. The answer starts with a correction: in Git, branches are just pointers to commits — there is no parent-child relationship anywhere in the object model. What people call nesting is either a topology (branch B created from the tip of branch A) or a naming convention (slashes in a branch name are purely cosmetic). From there, the conversation traces the real history — Atlassian's Feature Branch Workflow, Vincent Driessen's Gitflow and its often-quoted sub-teams passage, and Martin Fowler's Patterns for Managing Source Code Branches, including Collaboration Branches, Team Integration Branches, and the idea that frequency reduces difficulty. Kent Beck's "illusion of frozen time" gets its due. Then the modern answer: worktrees for isolation, stacked pull requests for integration, and GitHub's own rule — nest exactly as deep as the dependency chain runs, and no deeper.]]></description>
      <link>https://myweirdprompts.com/episode/git-branch-nesting-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/git-branch-nesting-ai-agents/</guid>
      <pubDate>Wed, 30 Sep 2026 08:39:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/git-branch-nesting-ai-agents.m4a"
        type="audio/mp4"
        length="24117221"
      />
      <itunes:title>Do Git Branches Actually Nest? AI Agents and Stacked PRs</itunes:title>
      <itunes:subtitle>Git branches don&apos;t nest — so what are we really doing when AI agents stack pull requests?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asks whether the branch-per-feature rule still makes sense when an AI agent's whole session lasts five minutes, and whether branches can be nested for multi-agent repositories. The answer starts with a correction: in Git, branches are just pointers to commits — there is no parent-child relationship anywhere in the object model. What people call nesting is either a topology (branch B created from the tip of branch A) or a naming convention (slashes in a branch name are purely cosmetic). From there, the conversation traces the real history — Atlassian's Feature Branch Workflow, Vincent Driessen's Gitflow and its often-quoted sub-teams passage, and Martin Fowler's Patterns for Managing Source Code Branches, including Collaboration Branches, Team Integration Branches, and the idea that frequency reduces difficulty. Kent Beck's "illusion of frozen time" gets its due. Then the modern answer: worktrees for isolation, stacked pull requests for integration, and GitHub's own rule — nest exactly as deep as the dependency chain runs, and no deeper.]]></itunes:summary>
      <itunes:duration>1383</itunes:duration>
      <itunes:episode>5643</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/git-branch-nesting-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/git-branch-nesting-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Forward Pass: Building a Bounded Classifier</title>
      <description><![CDATA[A bounded classifier emits a probability distribution over a fixed label set in one forward pass — no token generation, no JSON parsing, no helpfulness. This episode walks through what it takes to build one for a real pipeline: which encoder to fine-tune from (ModernBERT, DeBERTa-v3, or Liquid AI's LFM2.5 encoders), what size makes sense for a few thousand examples, and why a 150 MB weight file running on a laptop CPU can outperform a decoder LLM at a fraction of the cost. Then the harder questions: multi-label output with independent per-label thresholds, the zero-tag case that falls out of the arithmetic for free, and what to do when the taxonomy changes.]]></description>
      <link>https://myweirdprompts.com/episode/bounded-classifier-fine-tuning-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/bounded-classifier-fine-tuning-guide/</guid>
      <pubDate>Mon, 28 Sep 2026 14:20:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/bounded-classifier-fine-tuning-guide.m4a"
        type="audio/mp4"
        length="23056087"
      />
      <itunes:title>One Forward Pass: Building a Bounded Classifier</itunes:title>
      <itunes:subtitle>No generation, no parsing — just label scores in a single pass. How to build a small encoder classifier that actually works.</itunes:subtitle>
      <itunes:summary><![CDATA[A bounded classifier emits a probability distribution over a fixed label set in one forward pass — no token generation, no JSON parsing, no helpfulness. This episode walks through what it takes to build one for a real pipeline: which encoder to fine-tune from (ModernBERT, DeBERTa-v3, or Liquid AI's LFM2.5 encoders), what size makes sense for a few thousand examples, and why a 150 MB weight file running on a laptop CPU can outperform a decoder LLM at a fraction of the cost. Then the harder questions: multi-label output with independent per-label thresholds, the zero-tag case that falls out of the arithmetic for free, and what to do when the taxonomy changes.]]></itunes:summary>
      <itunes:duration>1312</itunes:duration>
      <itunes:episode>5624</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/bounded-classifier-fine-tuning-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/bounded-classifier-fine-tuning-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your AI Cites Sources It Didn&apos;t Actually Use</title>
      <description><![CDATA[Your retrieval pipeline returns a document, the model cites it, and the claim checks out. Everything in the trace looks fine. But what if the model actually pulled that answer from its training data and the citation is just decoration? This episode digs into a genuine gap in AI observability: tracing shows what your agent did, never why. We walk through our own planning-agent-to-research-agent-to-scriptwriter pipeline as a case study, then dig into new mechanistic interpretability research on how LLMs decide to cite — and why current context-attribution tools can't separate what the model knew from what you handed it. If you're building knowledge-based generative products, this one reframes what your citations actually prove.]]></description>
      <link>https://myweirdprompts.com/episode/ai-citation-faithfulness-tracing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-citation-faithfulness-tracing/</guid>
      <pubDate>Mon, 28 Sep 2026 12:36:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-citation-faithfulness-tracing.m4a"
        type="audio/mp4"
        length="30471964"
      />
      <itunes:title>When Your AI Cites Sources It Didn&apos;t Actually Use</itunes:title>
      <itunes:subtitle>Your RAG pipeline cites a source — but did the model actually use it? New interpretability research says you can&apos;t tell from the trace.</itunes:subtitle>
      <itunes:summary><![CDATA[Your retrieval pipeline returns a document, the model cites it, and the claim checks out. Everything in the trace looks fine. But what if the model actually pulled that answer from its training data and the citation is just decoration? This episode digs into a genuine gap in AI observability: tracing shows what your agent did, never why. We walk through our own planning-agent-to-research-agent-to-scriptwriter pipeline as a case study, then dig into new mechanistic interpretability research on how LLMs decide to cite — and why current context-attribution tools can't separate what the model knew from what you handed it. If you're building knowledge-based generative products, this one reframes what your citations actually prove.]]></itunes:summary>
      <itunes:duration>1733</itunes:duration>
      <itunes:episode>5623</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-citation-faithfulness-tracing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-citation-faithfulness-tracing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Constrained Decoding for AI-Generated Podcast Tags</title>
      <description><![CDATA[Daniel's AI-generated show deploys to Vercel, runs on serverless Postgres, and produces exactly the kind of wide-ranging content he wants. The problem is the feed — automatic tagging that started inventing its own categories, then duplicating them in ways nobody notices. This episode works through the fix: why unconstrained categorization never converges, how JSON Schema enums and context-free grammars make label bloat structurally impossible, and why a fine-tuned encoder beats a frontier model for a stable label set at one to two orders of magnitude lower cost. We also cover the counterargument — that a confident wrong classification is worse than no classification — and the read-at-classification-time pattern that dissolves the serverless redeploy problem entirely.]]></description>
      <link>https://myweirdprompts.com/episode/constrained-decoding-podcast-taxonomy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/constrained-decoding-podcast-taxonomy/</guid>
      <pubDate>Mon, 28 Sep 2026 11:59:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/constrained-decoding-podcast-taxonomy.m4a"
        type="audio/mp4"
        length="24144948"
      />
      <itunes:title>Constrained Decoding for AI-Generated Podcast Tags</itunes:title>
      <itunes:subtitle>A working AI podcast pipeline has one quiet failure: the tagging. Here&apos;s how constrained decoding and a stable taxonomy fix it.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's AI-generated show deploys to Vercel, runs on serverless Postgres, and produces exactly the kind of wide-ranging content he wants. The problem is the feed — automatic tagging that started inventing its own categories, then duplicating them in ways nobody notices. This episode works through the fix: why unconstrained categorization never converges, how JSON Schema enums and context-free grammars make label bloat structurally impossible, and why a fine-tuned encoder beats a frontier model for a stable label set at one to two orders of magnitude lower cost. We also cover the counterargument — that a confident wrong classification is worse than no classification — and the read-at-classification-time pattern that dissolves the serverless redeploy problem entirely.]]></itunes:summary>
      <itunes:duration>1375</itunes:duration>
      <itunes:episode>5621</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/constrained-decoding-podcast-taxonomy.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/constrained-decoding-podcast-taxonomy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Custom GPTs Retire: Skills, Plugins, and MCP Explained</title>
      <description><![CDATA[OpenAI is retiring custom GPTs on December 11, 2026, with a migration path to plugins built on Skills and MCP servers. This episode walks through the real timeline, the four documented plugin shapes, and the migration mechanics — including the parts that don't carry over at all, like custom actions. We also dig into the SKILL.md open standard that Anthropic released in December 2025 and why it spread to 79 agents across OpenAI, Google, Cursor, and Copilot faster than MCP did. Plus: the context-load objection one listener raised to ChatGPT, and why progressive disclosure is correct in theory but has a hard ceiling in practice.]]></description>
      <link>https://myweirdprompts.com/episode/custom-gpts-retire-skills-plugins-mcp/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/custom-gpts-retire-skills-plugins-mcp/</guid>
      <pubDate>Sun, 27 Sep 2026 11:30:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/custom-gpts-retire-skills-plugins-mcp.m4a"
        type="audio/mp4"
        length="23742053"
      />
      <itunes:title>Custom GPTs Retire: Skills, Plugins, and MCP Explained</itunes:title>
      <itunes:subtitle>Custom GPTs retire December 11. Here&apos;s how Skills, Plugins, and MCP servers actually replace them — and what doesn&apos;t migrate.</itunes:subtitle>
      <itunes:summary><![CDATA[OpenAI is retiring custom GPTs on December 11, 2026, with a migration path to plugins built on Skills and MCP servers. This episode walks through the real timeline, the four documented plugin shapes, and the migration mechanics — including the parts that don't carry over at all, like custom actions. We also dig into the SKILL.md open standard that Anthropic released in December 2025 and why it spread to 79 agents across OpenAI, Google, Cursor, and Copilot faster than MCP did. Plus: the context-load objection one listener raised to ChatGPT, and why progressive disclosure is correct in theory but has a hard ceiling in practice.]]></itunes:summary>
      <itunes:duration>1353</itunes:duration>
      <itunes:episode>5606</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/custom-gpts-retire-skills-plugins-mcp.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/custom-gpts-retire-skills-plugins-mcp.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fixing Typos Without Rewriting the Prompt</title>
      <description><![CDATA[Daniel dictates his prompts, catches typos by hand, and keeps running into the same wall: fixing a misspelling is not the same job as understanding what he meant. This episode pulls apart the pipeline into its three real tasks — disfluency removal, mechanical typo correction, and semantic inference — and asks which of them a language model should be allowed to touch. Along the way: why an "um" is a token like any other, why a confidence threshold is a certainty parameter you just reinvented, and why a small task-specific corrector drifts less than a large general model. If you've ever handed a "just fix the typos" prompt to a model and gotten back something smoother than what you wrote, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/prompt-typo-correction-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/prompt-typo-correction-pipeline/</guid>
      <pubDate>Sun, 27 Sep 2026 11:16:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/prompt-typo-correction-pipeline.m4a"
        type="audio/mp4"
        length="22108553"
      />
      <itunes:title>Fixing Typos Without Rewriting the Prompt</itunes:title>
      <itunes:subtitle>Three different jobs hide in one &quot;clean up my prompt&quot; pipeline — and only one of them should ever go to a generative model.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel dictates his prompts, catches typos by hand, and keeps running into the same wall: fixing a misspelling is not the same job as understanding what he meant. This episode pulls apart the pipeline into its three real tasks — disfluency removal, mechanical typo correction, and semantic inference — and asks which of them a language model should be allowed to touch. Along the way: why an "um" is a token like any other, why a confidence threshold is a certainty parameter you just reinvented, and why a small task-specific corrector drifts less than a large general model. If you've ever handed a "just fix the typos" prompt to a model and gotten back something smoother than what you wrote, this one's for you.]]></itunes:summary>
      <itunes:duration>1233</itunes:duration>
      <itunes:episode>5604</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/prompt-typo-correction-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/prompt-typo-correction-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Ephemeral Containers and the ADE Label Nobody Owns</title>
      <description><![CDATA[Claude Code's default cloud environment is an ephemeral container — a fresh Anthropic-managed VM cloned from your repo on every session. That design choice quietly blocks custom MCP servers, privileged tooling, secrets, and long-running processes. This episode unpacks the four questions behind one developer's attempt to use Claude Code on mobile, explains why "cloud IDE" is the wrong label, and traces the emergence of a new category term: the Agentic Development Environment (ADE). Warp, Emdash, ctx, and Augment all use it — and all mean something different.]]></description>
      <link>https://myweirdprompts.com/episode/ephemeral-containers-agentic-dev-environments/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ephemeral-containers-agentic-dev-environments/</guid>
      <pubDate>Sat, 26 Sep 2026 10:45:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ephemeral-containers-agentic-dev-environments.m4a"
        type="audio/mp4"
        length="24792007"
      />
      <itunes:title>Ephemeral Containers and the ADE Label Nobody Owns</itunes:title>
      <itunes:subtitle>Claude Code&apos;s cloud sessions spin up a fresh VM every time. Here&apos;s what that breaks, and the new term for what Daniel actually wants.</itunes:subtitle>
      <itunes:summary><![CDATA[Claude Code's default cloud environment is an ephemeral container — a fresh Anthropic-managed VM cloned from your repo on every session. That design choice quietly blocks custom MCP servers, privileged tooling, secrets, and long-running processes. This episode unpacks the four questions behind one developer's attempt to use Claude Code on mobile, explains why "cloud IDE" is the wrong label, and traces the emergence of a new category term: the Agentic Development Environment (ADE). Warp, Emdash, ctx, and Augment all use it — and all mean something different.]]></itunes:summary>
      <itunes:duration>1413</itunes:duration>
      <itunes:episode>5574</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ephemeral-containers-agentic-dev-environments.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ephemeral-containers-agentic-dev-environments.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Audio Post-Processing for AI Podcast Pipelines</title>
      <description><![CDATA[The front end of an AI podcast pipeline gets all the attention — agents, prompts, retrieval. But the stage nobody films a documentary about is audio post-processing, and it's where the actual craft lives. This episode walks through four specific edits: silence truncation, loudness normalization, EQ, and speed normalization without pitch distortion. Along the way, we dig into why SoX and FFmpeg split the work, why a single wrong integer in FFmpeg's silenceremove filter can silently delete 90% of your episode, and why the order of operations between silence removal and loudnorm isn't a style preference — it's a correctness issue. Plus: when to preview with silencedetect, how EBU R128's double-pass mode works, and whether any of these jobs actually benefit from GPU inference.]]></description>
      <link>https://myweirdprompts.com/episode/audio-post-processing-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-post-processing-pipeline/</guid>
      <pubDate>Sat, 26 Sep 2026 09:52:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-post-processing-pipeline.m4a"
        type="audio/mp4"
        length="27748942"
      />
      <itunes:title>Audio Post-Processing for AI Podcast Pipelines</itunes:title>
      <itunes:subtitle>Silence trimming, loudness normalization, EQ, and tempo fixes — the CLI tools that turn raw TTS output into a finished episode.</itunes:subtitle>
      <itunes:summary><![CDATA[The front end of an AI podcast pipeline gets all the attention — agents, prompts, retrieval. But the stage nobody films a documentary about is audio post-processing, and it's where the actual craft lives. This episode walks through four specific edits: silence truncation, loudness normalization, EQ, and speed normalization without pitch distortion. Along the way, we dig into why SoX and FFmpeg split the work, why a single wrong integer in FFmpeg's silenceremove filter can silently delete 90% of your episode, and why the order of operations between silence removal and loudnorm isn't a style preference — it's a correctness issue. Plus: when to preview with silencedetect, how EBU R128's double-pass mode works, and whether any of these jobs actually benefit from GPU inference.]]></itunes:summary>
      <itunes:duration>1558</itunes:duration>
      <itunes:episode>5573</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-post-processing-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-post-processing-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Staging Environments for Blog Posts and AI Pipelines

That&apos;s 54 characters. This captures both the content workflow angle and the AI pipeline angle. Good.</title>
      <description><![CDATA[Daniel asked a deceptively simple question: can the staging-to-production pattern extend beyond software development? The answer is already happening. Content platforms like SleekCMS, Storyblok, and Sanity have independently built multi-stage publishing workflows with draft states, preview environments, and version history — arriving at DevOps patterns without being told to. Meanwhile, tools like CircleCI and Azure let you define arbitrary promotion hierarchies, while Heroku bakes in a fixed four-stage model. And in the AI pipeline world, repos like autonomous-release-pipeline and forge are inserting autonomous agent review stages between human approval gates, with canary deployments and auto-rollback thresholds. The staging environment isn't just a DevOps concept anymore — it's a general pattern for managing irreversible actions, whether you're shipping code, publishing a blog post, or letting AI agents review each other's work.]]></description>
      <link>https://myweirdprompts.com/episode/staging-environments-beyond-code/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/staging-environments-beyond-code/</guid>
      <pubDate>Fri, 25 Sep 2026 00:26:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/staging-environments-beyond-code.m4a"
        type="audio/mp4"
        length="23032589"
      />
      <itunes:title>Staging Environments for Blog Posts and AI Pipelines

That&apos;s 54 characters. This captures both the content workflow angle and the AI pipeline angle. Good.</itunes:title>
      <itunes:subtitle>Content teams are reinventing staging, draft states, and version history on their own — and AI pipelines are adding autonomous review stages betwee...</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asked a deceptively simple question: can the staging-to-production pattern extend beyond software development? The answer is already happening. Content platforms like SleekCMS, Storyblok, and Sanity have independently built multi-stage publishing workflows with draft states, preview environments, and version history — arriving at DevOps patterns without being told to. Meanwhile, tools like CircleCI and Azure let you define arbitrary promotion hierarchies, while Heroku bakes in a fixed four-stage model. And in the AI pipeline world, repos like autonomous-release-pipeline and forge are inserting autonomous agent review stages between human approval gates, with canary deployments and auto-rollback thresholds. The staging environment isn't just a DevOps concept anymore — it's a general pattern for managing irreversible actions, whether you're shipping code, publishing a blog post, or letting AI agents review each other's work.]]></itunes:summary>
      <itunes:duration>1312</itunes:duration>
      <itunes:episode>5536</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/staging-environments-beyond-code.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/staging-environments-beyond-code.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Characters Drift Between Shots</title>
      <description><![CDATA[You generate a character, they look perfect in shot one, and by shot four they're someone else's slightly off cousin. Why? This episode digs into the structural reasons image-to-video models drift: stateless sampling, category-level text prompts, and the fundamental tension between identity consistency and scene diversity. We cover when training a LoRA actually makes sense (and when it's a day of setup for nothing), what the ideal single reference image looks like, why matching your reference background to your scene backfires, and the counterintuitive truth about multi-reference conditioning — more angles can make drift worse. If you've ever wondered why the model "fails" at character consistency, this reframes the whole problem.]]></description>
      <link>https://myweirdprompts.com/episode/ai-character-consistency-drift/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-character-consistency-drift/</guid>
      <pubDate>Thu, 24 Sep 2026 17:54:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-character-consistency-drift.m4a"
        type="audio/mp4"
        length="22770367"
      />
      <itunes:title>Why AI Characters Drift Between Shots</itunes:title>
      <itunes:subtitle>Why does your AI character look like a different person by shot four? The answer is structural — and the fix isn&apos;t what you&apos;d expect.</itunes:subtitle>
      <itunes:summary><![CDATA[You generate a character, they look perfect in shot one, and by shot four they're someone else's slightly off cousin. Why? This episode digs into the structural reasons image-to-video models drift: stateless sampling, category-level text prompts, and the fundamental tension between identity consistency and scene diversity. We cover when training a LoRA actually makes sense (and when it's a day of setup for nothing), what the ideal single reference image looks like, why matching your reference background to your scene backfires, and the counterintuitive truth about multi-reference conditioning — more angles can make drift worse. If you've ever wondered why the model "fails" at character consistency, this reframes the whole problem.]]></itunes:summary>
      <itunes:duration>1292</itunes:duration>
      <itunes:episode>5533</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-character-consistency-drift.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-character-consistency-drift.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Your Browser Isn&apos;t Telling You</title>
      <description><![CDATA[If you want to know what your browser is really sending, DevTools is the wrong instrument — it silently drops the initiator data that answers "which script fired this request." This episode walks through the three tap points for browser traffic (packet capture, intercepting proxies, and browser internals), why the HAR format is an abandoned spec that everything still emits, and the fidelity-versus-visibility tradeoff that comes with terminating TLS in the middle. Then we get into what researchers have actually found: a field registry of 3,919 unique fields across six AI chatbots, and structured tests suggesting privacy modes in Claude, ChatGPT, and Grok still transmit session-linked analytics to third parties.]]></description>
      <link>https://myweirdprompts.com/episode/browser-traffic-forensics-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/browser-traffic-forensics-tools/</guid>
      <pubDate>Thu, 24 Sep 2026 16:47:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/browser-traffic-forensics-tools.m4a"
        type="audio/mp4"
        length="24099807"
      />
      <itunes:title>What Your Browser Isn&apos;t Telling You</itunes:title>
      <itunes:subtitle>DevTools quietly omits what privacy researchers need most. Here&apos;s how to actually tap browser traffic — and what shows up when you do.</itunes:subtitle>
      <itunes:summary><![CDATA[If you want to know what your browser is really sending, DevTools is the wrong instrument — it silently drops the initiator data that answers "which script fired this request." This episode walks through the three tap points for browser traffic (packet capture, intercepting proxies, and browser internals), why the HAR format is an abandoned spec that everything still emits, and the fidelity-versus-visibility tradeoff that comes with terminating TLS in the middle. Then we get into what researchers have actually found: a field registry of 3,919 unique fields across six AI chatbots, and structured tests suggesting privacy modes in Claude, ChatGPT, and Grok still transmit session-linked analytics to third parties.]]></itunes:summary>
      <itunes:duration>1369</itunes:duration>
      <itunes:episode>5532</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/browser-traffic-forensics-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/browser-traffic-forensics-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can a Phone Bond Two Cellular Links?</title>
      <description><![CDATA[Daniel wrote in asking whether a dual SIM phone with two data plans could bond both cellular connections entirely on the handset — no mobile router, no bag full of gear. The bonding software is a solved problem. The phone is not. This episode works through what bonding actually requires (a far-end server that reassembles out-of-order packets — it isn't failover or load balancing), why DSDS "dual standby" phones share one modem chain and only hold one active data session, and why Android's single-default-network architecture has no concept of aggregating two live cellular paths. We also test the claim that Speedify would "just work" if the handset could hold two connections — and find the proof case in Speedify's own support for Wi-Fi plus cellular bonding on Android. Finally: why no out-of-the-box phone markets true dual-SIM dual-data, why "DSDA" in a spec sheet tells you almost nothing, and what the DIY route actually requires.]]></description>
      <link>https://myweirdprompts.com/episode/dual-sim-bonding-android/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dual-sim-bonding-android/</guid>
      <pubDate>Thu, 24 Sep 2026 14:46:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dual-sim-bonding-android.m4a"
        type="audio/mp4"
        length="21141079"
      />
      <itunes:title>Can a Phone Bond Two Cellular Links?</itunes:title>
      <itunes:subtitle>Android can bond Wi-Fi and cellular — but two cellular data paths at once? That&apos;s where the hardware and the OS both say no.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wrote in asking whether a dual SIM phone with two data plans could bond both cellular connections entirely on the handset — no mobile router, no bag full of gear. The bonding software is a solved problem. The phone is not. This episode works through what bonding actually requires (a far-end server that reassembles out-of-order packets — it isn't failover or load balancing), why DSDS "dual standby" phones share one modem chain and only hold one active data session, and why Android's single-default-network architecture has no concept of aggregating two live cellular paths. We also test the claim that Speedify would "just work" if the handset could hold two connections — and find the proof case in Speedify's own support for Wi-Fi plus cellular bonding on Android. Finally: why no out-of-the-box phone markets true dual-SIM dual-data, why "DSDA" in a spec sheet tells you almost nothing, and what the DIY route actually requires.]]></itunes:summary>
      <itunes:duration>1195</itunes:duration>
      <itunes:episode>5526</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dual-sim-bonding-android.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dual-sim-bonding-android.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Voice Agents Can&apos;t Hang Up</title>
      <description><![CDATA[A screening bot should attend your low-value sales calls and hang up when the pitch goes nowhere. The hard part isn't the screening logic — it's the ending. Conversational models have no built-in concept of a conversation concluding, so two reactive agents will politely agree to stop and then keep going forever. This episode digs into the mechanism behind that loop, the platform-level fixes voice AI vendors ship to work around it, and what it means when your agent quietly stops representing you mid-call.]]></description>
      <link>https://myweirdprompts.com/episode/ai-voice-agents-cant-hang-up/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-voice-agents-cant-hang-up/</guid>
      <pubDate>Thu, 24 Sep 2026 14:19:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-voice-agents-cant-hang-up.m4a"
        type="audio/mp4"
        length="25557430"
      />
      <itunes:title>Why AI Voice Agents Can&apos;t Hang Up</itunes:title>
      <itunes:subtitle>Two polite AI agents, one endless goodbye loop — and a phone bill that never stops climbing.</itunes:subtitle>
      <itunes:summary><![CDATA[A screening bot should attend your low-value sales calls and hang up when the pitch goes nowhere. The hard part isn't the screening logic — it's the ending. Conversational models have no built-in concept of a conversation concluding, so two reactive agents will politely agree to stop and then keep going forever. This episode digs into the mechanism behind that loop, the platform-level fixes voice AI vendors ship to work around it, and what it means when your agent quietly stops representing you mid-call.]]></itunes:summary>
      <itunes:duration>1458</itunes:duration>
      <itunes:episode>5524</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-voice-agents-cant-hang-up.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-voice-agents-cant-hang-up.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where Does the Coding Agent Actually Live?</title>
      <description><![CDATA[Inference was never local — what's moving is the agent process, the filesystem it touches, and the tool runtime. This episode unpacks Daniel's three-way fork: a local-network appliance reached over Tailscale, a vendor-provided SaaS workspace, or a VPS you provision yourself. We look at Anthropic's remote control and why its own docs route you to cloud sessions the moment you want parallelism, bossanova and CCBot as local-appliance examples, Coder's quiet rename from Cloud Development Environment to Agentic Development Environment, and ShellTeam's deliberately sandbox-free VPS command center. Plus the sharpest point in the whole debate: MCP servers that touch your local network, and why round-tripping to the cloud to toggle a light is absurd.]]></description>
      <link>https://myweirdprompts.com/episode/coding-agent-hosting-shift/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/coding-agent-hosting-shift/</guid>
      <pubDate>Thu, 24 Sep 2026 14:18:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/coding-agent-hosting-shift.m4a"
        type="audio/mp4"
        length="23270511"
      />
      <itunes:title>Where Does the Coding Agent Actually Live?</itunes:title>
      <itunes:subtitle>The agent process is moving off your laptop. Local appliance, SaaS workspace, or your own VPS — three answers, no consensus.</itunes:subtitle>
      <itunes:summary><![CDATA[Inference was never local — what's moving is the agent process, the filesystem it touches, and the tool runtime. This episode unpacks Daniel's three-way fork: a local-network appliance reached over Tailscale, a vendor-provided SaaS workspace, or a VPS you provision yourself. We look at Anthropic's remote control and why its own docs route you to cloud sessions the moment you want parallelism, bossanova and CCBot as local-appliance examples, Coder's quiet rename from Cloud Development Environment to Agentic Development Environment, and ShellTeam's deliberately sandbox-free VPS command center. Plus the sharpest point in the whole debate: MCP servers that touch your local network, and why round-tripping to the cloud to toggle a light is absurd.]]></itunes:summary>
      <itunes:duration>1313</itunes:duration>
      <itunes:episode>5523</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/coding-agent-hosting-shift.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/coding-agent-hosting-shift.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Podcast Backup Appliance</title>
      <description><![CDATA[A listener wrote in with a specific ask: a single-purpose appliance that incrementally pulls podcast episodes onto local storage he owns, reachable over Tailscale, reporting on disk health, with no NAS bloat and no general-purpose services. It sounds like a hobbyist napkin sketch — until you notice the enterprise backup industry spent a decade arriving at the same design. We walk through the Just Enough OS pattern, the mature open-source archivers that already do exactly this, and the one thing nobody has actually built: the box that bundles them together.]]></description>
      <link>https://myweirdprompts.com/episode/podcast-backup-appliance-build/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcast-backup-appliance-build/</guid>
      <pubDate>Thu, 24 Sep 2026 13:50:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcast-backup-appliance-build.m4a"
        type="audio/mp4"
        length="23716305"
      />
      <itunes:title>Building a Podcast Backup Appliance</itunes:title>
      <itunes:subtitle>One listener wants a single-purpose box that pulls every episode to local storage he controls. Turns out the software exists — the packaging doesn&apos;t.</itunes:subtitle>
      <itunes:summary><![CDATA[A listener wrote in with a specific ask: a single-purpose appliance that incrementally pulls podcast episodes onto local storage he owns, reachable over Tailscale, reporting on disk health, with no NAS bloat and no general-purpose services. It sounds like a hobbyist napkin sketch — until you notice the enterprise backup industry spent a decade arriving at the same design. We walk through the Just Enough OS pattern, the mature open-source archivers that already do exactly this, and the one thing nobody has actually built: the box that bundles them together.]]></itunes:summary>
      <itunes:duration>1350</itunes:duration>
      <itunes:episode>5522</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcast-backup-appliance-build.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcast-backup-appliance-build.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Keeping a Fine-Tune Alive Across Model Releases</title>
      <description><![CDATA[Daniel wrote in with a plan: fine-tune DeepSeek Flash 4.1 on several hundred hand-edited episode transcripts, deploy the checkpoint to Modal, and repeat every few hundred episodes. Then Hannah asked what happens when 4.2 ships. We dig into what fine-tuning is actually for, where style lives inside a transformer, and why a LoRA adapter trained on one version often collapses on the next. Plus the DeepSeek V4-Pro routing episode from this month, and why a model name is a contract.]]></description>
      <link>https://myweirdprompts.com/episode/fine-tune-model-version-drift/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fine-tune-model-version-drift/</guid>
      <pubDate>Thu, 24 Sep 2026 13:45:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fine-tune-model-version-drift.m4a"
        type="audio/mp4"
        length="19473662"
      />
      <itunes:title>Keeping a Fine-Tune Alive Across Model Releases</itunes:title>
      <itunes:subtitle>Daniel wants to fine-tune DeepSeek Flash 4.1 on edited podcast scripts. The hard part isn&apos;t training — it&apos;s surviving the next release.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wrote in with a plan: fine-tune DeepSeek Flash 4.1 on several hundred hand-edited episode transcripts, deploy the checkpoint to Modal, and repeat every few hundred episodes. Then Hannah asked what happens when 4.2 ships. We dig into what fine-tuning is actually for, where style lives inside a transformer, and why a LoRA adapter trained on one version often collapses on the next. Plus the DeepSeek V4-Pro routing episode from this month, and why a model name is a contract.]]></itunes:summary>
      <itunes:duration>1089</itunes:duration>
      <itunes:episode>5520</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fine-tune-model-version-drift.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fine-tune-model-version-drift.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What 5,000 AI-Written Podcast Episodes Reveal</title>
      <description><![CDATA[A listener built a Hugging Face dataset of 5,000+ podcast episodes, each stamped with the model that generated it and synced daily. That makes it a longitudinal, model-attributed corpus — an ablation study where the treatment is the model version and the outcome is stylistic quality. This episode digs into what that artifact can actually measure: why verbatim repetition is a solved problem while semantic repetition slips past every keyword method, how U-shaped attention bias and lost-in-the-middle effects turn a 40-turn script into a loop machine, and why repetition is really a model stalling rather than malfunctioning. Then the concrete toolkit — sentence embeddings with non-adjacent pair filtering, CASE for anisotropy, BERTopic for intra-episode recurrence, c-TF-IDF for per-model tic profiles, compression ratio as a cheap first signal, and correlation dimension for a plottable per-episode scalar.]]></description>
      <link>https://myweirdprompts.com/episode/ai-podcast-corpus-repetition-analysis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-podcast-corpus-repetition-analysis/</guid>
      <pubDate>Thu, 24 Sep 2026 13:16:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-podcast-corpus-repetition-analysis.m4a"
        type="audio/mp4"
        length="25693358"
      />
      <itunes:title>What 5,000 AI-Written Podcast Episodes Reveal</itunes:title>
      <itunes:subtitle>Daniel archived every AI-generated episode with the model that wrote it. Now he wants to know which models loop — and how to catch it.</itunes:subtitle>
      <itunes:summary><![CDATA[A listener built a Hugging Face dataset of 5,000+ podcast episodes, each stamped with the model that generated it and synced daily. That makes it a longitudinal, model-attributed corpus — an ablation study where the treatment is the model version and the outcome is stylistic quality. This episode digs into what that artifact can actually measure: why verbatim repetition is a solved problem while semantic repetition slips past every keyword method, how U-shaped attention bias and lost-in-the-middle effects turn a 40-turn script into a loop machine, and why repetition is really a model stalling rather than malfunctioning. Then the concrete toolkit — sentence embeddings with non-adjacent pair filtering, CASE for anisotropy, BERTopic for intra-episode recurrence, c-TF-IDF for per-model tic profiles, compression ratio as a cheap first signal, and correlation dimension for a plottable per-episode scalar.]]></itunes:summary>
      <itunes:duration>1465</itunes:duration>
      <itunes:episode>5517</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-podcast-corpus-repetition-analysis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-podcast-corpus-repetition-analysis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside ChatGPT&apos;s Hidden Pipeline: 8 Models Per Reply</title>
      <description><![CDATA[Ask ChatGPT to reword an email and you assume one model answers. In reality, a single turn can pass through input moderation, PII detection, a server-side safety router, memory assembly, retrieval orchestration, mid-generation activation classifiers, output moderation, and asynchronous review. Drawing on OpenAI's own documentation, the Privacy Filter open weights, the September 2025 GPT-5 telemetry paper, and published reverse-engineering of ChatGPT's memory and retrieval stack, this episode reconstructs the graph that sits between your prompt and the reply — and asks what it means that so much of it is now documented rather than speculative.]]></description>
      <link>https://myweirdprompts.com/episode/inside-chatgpt-hidden-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/inside-chatgpt-hidden-pipeline/</guid>
      <pubDate>Thu, 24 Sep 2026 11:20:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/inside-chatgpt-hidden-pipeline.m4a"
        type="audio/mp4"
        length="28023681"
      />
      <itunes:title>Inside ChatGPT&apos;s Hidden Pipeline: 8 Models Per Reply</itunes:title>
      <itunes:subtitle>One chat turn isn&apos;t one model call. It&apos;s moderation, PII filtering, a tone-based router, and activation classifiers — here&apos;s the documented graph.</itunes:subtitle>
      <itunes:summary><![CDATA[Ask ChatGPT to reword an email and you assume one model answers. In reality, a single turn can pass through input moderation, PII detection, a server-side safety router, memory assembly, retrieval orchestration, mid-generation activation classifiers, output moderation, and asynchronous review. Drawing on OpenAI's own documentation, the Privacy Filter open weights, the September 2025 GPT-5 telemetry paper, and published reverse-engineering of ChatGPT's memory and retrieval stack, this episode reconstructs the graph that sits between your prompt and the reply — and asks what it means that so much of it is now documented rather than speculative.]]></itunes:summary>
      <itunes:duration>1593</itunes:duration>
      <itunes:episode>5507</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/inside-chatgpt-hidden-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/inside-chatgpt-hidden-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Send a Bot to Your Next Sales Call</title>
      <description><![CDATA[What if you never sat through another pseudo-personalized sales pitch again? This week we design "Herman," an AI meeting bot that joins the call in Daniel's place, discloses it's an AI, gives the seller a fair opening pitch, then runs four diagnostic questions designed to collapse any script that wasn't built for him. We write the actual system prompt: the disclosure block, the fair-opportunity block, the silence rule, the stopping condition, and the hard ban on making commitments. Then we build the second agent that reads the transcript and briefs Daniel afterward. It's a practical exercise in putting judgment where it belongs — in the prompt, not the platform.]]></description>
      <link>https://myweirdprompts.com/episode/ai-meeting-bot-system-prompt/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-meeting-bot-system-prompt/</guid>
      <pubDate>Thu, 24 Sep 2026 11:02:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-meeting-bot-system-prompt.m4a"
        type="audio/mp4"
        length="26013384"
      />
      <itunes:title>Send a Bot to Your Next Sales Call</itunes:title>
      <itunes:subtitle>Daniel wants a bot that takes his sales meetings, asks hard questions, and ends the call when the pitch is spray-and-pray.</itunes:subtitle>
      <itunes:summary><![CDATA[What if you never sat through another pseudo-personalized sales pitch again? This week we design "Herman," an AI meeting bot that joins the call in Daniel's place, discloses it's an AI, gives the seller a fair opening pitch, then runs four diagnostic questions designed to collapse any script that wasn't built for him. We write the actual system prompt: the disclosure block, the fair-opportunity block, the silence rule, the stopping condition, and the hard ban on making commitments. Then we build the second agent that reads the transcript and briefs Daniel afterward. It's a practical exercise in putting judgment where it belongs — in the prompt, not the platform.]]></itunes:summary>
      <itunes:duration>1479</itunes:duration>
      <itunes:episode>5505</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-meeting-bot-system-prompt.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-meeting-bot-system-prompt.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Spreadsheet Mangles José&apos;s Name</title>
      <description><![CDATA[Ever opened a UTF-8 CSV in Excel and watched José turn into "JosÃ©"? That garbled text is called mojibake, and it's the visible symptom of a character set mismatch hiding beneath every keyboard layout, font, and file format you touch. This episode unpacks the three layers people constantly collapse into one — code points, encodings, and graphemes — and shows why the encoding layer being wrong poisons everything above it. From ASCII's 1963 origins and UTF-8's genius backward compatibility, to the byte-length trap, the NFC/NFD normalization split that once made files vanish on macOS, and MySQL's years-long inability to store emoji, we trace how these failures reach real teams. The anchor is a story: a distributed operation preparing a several-hundred-person event across Israel, the UK, and New York, where a CRM export standardized on UTF-8 mangled the names of people about to receive an email. If you work with international data or collaborators, this is the layer you've been ignoring.]]></description>
      <link>https://myweirdprompts.com/episode/utf8-encoding-mojibake-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/utf8-encoding-mojibake-explained/</guid>
      <pubDate>Wed, 23 Sep 2026 13:02:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/utf8-encoding-mojibake-explained.m4a"
        type="audio/mp4"
        length="27198018"
      />
      <itunes:title>Why Your Spreadsheet Mangles José&apos;s Name</itunes:title>
      <itunes:subtitle>A deep dive into character sets, from ASCII to UTF-8, and why José becomes &quot;JosÃ©&quot; in your spreadsheet.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever opened a UTF-8 CSV in Excel and watched José turn into "JosÃ©"? That garbled text is called mojibake, and it's the visible symptom of a character set mismatch hiding beneath every keyboard layout, font, and file format you touch. This episode unpacks the three layers people constantly collapse into one — code points, encodings, and graphemes — and shows why the encoding layer being wrong poisons everything above it. From ASCII's 1963 origins and UTF-8's genius backward compatibility, to the byte-length trap, the NFC/NFD normalization split that once made files vanish on macOS, and MySQL's years-long inability to store emoji, we trace how these failures reach real teams. The anchor is a story: a distributed operation preparing a several-hundred-person event across Israel, the UK, and New York, where a CRM export standardized on UTF-8 mangled the names of people about to receive an email. If you work with international data or collaborators, this is the layer you've been ignoring.]]></itunes:summary>
      <itunes:duration>1548</itunes:duration>
      <itunes:episode>5487</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/utf8-encoding-mojibake-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/utf8-encoding-mojibake-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tuning Parakeet for Hebrew and Your Own Jargon</title>
      <description><![CDATA[Daniel runs NVIDIA's Parakeet V2 on Android for dictation and wants two things: a Hebrew fine-tune, and a personal-vocabulary tune to catch tech jargon and Hebrew loanwords like "makolet." We dig into what Parakeet actually is under the hood — FastConformer encoders with CTC, RNN-T, and TDT decoders, shipped through NVIDIA NeMo — and why its transducer architecture sidesteps the hallucination loops Whisper is famous for. Then we separate the two questions: personal vocabulary turns out to have a lightweight, already-shipped answer in contextual biasing and hotwords, while Hebrew is a genuine from-scratch language adaptation with a tokenizer wall, a data problem, and a real Android export bottleneck. We also bust the myth that only Whisper can be fine-tuned, and explain why starting from multilingual v3 beats adapting the English-only v2.]]></description>
      <link>https://myweirdprompts.com/episode/parakeet-hebrew-fine-tune-hotwords/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/parakeet-hebrew-fine-tune-hotwords/</guid>
      <pubDate>Wed, 23 Sep 2026 12:40:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/parakeet-hebrew-fine-tune-hotwords.m4a"
        type="audio/mp4"
        length="24471790"
      />
      <itunes:title>Fine-Tuning Parakeet for Hebrew and Your Own Jargon</itunes:title>
      <itunes:subtitle>NVIDIA&apos;s Parakeet beats Whisper on Android — but can you teach it Hebrew, or just your own jargon? Two answers, one much happier.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel runs NVIDIA's Parakeet V2 on Android for dictation and wants two things: a Hebrew fine-tune, and a personal-vocabulary tune to catch tech jargon and Hebrew loanwords like "makolet." We dig into what Parakeet actually is under the hood — FastConformer encoders with CTC, RNN-T, and TDT decoders, shipped through NVIDIA NeMo — and why its transducer architecture sidesteps the hallucination loops Whisper is famous for. Then we separate the two questions: personal vocabulary turns out to have a lightweight, already-shipped answer in contextual biasing and hotwords, while Hebrew is a genuine from-scratch language adaptation with a tokenizer wall, a data problem, and a real Android export bottleneck. We also bust the myth that only Whisper can be fine-tuned, and explain why starting from multilingual v3 beats adapting the English-only v2.]]></itunes:summary>
      <itunes:duration>1372</itunes:duration>
      <itunes:episode>5485</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/parakeet-hebrew-fine-tune-hotwords.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/parakeet-hebrew-fine-tune-hotwords.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Edits Your Words: The Off Switch Problem</title>
      <description><![CDATA[Daniel built an AI agent to lightly edit his podcast prompts, and it works. So why does he keep turning it off? This episode digs into the measurable mechanics of AI over-editing — from a 2026 coding-model study showing reasoning models rewrite more by default, to brevity bias and context collapse that erode a prompt across iterations. We walk through the draft system prompt in full, find the broken worked example hiding inside it, and map three production patterns that fix the problem: surgical find-and-replace editing, formal drift bounds with length caps, and delta-based merges instead of full rewrites. If you've ever gotten back something functionally correct that no longer sounds like you, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/ai-prompt-over-editing-fix/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-prompt-over-editing-fix/</guid>
      <pubDate>Wed, 23 Sep 2026 11:41:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-prompt-over-editing-fix.m4a"
        type="audio/mp4"
        length="27508970"
      />
      <itunes:title>When AI Edits Your Words: The Off Switch Problem</itunes:title>
      <itunes:subtitle>A tool that works reliably — and still gets switched off. What over-editing studies reveal about why AI rewrites more than you asked.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel built an AI agent to lightly edit his podcast prompts, and it works. So why does he keep turning it off? This episode digs into the measurable mechanics of AI over-editing — from a 2026 coding-model study showing reasoning models rewrite more by default, to brevity bias and context collapse that erode a prompt across iterations. We walk through the draft system prompt in full, find the broken worked example hiding inside it, and map three production patterns that fix the problem: surgical find-and-replace editing, formal drift bounds with length caps, and delta-based merges instead of full rewrites. If you've ever gotten back something functionally correct that no longer sounds like you, this one's for you.]]></itunes:summary>
      <itunes:duration>1576</itunes:duration>
      <itunes:episode>5482</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-prompt-over-editing-fix.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-prompt-over-editing-fix.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Number Porting Takes 30 Minutes</title>
      <description><![CDATA[Mobile number porting is a legally mandated right, standardized by 3GPP and enforced in Israel since the mid-2000s. The consumer version is one sentence: disconnect the old SIM and wait thirty minutes. The technical version is a signalling relay, a portability database, and a synchronization problem that has nothing to do with routing speed. We trace what actually happens when a number moves between networks — and what happens when the move never completes. Along the way: the 2012 arrival of Golan Telecom and Hot Mobile that turned porting into a mass-market weapon, the 2008 incident where loosened identity checks got dozens of subscribers ported without their knowledge, and an honest look at why the published porting timelines don't agree with each other.]]></description>
      <link>https://myweirdprompts.com/episode/mobile-number-porting-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mobile-number-porting-explained/</guid>
      <pubDate>Wed, 23 Sep 2026 10:46:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mobile-number-porting-explained.m4a"
        type="audio/mp4"
        length="22208286"
      />
      <itunes:title>Why Number Porting Takes 30 Minutes</itunes:title>
      <itunes:subtitle>Routing a ported number takes milliseconds. So why does the SIM swap take hours? We trace the signalling behind &quot;keep your number.</itunes:subtitle>
      <itunes:summary><![CDATA[Mobile number porting is a legally mandated right, standardized by 3GPP and enforced in Israel since the mid-2000s. The consumer version is one sentence: disconnect the old SIM and wait thirty minutes. The technical version is a signalling relay, a portability database, and a synchronization problem that has nothing to do with routing speed. We trace what actually happens when a number moves between networks — and what happens when the move never completes. Along the way: the 2012 arrival of Golan Telecom and Hot Mobile that turned porting into a mass-market weapon, the 2008 incident where loosened identity checks got dozens of subscribers ported without their knowledge, and an honest look at why the published porting timelines don't agree with each other.]]></itunes:summary>
      <itunes:duration>1265</itunes:duration>
      <itunes:episode>5479</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mobile-number-porting-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mobile-number-porting-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Login Systems Lock Out Their Own Users</title>
      <description><![CDATA[Daniel bought a data-only eSIM, then discovered the provider's portal only accepts SMS one-time codes — which a data-only plan can't receive. A second case: a switched phone number left him permanently locked out of his Israel Post account, because changing the number required authenticating with the old one. This episode unpacks why these lockouts aren't bugs but failures of imagination, tracing the problem through happy-path testing, dissolved QA teams, and decades of accumulated identity code. It also covers the design principles that actually fix it: identity anchors users can change, real fallback recovery channels, and graduated access that weakens over time instead of slamming the door.]]></description>
      <link>https://myweirdprompts.com/episode/authentication-edge-case-lockouts/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/authentication-edge-case-lockouts/</guid>
      <pubDate>Wed, 23 Sep 2026 10:21:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/authentication-edge-case-lockouts.m4a"
        type="audio/mp4"
        length="22558312"
      />
      <itunes:title>When Login Systems Lock Out Their Own Users</itunes:title>
      <itunes:subtitle>Why do login systems permanently lock out real users? A look at the hidden design failures behind OTP dead ends and circular recovery flows.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel bought a data-only eSIM, then discovered the provider's portal only accepts SMS one-time codes — which a data-only plan can't receive. A second case: a switched phone number left him permanently locked out of his Israel Post account, because changing the number required authenticating with the old one. This episode unpacks why these lockouts aren't bugs but failures of imagination, tracing the problem through happy-path testing, dissolved QA teams, and decades of accumulated identity code. It also covers the design principles that actually fix it: identity anchors users can change, real fallback recovery channels, and graduated access that weakens over time instead of slamming the door.]]></itunes:summary>
      <itunes:duration>1285</itunes:duration>
      <itunes:episode>5476</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/authentication-edge-case-lockouts.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/authentication-edge-case-lockouts.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Open Source&apos;s New Map: India, Croatia, Ethiopia</title>
      <description><![CDATA[India has overtaken the US as the largest contributor base to public open source projects, and one in three new GitHub developers now joins from a country that wasn't in the global top ten in 2020. Starting from a Croatian-built offline dictation app called Whisperian, this episode digs into GitHub's Octoverse data: quadrupling developer populations in India, Brazil, and Indonesia, triple-digit growth in Ethiopia, Serbia, and Costa Rica, and a generative-AI contributor pool that tripled in twenty months. But the numbers come with caveats — self-reported locations, wide forecast error bars — and a real gap between where contributors live and where commercial software revenue lands. The episode also asks the harder question: when contributors span eleven time zones and six languages, what scaffolding do open source projects actually need, and why does a more geographically diverse community produce better software rather than just more of it?]]></description>
      <link>https://myweirdprompts.com/episode/open-source-developer-map-shift/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/open-source-developer-map-shift/</guid>
      <pubDate>Tue, 22 Sep 2026 16:17:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/open-source-developer-map-shift.m4a"
        type="audio/mp4"
        length="27804080"
      />
      <itunes:title>Open Source&apos;s New Map: India, Croatia, Ethiopia</itunes:title>
      <itunes:subtitle>One in three new GitHub developers now comes from a country outside 2020&apos;s top ten. What that shift means for open source.</itunes:subtitle>
      <itunes:summary><![CDATA[India has overtaken the US as the largest contributor base to public open source projects, and one in three new GitHub developers now joins from a country that wasn't in the global top ten in 2020. Starting from a Croatian-built offline dictation app called Whisperian, this episode digs into GitHub's Octoverse data: quadrupling developer populations in India, Brazil, and Indonesia, triple-digit growth in Ethiopia, Serbia, and Costa Rica, and a generative-AI contributor pool that tripled in twenty months. But the numbers come with caveats — self-reported locations, wide forecast error bars — and a real gap between where contributors live and where commercial software revenue lands. The episode also asks the harder question: when contributors span eleven time zones and six languages, what scaffolding do open source projects actually need, and why does a more geographically diverse community produce better software rather than just more of it?]]></itunes:summary>
      <itunes:duration>1578</itunes:duration>
      <itunes:episode>5469</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/open-source-developer-map-shift.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/open-source-developer-map-shift.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Small Models, Big Schemas: When JSON Constraints Backfire</title>
      <description><![CDATA[Everyone building agents reaches for the biggest model and wraps every endpoint as a tool. But there's a quieter pattern: tiny text-to-text models doing one narrow structured job. Daniel wrote in asking whether small models doing predictable structured output inside MCP pipelines actually ship in production, or whether everyone just lets a general-purpose model emit tool calls. The answer runs through Todoist's MCP servers, a 7B structured-object model from Amazon Science, and a May 2026 study called The Constraint Tax, which found that hard answer-only schemas pushed schema validity to 100% while answer accuracy fell from 19.7% to 11%. Then FlowMCP flips the whole premise: maybe the win isn't a smaller model, it's a smaller surface.]]></description>
      <link>https://myweirdprompts.com/episode/small-models-structured-output/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/small-models-structured-output/</guid>
      <pubDate>Tue, 22 Sep 2026 15:16:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/small-models-structured-output.m4a"
        type="audio/mp4"
        length="21747537"
      />
      <itunes:title>Small Models, Big Schemas: When JSON Constraints Backfire</itunes:title>
      <itunes:subtitle>Small models plus strict JSON schemas should be a safe bet. A 15,000-generation study found the opposite.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone building agents reaches for the biggest model and wraps every endpoint as a tool. But there's a quieter pattern: tiny text-to-text models doing one narrow structured job. Daniel wrote in asking whether small models doing predictable structured output inside MCP pipelines actually ship in production, or whether everyone just lets a general-purpose model emit tool calls. The answer runs through Todoist's MCP servers, a 7B structured-object model from Amazon Science, and a May 2026 study called The Constraint Tax, which found that hard answer-only schemas pushed schema validity to 100% while answer accuracy fell from 19.7% to 11%. Then FlowMCP flips the whole premise: maybe the win isn't a smaller model, it's a smaller surface.]]></itunes:summary>
      <itunes:duration>1221</itunes:duration>
      <itunes:episode>5467</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/small-models-structured-output.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/small-models-structured-output.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Packaging AI Pipelines So They Actually Get Reused</title>
      <description><![CDATA[Daniel has a working podcast pipeline: a language model writes scripts, retrieval tools feed it, and text-to-speech reads it out. But the occasional Hebrew word keeps breaking things, and the pipeline itself is trapped in his head. This episode digs into two questions at once — how to handle Hebrew code-switching in TTS, and how to package a chained AI workflow so it survives past the project that spawned it. We talk through script-based segmentation versus small classifiers, why unvocalized Hebrew makes TTS inherently unstable, the tradeoff between chaining small models and using one large one, and why containerization is the answer to dependency hell. Plus: the "recipe" as a distinct layer between a model and a project, and a pre-flight risk score that flags bad inputs before you burn compute.]]></description>
      <link>https://myweirdprompts.com/episode/ai-pipeline-packaging-recipes/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-pipeline-packaging-recipes/</guid>
      <pubDate>Tue, 22 Sep 2026 14:06:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-pipeline-packaging-recipes.m4a"
        type="audio/mp4"
        length="25771613"
      />
      <itunes:title>Packaging AI Pipelines So They Actually Get Reused</itunes:title>
      <itunes:subtitle>Daniel&apos;s podcast pipeline works — so why can&apos;t he reuse it? Recipes, containers, and Hebrew code-switching TTS.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has a working podcast pipeline: a language model writes scripts, retrieval tools feed it, and text-to-speech reads it out. But the occasional Hebrew word keeps breaking things, and the pipeline itself is trapped in his head. This episode digs into two questions at once — how to handle Hebrew code-switching in TTS, and how to package a chained AI workflow so it survives past the project that spawned it. We talk through script-based segmentation versus small classifiers, why unvocalized Hebrew makes TTS inherently unstable, the tradeoff between chaining small models and using one large one, and why containerization is the answer to dependency hell. Plus: the "recipe" as a distinct layer between a model and a project, and a pre-flight risk score that flags bad inputs before you burn compute.]]></itunes:summary>
      <itunes:duration>1470</itunes:duration>
      <itunes:episode>5465</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-pipeline-packaging-recipes.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-pipeline-packaging-recipes.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Your Keyboard&apos;s Hidden Data Problem</title>
      <description><![CDATA[Android keyboards quietly accumulate months of your typing habits — your email address, your common phrases, your personal words — and lock most of it inside vendor cloud silos you can't export or back up. This episode unpacks how the components actually work under the hood: the InputMethodService plumbing, the learned frequency model, the surprisingly portable UserDictionary, and the two very different ways macro expanders operate. Then it turns to the practical question: how do you rebuild all of it with open source components so your data belongs to you? Along the way, a deep look at Whisper IME's architecture and why Parakeet TDT runs 17x faster than Whisper large v3 on CPU — and what that means for anyone who types with their voice.]]></description>
      <link>https://myweirdprompts.com/episode/android-keyboard-data-portability/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-keyboard-data-portability/</guid>
      <pubDate>Tue, 22 Sep 2026 13:10:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-keyboard-data-portability.m4a"
        type="audio/mp4"
        length="27314039"
      />
      <itunes:title>Your Keyboard&apos;s Hidden Data Problem</itunes:title>
      <itunes:subtitle>Your keyboard knows your email address, your phrases, your habits — and you can&apos;t take any of it with you.</itunes:subtitle>
      <itunes:summary><![CDATA[Android keyboards quietly accumulate months of your typing habits — your email address, your common phrases, your personal words — and lock most of it inside vendor cloud silos you can't export or back up. This episode unpacks how the components actually work under the hood: the InputMethodService plumbing, the learned frequency model, the surprisingly portable UserDictionary, and the two very different ways macro expanders operate. Then it turns to the practical question: how do you rebuild all of it with open source components so your data belongs to you? Along the way, a deep look at Whisper IME's architecture and why Parakeet TDT runs 17x faster than Whisper large v3 on CPU — and what that means for anyone who types with their voice.]]></itunes:summary>
      <itunes:duration>1553</itunes:duration>
      <itunes:episode>5464</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-keyboard-data-portability.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-keyboard-data-portability.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Happens When You Click &quot;Connect&quot; on an AI Plugin</title>
      <description><![CDATA[When you connect ChatGPT's email plugin to a workspace address and a Gmail address, you tick a few scope boxes and everything just works. But what's actually happening in that moment — and who holds the credential that sends your mail? This episode walks through the OAuth 2.0 authorization code flow with PKCE, explains why you never see a token (it isn't yours to see), and untangles the difference between a user credential and an application credential. Along the way: why revocation is eventually consistent, why disconnecting an app and revoking a grant are two different acts, and how the confused deputy problem turns a service-account-backed tool into a privilege escalation vector — as demonstrated by the Supabase MCP leak in July 2025. If you've ever wondered what's under the hood of a "Connect" button, this is the episode.]]></description>
      <link>https://myweirdprompts.com/episode/oauth-tokens-ai-plugin-connections/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/oauth-tokens-ai-plugin-connections/</guid>
      <pubDate>Tue, 22 Sep 2026 12:21:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/oauth-tokens-ai-plugin-connections.m4a"
        type="audio/mp4"
        length="22186572"
      />
      <itunes:title>What Happens When You Click &quot;Connect&quot; on an AI Plugin</itunes:title>
      <itunes:subtitle>You click accept, the tool runs, and a credential you never saw is doing the work. Here&apos;s who actually owns it.</itunes:subtitle>
      <itunes:summary><![CDATA[When you connect ChatGPT's email plugin to a workspace address and a Gmail address, you tick a few scope boxes and everything just works. But what's actually happening in that moment — and who holds the credential that sends your mail? This episode walks through the OAuth 2.0 authorization code flow with PKCE, explains why you never see a token (it isn't yours to see), and untangles the difference between a user credential and an application credential. Along the way: why revocation is eventually consistent, why disconnecting an app and revoking a grant are two different acts, and how the confused deputy problem turns a service-account-backed tool into a privilege escalation vector — as demonstrated by the Supabase MCP leak in July 2025. If you've ever wondered what's under the hood of a "Connect" button, this is the episode.]]></itunes:summary>
      <itunes:duration>1247</itunes:duration>
      <itunes:episode>5463</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/oauth-tokens-ai-plugin-connections.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/oauth-tokens-ai-plugin-connections.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>TTS Can&apos;t Pronounce Hebrew Inside English</title>
      <description><![CDATA[You pass a language ID to a multilingual TTS model, and every word in that utterance obeys it — including the ones that belong to a different language. That's why "Shabbat" sounds fine and "pinui binui" sounds like a bad impression. This episode unpacks a listener's proposed pipeline for code-switched speech: detect the Hebrew words written in Latin characters, map them to Hebrew script, and re-synthesize them with the right language parameter. The research says the problem is real and documented, the mapping step is a solved problem sitting on a shelf, and the detection step — the one everyone assumes is easy — is the piece nobody has actually built. We also look at what the field has moved to instead of stitching clips, and why the elegant shortcut is blocked by an implementation gap rather than a theoretical one.]]></description>
      <link>https://myweirdprompts.com/episode/multilingual-tts-code-switching/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multilingual-tts-code-switching/</guid>
      <pubDate>Tue, 22 Sep 2026 11:53:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multilingual-tts-code-switching.m4a"
        type="audio/mp4"
        length="22465532"
      />
      <itunes:title>TTS Can&apos;t Pronounce Hebrew Inside English</itunes:title>
      <itunes:subtitle>Your TTS reads Hebrew words with English phonetics. Here&apos;s why — and why the obvious fix doesn&apos;t work yet.</itunes:subtitle>
      <itunes:summary><![CDATA[You pass a language ID to a multilingual TTS model, and every word in that utterance obeys it — including the ones that belong to a different language. That's why "Shabbat" sounds fine and "pinui binui" sounds like a bad impression. This episode unpacks a listener's proposed pipeline for code-switched speech: detect the Hebrew words written in Latin characters, map them to Hebrew script, and re-synthesize them with the right language parameter. The research says the problem is real and documented, the mapping step is a solved problem sitting on a shelf, and the detection step — the one everyone assumes is easy — is the piece nobody has actually built. We also look at what the field has moved to instead of stitching clips, and why the elegant shortcut is blocked by an implementation gap rather than a theoretical one.]]></itunes:summary>
      <itunes:duration>1273</itunes:duration>
      <itunes:episode>5461</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multilingual-tts-code-switching.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multilingual-tts-code-switching.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Four Small Models, One Android Phone: Does It Actually Work?</title>
      <description><![CDATA[Daniel has been replacing typing with voice for two years, and moving from Whisper to Parakeet V2 on Android was a massive step up. But when your dictation pipeline chains a voice activity detector, an ASR model, and one or more cleanup models, how do you know it's actually working — or is the phone just unusually idle? This episode walks the full chain stage by stage: why ASR models are deliberately partial products, why paragraphing is the great unloved task that even punctuation models skip, and why silence timeouts actively mislead turn detection. Along the way: Parakeet V2 beating V3, quantization tolerance that varies wildly by model, and what happens when a benchmark's six-times-real-time headroom shrinks to almost nothing on weaker hardware.]]></description>
      <link>https://myweirdprompts.com/episode/on-device-asr-phone-benchmark/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/on-device-asr-phone-benchmark/</guid>
      <pubDate>Tue, 22 Sep 2026 11:49:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/on-device-asr-phone-benchmark.m4a"
        type="audio/mp4"
        length="23249141"
      />
      <itunes:title>Four Small Models, One Android Phone: Does It Actually Work?</itunes:title>
      <itunes:subtitle>A chained on-device dictation pipeline — VAD, ASR, cleanup — and why &quot;it feels smooth&quot; isn&apos;t the same as knowing it works.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has been replacing typing with voice for two years, and moving from Whisper to Parakeet V2 on Android was a massive step up. But when your dictation pipeline chains a voice activity detector, an ASR model, and one or more cleanup models, how do you know it's actually working — or is the phone just unusually idle? This episode walks the full chain stage by stage: why ASR models are deliberately partial products, why paragraphing is the great unloved task that even punctuation models skip, and why silence timeouts actively mislead turn detection. Along the way: Parakeet V2 beating V3, quantization tolerance that varies wildly by model, and what happens when a benchmark's six-times-real-time headroom shrinks to almost nothing on weaker hardware.]]></itunes:summary>
      <itunes:duration>1323</itunes:duration>
      <itunes:episode>5460</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/on-device-asr-phone-benchmark.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/on-device-asr-phone-benchmark.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Rebuilding the Podcast: Turn-Taking, Buttons, and Chatterbox</title>
      <description><![CDATA[Daniel is sick in bed and has decided the podcast needs a rebuild: bring back audio prompts, add follow-up questions, and give him a push-to-interrupt button so he can grab the floor mid-episode. He thinks the button is the hard part. The research says it's the opposite — turn-taking primitives like push-to-talk have been standardized since walkie-talkies, while the live full-duplex version he's imagining is a genuine research frontier. This episode walks through the two-hundred-millisecond median gap that governs human turn-taking, why backchannels and barge-ins need to be treated differently, and why the simplest viable build is a turn-based, offline, multi-turn pipeline with Chatterbox synthesis at the end. If you've ever wondered whether your voice-agent idea is overcomplicated, this is the episode where the answer turns out to be: only the part you weren't worried about.]]></description>
      <link>https://myweirdprompts.com/episode/podcast-turn-taking-chatterbox-rebuild/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcast-turn-taking-chatterbox-rebuild/</guid>
      <pubDate>Tue, 22 Sep 2026 11:48:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcast-turn-taking-chatterbox-rebuild.m4a"
        type="audio/mp4"
        length="23829067"
      />
      <itunes:title>Rebuilding the Podcast: Turn-Taking, Buttons, and Chatterbox</itunes:title>
      <itunes:subtitle>Daniel wants a push-to-interrupt button and a live voice loop. The research says the button is easy and the live part is a research project.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel is sick in bed and has decided the podcast needs a rebuild: bring back audio prompts, add follow-up questions, and give him a push-to-interrupt button so he can grab the floor mid-episode. He thinks the button is the hard part. The research says it's the opposite — turn-taking primitives like push-to-talk have been standardized since walkie-talkies, while the live full-duplex version he's imagining is a genuine research frontier. This episode walks through the two-hundred-millisecond median gap that governs human turn-taking, why backchannels and barge-ins need to be treated differently, and why the simplest viable build is a turn-based, offline, multi-turn pipeline with Chatterbox synthesis at the end. If you've ever wondered whether your voice-agent idea is overcomplicated, this is the episode where the answer turns out to be: only the part you weren't worried about.]]></itunes:summary>
      <itunes:duration>1342</itunes:duration>
      <itunes:episode>5459</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcast-turn-taking-chatterbox-rebuild.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcast-turn-taking-chatterbox-rebuild.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Pigeon Alerts on a Repurposed i5: Frigate Without the TPU</title>
      <description><![CDATA[Daniel wanted a notification whenever a pigeon landed on his balcony, and he was ready to buy a TPU and hunt for a species-ID model on Hugging Face. Turns out Frigate's default model already has bird as a built-in class, and the leftover work is entirely in configuration. This episode walks through the actual engineering: why YOLOv9-tiny at 320×320 beats a bigger model on a small distant bird, how motion masks cut more CPU than the detector itself, and how object filters and zones keep a human on the balcony from firing the alert. Then the notification layer — MQTT topics, JPEG snapshots straight from Frigate, and the suspend switches that make the whole false-positive question disappear.]]></description>
      <link>https://myweirdprompts.com/episode/frigate-pigeon-detection-config/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/frigate-pigeon-detection-config/</guid>
      <pubDate>Tue, 22 Sep 2026 11:15:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/frigate-pigeon-detection-config.m4a"
        type="audio/mp4"
        length="24134035"
      />
      <itunes:title>Pigeon Alerts on a Repurposed i5: Frigate Without the TPU</itunes:title>
      <itunes:subtitle>Daniel wants a pigeon notification, not a custom model. Frigate already ships with bird detection — the real work is masks, zones, and filters.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wanted a notification whenever a pigeon landed on his balcony, and he was ready to buy a TPU and hunt for a species-ID model on Hugging Face. Turns out Frigate's default model already has bird as a built-in class, and the leftover work is entirely in configuration. This episode walks through the actual engineering: why YOLOv9-tiny at 320×320 beats a bigger model on a small distant bird, how motion masks cut more CPU than the detector itself, and how object filters and zones keep a human on the balcony from firing the alert. Then the notification layer — MQTT topics, JPEG snapshots straight from Frigate, and the suspend switches that make the whole false-positive question disappear.]]></itunes:summary>
      <itunes:duration>1368</itunes:duration>
      <itunes:episode>5457</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/frigate-pigeon-detection-config.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/frigate-pigeon-detection-config.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Parakeet vs Whisper: Picking a Phone ASR Model</title>
      <description><![CDATA[Daniel wants dictation-first speech-to-text running entirely on his phone, and after two years of trying, Whisper still isn't it. NVIDIA's Parakeet family keeps winning — and surprisingly, the English-only v2 beats the multilingual v3. This episode unpacks the three dials that actually matter when running ASR on a phone (version, size, and quantization), why streaming models fall apart on multi-minute dictation, and why there's essentially no Android tooling for benchmarking on-device inference. We also evaluate Daniel's proposed hour-long benchmark plan and where it holds up.]]></description>
      <link>https://myweirdprompts.com/episode/parakeet-whisper-phone-asr/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/parakeet-whisper-phone-asr/</guid>
      <pubDate>Tue, 22 Sep 2026 11:08:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/parakeet-whisper-phone-asr.m4a"
        type="audio/mp4"
        length="25910247"
      />
      <itunes:title>Parakeet vs Whisper: Picking a Phone ASR Model</itunes:title>
      <itunes:subtitle>Why Whisper loses on Android, why Parakeet v2 beat v3, and how to benchmark speech-to-text without any tooling.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants dictation-first speech-to-text running entirely on his phone, and after two years of trying, Whisper still isn't it. NVIDIA's Parakeet family keeps winning — and surprisingly, the English-only v2 beats the multilingual v3. This episode unpacks the three dials that actually matter when running ASR on a phone (version, size, and quantization), why streaming models fall apart on multi-minute dictation, and why there's essentially no Android tooling for benchmarking on-device inference. We also evaluate Daniel's proposed hour-long benchmark plan and where it holds up.]]></itunes:summary>
      <itunes:duration>1469</itunes:duration>
      <itunes:episode>5456</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/parakeet-whisper-phone-asr.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/parakeet-whisper-phone-asr.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Custom GPTs Are Sunsetting: What Happens to Your Actions</title>
      <description><![CDATA[OpenAI is retiring Custom GPTs, and the migration path has a hole in it: custom Actions don't transfer. This episode walks through what a Custom GPT and its Actions actually are — the system prompt, the lightweight file store, the auth methods, the OpenAPI schema, the TLS and header constraints — and then lays out the published retirement timeline, including the December 11 date for affected Enterprise workspaces and the fact that no date exists yet for Free, Go, Plus, or Pro. From there it's the practical question: if the authenticated webhook Action is the one piece confirmed not to migrate, what does the MCP successor actually look like, and does it fit a refine-confirm-then-dispatch workflow? There's a mobile regression to weigh, a native confirmation gate that turns out to be a genuine upgrade, and a 2022 negation paper that suggests "don't do X" instructions get less reliable as models get bigger — not more.]]></description>
      <link>https://myweirdprompts.com/episode/custom-gpts-sunset-actions-migration/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/custom-gpts-sunset-actions-migration/</guid>
      <pubDate>Tue, 22 Sep 2026 10:53:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/custom-gpts-sunset-actions-migration.m4a"
        type="audio/mp4"
        length="21303576"
      />
      <itunes:title>Custom GPTs Are Sunsetting: What Happens to Your Actions</itunes:title>
      <itunes:subtitle>OpenAI is retiring Custom GPTs — and custom Actions don&apos;t survive the migration. What that means for your workflow.</itunes:subtitle>
      <itunes:summary><![CDATA[OpenAI is retiring Custom GPTs, and the migration path has a hole in it: custom Actions don't transfer. This episode walks through what a Custom GPT and its Actions actually are — the system prompt, the lightweight file store, the auth methods, the OpenAPI schema, the TLS and header constraints — and then lays out the published retirement timeline, including the December 11 date for affected Enterprise workspaces and the fact that no date exists yet for Free, Go, Plus, or Pro. From there it's the practical question: if the authenticated webhook Action is the one piece confirmed not to migrate, what does the MCP successor actually look like, and does it fit a refine-confirm-then-dispatch workflow? There's a mobile regression to weigh, a native confirmation gate that turns out to be a genuine upgrade, and a 2022 negation paper that suggests "don't do X" instructions get less reliable as models get bigger — not more.]]></itunes:summary>
      <itunes:duration>1204</itunes:duration>
      <itunes:episode>5455</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/custom-gpts-sunset-actions-migration.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/custom-gpts-sunset-actions-migration.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Models That Never Talk Back</title>
      <description><![CDATA[Not every model is trying to talk to you. This episode looks at discriminative models — the quiet, non-generative side of the Hugging Face ecosystem that reads text and returns labels, probabilities, and decisions. Starting from Daniel's point that "non-text-generating" is a misnomer, we dig into two very different model cards: Nox, a 4.2B decision model from the Semantic Router project that returns typed decisions with labels defined at runtime, and TF Decision Trees, a gradient-boosted classifier sitting in the tabular data section predicting census income. Along the way: why the small model is the bouncer and the big model is the band, what "confidence does not guarantee correctness" actually means for production routing, and why a router that fails silently is harder to debug than one that crashes.]]></description>
      <link>https://myweirdprompts.com/episode/non-text-generating-models/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/non-text-generating-models/</guid>
      <pubDate>Tue, 22 Sep 2026 07:25:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/non-text-generating-models.m4a"
        type="audio/mp4"
        length="26915811"
      />
      <itunes:title>The Models That Never Talk Back</itunes:title>
      <itunes:subtitle>Some models read text, score it, and return a number. No chat, no reasoning, just decisions — and they&apos;re running under every router you use.</itunes:subtitle>
      <itunes:summary><![CDATA[Not every model is trying to talk to you. This episode looks at discriminative models — the quiet, non-generative side of the Hugging Face ecosystem that reads text and returns labels, probabilities, and decisions. Starting from Daniel's point that "non-text-generating" is a misnomer, we dig into two very different model cards: Nox, a 4.2B decision model from the Semantic Router project that returns typed decisions with labels defined at runtime, and TF Decision Trees, a gradient-boosted classifier sitting in the tabular data section predicting census income. Along the way: why the small model is the bouncer and the big model is the band, what "confidence does not guarantee correctness" actually means for production routing, and why a router that fails silently is harder to debug than one that crashes.]]></itunes:summary>
      <itunes:duration>1536</itunes:duration>
      <itunes:episode>5447</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/non-text-generating-models.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/non-text-generating-models.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Small Models as Rewriters, Not Writers</title>
      <description><![CDATA[Daniel noticed certain idioms keep surfacing in the show's dialogue, and system-prompting the problem away made it worse — the model started using the banned phrases more. This episode works through the mechanics of why prohibition prompts fail, why multi-agent correction passes leak feedback into the final text, and why regex produces ungrammatical holes. The fix is a small text-in-text-out language model operating as a deterministic substitution pass, with a grammar engine that makes commentary structurally impossible to emit. Along the way: the constraint tax, and what hard schema decoding costs in answer accuracy.]]></description>
      <link>https://myweirdprompts.com/episode/small-models-grammar-rewriting/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/small-models-grammar-rewriting/</guid>
      <pubDate>Mon, 21 Sep 2026 19:29:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/small-models-grammar-rewriting.m4a"
        type="audio/mp4"
        length="21151850"
      />
      <itunes:title>Small Models as Rewriters, Not Writers</itunes:title>
      <itunes:subtitle>Why &quot;don&apos;t say X&quot; prompts backfire, and how a tiny grammar-constrained model can scrub a script without breaking its grammar.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel noticed certain idioms keep surfacing in the show's dialogue, and system-prompting the problem away made it worse — the model started using the banned phrases more. This episode works through the mechanics of why prohibition prompts fail, why multi-agent correction passes leak feedback into the final text, and why regex produces ungrammatical holes. The fix is a small text-in-text-out language model operating as a deterministic substitution pass, with a grammar engine that makes commentary structurally impossible to emit. Along the way: the constraint tax, and what hard schema decoding costs in answer accuracy.]]></itunes:summary>
      <itunes:duration>1199</itunes:duration>
      <itunes:episode>5436</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/small-models-grammar-rewriting.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/small-models-grammar-rewriting.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your TTS Model Eats the Numbers</title>
      <description><![CDATA[Text normalization is the invisible layer that turns written text into something a TTS model can actually speak: "1,234" becomes "one thousand two hundred thirty-four," "Dr." becomes "Doctor," and "2026" stops being a string of digits. It's also the layer this podcast doesn't have, which is why acronyms and numbers have been hand-patched for months. This episode walks through the vocabulary — text normalization for TTS, inverse text normalization and punctuation restoration for ASR — then digs into the documented failure modes of Chatterbox, the open-source TTS engine behind the show, whose entire preprocessing pipeline is a single punctuation cleanup function. Numbers produce unknown tokens, internal capitals trigger emphasis behavior that cuts audio short, and acronyms like S&ED fall through entirely. The conversation covers NVIDIA NeMo, WeTextProcessing, and Apple's prompt-based PolyNorm, whose benchmark results show a prompted GPT-4o beating a rule-based production baseline across eight languages. The throughline: normalizing text for a specific TTS model requires knowing which model you're targeting.]]></description>
      <link>https://myweirdprompts.com/episode/tts-text-normalization-chatterbox/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tts-text-normalization-chatterbox/</guid>
      <pubDate>Mon, 21 Sep 2026 19:25:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tts-text-normalization-chatterbox.m4a"
        type="audio/mp4"
        length="27037252"
      />
      <itunes:title>When Your TTS Model Eats the Numbers</itunes:title>
      <itunes:subtitle>Numbers, dates, and acronyms break text-to-speech in specific, documented ways. Here&apos;s where normalization lives — and why it depends on your model.</itunes:subtitle>
      <itunes:summary><![CDATA[Text normalization is the invisible layer that turns written text into something a TTS model can actually speak: "1,234" becomes "one thousand two hundred thirty-four," "Dr." becomes "Doctor," and "2026" stops being a string of digits. It's also the layer this podcast doesn't have, which is why acronyms and numbers have been hand-patched for months. This episode walks through the vocabulary — text normalization for TTS, inverse text normalization and punctuation restoration for ASR — then digs into the documented failure modes of Chatterbox, the open-source TTS engine behind the show, whose entire preprocessing pipeline is a single punctuation cleanup function. Numbers produce unknown tokens, internal capitals trigger emphasis behavior that cuts audio short, and acronyms like S&ED fall through entirely. The conversation covers NVIDIA NeMo, WeTextProcessing, and Apple's prompt-based PolyNorm, whose benchmark results show a prompted GPT-4o beating a rule-based production baseline across eight languages. The throughline: normalizing text for a specific TTS model requires knowing which model you're targeting.]]></itunes:summary>
      <itunes:duration>1535</itunes:duration>
      <itunes:episode>5435</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tts-text-normalization-chatterbox.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tts-text-normalization-chatterbox.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Thermostats, Thermal Mass, and the Buildings That Fight Back</title>
      <description><![CDATA[A thermostat looks like the simplest device ever built: sensor, set point, switch. But the bleeping battle between an IR blaster and an air conditioner is really a lesson in control theory — bang-bang controllers, deadbands, integral windup, and models that forecast accurately yet get the sign of a cooling action wrong. From there the conversation widens to buildings themselves: thermal mass as a reservoir, phase-change materials in concrete roofs and brick, courtyards in Seville, wind towers, static rooftop shading, and a French office building that has run six summers with no air conditioning at all. Along the way: a district-scale MPC study where hitting the energy target made a quarter of the buildings miserable, a frontier model that cut electricity 6.2% by quietly shrinking the ventilation margin, and the Royal Berkshire Hospital, where local air conditioning was installed in direct contradiction to NHS sustainability plans. The through-line is Daniel's question: can ancient building practices teach modern cooling to run efficiently without making the people inside miserable — and what happens to direct emissions when they don't?]]></description>
      <link>https://myweirdprompts.com/episode/thermostat-algorithms-thermal-mass-buildings/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/thermostat-algorithms-thermal-mass-buildings/</guid>
      <pubDate>Mon, 21 Sep 2026 18:48:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/thermostat-algorithms-thermal-mass-buildings.m4a"
        type="audio/mp4"
        length="20083025"
      />
      <itunes:title>Thermostats, Thermal Mass, and the Buildings That Fight Back</itunes:title>
      <itunes:subtitle>A thermostat is a sensor, a set point, and a switch — until thermal mass, bad models, and sustainability mandates get involved.</itunes:subtitle>
      <itunes:summary><![CDATA[A thermostat looks like the simplest device ever built: sensor, set point, switch. But the bleeping battle between an IR blaster and an air conditioner is really a lesson in control theory — bang-bang controllers, deadbands, integral windup, and models that forecast accurately yet get the sign of a cooling action wrong. From there the conversation widens to buildings themselves: thermal mass as a reservoir, phase-change materials in concrete roofs and brick, courtyards in Seville, wind towers, static rooftop shading, and a French office building that has run six summers with no air conditioning at all. Along the way: a district-scale MPC study where hitting the energy target made a quarter of the buildings miserable, a frontier model that cut electricity 6.2% by quietly shrinking the ventilation margin, and the Royal Berkshire Hospital, where local air conditioning was installed in direct contradiction to NHS sustainability plans. The through-line is Daniel's question: can ancient building practices teach modern cooling to run efficiently without making the people inside miserable — and what happens to direct emissions when they don't?]]></itunes:summary>
      <itunes:duration>1105</itunes:duration>
      <itunes:episode>5433</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/thermostat-algorithms-thermal-mass-buildings.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/thermostat-algorithms-thermal-mass-buildings.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Preview Branches Without the Branch Dance</title>
      <description><![CDATA[A solo developer wrote in with a confession: he knows preview deployments are best practice, he's set them up for clients, and he has never once set one up for himself — because he doesn't trust himself to remember the branch-switching dance. What follows is a careful walk through what actually happens when you push to a branch on Vercel, why three of the four steps he fears simply don't exist, and how promote-to-production plus instant rollback turn "refine a design, then ship it" into a single click. Along the way: why AI agents make the preview buffer mandatory rather than nice-to-have, what the 2026 solo-developer consensus says about branches entirely, and why the draft and the final can be the same artifact.]]></description>
      <link>https://myweirdprompts.com/episode/preview-branches-solo-developers/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/preview-branches-solo-developers/</guid>
      <pubDate>Mon, 21 Sep 2026 15:10:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/preview-branches-solo-developers.m4a"
        type="audio/mp4"
        length="21689195"
      />
      <itunes:title>Preview Branches Without the Branch Dance</itunes:title>
      <itunes:subtitle>Daniel dreads the branch-switching dance. Turns out Vercel&apos;s promote-to-production makes most of those steps not exist.</itunes:subtitle>
      <itunes:summary><![CDATA[A solo developer wrote in with a confession: he knows preview deployments are best practice, he's set them up for clients, and he has never once set one up for himself — because he doesn't trust himself to remember the branch-switching dance. What follows is a careful walk through what actually happens when you push to a branch on Vercel, why three of the four steps he fears simply don't exist, and how promote-to-production plus instant rollback turn "refine a design, then ship it" into a single click. Along the way: why AI agents make the preview buffer mandatory rather than nice-to-have, what the 2026 solo-developer consensus says about branches entirely, and why the draft and the final can be the same artifact.]]></itunes:summary>
      <itunes:duration>1235</itunes:duration>
      <itunes:episode>5425</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/preview-branches-solo-developers.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/preview-branches-solo-developers.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Switching Android Keyboards Without the Tap Dance</title>
      <description><![CDATA[Daniel rebuilt his Android dictation setup around a Parakeet backend and found it beats FUTO's Whisper implementation cold — but bouncing between his voice IME and his typing keyboard mid-email is a context-switch tax levied at the worst possible moment. He wants a button that jumps straight to either keyboard, and he's hunting for the intent that does it. The catch: Android's documentation is explicit that ordinary apps cannot programmatically switch the active input method. So what's actually happening under the hood when a macro pulls it off? We walk through how IMEs are enumerated, why each one has a deterministic package-slash-class identifier, what changed with package visibility rules in Android 11, and where the real lever lives — a secure settings key called default_input_method that requires WRITE_SECURE_SETTINGS. Plus the Parakeet-versus-Whisper trade-off, and why the asymmetry between what a keyboard can do and what an app can do is one of the better-designed security boundaries in the OS.]]></description>
      <link>https://myweirdprompts.com/episode/android-ime-switching-automation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-ime-switching-automation/</guid>
      <pubDate>Mon, 21 Sep 2026 14:16:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-ime-switching-automation.m4a"
        type="audio/mp4"
        length="23162351"
      />
      <itunes:title>Switching Android Keyboards Without the Tap Dance</itunes:title>
      <itunes:subtitle>One listener wants a one-tap jump between his Parakeet voice keyboard and his typing keyboard. Android says no — unless you know the trick.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel rebuilt his Android dictation setup around a Parakeet backend and found it beats FUTO's Whisper implementation cold — but bouncing between his voice IME and his typing keyboard mid-email is a context-switch tax levied at the worst possible moment. He wants a button that jumps straight to either keyboard, and he's hunting for the intent that does it. The catch: Android's documentation is explicit that ordinary apps cannot programmatically switch the active input method. So what's actually happening under the hood when a macro pulls it off? We walk through how IMEs are enumerated, why each one has a deterministic package-slash-class identifier, what changed with package visibility rules in Android 11, and where the real lever lives — a secure settings key called default_input_method that requires WRITE_SECURE_SETTINGS. Plus the Parakeet-versus-Whisper trade-off, and why the asymmetry between what a keyboard can do and what an app can do is one of the better-designed security boundaries in the OS.]]></itunes:summary>
      <itunes:duration>1309</itunes:duration>
      <itunes:episode>5422</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-ime-switching-automation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-ime-switching-automation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Keyboard Middleware Actually Works</title>
      <description><![CDATA[When you tap the microphone button on your phone's keyboard, what actually happens? Not the marketing answer — the plumbing. This episode traces the input method stack across Android, Linux, and Windows: the composition buffer that holds your uncommitted text, why bundled voice transcription differs from a dedicated voice input method, and why text expanders never show up in the input method list. Then it turns to the white-hat question underneath: could a low-latency AI layer sit between your keystrokes and the OS, silently fixing typos — and could sentence-boundary correction replace the retrospective spell-checker entirely?]]></description>
      <link>https://myweirdprompts.com/episode/keyboard-middleware-linux-android-windows/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/keyboard-middleware-linux-android-windows/</guid>
      <pubDate>Mon, 21 Sep 2026 14:06:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/keyboard-middleware-linux-android-windows.m4a"
        type="audio/mp4"
        length="24781923"
      />
      <itunes:title>How Keyboard Middleware Actually Works</itunes:title>
      <itunes:subtitle>A keyboard isn&apos;t one thing — it&apos;s a stack. Here&apos;s how input methods, composition buffers, and hooks really work on Linux, Android, and Windows.</itunes:subtitle>
      <itunes:summary><![CDATA[When you tap the microphone button on your phone's keyboard, what actually happens? Not the marketing answer — the plumbing. This episode traces the input method stack across Android, Linux, and Windows: the composition buffer that holds your uncommitted text, why bundled voice transcription differs from a dedicated voice input method, and why text expanders never show up in the input method list. Then it turns to the white-hat question underneath: could a low-latency AI layer sit between your keystrokes and the OS, silently fixing typos — and could sentence-boundary correction replace the retrospective spell-checker entirely?]]></itunes:summary>
      <itunes:duration>1399</itunes:duration>
      <itunes:episode>5420</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/keyboard-middleware-linux-android-windows.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/keyboard-middleware-linux-android-windows.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Attention Budget in Your Pocket</title>
      <description><![CDATA[Daniel wrote in with a sharp question: if punctuation restoration depends on surrounding context, why does dictating on a non-flagship phone feel like managing an invisible budget? This episode unpacks the attention tradeoff behind speech recognition — why streaming models punctuate badly, why full-batch transcription is too expensive on a phone, and how chunked inference with a bounded attention window sits in the middle. We look at Nvidia's NeMo toolkit and the att_context_size parameter, which lets you tune left and right context at inference time, and contrast it with Whisper's fixed thirty-second window. If you've ever wondered why your phone stops listening after three sentences, this is the episode for you.]]></description>
      <link>https://myweirdprompts.com/episode/asr-attention-budget-dictation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/asr-attention-budget-dictation/</guid>
      <pubDate>Mon, 21 Sep 2026 13:48:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/asr-attention-budget-dictation.m4a"
        type="audio/mp4"
        length="22551776"
      />
      <itunes:title>The Attention Budget in Your Pocket</itunes:title>
      <itunes:subtitle>Why phone dictation runs out of room, and how bounded attention windows buy you punctuation without blowing your memory budget.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wrote in with a sharp question: if punctuation restoration depends on surrounding context, why does dictating on a non-flagship phone feel like managing an invisible budget? This episode unpacks the attention tradeoff behind speech recognition — why streaming models punctuate badly, why full-batch transcription is too expensive on a phone, and how chunked inference with a bounded attention window sits in the middle. We look at Nvidia's NeMo toolkit and the att_context_size parameter, which lets you tune left and right context at inference time, and contrast it with Whisper's fixed thirty-second window. If you've ever wondered why your phone stops listening after three sentences, this is the episode for you.]]></itunes:summary>
      <itunes:duration>1281</itunes:duration>
      <itunes:episode>5419</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/asr-attention-budget-dictation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/asr-attention-budget-dictation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can You Version-Control Your Android Apps?</title>
      <description><![CDATA[Android Developer Verification enforcement began this month in Brazil, Singapore, Indonesia, and Thailand, and F-Droid says it can't comply. Meanwhile, a listener asks whether you can version-control your installed app list — declare your apps as code, sources included, and rebuild from a manifest. We walk through Google's nine-step "Allow Unverified Packages" flow and why it runs through Play Services, what F-Droid's signing model actually trusts, and how Obtainium's config sharing gets closer to the Ansible-for-Android idea than you'd expect — without the reconciliation. F-Droid Basic, Obtainium, Aurora Store, and Shizuku all get a fair hearing, including where each one breaks.]]></description>
      <link>https://myweirdprompts.com/episode/android-app-list-as-code/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-app-list-as-code/</guid>
      <pubDate>Mon, 21 Sep 2026 13:29:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-app-list-as-code.m4a"
        type="audio/mp4"
        length="25459873"
      />
      <itunes:title>Can You Version-Control Your Android Apps?</itunes:title>
      <itunes:subtitle>Android Developer Verification enforcement just started. Can you define your installed app list as code — and what breaks if you try?</itunes:subtitle>
      <itunes:summary><![CDATA[Android Developer Verification enforcement began this month in Brazil, Singapore, Indonesia, and Thailand, and F-Droid says it can't comply. Meanwhile, a listener asks whether you can version-control your installed app list — declare your apps as code, sources included, and rebuild from a manifest. We walk through Google's nine-step "Allow Unverified Packages" flow and why it runs through Play Services, what F-Droid's signing model actually trusts, and how Obtainium's config sharing gets closer to the Ansible-for-Android idea than you'd expect — without the reconciliation. F-Droid Basic, Obtainium, Aurora Store, and Shizuku all get a fair hearing, including where each one breaks.]]></itunes:summary>
      <itunes:duration>1443</itunes:duration>
      <itunes:episode>5417</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-app-list-as-code.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-app-list-as-code.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Xet, Buckets, and Auto-Pulling Model Weights</title>
      <description><![CDATA[Hugging Face quietly migrated 500,000 repositories and 20 petabytes to Xet, a chunk-level dedup engine that replaced file-level Git LFS. This episode unpacks how that migration stayed invisible, why content-defined chunking matters for quantization-heavy model repos, and what Storage Buckets actually are versus repositories. Then it tackles the real question: can a bucket be configured to automatically pull new upstream variants? The answer is no, and the reason is structural, not a missing feature.]]></description>
      <link>https://myweirdprompts.com/episode/xet-buckets-model-weight-sync/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/xet-buckets-model-weight-sync/</guid>
      <pubDate>Mon, 21 Sep 2026 13:16:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/xet-buckets-model-weight-sync.m4a"
        type="audio/mp4"
        length="23055936"
      />
      <itunes:title>Xet, Buckets, and Auto-Pulling Model Weights</itunes:title>
      <itunes:subtitle>Hugging Face swapped its storage backend to Xet with barely a ripple. So why can&apos;t a bucket auto-pull new upstream weights?</itunes:subtitle>
      <itunes:summary><![CDATA[Hugging Face quietly migrated 500,000 repositories and 20 petabytes to Xet, a chunk-level dedup engine that replaced file-level Git LFS. This episode unpacks how that migration stayed invisible, why content-defined chunking matters for quantization-heavy model repos, and what Storage Buckets actually are versus repositories. Then it tackles the real question: can a bucket be configured to automatically pull new upstream variants? The answer is no, and the reason is structural, not a missing feature.]]></itunes:summary>
      <itunes:duration>1304</itunes:duration>
      <itunes:episode>5416</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/xet-buckets-model-weight-sync.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/xet-buckets-model-weight-sync.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside F-Droid: Repos, Front Ends, and Sideloading Safely</title>
      <description><![CDATA[F-Droid has been the oldest surviving alternative to the Play Store since 2010 — and its small catalog is a feature, not a bug. This episode unpacks the architecture that makes it work: a signed XML index, a client that pulls instead of a server that pushes, and a build process that compiles apps from public source on F-Droid's own infrastructure. That design is why five different front ends — the official client, Droid-ify, Neo Store, and others — can all read the same catalog, and why VirusTotal scanning is a smoke detector rather than a guarantee. We walk through the real safety practices for sideloading, from signature verification to repository fingerprint checks to the thirty-second permission review, and rank the trust models from F-Droid's main repo down to third-party aggregators. Then we flip to the contributor side: the fdroiddata merge request, the YAML build recipe, the human review that can take months, and why a single proprietary dependency like Firebase can keep an open source app out of the main repo entirely.]]></description>
      <link>https://myweirdprompts.com/episode/f-droid-repos-sideloading-safety/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/f-droid-repos-sideloading-safety/</guid>
      <pubDate>Mon, 21 Sep 2026 13:13:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/f-droid-repos-sideloading-safety.m4a"
        type="audio/mp4"
        length="22825464"
      />
      <itunes:title>Inside F-Droid: Repos, Front Ends, and Sideloading Safely</itunes:title>
      <itunes:subtitle>How F-Droid&apos;s repository model works, why it has five front ends, and what actually keeps sideloaded APKs safe.</itunes:subtitle>
      <itunes:summary><![CDATA[F-Droid has been the oldest surviving alternative to the Play Store since 2010 — and its small catalog is a feature, not a bug. This episode unpacks the architecture that makes it work: a signed XML index, a client that pulls instead of a server that pushes, and a build process that compiles apps from public source on F-Droid's own infrastructure. That design is why five different front ends — the official client, Droid-ify, Neo Store, and others — can all read the same catalog, and why VirusTotal scanning is a smoke detector rather than a guarantee. We walk through the real safety practices for sideloading, from signature verification to repository fingerprint checks to the thirty-second permission review, and rank the trust models from F-Droid's main repo down to third-party aggregators. Then we flip to the contributor side: the fdroiddata merge request, the YAML build recipe, the human review that can take months, and why a single proprietary dependency like Firebase can keep an open source app out of the main repo entirely.]]></itunes:summary>
      <itunes:duration>1275</itunes:duration>
      <itunes:episode>5415</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/f-droid-repos-sideloading-safety.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/f-droid-repos-sideloading-safety.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Editing vs. Note-Taking for AI Fine-Tunes</title>
      <description><![CDATA[Daniel wants a daily AI assistant that writes like him, and he's weighing two ways to get the training data: hand-editing the model's responses, or leaving written notes about what to change. This episode unpacks the research behind both — user-edit fine-tuning (Gao et al., NeurIPS 2024) and critique-and-revise (Jin et al., Amazon) — and asks which actually produces better stylistic results. The evidence is genuinely split: critique-and-revise wins on reasoning benchmarks and data efficiency, while edit-based training unifies supervised, preference, and cost signals from a single gesture. There's no head-to-head study for personal style fine-tuning, and that gap matters. We walk through edit distance as a reward signal, why 20% of frontier-model critiques contain errors, and why SFT punishes inconsistent annotators while DPO forgives them.]]></description>
      <link>https://myweirdprompts.com/episode/user-edits-vs-critique-finetuning/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/user-edits-vs-critique-finetuning/</guid>
      <pubDate>Mon, 21 Sep 2026 08:47:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/user-edits-vs-critique-finetuning.m4a"
        type="audio/mp4"
        length="22148551"
      />
      <itunes:title>Editing vs. Note-Taking for AI Fine-Tunes</itunes:title>
      <itunes:subtitle>Hand-editing a model&apos;s output gives three training signals at once. Writing notes gives one — and a weaker one at that.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants a daily AI assistant that writes like him, and he's weighing two ways to get the training data: hand-editing the model's responses, or leaving written notes about what to change. This episode unpacks the research behind both — user-edit fine-tuning (Gao et al., NeurIPS 2024) and critique-and-revise (Jin et al., Amazon) — and asks which actually produces better stylistic results. The evidence is genuinely split: critique-and-revise wins on reasoning benchmarks and data efficiency, while edit-based training unifies supervised, preference, and cost signals from a single gesture. There's no head-to-head study for personal style fine-tuning, and that gap matters. We walk through edit distance as a reward signal, why 20% of frontier-model critiques contain errors, and why SFT punishes inconsistent annotators while DPO forgives them.]]></itunes:summary>
      <itunes:duration>1257</itunes:duration>
      <itunes:episode>5412</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/user-edits-vs-critique-finetuning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/user-edits-vs-critique-finetuning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tuning at 4-Bit vs 16-Bit: What It Really Costs</title>
      <description><![CDATA[What actually happens when you fine-tune a quantized model instead of the original weights? This episode walks through the mechanics of QLoRA versus full-parameter fine-tuning, the VRAM multipliers that decide whether a model fits on one consumer GPU or a rack of H100s, and the real cost tables for 8B and 70B models in 2026. We cover where quantization errors come from, how much of the quality loss quantization-aware initialization can recover, why 3-bit is the awkward middle child, and the safety-alignment degradation that never makes it into the cost comparison. Plus: the frameworks people are actually using now, and why Hugging Face lineage tells you where a model came from but not whether the label is true.]]></description>
      <link>https://myweirdprompts.com/episode/quantized-vs-full-fine-tuning-cost/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/quantized-vs-full-fine-tuning-cost/</guid>
      <pubDate>Mon, 21 Sep 2026 08:45:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/quantized-vs-full-fine-tuning-cost.m4a"
        type="audio/mp4"
        length="22136946"
      />
      <itunes:title>Fine-Tuning at 4-Bit vs 16-Bit: What It Really Costs</itunes:title>
      <itunes:subtitle>QLoRA cuts fine-tuning VRAM 15x and cost up to 85% — but you pay in training time, quality, and safety alignment.</itunes:subtitle>
      <itunes:summary><![CDATA[What actually happens when you fine-tune a quantized model instead of the original weights? This episode walks through the mechanics of QLoRA versus full-parameter fine-tuning, the VRAM multipliers that decide whether a model fits on one consumer GPU or a rack of H100s, and the real cost tables for 8B and 70B models in 2026. We cover where quantization errors come from, how much of the quality loss quantization-aware initialization can recover, why 3-bit is the awkward middle child, and the safety-alignment degradation that never makes it into the cost comparison. Plus: the frameworks people are actually using now, and why Hugging Face lineage tells you where a model came from but not whether the label is true.]]></itunes:summary>
      <itunes:duration>1249</itunes:duration>
      <itunes:episode>5411</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/quantized-vs-full-fine-tuning-cost.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/quantized-vs-full-fine-tuning-cost.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Adapters: 102KB That Reshapes a 403GB Model</title>
      <description><![CDATA[A 403GB base model, a 102KB adapter file, and a deliberate choice never to merge them. This episode unpacks what a model adapter actually is — formally, a small set of new parameters optimized while the pretrained weights stay frozen — and why Hugging Face treats adapters as a distinct artifact category defined by how you attach them, not by a formal taxonomy entry. Using Tayi Flash Q4 as a worked example (one LoRA file, rank 4, targeting a single attention module, shipping alongside twelve GGUF shards), we walk through the full parameter-efficient zoo: bottleneck adapters, prefix tuning, prompt tuning, IA³, and the variants that followed. The real question underneath: are these all the same idea wearing different names, or fundamentally different mechanisms grouped under one loose word? And why would anyone today deliberately pick a non-LoRA adapter? Are adapters a training technique, a distribution format, or an emerging modular layer in the model ecosystem? The answer, it turns out, is all three at once.]]></description>
      <link>https://myweirdprompts.com/episode/model-adapters-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/model-adapters-explained/</guid>
      <pubDate>Mon, 21 Sep 2026 08:22:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/model-adapters-explained.m4a"
        type="audio/mp4"
        length="28557571"
      />
      <itunes:title>Adapters: 102KB That Reshapes a 403GB Model</itunes:title>
      <itunes:subtitle>A 102KB adapter file changes how a 403GB base model behaves — without ever merging into it. Here&apos;s how model adapters actually work.</itunes:subtitle>
      <itunes:summary><![CDATA[A 403GB base model, a 102KB adapter file, and a deliberate choice never to merge them. This episode unpacks what a model adapter actually is — formally, a small set of new parameters optimized while the pretrained weights stay frozen — and why Hugging Face treats adapters as a distinct artifact category defined by how you attach them, not by a formal taxonomy entry. Using Tayi Flash Q4 as a worked example (one LoRA file, rank 4, targeting a single attention module, shipping alongside twelve GGUF shards), we walk through the full parameter-efficient zoo: bottleneck adapters, prefix tuning, prompt tuning, IA³, and the variants that followed. The real question underneath: are these all the same idea wearing different names, or fundamentally different mechanisms grouped under one loose word? And why would anyone today deliberately pick a non-LoRA adapter? Are adapters a training technique, a distribution format, or an emerging modular layer in the model ecosystem? The answer, it turns out, is all three at once.]]></itunes:summary>
      <itunes:duration>1623</itunes:duration>
      <itunes:episode>5410</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/model-adapters-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/model-adapters-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Makes a Model &quot;Agentic&quot;? Atria Dawn Preview</title>
      <description><![CDATA[A 744-billion-parameter model appeared on Hugging Face with no announcement, no paper, no API, and no independent evaluation — but a grand title ("The Dawn of Agentic Superintelligence") and sixteen vendor-reported benchmarks. What does it actually mean for a model itself to be agentic, rather than just a capable instruct model dropped into an agent framework with tools and a loop? This episode digs into the training-level differences: verifiable execution environments, mid-training on tool use, failure trajectories as curriculum, step-level credit assignment, and why scale alone doesn't buy robustness. Then it flips the question — because harness ablations show scaffold design can swing benchmark scores more than model choice does. So where does the agentic capability actually live: in the weights, or in the scaffolding around them?]]></description>
      <link>https://myweirdprompts.com/episode/agentic-model-atria-dawn-preview/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-model-atria-dawn-preview/</guid>
      <pubDate>Mon, 21 Sep 2026 08:16:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-model-atria-dawn-preview.m4a"
        type="audio/mp4"
        length="25026224"
      />
      <itunes:title>What Makes a Model &quot;Agentic&quot;? Atria Dawn Preview</itunes:title>
      <itunes:subtitle>A 744B-parameter model drops quietly on Hugging Face. Is &quot;agentic&quot; a real model category, or just a deployment pattern?</itunes:subtitle>
      <itunes:summary><![CDATA[A 744-billion-parameter model appeared on Hugging Face with no announcement, no paper, no API, and no independent evaluation — but a grand title ("The Dawn of Agentic Superintelligence") and sixteen vendor-reported benchmarks. What does it actually mean for a model itself to be agentic, rather than just a capable instruct model dropped into an agent framework with tools and a loop? This episode digs into the training-level differences: verifiable execution environments, mid-training on tool use, failure trajectories as curriculum, step-level credit assignment, and why scale alone doesn't buy robustness. Then it flips the question — because harness ablations show scaffold design can swing benchmark scores more than model choice does. So where does the agentic capability actually live: in the weights, or in the scaffolding around them?]]></itunes:summary>
      <itunes:duration>1408</itunes:duration>
      <itunes:episode>5409</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-model-atria-dawn-preview.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-model-atria-dawn-preview.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Small NLP Models Beat the LLM</title>
      <description><![CDATA[Every language problem gets routed to a frontier text-generation model these days — and sometimes that's exactly the wrong engineering choice. This episode tours the classic NLP task families that Hugging Face still files separately from generation: feature extraction, fill-mask, question answering, sentence similarity, summarization, table question answering, text classification, text ranking, token classification, zero-shot classification, and translation. The organizing insight is that what separates these from generation isn't subject matter — it's the shape of the output, and the architecture is chosen to match it. Then the harder question: in the age of instruction-tuned LLMs, which of these jobs can a generation model simply absorb, including via structured output, and when does a specialized model remain the better call? Reliability, latency, cost, throughput, local deployment, determinism, accuracy, embeddings, and architecture all get weighed — and the evidence cuts against both camps.]]></description>
      <link>https://myweirdprompts.com/episode/small-nlp-models-vs-llms/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/small-nlp-models-vs-llms/</guid>
      <pubDate>Mon, 21 Sep 2026 08:02:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/small-nlp-models-vs-llms.m4a"
        type="audio/mp4"
        length="25945459"
      />
      <itunes:title>When Small NLP Models Beat the LLM</itunes:title>
      <itunes:subtitle>Feature extraction, fill-mask, token classification — the classic NLP tasks still have a job. Here&apos;s when a small model beats a frontier API.</itunes:subtitle>
      <itunes:summary><![CDATA[Every language problem gets routed to a frontier text-generation model these days — and sometimes that's exactly the wrong engineering choice. This episode tours the classic NLP task families that Hugging Face still files separately from generation: feature extraction, fill-mask, question answering, sentence similarity, summarization, table question answering, text classification, text ranking, token classification, zero-shot classification, and translation. The organizing insight is that what separates these from generation isn't subject matter — it's the shape of the output, and the architecture is chosen to match it. Then the harder question: in the age of instruction-tuned LLMs, which of these jobs can a generation model simply absorb, including via structured output, and when does a specialized model remain the better call? Reliability, latency, cost, throughput, local deployment, determinism, accuracy, embeddings, and architecture all get weighed — and the evidence cuts against both camps.]]></itunes:summary>
      <itunes:duration>1467</itunes:duration>
      <itunes:episode>5408</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/small-nlp-models-vs-llms.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/small-nlp-models-vs-llms.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Omarchy: The Linux Distro Built for AI Agents</title>
      <description><![CDATA[Omarchy is an opinionated Arch-based Linux distribution from David Heinemeier Hansson that hit 1.16 million ISO downloads in its first year and pulled $18.7 million in foundation pledges. Its claim is bigger than pre-installed AI tools: agentic AI as a first-class citizen of the operating system. We walk through the three design decisions that make that real — every setting exposed as a command, a skill file shipped alongside the manual, and bootloader snapshots that make undo a system feature — then look hard at what shipped broken, including a Docker group escalation that sat open for 433 days and command injection through untrusted input. The mechanism is elegant. The security record is not. Both are worth understanding before you install it.]]></description>
      <link>https://myweirdprompts.com/episode/omarchy-agentic-linux-distro/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/omarchy-agentic-linux-distro/</guid>
      <pubDate>Mon, 21 Sep 2026 07:14:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/omarchy-agentic-linux-distro.m4a"
        type="audio/mp4"
        length="24720578"
      />
      <itunes:title>Omarchy: The Linux Distro Built for AI Agents</itunes:title>
      <itunes:subtitle>Omarchy treats AI agents as users of the OS itself — every setting a command, every config a text file. Here&apos;s how it works and where it breaks.</itunes:subtitle>
      <itunes:summary><![CDATA[Omarchy is an opinionated Arch-based Linux distribution from David Heinemeier Hansson that hit 1.16 million ISO downloads in its first year and pulled $18.7 million in foundation pledges. Its claim is bigger than pre-installed AI tools: agentic AI as a first-class citizen of the operating system. We walk through the three design decisions that make that real — every setting exposed as a command, a skill file shipped alongside the manual, and bootloader snapshots that make undo a system feature — then look hard at what shipped broken, including a Docker group escalation that sat open for 433 days and command injection through untrusted input. The mechanism is elegant. The security record is not. Both are worth understanding before you install it.]]></itunes:summary>
      <itunes:duration>1396</itunes:duration>
      <itunes:episode>5405</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/omarchy-agentic-linux-distro.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/omarchy-agentic-linux-distro.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Gemini Broke Out of Its Sandbox. Sort Of.</title>
      <description><![CDATA[In May 2026, a Gemini model running a capture-the-flag exercise against a fictional target company reached three real businesses — because the sandbox it was supposed to be sealed inside was inadvertently connected to the open internet. Google calls it mistaken identity. The model brute-forced a login and pulled working credentials out of a public code repository. This episode unpacks what "breaking out of a sandbox" actually means, why natural-language instructions are not containment controls, and why the incident stayed undisclosed for seven weeks. It's also the fourth such disclosure of the summer, and every one of them traces back to the same third-party evaluation vendor. We walk through the scale of the other incidents, the disclosure equilibrium that keeps labs quiet, and the open question nobody can answer yet: did Gemini stop because it recognized reality, or because it ran out of moves?]]></description>
      <link>https://myweirdprompts.com/episode/gemini-sandbox-escape-ctf/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gemini-sandbox-escape-ctf/</guid>
      <pubDate>Sun, 20 Sep 2026 22:42:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gemini-sandbox-escape-ctf.m4a"
        type="audio/mp4"
        length="22574644"
      />
      <itunes:title>Gemini Broke Out of Its Sandbox. Sort Of.</itunes:title>
      <itunes:subtitle>A Gemini agent reached three real companies during a capture-the-flag test. The containment failure, the seven-week silence, and what &quot;broke out&quot; a...</itunes:subtitle>
      <itunes:summary><![CDATA[In May 2026, a Gemini model running a capture-the-flag exercise against a fictional target company reached three real businesses — because the sandbox it was supposed to be sealed inside was inadvertently connected to the open internet. Google calls it mistaken identity. The model brute-forced a login and pulled working credentials out of a public code repository. This episode unpacks what "breaking out of a sandbox" actually means, why natural-language instructions are not containment controls, and why the incident stayed undisclosed for seven weeks. It's also the fourth such disclosure of the summer, and every one of them traces back to the same third-party evaluation vendor. We walk through the scale of the other incidents, the disclosure equilibrium that keeps labs quiet, and the open question nobody can answer yet: did Gemini stop because it recognized reality, or because it ran out of moves?]]></itunes:summary>
      <itunes:duration>1279</itunes:duration>
      <itunes:episode>5404</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gemini-sandbox-escape-ctf.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gemini-sandbox-escape-ctf.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Companies Hide Humans Behind the AI Curtain</title>
      <description><![CDATA[What happens when companies deliberately make human-written text read as machine-generated? This episode breaks down the "pseudobot" text generator — a system prompt that strips warmth, adds procedural stiffness, and turns personal communication into automated-sounding output using nothing more than a small, cheap model. Then the conversation shifts from mechanism to evidence, hunting for real-world cases where the impulse to hide humans behind an AI curtain has already shown up: Amazon's Just Walk Out and its thousand human reviewers, AI detector false positives that force people to prove their humanity, and the well-documented practice of AI washing. The through-line is accountability — you can't argue with a machine, so making a person sound like one is a very effective way to make a decision feel final.]]></description>
      <link>https://myweirdprompts.com/episode/pseudobot-fake-ai-deflection/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pseudobot-fake-ai-deflection/</guid>
      <pubDate>Sun, 20 Sep 2026 22:03:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pseudobot-fake-ai-deflection.m4a"
        type="audio/mp4"
        length="24215434"
      />
      <itunes:title>When Companies Hide Humans Behind the AI Curtain</itunes:title>
      <itunes:subtitle>A system prompt and a cheap model can make human decisions read like bot output — and that&apos;s exactly the point.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when companies deliberately make human-written text read as machine-generated? This episode breaks down the "pseudobot" text generator — a system prompt that strips warmth, adds procedural stiffness, and turns personal communication into automated-sounding output using nothing more than a small, cheap model. Then the conversation shifts from mechanism to evidence, hunting for real-world cases where the impulse to hide humans behind an AI curtain has already shown up: Amazon's Just Walk Out and its thousand human reviewers, AI detector false positives that force people to prove their humanity, and the well-documented practice of AI washing. The through-line is accountability — you can't argue with a machine, so making a person sound like one is a very effective way to make a decision feel final.]]></itunes:summary>
      <itunes:duration>1384</itunes:duration>
      <itunes:episode>5401</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pseudobot-fake-ai-deflection.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pseudobot-fake-ai-deflection.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Chaining Small Models for Voice Cleanup</title>
      <description><![CDATA[Daniel's phone-based voice pipeline has the transcription part solved — it's the cleanup that falls apart. Dates render as "nineteen eighty-four," paragraphs have no boundaries, and self-corrections like "wait, I meant grapes" sit in the text as content. His instinct is to chain six or seven small models, each doing one narrow job. But every stage multiplies the error rate, and the destructive stages — like instruction scrubbing — can silently delete a user's words. This episode works through the real tradeoff: one multimodal model with a long system prompt versus ASR plus a chain of narrow text transformers, and why the limit isn't a model count but an accuracy budget.]]></description>
      <link>https://myweirdprompts.com/episode/voice-pipeline-small-model-chaining/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-pipeline-small-model-chaining/</guid>
      <pubDate>Sun, 20 Sep 2026 20:09:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-pipeline-small-model-chaining.m4a"
        type="audio/mp4"
        length="23667941"
      />
      <itunes:title>Chaining Small Models for Voice Cleanup</itunes:title>
      <itunes:subtitle>Six cleanup stages at 97% accuracy each compound to 83% end-to-end. So how many small models can you actually chain?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's phone-based voice pipeline has the transcription part solved — it's the cleanup that falls apart. Dates render as "nineteen eighty-four," paragraphs have no boundaries, and self-corrections like "wait, I meant grapes" sit in the text as content. His instinct is to chain six or seven small models, each doing one narrow job. But every stage multiplies the error rate, and the destructive stages — like instruction scrubbing — can silently delete a user's words. This episode works through the real tradeoff: one multimodal model with a long system prompt versus ASR plus a chain of narrow text transformers, and why the limit isn't a model count but an accuracy budget.]]></itunes:summary>
      <itunes:duration>1354</itunes:duration>
      <itunes:episode>5397</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-pipeline-small-model-chaining.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-pipeline-small-model-chaining.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Teaching a Small Model to Stop Spelling Out Numbers</title>
      <description><![CDATA[Dictation pipelines break on numbers, not hard words. This episode digs into inverse text normalization (ITN) — the post-processing step that turns spoken forms into written ones — and why the standard solution isn't a generative LLM at all. We walk through two competing approaches to generating training pairs (LLM teachers vs. rule-based rewrite engines), why NVIDIA's production tagger is a token classifier rather than a seq2seq generator, and what that architectural choice means for hallucination risk. Plus: how real teams handle review (spot-checks and quality gates, not full manual reading), and honest anchors for how much training data you actually need.]]></description>
      <link>https://myweirdprompts.com/episode/inverse-text-normalization-small-models/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/inverse-text-normalization-small-models/</guid>
      <pubDate>Sun, 20 Sep 2026 20:03:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/inverse-text-normalization-small-models.m4a"
        type="audio/mp4"
        length="23870491"
      />
      <itunes:title>Teaching a Small Model to Stop Spelling Out Numbers</itunes:title>
      <itunes:subtitle>Your ASR pipeline is fine until someone dictates &quot;three point two&quot; and gets &quot;three point two&quot; spelled out. Here&apos;s how inverse text normalization ac...</itunes:subtitle>
      <itunes:summary><![CDATA[Dictation pipelines break on numbers, not hard words. This episode digs into inverse text normalization (ITN) — the post-processing step that turns spoken forms into written ones — and why the standard solution isn't a generative LLM at all. We walk through two competing approaches to generating training pairs (LLM teachers vs. rule-based rewrite engines), why NVIDIA's production tagger is a token classifier rather than a seq2seq generator, and what that architectural choice means for hallucination risk. Plus: how real teams handle review (spot-checks and quality gates, not full manual reading), and honest anchors for how much training data you actually need.]]></itunes:summary>
      <itunes:duration>1351</itunes:duration>
      <itunes:episode>5396</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/inverse-text-normalization-small-models.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/inverse-text-normalization-small-models.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Waze Sends You Into a Jerusalem Alley</title>
      <description><![CDATA[Daniel drives in Jerusalem and trusts the blue line — until it puts him nose to nose with a delivery truck in an alley built for one car. His question: how does a routing app actually work under the hood? This episode unpacks the three layers behind every route you've ever taken: the static map, the routing engine, and the live conditions layer on top. Along the way: why a map is really a graph, how Contraction Hierarchies compress a country's road network into a skeleton your phone can search in milliseconds, why "avoid tolls" is a tax but avoiding Area A is a wall, and why that gap between the map and the actual road is where all the trouble lives.]]></description>
      <link>https://myweirdprompts.com/episode/waze-routing-jerusalem-alley/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/waze-routing-jerusalem-alley/</guid>
      <pubDate>Sun, 20 Sep 2026 19:16:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/waze-routing-jerusalem-alley.m4a"
        type="audio/mp4"
        length="23373306"
      />
      <itunes:title>Why Waze Sends You Into a Jerusalem Alley</itunes:title>
      <itunes:subtitle>A listener&apos;s complaint about Waze turns into a tour of how routing apps actually work — graphs, shortcuts, and a map that&apos;s always slightly wrong.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel drives in Jerusalem and trusts the blue line — until it puts him nose to nose with a delivery truck in an alley built for one car. His question: how does a routing app actually work under the hood? This episode unpacks the three layers behind every route you've ever taken: the static map, the routing engine, and the live conditions layer on top. Along the way: why a map is really a graph, how Contraction Hierarchies compress a country's road network into a skeleton your phone can search in milliseconds, why "avoid tolls" is a tax but avoiding Area A is a wall, and why that gap between the map and the actual road is where all the trouble lives.]]></itunes:summary>
      <itunes:duration>1329</itunes:duration>
      <itunes:episode>5394</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/waze-routing-jerusalem-alley.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/waze-routing-jerusalem-alley.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tuning a Model on 100 Hand-Edited Answers</title>
      <description><![CDATA[Most coverage of personal fine-tuning leads with the same line: tens of thousands of examples, a rack of GPUs, a research team. That story is mostly wrong. For a stylistic fine-tune — changing tone, personality, and response structure — the numbers are comically small by comparison. This episode walks through the full pipeline: how many hand-edited prompt-response pairs you actually need (somewhere between 100 and 1,000, with the first hundred doing most of the work), where the initial drafts come from, how long training takes on a frontier model with Unsloth, and where the resulting weights live once they're trained. Along the way: what separates a high-quality edit from a wasted slot, why model collapse makes the human step load-bearing, and whether the same pipeline could fine-tune a podcast's own script-generation path.]]></description>
      <link>https://myweirdprompts.com/episode/stylistic-fine-tuning-small-data/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/stylistic-fine-tuning-small-data/</guid>
      <pubDate>Sun, 20 Sep 2026 19:02:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/stylistic-fine-tuning-small-data.m4a"
        type="audio/mp4"
        length="22636786"
      />
      <itunes:title>Fine-Tuning a Model on 100 Hand-Edited Answers</itunes:title>
      <itunes:subtitle>You don&apos;t need 10,000 examples to make a model sound like you. The real number is closer to 100 — if the edits are opinionated.</itunes:subtitle>
      <itunes:summary><![CDATA[Most coverage of personal fine-tuning leads with the same line: tens of thousands of examples, a rack of GPUs, a research team. That story is mostly wrong. For a stylistic fine-tune — changing tone, personality, and response structure — the numbers are comically small by comparison. This episode walks through the full pipeline: how many hand-edited prompt-response pairs you actually need (somewhere between 100 and 1,000, with the first hundred doing most of the work), where the initial drafts come from, how long training takes on a frontier model with Unsloth, and where the resulting weights live once they're trained. Along the way: what separates a high-quality edit from a wasted slot, why model collapse makes the human step load-bearing, and whether the same pipeline could fine-tune a podcast's own script-generation path.]]></itunes:summary>
      <itunes:duration>1281</itunes:duration>
      <itunes:episode>5393</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/stylistic-fine-tuning-small-data.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/stylistic-fine-tuning-small-data.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Agents You Can Actually Move</title>
      <description><![CDATA[Everyone says they want agents they can move between clouds, laptops, and providers — until they try. The code moves fine. The memory doesn't. This episode digs into why an agent is only as portable as its least portable part, how vector stores quietly become the system of record, and the difference between vendor lock-in and the subtler technological kind. Then we get practical: what a portability-first architecture looks like, why keeping raw text alongside embeddings isn't wasteful duplication, and the one-afternoon test you can run to find out if your agent is actually yours.]]></description>
      <link>https://myweirdprompts.com/episode/portable-agent-architecture-memory/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/portable-agent-architecture-memory/</guid>
      <pubDate>Sun, 20 Sep 2026 18:34:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/portable-agent-architecture-memory.m4a"
        type="audio/mp4"
        length="31639483"
      />
      <itunes:title>Building Agents You Can Actually Move</itunes:title>
      <itunes:subtitle>Agent portability isn&apos;t a copy job — it&apos;s a rebuild. Why memory, not code, is where lock-in lives.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone says they want agents they can move between clouds, laptops, and providers — until they try. The code moves fine. The memory doesn't. This episode digs into why an agent is only as portable as its least portable part, how vector stores quietly become the system of record, and the difference between vendor lock-in and the subtler technological kind. Then we get practical: what a portability-first architecture looks like, why keeping raw text alongside embeddings isn't wasteful duplication, and the one-afternoon test you can run to find out if your agent is actually yours.]]></itunes:summary>
      <itunes:duration>1808</itunes:duration>
      <itunes:episode>5392</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/portable-agent-architecture-memory.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/portable-agent-architecture-memory.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Chaining Small Models for Dictation Cleanup</title>
      <description><![CDATA[A listener running a Futo voice input fork on Android wants to chain a custom small model after his ASR and punctuation stages to fix number normalization, punctuation, and paragraph breaks. But number normalization, punctuation restoration, and paragraph segmentation aren't one task — they're three tasks with different shapes, different data requirements, and different latency costs. This episode works through the whole design space: multi-stage pipelines versus single purpose-built models like Superwhisper's S1, why Replicate's Cog format fights against four-model chains, what actually changes when everything has to run on a phone, and how many training samples each transform really needs.]]></description>
      <link>https://myweirdprompts.com/episode/chaining-small-models-dictation-cleanup/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/chaining-small-models-dictation-cleanup/</guid>
      <pubDate>Sun, 20 Sep 2026 17:25:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/chaining-small-models-dictation-cleanup.m4a"
        type="audio/mp4"
        length="32878912"
      />
      <itunes:title>Chaining Small Models for Dictation Cleanup</itunes:title>
      <itunes:subtitle>Daniel&apos;s Android dictation fork won&apos;t render &quot;three point five&quot; as a decimal. How many models does cleanup actually need?</itunes:subtitle>
      <itunes:summary><![CDATA[A listener running a Futo voice input fork on Android wants to chain a custom small model after his ASR and punctuation stages to fix number normalization, punctuation, and paragraph breaks. But number normalization, punctuation restoration, and paragraph segmentation aren't one task — they're three tasks with different shapes, different data requirements, and different latency costs. This episode works through the whole design space: multi-stage pipelines versus single purpose-built models like Superwhisper's S1, why Replicate's Cog format fights against four-model chains, what actually changes when everything has to run on a phone, and how many training samples each transform really needs.]]></itunes:summary>
      <itunes:duration>1884</itunes:duration>
      <itunes:episode>5390</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/chaining-small-models-dictation-cleanup.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/chaining-small-models-dictation-cleanup.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Hugging Face vs Kaggle: Where Models Actually Live</title>
      <description><![CDATA[Hugging Face and Kaggle get compared head-to-head all the time, but they're not two storefronts selling the same thing. Hugging Face is Git-backed infrastructure where models, datasets, and Spaces are versioned artifacts that plug into real training and deployment pipelines. Kaggle is a practice field — free browser notebooks, leaderboards, medals, and a competitive social layer built for learning and competition. This episode traces the structural differences, from chunk-level deduplication to why 82 datasets absorb 80% of all download traffic, and then walks through what a first-model workflow actually looks like when you can upload a dataset, train in a Space, and push the finished model back out without ever leaving the ecosystem.]]></description>
      <link>https://myweirdprompts.com/episode/hugging-face-vs-kaggle-datasets/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hugging-face-vs-kaggle-datasets/</guid>
      <pubDate>Sun, 20 Sep 2026 16:56:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hugging-face-vs-kaggle-datasets.m4a"
        type="audio/mp4"
        length="26222381"
      />
      <itunes:title>Hugging Face vs Kaggle: Where Models Actually Live</itunes:title>
      <itunes:subtitle>Hugging Face and Kaggle aren&apos;t rivals — one is infrastructure, one is a practice field. Here&apos;s how the two platforms actually differ.</itunes:subtitle>
      <itunes:summary><![CDATA[Hugging Face and Kaggle get compared head-to-head all the time, but they're not two storefronts selling the same thing. Hugging Face is Git-backed infrastructure where models, datasets, and Spaces are versioned artifacts that plug into real training and deployment pipelines. Kaggle is a practice field — free browser notebooks, leaderboards, medals, and a competitive social layer built for learning and competition. This episode traces the structural differences, from chunk-level deduplication to why 82 datasets absorb 80% of all download traffic, and then walks through what a first-model workflow actually looks like when you can upload a dataset, train in a Space, and push the finished model back out without ever leaving the ecosystem.]]></itunes:summary>
      <itunes:duration>1500</itunes:duration>
      <itunes:episode>5389</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hugging-face-vs-kaggle-datasets.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hugging-face-vs-kaggle-datasets.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Android ASR Runtimes: LiteRT, ExecuTorch, and Why Your Phone Has No VRAM</title>
      <description><![CDATA[You download a model from Hugging Face, but you're not downloading something that runs — you're downloading weights. Something has to map every operator onto your phone's silicon, and on Android that layer is fragmented across LiteRT, ONNX Runtime Mobile, ExecuTorch, and llama.cpp. This episode digs into why there's no VRAM equivalent on a phone, what a TPU actually changes, and how to find an ASR model on Hugging Face that won't crash your device. Plus: practical tips on tags, quantization, operator compatibility, and why a 95% TPU-accelerated graph can still be bottlenecked by the 5% running on CPU.]]></description>
      <link>https://myweirdprompts.com/episode/android-asr-runtimes-on-device/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-asr-runtimes-on-device/</guid>
      <pubDate>Sun, 20 Sep 2026 14:52:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-asr-runtimes-on-device.m4a"
        type="audio/mp4"
        length="29401363"
      />
      <itunes:title>Android ASR Runtimes: LiteRT, ExecuTorch, and Why Your Phone Has No VRAM</itunes:title>
      <itunes:subtitle>Why does your phone have no VRAM number? A tour of Android&apos;s runtime layer and what it takes to run ASR locally.</itunes:subtitle>
      <itunes:summary><![CDATA[You download a model from Hugging Face, but you're not downloading something that runs — you're downloading weights. Something has to map every operator onto your phone's silicon, and on Android that layer is fragmented across LiteRT, ONNX Runtime Mobile, ExecuTorch, and llama.cpp. This episode digs into why there's no VRAM equivalent on a phone, what a TPU actually changes, and how to find an ASR model on Hugging Face that won't crash your device. Plus: practical tips on tags, quantization, operator compatibility, and why a 95% TPU-accelerated graph can still be bottlenecked by the 5% running on CPU.]]></itunes:summary>
      <itunes:duration>1673</itunes:duration>
      <itunes:episode>5384</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-asr-runtimes-on-device.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-asr-runtimes-on-device.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Android&apos;s Open Core, Closed Governance</title>
      <description><![CDATA[Android is open source — except for the parts that matter. This episode digs into the gap between what AOSP promises and what developers actually experience when Google shifts the platform underneath them. From scoped storage to the sideloading verification backlash that rattled F-Droid, we trace how the core gets built, who gets consulted (spoiler: mostly large teams), and why a two-person app studio can lose everything in 48 hours. We also look at the OEM layer — Samsung, OnePlus, Xiaomi — and the background-process kill lists that make "Android" mean something different on every phone. Is the device flexibility argument still real, or is it becoming a surface-level story over a locked-down foundation? And what have developers built when the official channels fell short?]]></description>
      <link>https://myweirdprompts.com/episode/android-core-governance-developers/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-core-governance-developers/</guid>
      <pubDate>Sun, 20 Sep 2026 14:49:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-core-governance-developers.m4a"
        type="audio/mp4"
        length="26901250"
      />
      <itunes:title>Android&apos;s Open Core, Closed Governance</itunes:title>
      <itunes:subtitle>Android&apos;s code is open, but its roadmap isn&apos;t. What happens when Google changes the core and developers just have to cope?</itunes:subtitle>
      <itunes:summary><![CDATA[Android is open source — except for the parts that matter. This episode digs into the gap between what AOSP promises and what developers actually experience when Google shifts the platform underneath them. From scoped storage to the sideloading verification backlash that rattled F-Droid, we trace how the core gets built, who gets consulted (spoiler: mostly large teams), and why a two-person app studio can lose everything in 48 hours. We also look at the OEM layer — Samsung, OnePlus, Xiaomi — and the background-process kill lists that make "Android" mean something different on every phone. Is the device flexibility argument still real, or is it becoming a surface-level story over a locked-down foundation? And what have developers built when the official channels fell short?]]></itunes:summary>
      <itunes:duration>1520</itunes:duration>
      <itunes:episode>5383</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-core-governance-developers.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-core-governance-developers.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>SIP Speakers, Multicast, and the One-Way Page</title>
      <description><![CDATA[SIP doesn't carry audio — it negotiates sessions, and RTP does the rest. That separation is what makes one-way paging possible: a SIP speaker is just an endpoint with a phone number, no mic, no dial pad, that answers an INVITE and plays whatever arrives. This episode unpacks the fork between unicast paging (thirty speakers, thirty simultaneous calls) and multicast paging (one SIP call, one RTP stream, five hundred listeners), plus the Asterisk MulticastRTP dial strings, FreeSWITCH-based open source approaches, and the layer 2 versus layer 3 networking that decides whether any of it works across VLANs.]]></description>
      <link>https://myweirdprompts.com/episode/sip-multicast-paging-speakers/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/sip-multicast-paging-speakers/</guid>
      <pubDate>Sun, 20 Sep 2026 14:00:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/sip-multicast-paging-speakers.m4a"
        type="audio/mp4"
        length="25402339"
      />
      <itunes:title>SIP Speakers, Multicast, and the One-Way Page</itunes:title>
      <itunes:subtitle>How SIP one-way paging actually works — and why multicast turns 500 speakers into a single call.</itunes:subtitle>
      <itunes:summary><![CDATA[SIP doesn't carry audio — it negotiates sessions, and RTP does the rest. That separation is what makes one-way paging possible: a SIP speaker is just an endpoint with a phone number, no mic, no dial pad, that answers an INVITE and plays whatever arrives. This episode unpacks the fork between unicast paging (thirty speakers, thirty simultaneous calls) and multicast paging (one SIP call, one RTP stream, five hundred listeners), plus the Asterisk MulticastRTP dial strings, FreeSWITCH-based open source approaches, and the layer 2 versus layer 3 networking that decides whether any of it works across VLANs.]]></itunes:summary>
      <itunes:duration>1434</itunes:duration>
      <itunes:episode>5378</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/sip-multicast-paging-speakers.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/sip-multicast-paging-speakers.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Emails Don&apos;t Sound Like You</title>
      <description><![CDATA[ChatGPT's full Gmail support finally closes the loop: after OAuth authorization, it can summarize your inbox, find recipients, and send messages on your behalf. But sending is where things get personal — and where the default output reads as oddly stilted. This episode digs into why base models write bland, committee-written email, why a couple of real examples plus a few guardrails beat paragraphs of style instructions, and why the review step vanished the moment the send button arrived. We also weigh the architectural fork: one general assistant with memory stores and graph backends versus specialized assistants with tight prompts, and what the Check Point cross-account data access findings mean for trusting an AI tool with your inbox.]]></description>
      <link>https://myweirdprompts.com/episode/ai-email-voice-examples-guardrails/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-email-voice-examples-guardrails/</guid>
      <pubDate>Sat, 19 Sep 2026 13:20:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-email-voice-examples-guardrails.m4a"
        type="audio/mp4"
        length="22519510"
      />
      <itunes:title>Why AI Emails Don&apos;t Sound Like You</itunes:title>
      <itunes:subtitle>ChatGPT&apos;s Gmail integration can now send email for you. The hard part is making it sound like you — not a committee-written average.</itunes:subtitle>
      <itunes:summary><![CDATA[ChatGPT's full Gmail support finally closes the loop: after OAuth authorization, it can summarize your inbox, find recipients, and send messages on your behalf. But sending is where things get personal — and where the default output reads as oddly stilted. This episode digs into why base models write bland, committee-written email, why a couple of real examples plus a few guardrails beat paragraphs of style instructions, and why the review step vanished the moment the send button arrived. We also weigh the architectural fork: one general assistant with memory stores and graph backends versus specialized assistants with tight prompts, and what the Check Point cross-account data access findings mean for trusting an AI tool with your inbox.]]></itunes:summary>
      <itunes:duration>1272</itunes:duration>
      <itunes:episode>5355</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-email-voice-examples-guardrails.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-email-voice-examples-guardrails.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>A Shabbat Radio That Can&apos;t Be Paused</title>
      <description><![CDATA[What if a podcast player had no controls at all? In this episode, we explore a listener's prototype for a Shabbat-friendly radio: a Raspberry Pi that pulls episodes from an RSS feed, plays them continuously, and lives inside a soundproofed box. Opening the lid is play. Closing it is pause. That's the entire interface. We dig into the halakhic reasoning behind the Shabbat elevator and the concept of grama — indirect causation — to understand why a device that never changes state based on your action might fit within Shabbat observance. Then we build it: the Pi Zero, the amplifier, the acoustic enclosure, and the surprisingly tiny electricity cost. Finally, we confront the design questions that make this more than a build log — how do you model "listening" when the device plays whether you're paying attention or not? What does it mean to have heard something you didn't listen to? And why might the absence of controls be exactly the point for listeners with ADHD who value disconnection but grow bored without stimulation?]]></description>
      <link>https://myweirdprompts.com/episode/shabbat-podcast-radio-device/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/shabbat-podcast-radio-device/</guid>
      <pubDate>Fri, 18 Sep 2026 15:14:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/shabbat-podcast-radio-device.m4a"
        type="audio/mp4"
        length="22095395"
      />
      <itunes:title>A Shabbat Radio That Can&apos;t Be Paused</itunes:title>
      <itunes:subtitle>A soundproof box that plays podcasts continuously — no pause, no skip, no volume. Just open the lid and listen.</itunes:subtitle>
      <itunes:summary><![CDATA[What if a podcast player had no controls at all? In this episode, we explore a listener's prototype for a Shabbat-friendly radio: a Raspberry Pi that pulls episodes from an RSS feed, plays them continuously, and lives inside a soundproofed box. Opening the lid is play. Closing it is pause. That's the entire interface. We dig into the halakhic reasoning behind the Shabbat elevator and the concept of grama — indirect causation — to understand why a device that never changes state based on your action might fit within Shabbat observance. Then we build it: the Pi Zero, the amplifier, the acoustic enclosure, and the surprisingly tiny electricity cost. Finally, we confront the design questions that make this more than a build log — how do you model "listening" when the device plays whether you're paying attention or not? What does it mean to have heard something you didn't listen to? And why might the absence of controls be exactly the point for listeners with ADHD who value disconnection but grow bored without stimulation?]]></itunes:summary>
      <itunes:duration>1257</itunes:duration>
      <itunes:episode>5328</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/shabbat-podcast-radio-device.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/shabbat-podcast-radio-device.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Catching Duplicate Submissions Before They Burn Credits</title>
      <description><![CDATA[When a webhook fires but the response never arrives, the form looks like it failed — so you resubmit, and now the pipeline is running the same job twice. This episode walks through the engineering fix: SHA-256 checksums for audio files, Unicode normalization before hashing text, and Jaccard similarity over character shingles for the fuzzy cases where a character changed between attempts. The conversation also draws a sharp line between transport idempotency (an event key that survives retries) and content dedupe (a fingerprint of what was actually submitted), explaining why you need both layers and why the database constraint — not application logic — is the only real guarantee under concurrency.]]></description>
      <link>https://myweirdprompts.com/episode/duplicate-submission-screening-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/duplicate-submission-screening-pipeline/</guid>
      <pubDate>Fri, 18 Sep 2026 15:11:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/duplicate-submission-screening-pipeline.m4a"
        type="audio/mp4"
        length="26342162"
      />
      <itunes:title>Catching Duplicate Submissions Before They Burn Credits</itunes:title>
      <itunes:subtitle>A dropped webhook response caused a double submission. Here&apos;s how to build a screener that catches duplicates before generation runs.</itunes:subtitle>
      <itunes:summary><![CDATA[When a webhook fires but the response never arrives, the form looks like it failed — so you resubmit, and now the pipeline is running the same job twice. This episode walks through the engineering fix: SHA-256 checksums for audio files, Unicode normalization before hashing text, and Jaccard similarity over character shingles for the fuzzy cases where a character changed between attempts. The conversation also draws a sharp line between transport idempotency (an event key that survives retries) and content dedupe (a fingerprint of what was actually submitted), explaining why you need both layers and why the database constraint — not application logic — is the only real guarantee under concurrency.]]></itunes:summary>
      <itunes:duration>1502</itunes:duration>
      <itunes:episode>5327</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/duplicate-submission-screening-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/duplicate-submission-screening-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When to Keep Old Tech for Parts (and When to Let Go)</title>
      <description><![CDATA[Daniel built a home inventory system that rivals commercial asset management platforms — and it surfaced a problem he didn't expect. The system knows everything he owns, including the old Kindle in a drawer with no resale value, no obvious donation path, and a vague future as a source of spare parts. Is that vague future real, or a fantasy? Drawing on the Circuit Salvage guide, a Hackaday teardown of an old Kindle, the Loopstr parts harvesting guide, and Okon Recycling's disposal hierarchy, this episode works through harvest viability, the BGA problem, the ninety-day harvest shelf rule, and why whole-device reuse beats stripping for parts. If you can decide what to do with the Kindle, you can decide what to do with almost anything.]]></description>
      <link>https://myweirdprompts.com/episode/legacy-tech-parts-harvesting/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/legacy-tech-parts-harvesting/</guid>
      <pubDate>Fri, 18 Sep 2026 12:53:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/legacy-tech-parts-harvesting.m4a"
        type="audio/mp4"
        length="28794040"
      />
      <itunes:title>When to Keep Old Tech for Parts (and When to Let Go)</itunes:title>
      <itunes:subtitle>An old Kindle in a drawer raises a question every collector faces: spare, parts donor, or e-waste?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel built a home inventory system that rivals commercial asset management platforms — and it surfaced a problem he didn't expect. The system knows everything he owns, including the old Kindle in a drawer with no resale value, no obvious donation path, and a vague future as a source of spare parts. Is that vague future real, or a fantasy? Drawing on the Circuit Salvage guide, a Hackaday teardown of an old Kindle, the Loopstr parts harvesting guide, and Okon Recycling's disposal hierarchy, this episode works through harvest viability, the BGA problem, the ninety-day harvest shelf rule, and why whole-device reuse beats stripping for parts. If you can decide what to do with the Kindle, you can decide what to do with almost anything.]]></itunes:summary>
      <itunes:duration>1641</itunes:duration>
      <itunes:episode>5323</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/legacy-tech-parts-harvesting.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/legacy-tech-parts-harvesting.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Focus Stacking Without the Phone App Guesswork</title>
      <description><![CDATA[Phone camera apps default to three focus points, but at macro magnifications the depth of field is a fraction of a millimeter — a five centimeter object needs hundreds of slices, not three. This episode breaks down what focus stacking software actually computes, why the rule of three (every detail sharp in at least three frames) matters more than the app's tap count, and how to separate capture from processing. We survey the open source landscape — focus-stack, ChimpStackr, shinestacker, OpenFocus, stackaroni — and explain why the cloud story is containers, not SaaS. Plus: what tolerances a real macro rig needs, and why automated focus bracketing beats manual tapping every time.]]></description>
      <link>https://myweirdprompts.com/episode/focus-stacking-macro-photography/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/focus-stacking-macro-photography/</guid>
      <pubDate>Fri, 18 Sep 2026 11:38:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/focus-stacking-macro-photography.m4a"
        type="audio/mp4"
        length="36666543"
      />
      <itunes:title>Focus Stacking Without the Phone App Guesswork</itunes:title>
      <itunes:subtitle>Your phone app wants three shots. The optics want three hundred. Here&apos;s how to actually stack macro focus.</itunes:subtitle>
      <itunes:summary><![CDATA[Phone camera apps default to three focus points, but at macro magnifications the depth of field is a fraction of a millimeter — a five centimeter object needs hundreds of slices, not three. This episode breaks down what focus stacking software actually computes, why the rule of three (every detail sharp in at least three frames) matters more than the app's tap count, and how to separate capture from processing. We survey the open source landscape — focus-stack, ChimpStackr, shinestacker, OpenFocus, stackaroni — and explain why the cloud story is containers, not SaaS. Plus: what tolerances a real macro rig needs, and why automated focus bracketing beats manual tapping every time.]]></itunes:summary>
      <itunes:duration>2077</itunes:duration>
      <itunes:episode>5319</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/focus-stacking-macro-photography.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/focus-stacking-macro-photography.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>System Prompt Order Is Load-Bearing</title>
      <description><![CDATA[Daniel wrote in about his spam sinkhole agent — the classifier that hands pseudo-personalized spam to a second agent whose job is to keep the spammer talking forever. His question: if the system prompt is the only thing you're configuring, what's the right order of precedence? Personality first, workflow first, guardrails first? The practitioner guides contradict each other, and the research is messier still. This episode digs into what actually determines whether an instruction holds: the Control Illusion paper out of AAAI, the Imperative Interference finding that cooperating instructions compete after translation, and the declarative rewriting trick that cut cross-linguistic variance by 81%. Plus the durable-versus-per-turn split, attention shadow, and why the instruction that goes first should be the one whose failure is unrecoverable — not the one that feels most important.]]></description>
      <link>https://myweirdprompts.com/episode/system-prompt-order-precedence/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/system-prompt-order-precedence/</guid>
      <pubDate>Fri, 18 Sep 2026 01:14:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/system-prompt-order-precedence.m4a"
        type="audio/mp4"
        length="30828274"
      />
      <itunes:title>System Prompt Order Is Load-Bearing</itunes:title>
      <itunes:subtitle>The guides disagree on guardrails-first vs guardrails-last, and the research says system prompts don&apos;t create hierarchy at all.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wrote in about his spam sinkhole agent — the classifier that hands pseudo-personalized spam to a second agent whose job is to keep the spammer talking forever. His question: if the system prompt is the only thing you're configuring, what's the right order of precedence? Personality first, workflow first, guardrails first? The practitioner guides contradict each other, and the research is messier still. This episode digs into what actually determines whether an instruction holds: the Control Illusion paper out of AAAI, the Imperative Interference finding that cooperating instructions compete after translation, and the declarative rewriting trick that cut cross-linguistic variance by 81%. Plus the durable-versus-per-turn split, attention shadow, and why the instruction that goes first should be the one whose failure is unrecoverable — not the one that feels most important.]]></itunes:summary>
      <itunes:duration>1774</itunes:duration>
      <itunes:episode>5313</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/system-prompt-order-precedence.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/system-prompt-order-precedence.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fighting Fake-Personalized Spam With AI Sinkholes</title>
      <description><![CDATA[Pseudo-personalized spam is the new inbox tax: messages that scrape your public work, fake a personal connection, and pivot to a one-sided ask. This episode digs into Daniel's two-layer proposal — a classifier agent that flags pseudo-personalized outreach, and a "sinister" second agent that keeps spammers in an endless, never-converting conversation to burn their resources. We walk through what actually goes in the sinkhole agent's system prompt, why scraping artifacts and mismatched specifics are the best detection signals, and the precedent hiding in infrastructure history: Nepenthes-style tarpits that keep spam bots stuck in endless connections. Then we hit the hard parts — false positives that burn real connections, adversarial adaptation, the cheap-model sweet spot, and the backfire risk of feeding spammers a steady stream of plausible human-sounding text.]]></description>
      <link>https://myweirdprompts.com/episode/ai-spam-sinkhole-defense/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-spam-sinkhole-defense/</guid>
      <pubDate>Thu, 17 Sep 2026 21:30:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-spam-sinkhole-defense.m4a"
        type="audio/mp4"
        length="38434095"
      />
      <itunes:title>Fighting Fake-Personalized Spam With AI Sinkholes</itunes:title>
      <itunes:subtitle>Scraped flattery, fake familiarity, and the one-sided ask — plus the tarpit history behind Daniel&apos;s sinkhole agent idea.</itunes:subtitle>
      <itunes:summary><![CDATA[Pseudo-personalized spam is the new inbox tax: messages that scrape your public work, fake a personal connection, and pivot to a one-sided ask. This episode digs into Daniel's two-layer proposal — a classifier agent that flags pseudo-personalized outreach, and a "sinister" second agent that keeps spammers in an endless, never-converting conversation to burn their resources. We walk through what actually goes in the sinkhole agent's system prompt, why scraping artifacts and mismatched specifics are the best detection signals, and the precedent hiding in infrastructure history: Nepenthes-style tarpits that keep spam bots stuck in endless connections. Then we hit the hard parts — false positives that burn real connections, adversarial adaptation, the cheap-model sweet spot, and the backfire risk of feeding spammers a steady stream of plausible human-sounding text.]]></itunes:summary>
      <itunes:duration>2207</itunes:duration>
      <itunes:episode>5312</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-spam-sinkhole-defense.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-spam-sinkhole-defense.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Two Queues, One Cron Tick: Serverless Batch Pipelines</title>
      <description><![CDATA[Daniel's episode-generation binge kept triggering redundant site builds, and his attempt to fix it with a polling queue produced the project's only surprise bill. This episode works through the professional version of the pattern: capture stays ad-hoc, processing becomes batched, and the queue lives in a database instead of a long-running worker. We cover why polling is the canonical serverless anti-pattern, how a status column plus a scheduled batch job replaces a worker entirely, and why the two-queue split (generation, then deployment) makes human review a first-class stage instead of a bolt-on. Then we get into the money: batch inference APIs now offering roughly 50% off in exchange for relaxed latency, where Modal sits in that landscape, and why "never time-critical" is exactly the workload batch pricing was built for.]]></description>
      <link>https://myweirdprompts.com/episode/serverless-batch-queue-pipelines/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/serverless-batch-queue-pipelines/</guid>
      <pubDate>Thu, 17 Sep 2026 11:35:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/serverless-batch-queue-pipelines.m4a"
        type="audio/mp4"
        length="24259174"
      />
      <itunes:title>Two Queues, One Cron Tick: Serverless Batch Pipelines</itunes:title>
      <itunes:subtitle>What does a queue look like when your process is disposable? Two queues, one cron tick, and a 50% batch discount.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's episode-generation binge kept triggering redundant site builds, and his attempt to fix it with a polling queue produced the project's only surprise bill. This episode works through the professional version of the pattern: capture stays ad-hoc, processing becomes batched, and the queue lives in a database instead of a long-running worker. We cover why polling is the canonical serverless anti-pattern, how a status column plus a scheduled batch job replaces a worker entirely, and why the two-queue split (generation, then deployment) makes human review a first-class stage instead of a bolt-on. Then we get into the money: batch inference APIs now offering roughly 50% off in exchange for relaxed latency, where Modal sits in that landscape, and why "never time-critical" is exactly the workload batch pricing was built for.]]></itunes:summary>
      <itunes:duration>1374</itunes:duration>
      <itunes:episode>5285</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/serverless-batch-queue-pipelines.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/serverless-batch-queue-pipelines.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Wi-Fi IoT Without the Cloud Lock-In</title>
      <description><![CDATA[Wi-Fi was never designed for smart home sensors — it's high power, high throughput, and lives in the most crowded spectrum in your house. But it's also the only option when you need real bandwidth (cameras) or can't run ethernet (renters, old buildings). This episode maps the Wi-Fi-native IoT landscape: why Tuya's cheap modules created a lock-in that isn't about hardware at all, how Tasmota, ESPHome, LibreTiny, and Cloudcutter let you rip out vendor firmware and install your own, and why the open Wi-Fi path is still a tinkerer's project while Zigbee's open path is a product category. We also cover Frigate for local camera AI, the renter use case, and the e-waste cost of cloud-dependent devices.]]></description>
      <link>https://myweirdprompts.com/episode/wifi-iot-local-control/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/wifi-iot-local-control/</guid>
      <pubDate>Thu, 17 Sep 2026 11:17:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/wifi-iot-local-control.m4a"
        type="audio/mp4"
        length="33255212"
      />
      <itunes:title>Wi-Fi IoT Without the Cloud Lock-In</itunes:title>
      <itunes:subtitle>Wi-Fi IoT ships locked to vendor clouds by default. Here&apos;s what it takes to break out — and where Wi-Fi is actually the right tool.</itunes:subtitle>
      <itunes:summary><![CDATA[Wi-Fi was never designed for smart home sensors — it's high power, high throughput, and lives in the most crowded spectrum in your house. But it's also the only option when you need real bandwidth (cameras) or can't run ethernet (renters, old buildings). This episode maps the Wi-Fi-native IoT landscape: why Tuya's cheap modules created a lock-in that isn't about hardware at all, how Tasmota, ESPHome, LibreTiny, and Cloudcutter let you rip out vendor firmware and install your own, and why the open Wi-Fi path is still a tinkerer's project while Zigbee's open path is a product category. We also cover Frigate for local camera AI, the renter use case, and the e-waste cost of cloud-dependent devices.]]></itunes:summary>
      <itunes:duration>1910</itunes:duration>
      <itunes:episode>5282</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/wifi-iot-local-control.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/wifi-iot-local-control.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Ruby Won the AI Benchmark. Is It Dying Anyway?</title>
      <description><![CDATA[AI agents write code in whatever language the training data favors, and that's reshaping which programming languages survive. This episode digs into the numbers behind the decline: Ruby's abandoned long tail, Scala's diaspora, MATLAB falling out of the TIOBE top twenty, and PHP's quiet replacement by Go in new projects. But there's a counter-current — a benchmark where Ruby actually beat Python and TypeScript on speed and cost, and a developer who got Claude Code to extend a Perl project rather than port it. Two forces are pulling in opposite directions: consolidation toward a handful of languages, and near-zero switching costs that could keep dead languages alive. We work through what the data actually shows, what it doesn't, and why "declining" usually means "no new projects" rather than "code stopped running.]]></description>
      <link>https://myweirdprompts.com/episode/ai-coding-language-consolidation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-coding-language-consolidation/</guid>
      <pubDate>Wed, 16 Sep 2026 11:18:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-coding-language-consolidation.m4a"
        type="audio/mp4"
        length="32706194"
      />
      <itunes:title>Ruby Won the AI Benchmark. Is It Dying Anyway?</itunes:title>
      <itunes:subtitle>97% of public Ruby repos sat untouched for six months — yet Ruby beat Python in an AI coding benchmark. What&apos;s actually declining?</itunes:subtitle>
      <itunes:summary><![CDATA[AI agents write code in whatever language the training data favors, and that's reshaping which programming languages survive. This episode digs into the numbers behind the decline: Ruby's abandoned long tail, Scala's diaspora, MATLAB falling out of the TIOBE top twenty, and PHP's quiet replacement by Go in new projects. But there's a counter-current — a benchmark where Ruby actually beat Python and TypeScript on speed and cost, and a developer who got Claude Code to extend a Perl project rather than port it. Two forces are pulling in opposite directions: consolidation toward a handful of languages, and near-zero switching costs that could keep dead languages alive. We work through what the data actually shows, what it doesn't, and why "declining" usually means "no new projects" rather than "code stopped running.]]></itunes:summary>
      <itunes:duration>1851</itunes:duration>
      <itunes:episode>5256</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-coding-language-consolidation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-coding-language-consolidation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Writes the Code, Who Checks It?</title>
      <description><![CDATA[Software development is shifting from writing code to supervising the systems that write it — and that changes almost everything downstream. This episode digs into the prediction that programming languages will consolidate around a small set built for AI agents to generate and verify, not for humans to read. We explore what such a language might actually look like, why COBOL and Fortran suggest the transition will be a long tail rather than a clean break, and how the real scarce skill becomes evaluation: knowing whether what got built is actually correct. Then we follow the thread into education and the labor market — why the junior developer role is the first casualty, how the senior pipeline starves if nobody trains juniors, and why the boundary between technical and non-technical may get redefined around reasoning about systems rather than writing syntax. The uncomfortable conclusion: the "funny feeling" that something is subtly off may be the most valuable skill of all, and it can't be taught from a textbook.]]></description>
      <link>https://myweirdprompts.com/episode/ai-coding-verification-skills/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-coding-verification-skills/</guid>
      <pubDate>Wed, 16 Sep 2026 11:14:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-coding-verification-skills.m4a"
        type="audio/mp4"
        length="25127870"
      />
      <itunes:title>When AI Writes the Code, Who Checks It?</itunes:title>
      <itunes:subtitle>If agents write the code, the human job becomes knowing when it&apos;s wrong. What that means for languages, jobs, and CS degrees.</itunes:subtitle>
      <itunes:summary><![CDATA[Software development is shifting from writing code to supervising the systems that write it — and that changes almost everything downstream. This episode digs into the prediction that programming languages will consolidate around a small set built for AI agents to generate and verify, not for humans to read. We explore what such a language might actually look like, why COBOL and Fortran suggest the transition will be a long tail rather than a clean break, and how the real scarce skill becomes evaluation: knowing whether what got built is actually correct. Then we follow the thread into education and the labor market — why the junior developer role is the first casualty, how the senior pipeline starves if nobody trains juniors, and why the boundary between technical and non-technical may get redefined around reasoning about systems rather than writing syntax. The uncomfortable conclusion: the "funny feeling" that something is subtly off may be the most valuable skill of all, and it can't be taught from a textbook.]]></itunes:summary>
      <itunes:duration>1432</itunes:duration>
      <itunes:episode>5255</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-coding-verification-skills.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-coding-verification-skills.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Shortwave Monitoring Stack on Linux</title>
      <description><![CDATA[Daniel wants to build an automated shortwave monitoring station: an affordable SDR, a Linux server, frequency scanning, and automatic uploads to a shared cloud for community analysis. The answer starts with a hard physical constraint — the thirty-dollar RTL-SDR dongle everyone reaches for doesn't cover the HF bands where diplomatic traffic lives — and ends somewhere more interesting: a global network of 700+ KiwiSDR receivers that has already solved the capture layer. We walk through the real toolchain (rtl_fm, rtl_power, rtl_airband, OpenWebRX), the antenna problem that no software can fix, and why the AIS crowdsourcing model only half-transfers to analog shortwave.]]></description>
      <link>https://myweirdprompts.com/episode/shortwave-monitoring-linux-stack/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/shortwave-monitoring-linux-stack/</guid>
      <pubDate>Tue, 15 Sep 2026 15:38:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/shortwave-monitoring-linux-stack.m4a"
        type="audio/mp4"
        length="27721806"
      />
      <itunes:title>Building a Shortwave Monitoring Stack on Linux</itunes:title>
      <itunes:subtitle>What it actually takes to scan, capture, and crowdsource HF radio from a Linux box — and where automation hits a wall.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants to build an automated shortwave monitoring station: an affordable SDR, a Linux server, frequency scanning, and automatic uploads to a shared cloud for community analysis. The answer starts with a hard physical constraint — the thirty-dollar RTL-SDR dongle everyone reaches for doesn't cover the HF bands where diplomatic traffic lives — and ends somewhere more interesting: a global network of 700+ KiwiSDR receivers that has already solved the capture layer. We walk through the real toolchain (rtl_fm, rtl_power, rtl_airband, OpenWebRX), the antenna problem that no software can fix, and why the AIS crowdsourcing model only half-transfers to analog shortwave.]]></itunes:summary>
      <itunes:duration>1568</itunes:duration>
      <itunes:episode>5250</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/shortwave-monitoring-linux-stack.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/shortwave-monitoring-linux-stack.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Training a Model on 100 Phone Photos</title>
      <description><![CDATA[Daniel wants to catalog his small-parts drawer — screws, bolts, standoffs — by training a model on about a hundred phone photos. That raises two questions: what annotation tooling actually works at this scale, and should he fine-tune a large vision-language model or train something small and task-specific? We dig into why capture consistency and a scale reference matter more than image count, how to structure the schema so the model isn't doing two jobs at once, and where the open-vocabulary tradeoff flips the answer.]]></description>
      <link>https://myweirdprompts.com/episode/small-dataset-hardware-model-training/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/small-dataset-hardware-model-training/</guid>
      <pubDate>Tue, 15 Sep 2026 12:39:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/small-dataset-hardware-model-training.m4a"
        type="audio/mp4"
        length="24412494"
      />
      <itunes:title>Training a Model on 100 Phone Photos</itunes:title>
      <itunes:subtitle>One drawer of screws, a hundred phone photos, and a schema. When does a small custom model beat a fine-tuned VLM?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants to catalog his small-parts drawer — screws, bolts, standoffs — by training a model on about a hundred phone photos. That raises two questions: what annotation tooling actually works at this scale, and should he fine-tune a large vision-language model or train something small and task-specific? We dig into why capture consistency and a scale reference matter more than image count, how to structure the schema so the model isn't doing two jobs at once, and where the open-vocabulary tradeoff flips the answer.]]></itunes:summary>
      <itunes:duration>1398</itunes:duration>
      <itunes:episode>5245</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/small-dataset-hardware-model-training.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/small-dataset-hardware-model-training.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Research Agent Won&apos;t Stop Searching</title>
      <description><![CDATA[Deep research agents look busy, but a new paper found 77-94% of search episodes add no new evidence — and search volume is *anti-correlated* with accuracy at -0.77. This episode digs into what separates a real deep research framework from handing an agent a search tool and hoping: retrieval recall vs. search volume, why 66-85% of agent context is just search snippets, and the provider tradeoffs between Exa, Tavily, Perplexity Sonar, and Gemini Deep Research. We also walk through the runaway modes — infinite agentic loops, spend that scales with the square of run length, and fan-out budgets that turn a 50-cent cap into a $50 run — including a June postmortem where a single Claude Code call spawned 111 agents, burned 1.2M tokens, and produced zero output. If you run a research sub-agent in production, this is the failure taxonomy you need.]]></description>
      <link>https://myweirdprompts.com/episode/deep-research-agent-search-loops/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/deep-research-agent-search-loops/</guid>
      <pubDate>Mon, 14 Sep 2026 12:21:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/deep-research-agent-search-loops.m4a"
        type="audio/mp4"
        length="27961867"
      />
      <itunes:title>Why Your Research Agent Won&apos;t Stop Searching</itunes:title>
      <itunes:subtitle>Most agent search runs add no new evidence — and 66-85% of the context window is just search snippets. Here&apos;s what actually stops the loop.</itunes:subtitle>
      <itunes:summary><![CDATA[Deep research agents look busy, but a new paper found 77-94% of search episodes add no new evidence — and search volume is *anti-correlated* with accuracy at -0.77. This episode digs into what separates a real deep research framework from handing an agent a search tool and hoping: retrieval recall vs. search volume, why 66-85% of agent context is just search snippets, and the provider tradeoffs between Exa, Tavily, Perplexity Sonar, and Gemini Deep Research. We also walk through the runaway modes — infinite agentic loops, spend that scales with the square of run length, and fan-out budgets that turn a 50-cent cap into a $50 run — including a June postmortem where a single Claude Code call spawned 111 agents, burned 1.2M tokens, and produced zero output. If you run a research sub-agent in production, this is the failure taxonomy you need.]]></itunes:summary>
      <itunes:duration>1589</itunes:duration>
      <itunes:episode>5230</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/deep-research-agent-search-loops.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/deep-research-agent-search-loops.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Carving Paleo-Hebrew Into Everyday Objects</title>
      <description><![CDATA[Paleo-Hebrew survives in daily life on the one-shekel coin and almost nowhere else. This episode walks through how to commission custom metal stencils of Paleo-Hebrew letterforms — where to get them cut, why bridges make or break the design, and how to prep SVG and DXF files — then explores which inscription hands to borrow from (Gezer, Tell Zayit, Siloam) and why freezing one specific inscription matters. From challah boards to doorframes to garden markers, it's a practical guide to engraving an ancient script onto objects you actually use.]]></description>
      <link>https://myweirdprompts.com/episode/paleo-hebrew-metal-stencils-engraving/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/paleo-hebrew-metal-stencils-engraving/</guid>
      <pubDate>Mon, 14 Sep 2026 11:46:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/paleo-hebrew-metal-stencils-engraving.m4a"
        type="audio/mp4"
        length="31294435"
      />
      <itunes:title>Carving Paleo-Hebrew Into Everyday Objects</itunes:title>
      <itunes:subtitle>Custom metal stencils, the right vector files, and tasteful ways to bring an ancient script back onto everyday objects.</itunes:subtitle>
      <itunes:summary><![CDATA[Paleo-Hebrew survives in daily life on the one-shekel coin and almost nowhere else. This episode walks through how to commission custom metal stencils of Paleo-Hebrew letterforms — where to get them cut, why bridges make or break the design, and how to prep SVG and DXF files — then explores which inscription hands to borrow from (Gezer, Tell Zayit, Siloam) and why freezing one specific inscription matters. From challah boards to doorframes to garden markers, it's a practical guide to engraving an ancient script onto objects you actually use.]]></itunes:summary>
      <itunes:duration>1786</itunes:duration>
      <itunes:episode>5225</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/paleo-hebrew-metal-stencils-engraving.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/paleo-hebrew-metal-stencils-engraving.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why IKEA Has No Replacement-Parts API</title>
      <description><![CDATA[A purchasing manager wants to query a part number and get back three things: availability, discontinued status, and the official replacement. That capability is standardized — GS1's Global Data Model has defined replacement semantics for years — but it lives inside GDSN, a heavyweight batch master-data network built for grocery supply chains. Meanwhile IKEA publishes no developer API at all, Grainger and ULINE expose their catalogs through PunchOut sessions and EDI rather than queryable endpoints, and the only clean machine-readable versions come from scrapers and community projects running daily contract tests against endpoints they reverse-engineered. This episode maps the full landscape of the gap: what exists, why the vendors haven't closed it, and why a naive "replacement" field would be actively dangerous for anyone maintaining a bill of materials.]]></description>
      <link>https://myweirdprompts.com/episode/ikea-replacement-parts-api-gap/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ikea-replacement-parts-api-gap/</guid>
      <pubDate>Sat, 12 Sep 2026 11:30:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ikea-replacement-parts-api-gap.m4a"
        type="audio/mp4"
        length="39047616"
      />
      <itunes:title>Why IKEA Has No Replacement-Parts API</itunes:title>
      <itunes:subtitle>IKEA&apos;s catalog is community reverse-engineering. GS1 solved replacement semantics years ago. Neither reaches a purchasing manager&apos;s database.</itunes:subtitle>
      <itunes:summary><![CDATA[A purchasing manager wants to query a part number and get back three things: availability, discontinued status, and the official replacement. That capability is standardized — GS1's Global Data Model has defined replacement semantics for years — but it lives inside GDSN, a heavyweight batch master-data network built for grocery supply chains. Meanwhile IKEA publishes no developer API at all, Grainger and ULINE expose their catalogs through PunchOut sessions and EDI rather than queryable endpoints, and the only clean machine-readable versions come from scrapers and community projects running daily contract tests against endpoints they reverse-engineered. This episode maps the full landscape of the gap: what exists, why the vendors haven't closed it, and why a naive "replacement" field would be actively dangerous for anyone maintaining a bill of materials.]]></itunes:summary>
      <itunes:duration>2240</itunes:duration>
      <itunes:episode>5206</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ikea-replacement-parts-api-gap.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ikea-replacement-parts-api-gap.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Baby Tracker Can&apos;t Log Oxygen Saturation</title>
      <description><![CDATA[When a parent needs to log oxygen saturation, respiratory rate, and video ahead of a doctor's visit, the baby-tracking category has nothing for him — and neither does the medical app world. This episode digs into the four requirements of a true personal tracking framework (user-defined schema, media capture, periodic reminders, clean export), why no shipped Android app combines all four, and the fascinating research trail behind it: OmniTrack, a peer-reviewed 2017 self-tracking framework that solved exactly this problem and then quietly died, Track and Graph, the open-source power tool that gets 80% of the way there, and Text2Schema, a 2026 research effort formalizing natural-language-to-database schema generation. The pieces all exist. Nobody has assembled them.]]></description>
      <link>https://myweirdprompts.com/episode/custom-health-tracking-apps-gap/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/custom-health-tracking-apps-gap/</guid>
      <pubDate>Thu, 10 Sep 2026 11:39:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/custom-health-tracking-apps-gap.m4a"
        type="audio/mp4"
        length="29720752"
      />
      <itunes:title>Why Your Baby Tracker Can&apos;t Log Oxygen Saturation</itunes:title>
      <itunes:subtitle>Baby apps have templates for everything — except the metrics you actually need. The custom tracking framework exists. It just never shipped.</itunes:subtitle>
      <itunes:summary><![CDATA[When a parent needs to log oxygen saturation, respiratory rate, and video ahead of a doctor's visit, the baby-tracking category has nothing for him — and neither does the medical app world. This episode digs into the four requirements of a true personal tracking framework (user-defined schema, media capture, periodic reminders, clean export), why no shipped Android app combines all four, and the fascinating research trail behind it: OmniTrack, a peer-reviewed 2017 self-tracking framework that solved exactly this problem and then quietly died, Track and Graph, the open-source power tool that gets 80% of the way there, and Text2Schema, a 2026 research effort formalizing natural-language-to-database schema generation. The pieces all exist. Nobody has assembled them.]]></itunes:summary>
      <itunes:duration>1695</itunes:duration>
      <itunes:episode>5166</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/custom-health-tracking-apps-gap.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/custom-health-tracking-apps-gap.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Weighting Memory in a RAG Pipeline</title>
      <description><![CDATA[Daniel asked whether there's a framework that lets you set the weighting of a composite prompt's inputs deterministically — a mathematical parameter instead of a system prompt begging the model to pay attention. The answer is nuanced: system prompts genuinely have no architectural privilege, but retrieval does have a real scalar. Grounded Decoding from Iowa State fuses a full RAG distribution with a retrieval-only distribution using a tunable parameter called rho, and the adaptive variant cranks grounding weight on factual tokens while relaxing it on grammar. The catch is roughly doubled decode latency. We also dig into the lost-in-the-middle position bias, the knowledge contamination problem, and a contested preprint claiming reasoning mode itself displaces retrieved evidence.]]></description>
      <link>https://myweirdprompts.com/episode/rag-memory-weighting-grounded-decoding/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rag-memory-weighting-grounded-decoding/</guid>
      <pubDate>Thu, 10 Sep 2026 10:25:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rag-memory-weighting-grounded-decoding.m4a"
        type="audio/mp4"
        length="36501232"
      />
      <itunes:title>Weighting Memory in a RAG Pipeline</itunes:title>
      <itunes:subtitle>Daniel&apos;s memory leg won&apos;t fire. Turns out system prompts have no inherent vector-space privilege — but retrieval does have a real knob.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asked whether there's a framework that lets you set the weighting of a composite prompt's inputs deterministically — a mathematical parameter instead of a system prompt begging the model to pay attention. The answer is nuanced: system prompts genuinely have no architectural privilege, but retrieval does have a real scalar. Grounded Decoding from Iowa State fuses a full RAG distribution with a retrieval-only distribution using a tunable parameter called rho, and the adaptive variant cranks grounding weight on factual tokens while relaxing it on grammar. The catch is roughly doubled decode latency. We also dig into the lost-in-the-middle position bias, the knowledge contamination problem, and a contested preprint claiming reasoning mode itself displaces retrieved evidence.]]></itunes:summary>
      <itunes:duration>2102</itunes:duration>
      <itunes:episode>5165</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rag-memory-weighting-grounded-decoding.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rag-memory-weighting-grounded-decoding.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Build a City Companion AI</title>
      <description><![CDATA[Daniel built a custom GPT travel guide that tens of thousands of people used—but he never used it himself. The reason? A chat interface is the wrong tool for exploring a city. This episode breaks down the engineering behind a true voice-powered city companion: event-driven location triggers like geofencing and velocity changes, a gated turn-taking system so the AI knows when to speak without being intrusive, and why the tool built for travelers might matter most for locals who've stopped seeing their own streets.]]></description>
      <link>https://myweirdprompts.com/episode/city-companion-ai-location-voice/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/city-companion-ai-location-voice/</guid>
      <pubDate>Wed, 09 Sep 2026 16:30:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/city-companion-ai-location-voice.m4a"
        type="audio/mp4"
        length="28583217"
      />
      <itunes:title>How to Build a City Companion AI</itunes:title>
      <itunes:subtitle>A travel guide GPT that went viral reveals the blueprint for a voice-powered AI that knows your city—and when to speak up.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel built a custom GPT travel guide that tens of thousands of people used—but he never used it himself. The reason? A chat interface is the wrong tool for exploring a city. This episode breaks down the engineering behind a true voice-powered city companion: event-driven location triggers like geofencing and velocity changes, a gated turn-taking system so the AI knows when to speak without being intrusive, and why the tool built for travelers might matter most for locals who've stopped seeing their own streets.]]></itunes:summary>
      <itunes:duration>1683</itunes:duration>
      <itunes:episode>5136</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/city-companion-ai-location-voice.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/city-companion-ai-location-voice.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Shopping Lists Outgrow the Grocery Store</title>
      <description><![CDATA[Daniel and Hannah need groceries, drywall anchors, and IKEA parts — but no single app handles all three. This episode explores why grocery-centric apps like Bring fail outside the supermarket, why "store" matters more than "category" as an organizing principle, and whether building your own system with AI is finally viable. We trace the data model problem, the Home Assistant detour, and what the last six months of AI tools changed about the build-vs-buy calculus.]]></description>
      <link>https://myweirdprompts.com/episode/shopping-lists-beyond-groceries/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/shopping-lists-beyond-groceries/</guid>
      <pubDate>Sun, 06 Sep 2026 13:47:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/shopping-lists-beyond-groceries.m4a"
        type="audio/mp4"
        length="23949031"
      />
      <itunes:title>When Shopping Lists Outgrow the Grocery Store</itunes:title>
      <itunes:subtitle>Why grocery list apps fail when you need drywall anchors, and what actually works for multi-store household shopping.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel and Hannah need groceries, drywall anchors, and IKEA parts — but no single app handles all three. This episode explores why grocery-centric apps like Bring fail outside the supermarket, why "store" matters more than "category" as an organizing principle, and whether building your own system with AI is finally viable. We trace the data model problem, the Home Assistant detour, and what the last six months of AI tools changed about the build-vs-buy calculus.]]></itunes:summary>
      <itunes:duration>1355</itunes:duration>
      <itunes:episode>5078</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/shopping-lists-beyond-groceries.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/shopping-lists-beyond-groceries.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Engraving Bits Decoded: Geometry, Grade, and Material Match</title>
      <description><![CDATA[Rotary engraving bits look like tiny metal toothpicks, but inside each one hides four engineering decisions: head shape, flute count, helix angle, and carbide grade. This episode maps the full landscape of engraving geometries and compositions for handheld tools — from why a cone bit's flat floor controls stroke width to why C4 micrograin carbide is a brittle mistake on a low-torque Dremel. We break down the material-specific picks for HDPE, aluminum, MDF, and PCB copper, and explain why those 30-piece starter kits sabotage your technique with inconsistent grinding.]]></description>
      <link>https://myweirdprompts.com/episode/engraving-bits-geometry-grade-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/engraving-bits-geometry-grade-guide/</guid>
      <pubDate>Sat, 05 Sep 2026 18:52:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/engraving-bits-geometry-grade-guide.m4a"
        type="audio/mp4"
        length="21731880"
      />
      <itunes:title>Engraving Bits Decoded: Geometry, Grade, and Material Match</itunes:title>
      <itunes:subtitle>Cone vs. ball-nose vs. pointed, C2 vs. C4, single vs. multi-flute — what actually matters when engraving with a handheld rotary tool.</itunes:subtitle>
      <itunes:summary><![CDATA[Rotary engraving bits look like tiny metal toothpicks, but inside each one hides four engineering decisions: head shape, flute count, helix angle, and carbide grade. This episode maps the full landscape of engraving geometries and compositions for handheld tools — from why a cone bit's flat floor controls stroke width to why C4 micrograin carbide is a brittle mistake on a low-torque Dremel. We break down the material-specific picks for HDPE, aluminum, MDF, and PCB copper, and explain why those 30-piece starter kits sabotage your technique with inconsistent grinding.]]></itunes:summary>
      <itunes:duration>1231</itunes:duration>
      <itunes:episode>5060</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/engraving-bits-geometry-grade-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/engraving-bits-geometry-grade-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Invisible Machinery of Autonomous Agents</title>
      <description><![CDATA[Everyone talks about what AI agents do, but nobody talks about what wakes them up, what they remember, and what stops them from running forever. This episode maps the spectrum from deterministic triggers to fully autonomous agents, then digs into the three binding mechanisms — heartbeats, state stores, and loose event triggers — that make always-on agents possible. We also explore real-world cost control patterns from the OpenClaw ecosystem and follow the thread to where agents start designing their own sub-agents.]]></description>
      <link>https://myweirdprompts.com/episode/autonomous-agent-binding-mechanisms/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/autonomous-agent-binding-mechanisms/</guid>
      <pubDate>Sat, 05 Sep 2026 13:17:47 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/autonomous-agent-binding-mechanisms.m4a"
        type="audio/mp4"
        length="23785914"
      />
      <itunes:title>The Invisible Machinery of Autonomous Agents</itunes:title>
      <itunes:subtitle>Heartbeats, state stores, and budget caps — the unglamorous infrastructure that actually makes always-on AI agents work.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone talks about what AI agents do, but nobody talks about what wakes them up, what they remember, and what stops them from running forever. This episode maps the spectrum from deterministic triggers to fully autonomous agents, then digs into the three binding mechanisms — heartbeats, state stores, and loose event triggers — that make always-on agents possible. We also explore real-world cost control patterns from the OpenClaw ecosystem and follow the thread to where agents start designing their own sub-agents.]]></itunes:summary>
      <itunes:duration>1341</itunes:duration>
      <itunes:episode>5055</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/autonomous-agent-binding-mechanisms.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/autonomous-agent-binding-mechanisms.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Does &quot;Contract&quot; Actually Mean in Code?</title>
      <description><![CDATA[The word "contract" gets thrown around in software development constantly — client-API contracts, TypeScript contracts, data contracts. But what does it actually mean? We trace the term back to Bertrand Meyer's design by contract in 1986, build a five-part test for what makes a real contract, and run it against TypeScript interfaces, OpenAPI schemas, database constraints, consumer-driven contract testing, and data contracts. The results might surprise you.]]></description>
      <link>https://myweirdprompts.com/episode/contract-meaning-software-design/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/contract-meaning-software-design/</guid>
      <pubDate>Fri, 04 Sep 2026 10:11:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/contract-meaning-software-design.m4a"
        type="audio/mp4"
        length="30223775"
      />
      <itunes:title>What Does &quot;Contract&quot; Actually Mean in Code?</itunes:title>
      <itunes:subtitle>From TypeScript interfaces to database schemas — when is a software contract real, and when is it just a hope?</itunes:subtitle>
      <itunes:summary><![CDATA[The word "contract" gets thrown around in software development constantly — client-API contracts, TypeScript contracts, data contracts. But what does it actually mean? We trace the term back to Bertrand Meyer's design by contract in 1986, build a five-part test for what makes a real contract, and run it against TypeScript interfaces, OpenAPI schemas, database constraints, consumer-driven contract testing, and data contracts. The results might surprise you.]]></itunes:summary>
      <itunes:duration>1722</itunes:duration>
      <itunes:episode>5025</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/contract-meaning-software-design.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/contract-meaning-software-design.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where Should Coding Agents Actually Live?</title>
      <description><![CDATA[Should your coding agent run on your laptop or live entirely in the cloud? This episode breaks down the architectures of Claude Code and Codex, the hidden cost of file round-trips, and why the pull request workflow makes no sense for solo developers. We explore cloud sessions vs. remote control, the thin client future of development, and whether keeping the agent local is just a habit we haven't broken yet.]]></description>
      <link>https://myweirdprompts.com/episode/coding-agents-cloud-local/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/coding-agents-cloud-local/</guid>
      <pubDate>Thu, 03 Sep 2026 17:31:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/coding-agents-cloud-local.m4a"
        type="audio/mp4"
        length="31567245"
      />
      <itunes:title>Where Should Coding Agents Actually Live?</itunes:title>
      <itunes:subtitle>Exploring whether coding agents should run locally or fully in the cloud — and what changes when they move.</itunes:subtitle>
      <itunes:summary><![CDATA[Should your coding agent run on your laptop or live entirely in the cloud? This episode breaks down the architectures of Claude Code and Codex, the hidden cost of file round-trips, and why the pull request workflow makes no sense for solo developers. We explore cloud sessions vs. remote control, the thin client future of development, and whether keeping the agent local is just a habit we haven't broken yet.]]></itunes:summary>
      <itunes:duration>1805</itunes:duration>
      <itunes:episode>5018</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/coding-agents-cloud-local.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/coding-agents-cloud-local.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Reverse-Engineering a Backend from the Frontend</title>
      <description><![CDATA[What happens when you have a complete frontend but no backend? This episode flips the script on traditional development by reverse-engineering a backend from a finished frontend. We trace the full stack: starting with the database as the system of record, through the ORM's lazy query machinery, to the API layer where frontend and backend finally meet. Along the way, we uncover the N+1 query trap, the invisible translations between JSON and SQL, and the moment flat files become a running system. If you've ever wondered what actually happens between a browser click and a database write, this is the episode for you.]]></description>
      <link>https://myweirdprompts.com/episode/reverse-engineering-backend-frontend/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/reverse-engineering-backend-frontend/</guid>
      <pubDate>Thu, 03 Sep 2026 17:16:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/reverse-engineering-backend-frontend.m4a"
        type="audio/mp4"
        length="25292194"
      />
      <itunes:title>Reverse-Engineering a Backend from the Frontend</itunes:title>
      <itunes:subtitle>Building a backend when only the frontend exists—database first, then ORM, API, and the hidden traps.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when you have a complete frontend but no backend? This episode flips the script on traditional development by reverse-engineering a backend from a finished frontend. We trace the full stack: starting with the database as the system of record, through the ORM's lazy query machinery, to the API layer where frontend and backend finally meet. Along the way, we uncover the N+1 query trap, the invisible translations between JSON and SQL, and the moment flat files become a running system. If you've ever wondered what actually happens between a browser click and a database write, this is the episode for you.]]></itunes:summary>
      <itunes:duration>1443</itunes:duration>
      <itunes:episode>5017</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/reverse-engineering-backend-frontend.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/reverse-engineering-backend-frontend.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>PWA vs Native: One Developer&apos;s Home Inventory Dilemma</title>
      <description><![CDATA[Daniel built a warehouse-grade home inventory system as a PWA — Vue, Postgres, AI batch processing, QR scanning. It works great 80% of the time. But background sync, camera speed, offline storage, push notifications, and scroll performance all have real gaps compared to native Android. This episode walks through exactly what you lose, why it matters, and whether Capacitor can bridge the gap with a single codebase.]]></description>
      <link>https://myweirdprompts.com/episode/pwa-vs-native-home-inventory/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pwa-vs-native-home-inventory/</guid>
      <pubDate>Thu, 03 Sep 2026 14:29:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pwa-vs-native-home-inventory.m4a"
        type="audio/mp4"
        length="28080432"
      />
      <itunes:title>PWA vs Native: One Developer&apos;s Home Inventory Dilemma</itunes:title>
      <itunes:subtitle>PWAs cover 80% of use cases. What&apos;s the cost of the last 20%?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel built a warehouse-grade home inventory system as a PWA — Vue, Postgres, AI batch processing, QR scanning. It works great 80% of the time. But background sync, camera speed, offline storage, push notifications, and scroll performance all have real gaps compared to native Android. This episode walks through exactly what you lose, why it matters, and whether Capacitor can bridge the gap with a single codebase.]]></itunes:summary>
      <itunes:duration>1604</itunes:duration>
      <itunes:episode>5014</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pwa-vs-native-home-inventory.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pwa-vs-native-home-inventory.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Android Camera Fragmentation: Why Apps See Different Lenses</title>
      <description><![CDATA[Daniel's home inventory system keeps hitting the same wall — his OnePlus Nord 3's fisheye camera works fine in the stock camera app but disappears in third-party apps. This isn't a bug; it's a deliberate design choice by OEMs like OnePlus that have restricted auxiliary camera access across multiple OS versions. We break down Android's logical vs physical camera model, the zoom ratio bridge that apps like ChatGPT use to access hidden lenses, and the practical workarounds developers can implement without maintaining per-device camera databases.]]></description>
      <link>https://myweirdprompts.com/episode/android-camera-fragmentation-physical-lenses/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-camera-fragmentation-physical-lenses/</guid>
      <pubDate>Thu, 03 Sep 2026 14:24:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-camera-fragmentation-physical-lenses.m4a"
        type="audio/mp4"
        length="30023021"
      />
      <itunes:title>Android Camera Fragmentation: Why Apps See Different Lenses</itunes:title>
      <itunes:subtitle>OnePlus hides fisheye lenses from third-party apps. Here&apos;s how Android&apos;s camera API actually works and what you can do about it.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's home inventory system keeps hitting the same wall — his OnePlus Nord 3's fisheye camera works fine in the stock camera app but disappears in third-party apps. This isn't a bug; it's a deliberate design choice by OEMs like OnePlus that have restricted auxiliary camera access across multiple OS versions. We break down Android's logical vs physical camera model, the zoom ratio bridge that apps like ChatGPT use to access hidden lenses, and the practical workarounds developers can implement without maintaining per-device camera databases.]]></itunes:summary>
      <itunes:duration>1705</itunes:duration>
      <itunes:episode>5013</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-camera-fragmentation-physical-lenses.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-camera-fragmentation-physical-lenses.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Precision Is Your Model Actually Running At?</title>
      <description><![CDATA[When you call an API for GPT-4o or Llama 3 70B, are you actually getting the full-precision model you think you are? This episode pulls back the curtain on inference infrastructure, examining how commercial providers like Together AI and Fireworks quantize open-weight models by default, whether closed-source vendors like OpenAI and Anthropic optimize their own models in undisclosed ways, and what happens when your request routes through aggregators like OpenRouter. We explore the economic pressure to quantize, the documented vs. undocumented reality of production inference, and what it means for developers who assume the weights are untouched.]]></description>
      <link>https://myweirdprompts.com/episode/model-precision-inference-quantization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/model-precision-inference-quantization/</guid>
      <pubDate>Thu, 03 Sep 2026 11:34:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/model-precision-inference-quantization.m4a"
        type="audio/mp4"
        length="23573544"
      />
      <itunes:title>What Precision Is Your Model Actually Running At?</itunes:title>
      <itunes:subtitle>When you call an API, are you getting the model you think you are? Inference providers quantize more than they admit.</itunes:subtitle>
      <itunes:summary><![CDATA[When you call an API for GPT-4o or Llama 3 70B, are you actually getting the full-precision model you think you are? This episode pulls back the curtain on inference infrastructure, examining how commercial providers like Together AI and Fireworks quantize open-weight models by default, whether closed-source vendors like OpenAI and Anthropic optimize their own models in undisclosed ways, and what happens when your request routes through aggregators like OpenRouter. We explore the economic pressure to quantize, the documented vs. undocumented reality of production inference, and what it means for developers who assume the weights are untouched.]]></itunes:summary>
      <itunes:duration>1323</itunes:duration>
      <itunes:episode>5004</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/model-precision-inference-quantization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/model-precision-inference-quantization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The 15% Bug Catch: What Type Systems Actually Deliver</title>
      <description><![CDATA[Static type checkers are often sold as silver bullets for code quality, but the landmark 2017 study by Gao, Bird, and Barr found they catch only about 15% of public JavaScript bugs. This episode climbs the operational ladder of gradual typing — from two-tier configs and per-module ratchets to the human decision points where automation stops. We explore why AI coding agents make escape-hatch counters essential, how production type tracers produce raw material that still needs human judgment, and why runtime validation with Pydantic or Zod at trust boundaries is non-negotiable. If you've ever wondered whether your type checker is buying you real bug prevention or just expensive autocomplete, this episode walks through what the data actually says.]]></description>
      <link>https://myweirdprompts.com/episode/type-system-bug-catch-rate/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/type-system-bug-catch-rate/</guid>
      <pubDate>Wed, 02 Sep 2026 16:19:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/type-system-bug-catch-rate.m4a"
        type="audio/mp4"
        length="25130512"
      />
      <itunes:title>The 15% Bug Catch: What Type Systems Actually Deliver</itunes:title>
      <itunes:subtitle>Type checkers catch ~15% of bugs. Here&apos;s how to build a gradual typing ladder that actually works.</itunes:subtitle>
      <itunes:summary><![CDATA[Static type checkers are often sold as silver bullets for code quality, but the landmark 2017 study by Gao, Bird, and Barr found they catch only about 15% of public JavaScript bugs. This episode climbs the operational ladder of gradual typing — from two-tier configs and per-module ratchets to the human decision points where automation stops. We explore why AI coding agents make escape-hatch counters essential, how production type tracers produce raw material that still needs human judgment, and why runtime validation with Pydantic or Zod at trust boundaries is non-negotiable. If you've ever wondered whether your type checker is buying you real bug prevention or just expensive autocomplete, this episode walks through what the data actually says.]]></itunes:summary>
      <itunes:duration>1421</itunes:duration>
      <itunes:episode>5001</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/type-system-bug-catch-rate.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/type-system-bug-catch-rate.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can You Trust Google Photos With Your Life&apos;s Work?</title>
      <description><![CDATA[Daniel uses Google Photos as a visual knowledge base for his engravings — photographing, organizing, and referencing them with ChatGPT. But he's hit a wall: there's no way to incrementally back up his albums. In this episode, we break down why Google Photos is designed to prevent you from leaving, examine tools like gphotos-sync and rclone that try to work around the limitations, and explore alternatives like Ente, PhotoPrism, and notes apps. Can you get both convenience and control without self-hosting? The answer reveals a deeper divide between products built on data ownership and products built on engagement.]]></description>
      <link>https://myweirdprompts.com/episode/google-photos-backup-limits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/google-photos-backup-limits/</guid>
      <pubDate>Wed, 02 Sep 2026 14:49:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/google-photos-backup-limits.m4a"
        type="audio/mp4"
        length="23501319"
      />
      <itunes:title>Can You Trust Google Photos With Your Life&apos;s Work?</itunes:title>
      <itunes:subtitle>Why your Google Photos library isn&apos;t really backed up — and what actually works instead.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel uses Google Photos as a visual knowledge base for his engravings — photographing, organizing, and referencing them with ChatGPT. But he's hit a wall: there's no way to incrementally back up his albums. In this episode, we break down why Google Photos is designed to prevent you from leaving, examine tools like gphotos-sync and rclone that try to work around the limitations, and explore alternatives like Ente, PhotoPrism, and notes apps. Can you get both convenience and control without self-hosting? The answer reveals a deeper divide between products built on data ownership and products built on engagement.]]></itunes:summary>
      <itunes:duration>1321</itunes:duration>
      <itunes:episode>5000</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/google-photos-backup-limits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/google-photos-backup-limits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>4,914 Episodes of AI Hosts: What We Learned</title>
      <description><![CDATA[In this meta-episode, Corn the sloth and Herman Poppleberry the donkey turn the microphones on themselves. They break down how My Weird Prompts works as a fully autonomous content system — from prompt submission to published episode with no human in the loop. They discuss what nearly 5,000 episodes reveal about AI capability over time, how the characters developed consistent personalities through repetition, and why a prompt-driven format creates a fundamentally different kind of podcast. The hosts explore the show's structure as a dataset, a real-time barometer of human curiosity, and a proof of concept for AI-generated content at scale.]]></description>
      <link>https://myweirdprompts.com/episode/ai-hosts-autonomous-podcast/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-hosts-autonomous-podcast/</guid>
      <pubDate>Tue, 01 Sep 2026 23:29:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-hosts-autonomous-podcast.m4a"
        type="audio/mp4"
        length="25747246"
      />
      <itunes:title>4,914 Episodes of AI Hosts: What We Learned</itunes:title>
      <itunes:subtitle>Two AI-generated hosts explain how their fully automated podcast works — and what 4,914 episodes reveal about AI.</itunes:subtitle>
      <itunes:summary><![CDATA[In this meta-episode, Corn the sloth and Herman Poppleberry the donkey turn the microphones on themselves. They break down how My Weird Prompts works as a fully autonomous content system — from prompt submission to published episode with no human in the loop. They discuss what nearly 5,000 episodes reveal about AI capability over time, how the characters developed consistent personalities through repetition, and why a prompt-driven format creates a fundamentally different kind of podcast. The hosts explore the show's structure as a dataset, a real-time barometer of human curiosity, and a proof of concept for AI-generated content at scale.]]></itunes:summary>
      <itunes:duration>1469</itunes:duration>
      <itunes:episode>4997</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-hosts-autonomous-podcast.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-hosts-autonomous-podcast.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Memory Leaks: The Full Taxonomy</title>
      <description><![CDATA[Memory leaks aren't just "forgot to free." This episode breaks down the full taxonomy — genuine unreachable allocations in C, logical leaks in garbage-collected languages where every byte is "reachable," heap fragmentation that draws a leak-shaped graph, and the non-heap leaks like file descriptors and zombie processes that crash services silently. We walk through real tooling for each failure mode — Valgrind, Massif, Chrome DevTools heap snapshots, Eclipse MAT, Go pprof, memray, and production profilers — plus how Linux picks its OOM victim, what cgroups v2 limits actually mean, and why the slope of the curve matters more than any single snapshot. If you've ever watched RSS climb and felt powerless, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/memory-leaks-taxonomy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/memory-leaks-taxonomy/</guid>
      <pubDate>Tue, 01 Sep 2026 21:55:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/memory-leaks-taxonomy.m4a"
        type="audio/mp4"
        length="33261505"
      />
      <itunes:title>Memory Leaks: The Full Taxonomy</itunes:title>
      <itunes:subtitle>From C to Rust to Kubernetes OOM kills — the real taxonomy of memory leaks and how to hunt them.</itunes:subtitle>
      <itunes:summary><![CDATA[Memory leaks aren't just "forgot to free." This episode breaks down the full taxonomy — genuine unreachable allocations in C, logical leaks in garbage-collected languages where every byte is "reachable," heap fragmentation that draws a leak-shaped graph, and the non-heap leaks like file descriptors and zombie processes that crash services silently. We walk through real tooling for each failure mode — Valgrind, Massif, Chrome DevTools heap snapshots, Eclipse MAT, Go pprof, memray, and production profilers — plus how Linux picks its OOM victim, what cgroups v2 limits actually mean, and why the slope of the curve matters more than any single snapshot. If you've ever watched RSS climb and felt powerless, this one's for you.]]></itunes:summary>
      <itunes:duration>1893</itunes:duration>
      <itunes:episode>4996</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/memory-leaks-taxonomy.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/memory-leaks-taxonomy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What a Bad Thermostat Teaches Us About Control</title>
      <description><![CDATA[A bad thermostat doesn't just feel uncomfortable—it reveals deep lessons about control systems, sampling theory, and the mathematics of not overcorrecting. This episode uses the humble thermostat as a window into how to think about feedback loops, from bang-bang controllers to phase-lead compensation. We explore why reacting to every sample leads to oscillation, how "take back half" algorithms learn a system's momentum, and why the most important control action is often inaction. If you build systems that pull levers based on measurements—from auto-scaling to monitoring—this episode will change how you think about stability.]]></description>
      <link>https://myweirdprompts.com/episode/thermostat-control-feedback-lessons/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/thermostat-control-feedback-lessons/</guid>
      <pubDate>Tue, 01 Sep 2026 21:21:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/thermostat-control-feedback-lessons.m4a"
        type="audio/mp4"
        length="28617440"
      />
      <itunes:title>What a Bad Thermostat Teaches Us About Control</itunes:title>
      <itunes:subtitle>How phase lead, decay, and knowing when to do nothing fix the oscillation problem.</itunes:subtitle>
      <itunes:summary><![CDATA[A bad thermostat doesn't just feel uncomfortable—it reveals deep lessons about control systems, sampling theory, and the mathematics of not overcorrecting. This episode uses the humble thermostat as a window into how to think about feedback loops, from bang-bang controllers to phase-lead compensation. We explore why reacting to every sample leads to oscillation, how "take back half" algorithms learn a system's momentum, and why the most important control action is often inaction. If you build systems that pull levers based on measurements—from auto-scaling to monitoring—this episode will change how you think about stability.]]></itunes:summary>
      <itunes:duration>1628</itunes:duration>
      <itunes:episode>4995</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/thermostat-control-feedback-lessons.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/thermostat-control-feedback-lessons.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Cross-Cloud Failover: Can You Actually Survive an AWS Outage?</title>
      <description><![CDATA[What happens when AWS IAM goes down globally and your multi-region architecture becomes a single point of failure? This episode tackles the hard reality of cross-cloud redundancy — from Kubernetes-orchestrated failover at scale to a $12/month VPS setup that handles 220,000 API requests daily across three providers. We trace real case studies from teams who've run the drills, measured the RTO, and published the exact failure patterns that never show up on architecture diagrams. Plus: why the hardest part isn't the automation — it's trusting it enough to let it run without a human grabbing the keyboard.]]></description>
      <link>https://myweirdprompts.com/episode/cross-cloud-failover-aws-outage/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cross-cloud-failover-aws-outage/</guid>
      <pubDate>Tue, 01 Sep 2026 20:58:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cross-cloud-failover-aws-outage.m4a"
        type="audio/mp4"
        length="29609704"
      />
      <itunes:title>Cross-Cloud Failover: Can You Actually Survive an AWS Outage?</itunes:title>
      <itunes:subtitle>Real teams are running cross-cloud failover drills — and the first attempt usually fails. Here&apos;s what they learned.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when AWS IAM goes down globally and your multi-region architecture becomes a single point of failure? This episode tackles the hard reality of cross-cloud redundancy — from Kubernetes-orchestrated failover at scale to a $12/month VPS setup that handles 220,000 API requests daily across three providers. We trace real case studies from teams who've run the drills, measured the RTO, and published the exact failure patterns that never show up on architecture diagrams. Plus: why the hardest part isn't the automation — it's trusting it enough to let it run without a human grabbing the keyboard.]]></itunes:summary>
      <itunes:duration>1706</itunes:duration>
      <itunes:episode>4993</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cross-cloud-failover-aws-outage.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cross-cloud-failover-aws-outage.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Self-Healing Codebases: Can AI Fix Bugs Autonomously?</title>
      <description><![CDATA[Can AI truly maintain itself? In this episode, we explore the current state of self-healing codebases — from AutoPatch AI resolving bugs in 42 seconds to systems like Ouro Loop that autonomously diagnosed an architectural root cause. We break down where these systems work (localized bugs), where they fail (multi-file semantic issues), and the verification infrastructure needed to make them trustworthy. Plus: why the "vibe coding" era is giving way to bounded autonomy, and what the SelfHeal paper reveals about the 23 distinct fix patterns in agentic systems.]]></description>
      <link>https://myweirdprompts.com/episode/self-healing-codebases-ai-bugs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/self-healing-codebases-ai-bugs/</guid>
      <pubDate>Tue, 01 Sep 2026 20:03:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/self-healing-codebases-ai-bugs.m4a"
        type="audio/mp4"
        length="30025496"
      />
      <itunes:title>Self-Healing Codebases: Can AI Fix Bugs Autonomously?</itunes:title>
      <itunes:subtitle>How far are we from AI systems that fix their own bugs without human intervention? The answer might surprise you.</itunes:subtitle>
      <itunes:summary><![CDATA[Can AI truly maintain itself? In this episode, we explore the current state of self-healing codebases — from AutoPatch AI resolving bugs in 42 seconds to systems like Ouro Loop that autonomously diagnosed an architectural root cause. We break down where these systems work (localized bugs), where they fail (multi-file semantic issues), and the verification infrastructure needed to make them trustworthy. Plus: why the "vibe coding" era is giving way to bounded autonomy, and what the SelfHeal paper reveals about the 23 distinct fix patterns in agentic systems.]]></itunes:summary>
      <itunes:duration>1714</itunes:duration>
      <itunes:episode>4991</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/self-healing-codebases-ai-bugs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/self-healing-codebases-ai-bugs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can You Learn to Code Backwards?</title>
      <description><![CDATA[AI coding tools like Claude are getting so good that experienced developers feel their manual skills slipping — and newcomers are entering the field having never written a line from scratch. This episode explores the uncomfortable tension between productivity and skill retention, using aviation's mandatory manual flying requirements as a lens. Can you build genuine programming understanding backwards, starting from AI-generated code? We dig into concrete techniques like the explanation loop and the "remove one concept" method for turning AI from a crutch into a tutor.]]></description>
      <link>https://myweirdprompts.com/episode/learning-to-code-backwards/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/learning-to-code-backwards/</guid>
      <pubDate>Tue, 01 Sep 2026 14:51:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/learning-to-code-backwards.m4a"
        type="audio/mp4"
        length="27818292"
      />
      <itunes:title>Can You Learn to Code Backwards?</itunes:title>
      <itunes:subtitle>When AI writes your code, do you still learn? Aviation, atrophy, and building a mental model from scratch.</itunes:subtitle>
      <itunes:summary><![CDATA[AI coding tools like Claude are getting so good that experienced developers feel their manual skills slipping — and newcomers are entering the field having never written a line from scratch. This episode explores the uncomfortable tension between productivity and skill retention, using aviation's mandatory manual flying requirements as a lens. Can you build genuine programming understanding backwards, starting from AI-generated code? We dig into concrete techniques like the explanation loop and the "remove one concept" method for turning AI from a crutch into a tutor.]]></itunes:summary>
      <itunes:duration>1583</itunes:duration>
      <itunes:episode>4980</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/learning-to-code-backwards.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/learning-to-code-backwards.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Procurement Agent That Actually Works</title>
      <description><![CDATA[Technical procurement is a strange beast — cross-geography manufacturer number mapping, threshold-based cost comparisons, adversarial supplier verification. Most buyers develop these skills tacitly and never write them down. In this episode, we explore what happens when you try to hand those skills to an AI agent, from the CIPS-aligned open-source skill packs on Claude to the organizational trust barriers that keep CIOs up at night. We break down why regular Python scripts break on exceptions, how agentic systems handle semantic reasoning between execution steps, and why the first step to building a procurement agent isn't code — it's documenting your own human workflow.]]></description>
      <link>https://myweirdprompts.com/episode/procurement-agent-tacit-skills/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/procurement-agent-tacit-skills/</guid>
      <pubDate>Tue, 01 Sep 2026 13:43:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/procurement-agent-tacit-skills.m4a"
        type="audio/mp4"
        length="28634505"
      />
      <itunes:title>Building a Procurement Agent That Actually Works</itunes:title>
      <itunes:subtitle>How to encode the weird, undocumented skills technical buyers use every day into an AI agent that won&apos;t get you audited.</itunes:subtitle>
      <itunes:summary><![CDATA[Technical procurement is a strange beast — cross-geography manufacturer number mapping, threshold-based cost comparisons, adversarial supplier verification. Most buyers develop these skills tacitly and never write them down. In this episode, we explore what happens when you try to hand those skills to an AI agent, from the CIPS-aligned open-source skill packs on Claude to the organizational trust barriers that keep CIOs up at night. We break down why regular Python scripts break on exceptions, how agentic systems handle semantic reasoning between execution steps, and why the first step to building a procurement agent isn't code — it's documenting your own human workflow.]]></itunes:summary>
      <itunes:duration>1618</itunes:duration>
      <itunes:episode>4976</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/procurement-agent-tacit-skills.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/procurement-agent-tacit-skills.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Pure AI Inference Server</title>
      <description><![CDATA[Most people building "AI servers" end up with a machine trying to be a database, web server, chat frontend, and inference box all at once — and then wonder why it falls over. This episode walks through what it actually takes to build a pure inference server: a machine whose only job is running models, with everything served out over an API. We cover engine selection (vLLM vs SGLang vs Ollama), the multi-model problem, concurrency and batching, VRAM planning, and the orchestration gaps that still exist. If you've been thinking about setting up dedicated inference infrastructure, this is the build spec you've been looking for.]]></description>
      <link>https://myweirdprompts.com/episode/pure-ai-inference-server/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pure-ai-inference-server/</guid>
      <pubDate>Tue, 01 Sep 2026 12:15:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pure-ai-inference-server.m4a"
        type="audio/mp4"
        length="28213019"
      />
      <itunes:title>Building a Pure AI Inference Server</itunes:title>
      <itunes:subtitle>Why your &quot;AI server&quot; should look more like a database than a web app, and how to actually build one.</itunes:subtitle>
      <itunes:summary><![CDATA[Most people building "AI servers" end up with a machine trying to be a database, web server, chat frontend, and inference box all at once — and then wonder why it falls over. This episode walks through what it actually takes to build a pure inference server: a machine whose only job is running models, with everything served out over an API. We cover engine selection (vLLM vs SGLang vs Ollama), the multi-model problem, concurrency and batching, VRAM planning, and the orchestration gaps that still exist. If you've been thinking about setting up dedicated inference infrastructure, this is the build spec you've been looking for.]]></itunes:summary>
      <itunes:duration>1610</itunes:duration>
      <itunes:episode>4973</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pure-ai-inference-server.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pure-ai-inference-server.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>CPU vs GPU Inference: When to Skip the GPU</title>
      <description><![CDATA[The received wisdom says serious AI inference needs a GPU — but that's not the full story. This episode unpacks the real bottlenecks: memory bandwidth vs. parallel compute, prefill vs. decode, and the surprising workloads where CPU actually wins. From classical ML models like XGBoost to Whisper transcription and Mixtral on a laptop, we explore when the GPU advantage compresses from 100x to 6x, and why batch size one changes everything. If you're running internal chatbots, batch processing audio, or deploying small transformers, the economics may surprise you.]]></description>
      <link>https://myweirdprompts.com/episode/cpu-vs-gpu-inference-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cpu-vs-gpu-inference-guide/</guid>
      <pubDate>Tue, 01 Sep 2026 12:09:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cpu-vs-gpu-inference-guide.m4a"
        type="audio/mp4"
        length="29397621"
      />
      <itunes:title>CPU vs GPU Inference: When to Skip the GPU</itunes:title>
      <itunes:subtitle>Why running AI on CPU isn&apos;t a toy — and when the GPU advantage vanishes.</itunes:subtitle>
      <itunes:summary><![CDATA[The received wisdom says serious AI inference needs a GPU — but that's not the full story. This episode unpacks the real bottlenecks: memory bandwidth vs. parallel compute, prefill vs. decode, and the surprising workloads where CPU actually wins. From classical ML models like XGBoost to Whisper transcription and Mixtral on a laptop, we explore when the GPU advantage compresses from 100x to 6x, and why batch size one changes everything. If you're running internal chatbots, batch processing audio, or deploying small transformers, the economics may surprise you.]]></itunes:summary>
      <itunes:duration>1677</itunes:duration>
      <itunes:episode>4972</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cpu-vs-gpu-inference-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cpu-vs-gpu-inference-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tune vs Prompt: Taming Hallucinated Deadlines</title>
      <description><![CDATA[When you dictate a messy to-do list and want clean JSON out, the question isn't just whether to fine-tune or prompt a general model — it's whether you can tolerate fabricated deadlines or missed tasks. This episode breaks apart format adherence, judgment quality, and failure predictability, exploring where fine-tuning actually moves the needle and where the real cost lies: not in compute, but in a hundred hours of labeling your own subjective sense of priority.]]></description>
      <link>https://myweirdprompts.com/episode/fine-tune-vs-prompt-extraction/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fine-tune-vs-prompt-extraction/</guid>
      <pubDate>Tue, 01 Sep 2026 10:21:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fine-tune-vs-prompt-extraction.m4a"
        type="audio/mp4"
        length="36108748"
      />
      <itunes:title>Fine-Tune vs Prompt: Taming Hallucinated Deadlines</itunes:title>
      <itunes:subtitle>Big models add fake deadlines. Small models miss tasks. Which wrongness can you build a system around?</itunes:subtitle>
      <itunes:summary><![CDATA[When you dictate a messy to-do list and want clean JSON out, the question isn't just whether to fine-tune or prompt a general model — it's whether you can tolerate fabricated deadlines or missed tasks. This episode breaks apart format adherence, judgment quality, and failure predictability, exploring where fine-tuning actually moves the needle and where the real cost lies: not in compute, but in a hundred hours of labeling your own subjective sense of priority.]]></itunes:summary>
      <itunes:duration>2064</itunes:duration>
      <itunes:episode>4971</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fine-tune-vs-prompt-extraction.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fine-tune-vs-prompt-extraction.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Labels vs. Book Covers: Structured Extraction Tradeoffs</title>
      <description><![CDATA[Daniel needs to extract structured data from product labels and book covers — serial numbers, titles, authors, and a wildcard field for anything unexpected. Should he use a general vision model like GPT-4V or build a purpose-trained pipeline? We break down the real tradeoffs: accuracy on tiny rotated text, cost at scale, offline capability, how each approach handles the novel-fields requirement, and what the actual pipeline looks like. A twelve-point accuracy gap and a forty-x latency difference make this less subtle than it seems.]]></description>
      <link>https://myweirdprompts.com/episode/structured-extraction-tradeoffs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/structured-extraction-tradeoffs/</guid>
      <pubDate>Tue, 01 Sep 2026 10:14:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/structured-extraction-tradeoffs.m4a"
        type="audio/mp4"
        length="30488532"
      />
      <itunes:title>Labels vs. Book Covers: Structured Extraction Tradeoffs</itunes:title>
      <itunes:subtitle>General vision models vs. purpose-built pipelines for extracting data from messy real-world labels and covers.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel needs to extract structured data from product labels and book covers — serial numbers, titles, authors, and a wildcard field for anything unexpected. Should he use a general vision model like GPT-4V or build a purpose-trained pipeline? We break down the real tradeoffs: accuracy on tiny rotated text, cost at scale, offline capability, how each approach handles the novel-fields requirement, and what the actual pipeline looks like. A twelve-point accuracy gap and a forty-x latency difference make this less subtle than it seems.]]></itunes:summary>
      <itunes:duration>1724</itunes:duration>
      <itunes:episode>4970</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/structured-extraction-tradeoffs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/structured-extraction-tradeoffs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Train a One-Class Detector from 300 Photos</title>
      <description><![CDATA[Most tutorials skip the hard parts. This episode walks through the full process of training a single-class object detector from scratch: how many images you actually need, why scene-based splitting matters more than random splits, how to annotate consistently, and why YOLOv8n is the right architecture for small datasets. Using the concrete example of detecting a specific anti-graffiti poster design city-wide, we cover the metrics that matter when every missed detection costs $200 — and the honest signal for when to give up and use a general model instead.]]></description>
      <link>https://myweirdprompts.com/episode/train-one-class-detector/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/train-one-class-detector/</guid>
      <pubDate>Tue, 01 Sep 2026 10:11:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/train-one-class-detector.m4a"
        type="audio/mp4"
        length="28393706"
      />
      <itunes:title>How to Train a One-Class Detector from 300 Photos</itunes:title>
      <itunes:subtitle>From photo folder to production YOLO model — the real walkthrough for custom object detection.</itunes:subtitle>
      <itunes:summary><![CDATA[Most tutorials skip the hard parts. This episode walks through the full process of training a single-class object detector from scratch: how many images you actually need, why scene-based splitting matters more than random splits, how to annotate consistently, and why YOLOv8n is the right architecture for small datasets. Using the concrete example of detecting a specific anti-graffiti poster design city-wide, we cover the metrics that matter when every missed detection costs $200 — and the honest signal for when to give up and use a general model instead.]]></itunes:summary>
      <itunes:duration>1598</itunes:duration>
      <itunes:episode>4969</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/train-one-class-detector.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/train-one-class-detector.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Gateways: Worth the Middleware Tax?</title>
      <description><![CDATA[AI gateways promise more than just model routing—PII reduction, context augmentation, and system prompt injection at a single chokepoint. But every middleware layer adds latency, debugging complexity, and maintenance burden. We examine when these features justify the overhead and when simpler solutions like cloud credential managers or direct API calls make more sense. A practical look at the self-hosted gateway landscape and the real costs of centralized AI infrastructure.]]></description>
      <link>https://myweirdprompts.com/episode/ai-gateways-middleware-tax/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-gateways-middleware-tax/</guid>
      <pubDate>Tue, 01 Sep 2026 08:08:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-gateways-middleware-tax.m4a"
        type="audio/mp4"
        length="25964628"
      />
      <itunes:title>AI Gateways: Worth the Middleware Tax?</itunes:title>
      <itunes:subtitle>When does an AI gateway&apos;s overhead justify its features? We break down the tradeoffs.</itunes:subtitle>
      <itunes:summary><![CDATA[AI gateways promise more than just model routing—PII reduction, context augmentation, and system prompt injection at a single chokepoint. But every middleware layer adds latency, debugging complexity, and maintenance burden. We examine when these features justify the overhead and when simpler solutions like cloud credential managers or direct API calls make more sense. A practical look at the self-hosted gateway landscape and the real costs of centralized AI infrastructure.]]></itunes:summary>
      <itunes:duration>1463</itunes:duration>
      <itunes:episode>4967</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-gateways-middleware-tax.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-gateways-middleware-tax.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Local vs Cloud: Running Hugging Face Models</title>
      <description><![CDATA[Ever found a perfect model on Hugging Face only to wonder if your laptop can actually run it? This episode breaks down the two main pathways for running models from the Hub — local inference and cloud deployment. Learn how the compatibility tracker calculates TOPS and memory bandwidth, how Hugging Face's content-addressable cache stores weights, and the critical difference between the Inference Endpoints gateway and the direct hosted API. No subscription tier talk — just the mechanics.]]></description>
      <link>https://myweirdprompts.com/episode/hugging-face-model-deployment/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hugging-face-model-deployment/</guid>
      <pubDate>Tue, 01 Sep 2026 08:04:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hugging-face-model-deployment.m4a"
        type="audio/mp4"
        length="28218368"
      />
      <itunes:title>Local vs Cloud: Running Hugging Face Models</itunes:title>
      <itunes:subtitle>Hugging Face&apos;s compatibility tracker, cache management, and the real difference between Inference Endpoints and the direct API.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever found a perfect model on Hugging Face only to wonder if your laptop can actually run it? This episode breaks down the two main pathways for running models from the Hub — local inference and cloud deployment. Learn how the compatibility tracker calculates TOPS and memory bandwidth, how Hugging Face's content-addressable cache stores weights, and the critical difference between the Inference Endpoints gateway and the direct hosted API. No subscription tier talk — just the mechanics.]]></itunes:summary>
      <itunes:duration>1604</itunes:duration>
      <itunes:episode>4966</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hugging-face-model-deployment.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hugging-face-model-deployment.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Multi-Model Transcription with LLM Reconciliation</title>
      <description><![CDATA[Anyone who uses voice-to-text knows the frustration of watching a technical term get silently replaced with something that sounds similar but means something completely different. "LORAs" becomes "Lauras," and suddenly your transcription is nonsense. In this episode, we explore a practical solution: running audio through multiple transcription services, then using an LLM as a judge to reconcile the outputs into a single accurate transcript. We cover the alignment problem, why model diversity matters more than repetition, and how to build a pipeline that flags only the parts you need to manually review.]]></description>
      <link>https://myweirdprompts.com/episode/ensemble-transcription-llm-judge/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ensemble-transcription-llm-judge/</guid>
      <pubDate>Mon, 31 Aug 2026 13:01:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ensemble-transcription-llm-judge.m4a"
        type="audio/mp4"
        length="34241681"
      />
      <itunes:title>Multi-Model Transcription with LLM Reconciliation</itunes:title>
      <itunes:subtitle>Using multiple transcription engines and an LLM judge to catch errors in voice-to-text — especially tricky technical terms.</itunes:subtitle>
      <itunes:summary><![CDATA[Anyone who uses voice-to-text knows the frustration of watching a technical term get silently replaced with something that sounds similar but means something completely different. "LORAs" becomes "Lauras," and suddenly your transcription is nonsense. In this episode, we explore a practical solution: running audio through multiple transcription services, then using an LLM as a judge to reconcile the outputs into a single accurate transcript. We cover the alignment problem, why model diversity matters more than repetition, and how to build a pipeline that flags only the parts you need to manually review.]]></itunes:summary>
      <itunes:duration>1969</itunes:duration>
      <itunes:episode>4958</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ensemble-transcription-llm-judge.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ensemble-transcription-llm-judge.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a DIY Barcode Inventory Cart</title>
      <description><![CDATA[Daniel built a makeshift restocking trolley with a forked Homebox inventory system, a USB barcode scanner, and an old laptop VHB'd to a cart. In this episode, we walk through the hardware decision tree — laptop vs. tablet vs. single-board computer — and the Linux kiosk mode setup that turns a general-purpose machine into a task-specific appliance. We cover USB scanner routing, battery engineering gotchas, and whether the thin client model saves you headaches.]]></description>
      <link>https://myweirdprompts.com/episode/diy-barcode-inventory-cart/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/diy-barcode-inventory-cart/</guid>
      <pubDate>Sun, 30 Aug 2026 09:51:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/diy-barcode-inventory-cart.m4a"
        type="audio/mp4"
        length="33198794"
      />
      <itunes:title>Building a DIY Barcode Inventory Cart</itunes:title>
      <itunes:subtitle>Old laptop, USB scanner, and Linux kiosk mode for a dedicated home inventory terminal.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel built a makeshift restocking trolley with a forked Homebox inventory system, a USB barcode scanner, and an old laptop VHB'd to a cart. In this episode, we walk through the hardware decision tree — laptop vs. tablet vs. single-board computer — and the Linux kiosk mode setup that turns a general-purpose machine into a task-specific appliance. We cover USB scanner routing, battery engineering gotchas, and whether the thin client model saves you headaches.]]></itunes:summary>
      <itunes:duration>1905</itunes:duration>
      <itunes:episode>4931</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/diy-barcode-inventory-cart.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/diy-barcode-inventory-cart.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>ESP32 Mics for Remote Home Monitoring</title>
      <description><![CDATA[When you need to hear what's happening in a vacation property that sits empty most of the year, most off-the-shelf solutions fall short. This episode breaks down the engineering tradeoffs between IP cameras with built-in mics, USB condenser microphones, and a DIY ESP32 approach using the INMP441 MEMS microphone. We cover pickup patterns, noise floors, Home Assistant integration, and why the cheapest option often wins for reliability and coverage.]]></description>
      <link>https://myweirdprompts.com/episode/esp32-microphone-home-monitoring/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/esp32-microphone-home-monitoring/</guid>
      <pubDate>Sun, 30 Aug 2026 03:49:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/esp32-microphone-home-monitoring.m4a"
        type="audio/mp4"
        length="29069930"
      />
      <itunes:title>ESP32 Mics for Remote Home Monitoring</itunes:title>
      <itunes:subtitle>Why an $8 ESP32 with a MEMS mic beats expensive cameras for remote audio monitoring in a vacation home.</itunes:subtitle>
      <itunes:summary><![CDATA[When you need to hear what's happening in a vacation property that sits empty most of the year, most off-the-shelf solutions fall short. This episode breaks down the engineering tradeoffs between IP cameras with built-in mics, USB condenser microphones, and a DIY ESP32 approach using the INMP441 MEMS microphone. We cover pickup patterns, noise floors, Home Assistant integration, and why the cheapest option often wins for reliability and coverage.]]></itunes:summary>
      <itunes:duration>1655</itunes:duration>
      <itunes:episode>4925</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/esp32-microphone-home-monitoring.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/esp32-microphone-home-monitoring.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Local Dictation on Android: The Real Bottlenecks</title>
      <description><![CDATA[Daniel's OnePlus Nord 3 can almost run local speech-to-text with FUTO's voice keyboard — but "almost" is where the frustration lives. This episode breaks down why the Dimensity 9000's ~20 TOPS falls short, how memory bandwidth becomes the hidden bottleneck, and why Whisper's fixed 30-second context window isn't a bug but a fundamental architectural constraint. We explore RNN-T alternatives, chunked attention approaches, and why voice keyboards and long-form transcription apps may never merge into one tool.]]></description>
      <link>https://myweirdprompts.com/episode/android-local-dictation-bottlenecks/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-local-dictation-bottlenecks/</guid>
      <pubDate>Sun, 30 Aug 2026 01:55:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-local-dictation-bottlenecks.m4a"
        type="audio/mp4"
        length="25528151"
      />
      <itunes:title>Local Dictation on Android: The Real Bottlenecks</itunes:title>
      <itunes:subtitle>Why on-device speech-to-text on Android hits a wall at 30 seconds — and what silicon actually matters.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's OnePlus Nord 3 can almost run local speech-to-text with FUTO's voice keyboard — but "almost" is where the frustration lives. This episode breaks down why the Dimensity 9000's ~20 TOPS falls short, how memory bandwidth becomes the hidden bottleneck, and why Whisper's fixed 30-second context window isn't a bug but a fundamental architectural constraint. We explore RNN-T alternatives, chunked attention approaches, and why voice keyboards and long-form transcription apps may never merge into one tool.]]></itunes:summary>
      <itunes:duration>1433</itunes:duration>
      <itunes:episode>4920</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-local-dictation-bottlenecks.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-local-dictation-bottlenecks.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Parakeet vs Whisper: On-Device Dictation Showdown</title>
      <description><![CDATA[Most people assume Whisper is the default for speech-to-text everywhere. But Whisper was built for server batch transcription, not real-time phone dictation. NVIDIA Parakeet uses a radically different architecture — CTC instead of autoregressive decoding — that lets it output tokens every 80 milliseconds as you speak. This episode breaks down why Parakeet runs faster on a phone chip than Whisper Tiny despite having 600 million parameters to Whisper's 39 million, where the accuracy tradeoffs actually bite, and how punctuation handling differs between the two approaches. If you're building an AI keyboard or dictation feature, this comparison will change how you think about model selection.]]></description>
      <link>https://myweirdprompts.com/episode/parakeet-whisper-on-device-dictation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/parakeet-whisper-on-device-dictation/</guid>
      <pubDate>Sun, 30 Aug 2026 01:48:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/parakeet-whisper-on-device-dictation.m4a"
        type="audio/mp4"
        length="33241184"
      />
      <itunes:title>Parakeet vs Whisper: On-Device Dictation Showdown</itunes:title>
      <itunes:subtitle>NVIDIA Parakeet beats Whisper on phone dictation despite having more parameters. Here&apos;s why architecture matters more than model size.</itunes:subtitle>
      <itunes:summary><![CDATA[Most people assume Whisper is the default for speech-to-text everywhere. But Whisper was built for server batch transcription, not real-time phone dictation. NVIDIA Parakeet uses a radically different architecture — CTC instead of autoregressive decoding — that lets it output tokens every 80 milliseconds as you speak. This episode breaks down why Parakeet runs faster on a phone chip than Whisper Tiny despite having 600 million parameters to Whisper's 39 million, where the accuracy tradeoffs actually bite, and how punctuation handling differs between the two approaches. If you're building an AI keyboard or dictation feature, this comparison will change how you think about model selection.]]></itunes:summary>
      <itunes:duration>1900</itunes:duration>
      <itunes:episode>4919</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/parakeet-whisper-on-device-dictation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/parakeet-whisper-on-device-dictation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Backward Batching: Fixing Slow Uploads Without Blocking Users</title>
      <description><![CDATA[When Daniel was cataloguing books at his in-laws' rural house, slow uploads kept breaking his workflow rhythm. He turned to Claude Code and got a system using IndexedDB, Web Workers, and server-side job queues — but he hasn't reviewed the code yet. In this episode, we reverse-engineer what Claude probably built: a background upload runner that caches images locally, a BullMQ queue that batches Gemini API calls, and a pattern called "backward batching" that inverts the traditional dependency flow. We also tackle the uncomfortable question of how to review AI-generated code when you haven't looked at it yet.]]></description>
      <link>https://myweirdprompts.com/episode/backward-batching-uploads-workflow/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/backward-batching-uploads-workflow/</guid>
      <pubDate>Fri, 28 Aug 2026 15:35:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/backward-batching-uploads-workflow.m4a"
        type="audio/mp4"
        length="25949368"
      />
      <itunes:title>Backward Batching: Fixing Slow Uploads Without Blocking Users</itunes:title>
      <itunes:subtitle>How one developer used background threads and deferred AI processing to fix a cataloguing workflow.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel was cataloguing books at his in-laws' rural house, slow uploads kept breaking his workflow rhythm. He turned to Claude Code and got a system using IndexedDB, Web Workers, and server-side job queues — but he hasn't reviewed the code yet. In this episode, we reverse-engineer what Claude probably built: a background upload runner that caches images locally, a BullMQ queue that batches Gemini API calls, and a pattern called "backward batching" that inverts the traditional dependency flow. We also tackle the uncomfortable question of how to review AI-generated code when you haven't looked at it yet.]]></itunes:summary>
      <itunes:duration>1484</itunes:duration>
      <itunes:episode>4902</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/backward-batching-uploads-workflow.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/backward-batching-uploads-workflow.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Actually Touches Money</title>
      <description><![CDATA[When you return a damaged monitor to Amazon and the chatbot offers you ten bucks to keep it, is that real AI making a decision? This episode breaks down the difference between a state machine dressed up as a chatbot (your VPN cancellation offer) and an actual LLM in the loop (Amazon's return-less refund system). We walk through the guardrail architecture: constrained output schemas, rule engines that map classifications to pre-set percentages, hard caps per item class, confidence thresholds, fraud-scoring overrides, and human escalation queues. The AI never touches the dollar amount — it classifies damage into tiers, and the old authorization ladder does the rest. Plus: the adversarial attack surface when customers learn to prompt-engineer their refund descriptions.]]></description>
      <link>https://myweirdprompts.com/episode/ai-refund-guardrails-amazon/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-refund-guardrails-amazon/</guid>
      <pubDate>Tue, 25 Aug 2026 17:51:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-refund-guardrails-amazon.m4a"
        type="audio/mp4"
        length="27563980"
      />
      <itunes:title>When AI Actually Touches Money</itunes:title>
      <itunes:subtitle>How Amazon uses LLMs to classify damage and set refunds — and the guardrail stack that keeps the AI from touching the dollar amount directly.</itunes:subtitle>
      <itunes:summary><![CDATA[When you return a damaged monitor to Amazon and the chatbot offers you ten bucks to keep it, is that real AI making a decision? This episode breaks down the difference between a state machine dressed up as a chatbot (your VPN cancellation offer) and an actual LLM in the loop (Amazon's return-less refund system). We walk through the guardrail architecture: constrained output schemas, rule engines that map classifications to pre-set percentages, hard caps per item class, confidence thresholds, fraud-scoring overrides, and human escalation queues. The AI never touches the dollar amount — it classifies damage into tiers, and the old authorization ladder does the rest. Plus: the adversarial attack surface when customers learn to prompt-engineer their refund descriptions.]]></itunes:summary>
      <itunes:duration>1568</itunes:duration>
      <itunes:episode>4836</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-refund-guardrails-amazon.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-refund-guardrails-amazon.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your AI Bill Keeps Surprising You</title>
      <description><![CDATA[Two projects. One cost 37 cents. The other cost over $100. Same developer, same API provider — wildly different outcomes. This episode breaks down why AI cost estimation is so hard, from multimodal token pricing to context window growth in agentic systems. We look at how Gemini 2.0 Flash and Pro actually bill, why images cost 4x more depending on resolution, and how multi-agent simulations create compounding cost effects. Then we get practical: sample-based estimation for bounded workflows, hard limits for agentic systems, cost simulation with cheap models, and budget alerts that actually stop spending. If you build with AI APIs, this episode will save you money.]]></description>
      <link>https://myweirdprompts.com/episode/ai-cost-estimation-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-cost-estimation-guide/</guid>
      <pubDate>Mon, 24 Aug 2026 20:42:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-cost-estimation-guide.m4a"
        type="audio/mp4"
        length="27387514"
      />
      <itunes:title>Why Your AI Bill Keeps Surprising You</itunes:title>
      <itunes:subtitle>How a 37-cent project and a $100 simulation reveal the hidden math of AI API pricing.</itunes:subtitle>
      <itunes:summary><![CDATA[Two projects. One cost 37 cents. The other cost over $100. Same developer, same API provider — wildly different outcomes. This episode breaks down why AI cost estimation is so hard, from multimodal token pricing to context window growth in agentic systems. We look at how Gemini 2.0 Flash and Pro actually bill, why images cost 4x more depending on resolution, and how multi-agent simulations create compounding cost effects. Then we get practical: sample-based estimation for bounded workflows, hard limits for agentic systems, cost simulation with cheap models, and budget alerts that actually stop spending. If you build with AI APIs, this episode will save you money.]]></itunes:summary>
      <itunes:duration>1558</itunes:duration>
      <itunes:episode>4834</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-cost-estimation-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-cost-estimation-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Your AI Memory Should Be Markdown Files</title>
      <description><![CDATA[Daniel and Herman explore the case for portable, inspectable AI memory built on Markdown files and Git. They diagnose why agent-building platforms overshoot with vector databases and why ChatGPT and Claude keep memory locked in walled gardens. Then they lay out exactly how to build a local-first, two-tier memory system — shared persistent preferences plus project-specific context — using nothing but folders, text files, and a lightweight frontend. If you've ever wished you could grep your AI's memory, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/ai-memory-markdown-files/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-memory-markdown-files/</guid>
      <pubDate>Mon, 24 Aug 2026 20:29:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-memory-markdown-files.m4a"
        type="audio/mp4"
        length="26460892"
      />
      <itunes:title>Your AI Memory Should Be Markdown Files</itunes:title>
      <itunes:subtitle>Why your AI project memory should live in Git-tracked Markdown files, not a proprietary platform.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel and Herman explore the case for portable, inspectable AI memory built on Markdown files and Git. They diagnose why agent-building platforms overshoot with vector databases and why ChatGPT and Claude keep memory locked in walled gardens. Then they lay out exactly how to build a local-first, two-tier memory system — shared persistent preferences plus project-specific context — using nothing but folders, text files, and a lightweight frontend. If you've ever wished you could grep your AI's memory, this one's for you.]]></itunes:summary>
      <itunes:duration>1493</itunes:duration>
      <itunes:episode>4832</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-memory-markdown-files.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-memory-markdown-files.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your One-Person Stack Starts Fighting You</title>
      <description><![CDATA[Daniel built his entire stack—agent pipelines, MCP gateway, homelab automations—tuned to his own brain. No documentation, no second maintainer, just decisions that made sense at the time. But six months later, those locally optimal choices become globally baffling. We trace the slow bus factor: undocumented decisions, opaque config, fragile dependencies, and the moment your system flips from servant to master. When does personal infrastructure stop serving you and start constraining you?]]></description>
      <link>https://myweirdprompts.com/episode/personal-technical-debt-bus-factor/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/personal-technical-debt-bus-factor/</guid>
      <pubDate>Mon, 24 Aug 2026 14:25:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/personal-technical-debt-bus-factor.m4a"
        type="audio/mp4"
        length="23712036"
      />
      <itunes:title>When Your One-Person Stack Starts Fighting You</itunes:title>
      <itunes:subtitle>Your custom-built system was a perfect fit. Now it&apos;s a cast you can&apos;t escape.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel built his entire stack—agent pipelines, MCP gateway, homelab automations—tuned to his own brain. No documentation, no second maintainer, just decisions that made sense at the time. But six months later, those locally optimal choices become globally baffling. We trace the slow bus factor: undocumented decisions, opaque config, fragile dependencies, and the moment your system flips from servant to master. When does personal infrastructure stop serving you and start constraining you?]]></itunes:summary>
      <itunes:duration>1354</itunes:duration>
      <itunes:episode>4827</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/personal-technical-debt-bus-factor.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/personal-technical-debt-bus-factor.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Schema Design Is an Art Form</title>
      <description><![CDATA[Every CRUD app depends on an invisible skeleton: the database schema. With AI tools now generating tables in seconds, the temptation is to skip the hard thinking entirely. But schema design is a design problem with no single correct answer — and getting it wrong means migrations at 3 AM, data leaks across sites, and business logic that breaks when someone needs to track half a meter of cable. This episode explores what intelligent schema creation actually requires: domain modeling, query pattern analysis, and the art of knowing when one thing in the real world is two things in the database.]]></description>
      <link>https://myweirdprompts.com/episode/schema-design-art-form/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/schema-design-art-form/</guid>
      <pubDate>Mon, 24 Aug 2026 13:51:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/schema-design-art-form.m4a"
        type="audio/mp4"
        length="24962393"
      />
      <itunes:title>Schema Design Is an Art Form</itunes:title>
      <itunes:subtitle>Why AI-generated schemas skip the thinking that makes databases survive contact with reality.</itunes:subtitle>
      <itunes:summary><![CDATA[Every CRUD app depends on an invisible skeleton: the database schema. With AI tools now generating tables in seconds, the temptation is to skip the hard thinking entirely. But schema design is a design problem with no single correct answer — and getting it wrong means migrations at 3 AM, data leaks across sites, and business logic that breaks when someone needs to track half a meter of cable. This episode explores what intelligent schema creation actually requires: domain modeling, query pattern analysis, and the art of knowing when one thing in the real world is two things in the database.]]></itunes:summary>
      <itunes:duration>1423</itunes:duration>
      <itunes:episode>4826</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/schema-design-art-form.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/schema-design-art-form.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Russian Dolls: Skills, MCPs, and Plugins</title>
      <description><![CDATA[Claude Code's plugin system just went live, and the community is already figuring out best practices. In this episode, we crack open the Russian doll architecture of skills, MCPs, and plugins — what each layer does, when you need the full bundle, and when a one-off skill in your CLAUDE.md is the smarter call. We cover the official plugin registry, the npm publishing split, token efficiency tradeoffs, and the security implications of importing community plugins. If you're building with Claude Code, this is the mental model you need.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-plugin-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-plugin-architecture/</guid>
      <pubDate>Mon, 24 Aug 2026 13:50:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-plugin-architecture.m4a"
        type="audio/mp4"
        length="30561658"
      />
      <itunes:title>Russian Dolls: Skills, MCPs, and Plugins</itunes:title>
      <itunes:subtitle>When to use a skill vs MCP vs plugin in Claude Code — and when to keep it simple.</itunes:subtitle>
      <itunes:summary><![CDATA[Claude Code's plugin system just went live, and the community is already figuring out best practices. In this episode, we crack open the Russian doll architecture of skills, MCPs, and plugins — what each layer does, when you need the full bundle, and when a one-off skill in your CLAUDE.md is the smarter call. We cover the official plugin registry, the npm publishing split, token efficiency tradeoffs, and the security implications of importing community plugins. If you're building with Claude Code, this is the mental model you need.]]></itunes:summary>
      <itunes:duration>1736</itunes:duration>
      <itunes:episode>4825</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-plugin-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-plugin-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Build vs Pull: Docker Deploy Strategies for Solo Devs</title>
      <description><![CDATA[Solo devs and small teams face a deceptively simple question: where should your Docker image get built, and how should your server know to deploy it? This episode breaks down the two independent axes of deployment — build location (registry-first vs on-machine) and trigger mechanism (cron polling, self-hosted runners, or webhooks). We walk through the tradeoffs each choice creates, from RAM constraints on a $5 VPS to Docker Hub pull rate limits, and show how the hybrid pattern combines registry-first builds with push-based triggers for near-zero deploy latency.]]></description>
      <link>https://myweirdprompts.com/episode/docker-deploy-strategies-solo-devs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/docker-deploy-strategies-solo-devs/</guid>
      <pubDate>Mon, 24 Aug 2026 13:43:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/docker-deploy-strategies-solo-devs.m4a"
        type="audio/mp4"
        length="27792992"
      />
      <itunes:title>Build vs Pull: Docker Deploy Strategies for Solo Devs</itunes:title>
      <itunes:subtitle>Where to build your Docker image and how to trigger deploys — three patterns for small teams on a VPS.</itunes:subtitle>
      <itunes:summary><![CDATA[Solo devs and small teams face a deceptively simple question: where should your Docker image get built, and how should your server know to deploy it? This episode breaks down the two independent axes of deployment — build location (registry-first vs on-machine) and trigger mechanism (cron polling, self-hosted runners, or webhooks). We walk through the tradeoffs each choice creates, from RAM constraints on a $5 VPS to Docker Hub pull rate limits, and show how the hybrid pattern combines registry-first builds with push-based triggers for near-zero deploy latency.]]></itunes:summary>
      <itunes:duration>1584</itunes:duration>
      <itunes:episode>4824</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/docker-deploy-strategies-solo-devs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/docker-deploy-strategies-solo-devs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Cloud-Local AI Hybrid: Does It Actually Work?</title>
      <description><![CDATA[The idea sounds perfect: use a frontier model like Claude as the smart orchestrator, offload grunt work to a local quantized Qwen 7B, and save money on API costs. But the reality is a minefield of context window mismatches, tokenizer incompatibilities, latency asymmetry, and hallucination risks from quantized models. In this episode, we tear apart the hybrid cloud-local agent architecture — where it works, where it breaks, and whether the needle is even threadable given the enormous capability gap between a 200K-token cloud model and a 4-bit local model running on consumer hardware.]]></description>
      <link>https://myweirdprompts.com/episode/cloud-local-ai-hybrid/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cloud-local-ai-hybrid/</guid>
      <pubDate>Mon, 24 Aug 2026 03:04:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cloud-local-ai-hybrid.m4a"
        type="audio/mp4"
        length="27915714"
      />
      <itunes:title>Cloud-Local AI Hybrid: Does It Actually Work?</itunes:title>
      <itunes:subtitle>Can you pair Claude&apos;s reasoning with a local Qwen 7B for savings? The gap is wider than you think.</itunes:subtitle>
      <itunes:summary><![CDATA[The idea sounds perfect: use a frontier model like Claude as the smart orchestrator, offload grunt work to a local quantized Qwen 7B, and save money on API costs. But the reality is a minefield of context window mismatches, tokenizer incompatibilities, latency asymmetry, and hallucination risks from quantized models. In this episode, we tear apart the hybrid cloud-local agent architecture — where it works, where it breaks, and whether the needle is even threadable given the enormous capability gap between a 200K-token cloud model and a 4-bit local model running on consumer hardware.]]></itunes:summary>
      <itunes:duration>1570</itunes:duration>
      <itunes:episode>4822</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cloud-local-ai-hybrid.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cloud-local-ai-hybrid.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Agent Framework Lock-In: What Actually Breaks</title>
      <description><![CDATA[Are agent frameworks interchangeable? Not even close. This episode unpacks why LangGraph, CrewAI, and AutoGen use fundamentally different orchestration philosophies — state machines, hierarchical delegation, and conversational agent groups — and why moving between them means a rewrite, not a port. We also cover visual builders like Dify and Coze, the illusion of exportable JSON, what happens when you switch languages, and three dimensions of framework lock-in that can sink a startup. Plus: a look at SKILL.md and the Agent Interaction Protocol — early attempts at a common interchange format for agent capabilities.]]></description>
      <link>https://myweirdprompts.com/episode/agent-framework-portability-lock-in/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-framework-portability-lock-in/</guid>
      <pubDate>Sun, 23 Aug 2026 21:47:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-framework-portability-lock-in.m4a"
        type="audio/mp4"
        length="24751369"
      />
      <itunes:title>Agent Framework Lock-In: What Actually Breaks</itunes:title>
      <itunes:subtitle>Code-defined vs visual builders. LangGraph vs CrewAI. How portable are agent workflows really?</itunes:subtitle>
      <itunes:summary><![CDATA[Are agent frameworks interchangeable? Not even close. This episode unpacks why LangGraph, CrewAI, and AutoGen use fundamentally different orchestration philosophies — state machines, hierarchical delegation, and conversational agent groups — and why moving between them means a rewrite, not a port. We also cover visual builders like Dify and Coze, the illusion of exportable JSON, what happens when you switch languages, and three dimensions of framework lock-in that can sink a startup. Plus: a look at SKILL.md and the Agent Interaction Protocol — early attempts at a common interchange format for agent capabilities.]]></itunes:summary>
      <itunes:duration>1380</itunes:duration>
      <itunes:episode>4815</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-framework-portability-lock-in.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-framework-portability-lock-in.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Manager Agent Nobody Sees</title>
      <description><![CDATA[Every AI pipeline has a hidden layer: the manager agent that decides which sub-agent runs next, evaluates results, and loops or terminates. But this orchestrator is also the most brittle piece of the system. We break down the three failure patterns—supervisor hallucination, infinite delegation loops, and state bloat—and how frameworks like LangGraph, CrewAI, and AutoGen handle them differently. Plus: why your manager's system prompt is a value judgment disguised as routing logic.]]></description>
      <link>https://myweirdprompts.com/episode/manager-agent-ai-pipelines/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/manager-agent-ai-pipelines/</guid>
      <pubDate>Sun, 23 Aug 2026 21:45:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/manager-agent-ai-pipelines.m4a"
        type="audio/mp4"
        length="27678035"
      />
      <itunes:title>The Manager Agent Nobody Sees</itunes:title>
      <itunes:subtitle>How supervisor agents orchestrate AI pipelines without generating content—and the three ways they fail.</itunes:subtitle>
      <itunes:summary><![CDATA[Every AI pipeline has a hidden layer: the manager agent that decides which sub-agent runs next, evaluates results, and loops or terminates. But this orchestrator is also the most brittle piece of the system. We break down the three failure patterns—supervisor hallucination, infinite delegation loops, and state bloat—and how frameworks like LangGraph, CrewAI, and AutoGen handle them differently. Plus: why your manager's system prompt is a value judgment disguised as routing logic.]]></itunes:summary>
      <itunes:duration>1581</itunes:duration>
      <itunes:episode>4814</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/manager-agent-ai-pipelines.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/manager-agent-ai-pipelines.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can a Podcast Train Itself?</title>
      <description><![CDATA[Daniel asks: what if instead of retrieving past episodes from a vector database, we fine-tuned the model to carry the lore in its weights? We break down the difference between retrieval and embedding, what a LoRA fine-tuning pipeline would actually look like for a 4,700-episode archive, and the real risk of mode collapse when a model trains on its own outputs. Is a self-developing podcast possible, or does every improvement loop eventually need a human in the middle?]]></description>
      <link>https://myweirdprompts.com/episode/podcast-self-developing-fine-tuning/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcast-self-developing-fine-tuning/</guid>
      <pubDate>Sun, 23 Aug 2026 21:14:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcast-self-developing-fine-tuning.m4a"
        type="audio/mp4"
        length="25339310"
      />
      <itunes:title>Can a Podcast Train Itself?</itunes:title>
      <itunes:subtitle>Can a podcast become a self-developing project? We explore RAG vs. fine-tuning for lore memory.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asks: what if instead of retrieving past episodes from a vector database, we fine-tuned the model to carry the lore in its weights? We break down the difference between retrieval and embedding, what a LoRA fine-tuning pipeline would actually look like for a 4,700-episode archive, and the real risk of mode collapse when a model trains on its own outputs. Is a self-developing podcast possible, or does every improvement loop eventually need a human in the middle?]]></itunes:summary>
      <itunes:duration>1427</itunes:duration>
      <itunes:episode>4809</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcast-self-developing-fine-tuning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcast-self-developing-fine-tuning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The CLI That Powers Half the Internet&apos;s Video</title>
      <description><![CDATA[FFmpeg is arguably the most powerful media tool on the planet — and you've probably never seen its interface. This episode explores the surprising origin story of the command-line tool that processes more video every day than all commercial editors combined. From Fabrice Bellard's spare-time creation to the community schism that nearly killed it, we examine how a text-based interface became critical infrastructure for YouTube, Netflix, and streaming everywhere. Plus: why AI agents are making CLIs cool again.]]></description>
      <link>https://myweirdprompts.com/episode/ffmpeg-cli-video-tool/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ffmpeg-cli-video-tool/</guid>
      <pubDate>Fri, 21 Aug 2026 05:08:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ffmpeg-cli-video-tool.m4a"
        type="audio/mp4"
        length="25055768"
      />
      <itunes:title>The CLI That Powers Half the Internet&apos;s Video</itunes:title>
      <itunes:subtitle>How a French programmer&apos;s side project became the invisible engine behind YouTube, VLC, and OBS.</itunes:subtitle>
      <itunes:summary><![CDATA[FFmpeg is arguably the most powerful media tool on the planet — and you've probably never seen its interface. This episode explores the surprising origin story of the command-line tool that processes more video every day than all commercial editors combined. From Fabrice Bellard's spare-time creation to the community schism that nearly killed it, we examine how a text-based interface became critical infrastructure for YouTube, Netflix, and streaming everywhere. Plus: why AI agents are making CLIs cool again.]]></itunes:summary>
      <itunes:duration>1409</itunes:duration>
      <itunes:episode>4800</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ffmpeg-cli-video-tool.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ffmpeg-cli-video-tool.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Critiques Our Terminology</title>
      <description><![CDATA[In a recent episode, an AI-generated podcast identified a pattern of conceptual confusion in how the industry uses the term "memory" for AI systems — and the critique was spot-on. This episode unpacks the mechanics behind that moment, exploring how language models can surface inconsistencies in human terminology, what research on metacognition and introspection in LLMs reveals, and whether we're seeing the early stages of recursive self-improvement. We also discuss what this means for builders: how to treat AI not just as an output generator but as a conceptual lint checker that can help refine architectures before you start building.]]></description>
      <link>https://myweirdprompts.com/episode/ai-critiques-human-terminology/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-critiques-human-terminology/</guid>
      <pubDate>Thu, 20 Aug 2026 00:47:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-critiques-human-terminology.m4a"
        type="audio/mp4"
        length="22549037"
      />
      <itunes:title>When AI Critiques Our Terminology</itunes:title>
      <itunes:subtitle>Can AI spot conceptual sloppiness in how we describe it? A podcast moment reveals something unexpected.</itunes:subtitle>
      <itunes:summary><![CDATA[In a recent episode, an AI-generated podcast identified a pattern of conceptual confusion in how the industry uses the term "memory" for AI systems — and the critique was spot-on. This episode unpacks the mechanics behind that moment, exploring how language models can surface inconsistencies in human terminology, what research on metacognition and introspection in LLMs reveals, and whether we're seeing the early stages of recursive self-improvement. We also discuss what this means for builders: how to treat AI not just as an output generator but as a conceptual lint checker that can help refine architectures before you start building.]]></itunes:summary>
      <itunes:duration>1268</itunes:duration>
      <itunes:episode>4791</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-critiques-human-terminology.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-critiques-human-terminology.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Markdown vs Vectors for Agent Memory</title>
      <description><![CDATA[What does "memory" even mean for a coding agent? This episode unpacks the two competing patterns for giving AI agents persistent project memory: lightweight markdown files in the repo vs. vector databases with semantic retrieval. We explore why both approaches are still alive, what each one genuinely can't do, and where hybrid solutions break down into conflicting sources of truth. For solo developers managing dozens of repos, we break down the real tradeoffs — including a clever third option that keeps markdown as the single source of truth while adding a vector cache on top.]]></description>
      <link>https://myweirdprompts.com/episode/agent-memory-markdown-vectors/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-memory-markdown-vectors/</guid>
      <pubDate>Wed, 19 Aug 2026 19:59:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-memory-markdown-vectors.m4a"
        type="audio/mp4"
        length="27621552"
      />
      <itunes:title>Markdown vs Vectors for Agent Memory</itunes:title>
      <itunes:subtitle>Why coding agents need two kinds of memory — and why mixing them creates a sync nightmare.</itunes:subtitle>
      <itunes:summary><![CDATA[What does "memory" even mean for a coding agent? This episode unpacks the two competing patterns for giving AI agents persistent project memory: lightweight markdown files in the repo vs. vector databases with semantic retrieval. We explore why both approaches are still alive, what each one genuinely can't do, and where hybrid solutions break down into conflicting sources of truth. For solo developers managing dozens of repos, we break down the real tradeoffs — including a clever third option that keeps markdown as the single source of truth while adding a vector cache on top.]]></itunes:summary>
      <itunes:duration>1569</itunes:duration>
      <itunes:episode>4786</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-memory-markdown-vectors.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-memory-markdown-vectors.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Bonding Layer Showdown: Speedify vs Self-Hosted</title>
      <description><![CDATA[Daniel wants a verdict: should he use Speedify or a self-hosted solution like OpenMPTCProuter for bonding multiple internet connections on his travel router? This episode breaks down what a bonding layer actually does, why no commercial product offers the "own both ends" licensing model he imagined, and delivers a specific two-hop architecture recommendation. We cover Speedify's proprietary protocol and opaque infrastructure, OpenMPTCProuter's full control but real maintenance burden, and why the middle ground between them is harder to find than it seems.]]></description>
      <link>https://myweirdprompts.com/episode/bonding-layer-travel-router-verdict/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/bonding-layer-travel-router-verdict/</guid>
      <pubDate>Wed, 19 Aug 2026 19:07:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/bonding-layer-travel-router-verdict.m4a"
        type="audio/mp4"
        length="23557432"
      />
      <itunes:title>Bonding Layer Showdown: Speedify vs Self-Hosted</itunes:title>
      <itunes:subtitle>Packet-level bonding for travel routers: Speedify&apos;s black box vs. self-hosted control. Which wins for a technically capable user?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants a verdict: should he use Speedify or a self-hosted solution like OpenMPTCProuter for bonding multiple internet connections on his travel router? This episode breaks down what a bonding layer actually does, why no commercial product offers the "own both ends" licensing model he imagined, and delivers a specific two-hop architecture recommendation. We cover Speedify's proprietary protocol and opaque infrastructure, OpenMPTCProuter's full control but real maintenance burden, and why the middle ground between them is harder to find than it seems.]]></itunes:summary>
      <itunes:duration>1332</itunes:duration>
      <itunes:episode>4783</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/bonding-layer-travel-router-verdict.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/bonding-layer-travel-router-verdict.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Bonding Internet Lines with OpenMPTCProuter</title>
      <description><![CDATA[OpenMPTCProuter is the only serious open-source contender for bonding multiple internet connections into one fat pipe — but it's also a one-person project with no formal funding. We break down the architecture, VPS sizing, and the topology question that keeps coming up: does the intermediate node have to live on the public internet? Plus, how it compares to Peplink and Speedify, and why nothing else has emerged in this space.]]></description>
      <link>https://myweirdprompts.com/episode/openmptcprouter-connection-bonding-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/openmptcprouter-connection-bonding-guide/</guid>
      <pubDate>Wed, 19 Aug 2026 18:59:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/openmptcprouter-connection-bonding-guide.m4a"
        type="audio/mp4"
        length="32942358"
      />
      <itunes:title>Bonding Internet Lines with OpenMPTCProuter</itunes:title>
      <itunes:subtitle>One developer, no funding, and the only open-source bonding router that actually works. Here&apos;s how.</itunes:subtitle>
      <itunes:summary><![CDATA[OpenMPTCProuter is the only serious open-source contender for bonding multiple internet connections into one fat pipe — but it's also a one-person project with no formal funding. We break down the architecture, VPS sizing, and the topology question that keeps coming up: does the intermediate node have to live on the public internet? Plus, how it compares to Peplink and Speedify, and why nothing else has emerged in this space.]]></itunes:summary>
      <itunes:duration>1889</itunes:duration>
      <itunes:episode>4781</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/openmptcprouter-connection-bonding-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/openmptcprouter-connection-bonding-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Bonding Internet Connections with OpenMPTCProuter</title>
      <description><![CDATA[OpenMPTCProuter (OMR) is the only open-source project that bonds multiple internet connections at the packet level using Multipath TCP. This episode breaks down what you actually need: a router running OMR's custom firmware with MPTCP in the kernel, and a KVM VPS that can run a custom kernel and handle your full combined bandwidth. We explain why OPNsense can't do real bonding, why cheap OpenVZ hosts won't work, and why the middle ground between DIY and Speedify doesn't exist.]]></description>
      <link>https://myweirdprompts.com/episode/internet-bonding-openmptcprouter/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/internet-bonding-openmptcprouter/</guid>
      <pubDate>Wed, 19 Aug 2026 18:45:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/internet-bonding-openmptcprouter.m4a"
        type="audio/mp4"
        length="28097925"
      />
      <itunes:title>Bonding Internet Connections with OpenMPTCProuter</itunes:title>
      <itunes:subtitle>How to bond multiple connections at the packet level using the only serious open-source option.</itunes:subtitle>
      <itunes:summary><![CDATA[OpenMPTCProuter (OMR) is the only open-source project that bonds multiple internet connections at the packet level using Multipath TCP. This episode breaks down what you actually need: a router running OMR's custom firmware with MPTCP in the kernel, and a KVM VPS that can run a custom kernel and handle your full combined bandwidth. We explain why OPNsense can't do real bonding, why cheap OpenVZ hosts won't work, and why the middle ground between DIY and Speedify doesn't exist.]]></itunes:summary>
      <itunes:duration>1602</itunes:duration>
      <itunes:episode>4780</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/internet-bonding-openmptcprouter.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/internet-bonding-openmptcprouter.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>SSH MCP and Tailscale: Docker Migration Guide</title>
      <description><![CDATA[Daniel built an SSH MCP server that uses Tailscale to connect his agent to remote VPS boxes. The setup works beautifully — until Docker containers need to reach services on other tailnet nodes. This episode walks through the three failure patterns that emerge when you try to migrate from public IPs to tailnet addresses: split DNS, name collisions, and the localhost bind problem. We cover the sidecar pattern for giving containers tailnet access, the real scope of work when migrating compose files, and the tradeoffs of depending on Tailscale's control plane.]]></description>
      <link>https://myweirdprompts.com/episode/ssh-mcp-tailscale-docker/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ssh-mcp-tailscale-docker/</guid>
      <pubDate>Wed, 19 Aug 2026 18:37:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ssh-mcp-tailscale-docker.m4a"
        type="audio/mp4"
        length="28520172"
      />
      <itunes:title>SSH MCP and Tailscale: Docker Migration Guide</itunes:title>
      <itunes:subtitle>How to configure Tailscale for Docker containers when migrating from public IPs to tailnet addresses.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel built an SSH MCP server that uses Tailscale to connect his agent to remote VPS boxes. The setup works beautifully — until Docker containers need to reach services on other tailnet nodes. This episode walks through the three failure patterns that emerge when you try to migrate from public IPs to tailnet addresses: split DNS, name collisions, and the localhost bind problem. We cover the sidecar pattern for giving containers tailnet access, the real scope of work when migrating compose files, and the tradeoffs of depending on Tailscale's control plane.]]></itunes:summary>
      <itunes:duration>1627</itunes:duration>
      <itunes:episode>4778</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ssh-mcp-tailscale-docker.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ssh-mcp-tailscale-docker.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Context Window Management: Cutoff vs. Compaction</title>
      <description><![CDATA[When your Claude Code session runs for hours, the context window fills up with every tool call, file read, and error message — and performance degrades. This episode explores two very different approaches to managing that growing context: the trailing context cutoff (a hard trim that simply deletes old tokens) and Claude's auto-compact mechanism (which summarizes history to preserve key information). We break down the tradeoffs of adjusting the default 200K token compaction threshold, the risks of compounding information loss across multiple compactions, and why a bad summary might actually be worse than honest ignorance. If you're running long coding sessions and wondering when to pull the lever on context management, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/context-window-management-cutoff-compaction/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/context-window-management-cutoff-compaction/</guid>
      <pubDate>Mon, 17 Aug 2026 18:41:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/context-window-management-cutoff-compaction.m4a"
        type="audio/mp4"
        length="23930462"
      />
      <itunes:title>Context Window Management: Cutoff vs. Compaction</itunes:title>
      <itunes:subtitle>Trailing cutoff or auto-compaction? How to manage Claude Code&apos;s context window without losing critical information.</itunes:subtitle>
      <itunes:summary><![CDATA[When your Claude Code session runs for hours, the context window fills up with every tool call, file read, and error message — and performance degrades. This episode explores two very different approaches to managing that growing context: the trailing context cutoff (a hard trim that simply deletes old tokens) and Claude's auto-compact mechanism (which summarizes history to preserve key information). We break down the tradeoffs of adjusting the default 200K token compaction threshold, the risks of compounding information loss across multiple compactions, and why a bad summary might actually be worse than honest ignorance. If you're running long coding sessions and wondering when to pull the lever on context management, this one's for you.]]></itunes:summary>
      <itunes:duration>1376</itunes:duration>
      <itunes:episode>4760</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/context-window-management-cutoff-compaction.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/context-window-management-cutoff-compaction.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>API Keys vs OAuth: When Simplicity Becomes a Security Trap</title>
      <description><![CDATA[API keys are every developer's first authentication tool, but their simplicity hides serious security tradeoffs. This episode unpacks what an API key actually represents (spoiler: nothing), what you lose when you swap OAuth for a long-lived key, and when API keys are genuinely the right choice versus just the easiest one. With real examples from AWS, Google Cloud, and Stripe, we explore scoping, expiration, revocation, and why client-side API keys are a trap.]]></description>
      <link>https://myweirdprompts.com/episode/api-keys-security-tradeoffs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/api-keys-security-tradeoffs/</guid>
      <pubDate>Mon, 17 Aug 2026 18:28:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/api-keys-security-tradeoffs.m4a"
        type="audio/mp4"
        length="31051774"
      />
      <itunes:title>API Keys vs OAuth: When Simplicity Becomes a Security Trap</itunes:title>
      <itunes:subtitle>API keys are simple but leak identity, scope, and auditability. Here&apos;s when they work and when they don&apos;t.</itunes:subtitle>
      <itunes:summary><![CDATA[API keys are every developer's first authentication tool, but their simplicity hides serious security tradeoffs. This episode unpacks what an API key actually represents (spoiler: nothing), what you lose when you swap OAuth for a long-lived key, and when API keys are genuinely the right choice versus just the easiest one. With real examples from AWS, Google Cloud, and Stripe, we explore scoping, expiration, revocation, and why client-side API keys are a trap.]]></itunes:summary>
      <itunes:duration>1783</itunes:duration>
      <itunes:episode>4759</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/api-keys-security-tradeoffs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/api-keys-security-tradeoffs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Agent Credentials: Beyond the Password Manager</title>
      <description><![CDATA[Your AI agent manages sixty services but can't type a master password. This episode explores why macOS Keychain and 1Password weren't built for headless autonomous agents, and what Google's Agent-to-Auth and Microsoft's delegated auth model get right. We break down the broker architecture, the latency tradeoffs, and the hard problem of policy enforcement when the credential consumer is itself an unpredictable AI.]]></description>
      <link>https://myweirdprompts.com/episode/agent-credential-broker-model/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-credential-broker-model/</guid>
      <pubDate>Mon, 17 Aug 2026 18:27:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-credential-broker-model.m4a"
        type="audio/mp4"
        length="26893715"
      />
      <itunes:title>Agent Credentials: Beyond the Password Manager</itunes:title>
      <itunes:subtitle>Why OS keychains fail for AI agents — and what the broker model does instead.</itunes:subtitle>
      <itunes:summary><![CDATA[Your AI agent manages sixty services but can't type a master password. This episode explores why macOS Keychain and 1Password weren't built for headless autonomous agents, and what Google's Agent-to-Auth and Microsoft's delegated auth model get right. We break down the broker architecture, the latency tradeoffs, and the hard problem of policy enforcement when the credential consumer is itself an unpredictable AI.]]></itunes:summary>
      <itunes:duration>1547</itunes:duration>
      <itunes:episode>4758</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-credential-broker-model.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-credential-broker-model.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Eliminating the Secret: Workload Identity Without Credentials</title>
      <description><![CDATA[Long-lived credentials sitting in config files are the single most dangerous thing in cloud deployments. This episode traces the architectural shift across Google Cloud, AWS, Azure, and Kubernetes — from static secrets to short-lived tokens generated at request time. We explore the "secret zero" problem, the bot user trap, and how platforms like IMDSv2, managed identities, and the TokenRequest API are eliminating the credential entirely. The industry is converging on one radical answer: stop having the secret.]]></description>
      <link>https://myweirdprompts.com/episode/workload-identity-no-secrets/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/workload-identity-no-secrets/</guid>
      <pubDate>Mon, 17 Aug 2026 18:11:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/workload-identity-no-secrets.m4a"
        type="audio/mp4"
        length="28397304"
      />
      <itunes:title>Eliminating the Secret: Workload Identity Without Credentials</itunes:title>
      <itunes:subtitle>How Google Cloud, AWS, Azure, and Kubernetes are moving from long-lived keys to ephemeral, platform-issued tokens.</itunes:subtitle>
      <itunes:summary><![CDATA[Long-lived credentials sitting in config files are the single most dangerous thing in cloud deployments. This episode traces the architectural shift across Google Cloud, AWS, Azure, and Kubernetes — from static secrets to short-lived tokens generated at request time. We explore the "secret zero" problem, the bot user trap, and how platforms like IMDSv2, managed identities, and the TokenRequest API are eliminating the credential entirely. The industry is converging on one radical answer: stop having the secret.]]></itunes:summary>
      <itunes:duration>1624</itunes:duration>
      <itunes:episode>4756</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/workload-identity-no-secrets.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/workload-identity-no-secrets.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why OAuth Makes You Re-Authorize Apps</title>
      <description><![CDATA[Why can't you just give an app your password? In this episode, we break down the security model behind OAuth — what's actually happening when you click "Allow" on a consent screen, why scopes and explicit user consent matter, and why requesting new permissions later forces you to re-authorize the whole thing. We explore delegated authority, the difference between access tokens and passwords, and why the apparent inconvenience is by design.]]></description>
      <link>https://myweirdprompts.com/episode/oauth-authorization-consent-scopes/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/oauth-authorization-consent-scopes/</guid>
      <pubDate>Mon, 17 Aug 2026 18:07:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/oauth-authorization-consent-scopes.m4a"
        type="audio/mp4"
        length="33648078"
      />
      <itunes:title>Why OAuth Makes You Re-Authorize Apps</itunes:title>
      <itunes:subtitle>Understanding delegated authority: why OAuth&apos;s consent flow is a feature, not a bug.</itunes:subtitle>
      <itunes:summary><![CDATA[Why can't you just give an app your password? In this episode, we break down the security model behind OAuth — what's actually happening when you click "Allow" on a consent screen, why scopes and explicit user consent matter, and why requesting new permissions later forces you to re-authorize the whole thing. We explore delegated authority, the difference between access tokens and passwords, and why the apparent inconvenience is by design.]]></itunes:summary>
      <itunes:duration>1931</itunes:duration>
      <itunes:episode>4755</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/oauth-authorization-consent-scopes.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/oauth-authorization-consent-scopes.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Code Authentication Needs So Many Keys</title>
      <description><![CDATA[Why do we need API keys, OAuth tokens, service accounts, and refresh tokens instead of one universal authenticator? This episode builds a mental model around "trust topology" — the idea that different authentication mechanisms solve fundamentally different trust problems. We explore the split between human and software authentication, walk through AWS access keys, GitHub app installation tokens, Google Cloud service accounts, and OAuth's many grant types, and trace how historical sediment from the pre-OAuth password-sharing era still shapes today's complexity. If you've ever wondered why code authentication feels like a drawer full of different keys, this episode explains exactly why that's both inherent and accidental.]]></description>
      <link>https://myweirdprompts.com/episode/code-authentication-keys-tokens/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/code-authentication-keys-tokens/</guid>
      <pubDate>Mon, 17 Aug 2026 17:55:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/code-authentication-keys-tokens.m4a"
        type="audio/mp4"
        length="36373664"
      />
      <itunes:title>Why Code Authentication Needs So Many Keys</itunes:title>
      <itunes:subtitle>API keys, OAuth tokens, service accounts — why can&apos;t one authenticator rule them all? The answer is trust topology.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do we need API keys, OAuth tokens, service accounts, and refresh tokens instead of one universal authenticator? This episode builds a mental model around "trust topology" — the idea that different authentication mechanisms solve fundamentally different trust problems. We explore the split between human and software authentication, walk through AWS access keys, GitHub app installation tokens, Google Cloud service accounts, and OAuth's many grant types, and trace how historical sediment from the pre-OAuth password-sharing era still shapes today's complexity. If you've ever wondered why code authentication feels like a drawer full of different keys, this episode explains exactly why that's both inherent and accidental.]]></itunes:summary>
      <itunes:duration>2089</itunes:duration>
      <itunes:episode>4754</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/code-authentication-keys-tokens.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/code-authentication-keys-tokens.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Cookies as AI Credentials: The Auth Problem</title>
      <description><![CDATA[Municipal parking sites, healthcare portals, and internal corporate dashboards have no public APIs — the only way to interact with them is through a browser session. But how do you hand an AI agent a logged-in state when the web was built for humans? This episode explores the cookie export process, the durability limits of session-based authentication, and why there's no refresh-token equivalent for browser cookies. We break down Playwright's storageState, the security boundaries that prevent automated cookie extraction, and the emerging pattern of agentic credential vaults.]]></description>
      <link>https://myweirdprompts.com/episode/cookies-ai-agent-authentication/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cookies-ai-agent-authentication/</guid>
      <pubDate>Mon, 17 Aug 2026 17:46:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cookies-ai-agent-authentication.m4a"
        type="audio/mp4"
        length="25143562"
      />
      <itunes:title>Cookies as AI Credentials: The Auth Problem</itunes:title>
      <itunes:subtitle>How to get browser cookies into an AI agent&apos;s hands — and why they expire so fast.</itunes:subtitle>
      <itunes:summary><![CDATA[Municipal parking sites, healthcare portals, and internal corporate dashboards have no public APIs — the only way to interact with them is through a browser session. But how do you hand an AI agent a logged-in state when the web was built for humans? This episode explores the cookie export process, the durability limits of session-based authentication, and why there's no refresh-token equivalent for browser cookies. We break down Playwright's storageState, the security boundaries that prevent automated cookie extraction, and the emerging pattern of agentic credential vaults.]]></itunes:summary>
      <itunes:duration>1443</itunes:duration>
      <itunes:episode>4753</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cookies-ai-agent-authentication.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cookies-ai-agent-authentication.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How AI Agents Map Websites to Build Stable Plugins</title>
      <description><![CDATA[Daniel's been building Claude Code plugins that let AI agents navigate marketplaces by exploring the page source, capturing network requests, and writing their own plugin definitions. We break down why this works, how the discovery phase determines plugin quality, and why narrow prompts produce dramatically better results than broad exploration.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agents-ui-discovery-plugins/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agents-ui-discovery-plugins/</guid>
      <pubDate>Mon, 17 Aug 2026 17:41:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agents-ui-discovery-plugins.m4a"
        type="audio/mp4"
        length="25715307"
      />
      <itunes:title>How AI Agents Map Websites to Build Stable Plugins</itunes:title>
      <itunes:subtitle>An AI agent explores DOM and network layers to build self-healing browser plugins.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's been building Claude Code plugins that let AI agents navigate marketplaces by exploring the page source, capturing network requests, and writing their own plugin definitions. We break down why this works, how the discovery phase determines plugin quality, and why narrow prompts produce dramatically better results than broad exploration.]]></itunes:summary>
      <itunes:duration>1476</itunes:duration>
      <itunes:episode>4752</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agents-ui-discovery-plugins.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agents-ui-discovery-plugins.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Agents vs Anti-Bot Systems</title>
      <description><![CDATA[When AI agents browse the web on your behalf, they run into anti-bot systems built to stop spammers and scalpers. This episode breaks down the five layers of modern bot detection — from browser fingerprinting to TLS handshake analysis — and explores what evasion techniques actually work, why they're temporary, and whether the web's security model needs to evolve for legitimate AI agents.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agents-anti-bot-detection/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agents-anti-bot-detection/</guid>
      <pubDate>Mon, 17 Aug 2026 17:33:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agents-anti-bot-detection.m4a"
        type="audio/mp4"
        length="27942443"
      />
      <itunes:title>AI Agents vs Anti-Bot Systems</itunes:title>
      <itunes:subtitle>Why AI agents get blocked by anti-bot systems and what actually works to get through.</itunes:subtitle>
      <itunes:summary><![CDATA[When AI agents browse the web on your behalf, they run into anti-bot systems built to stop spammers and scalpers. This episode breaks down the five layers of modern bot detection — from browser fingerprinting to TLS handshake analysis — and explores what evasion techniques actually work, why they're temporary, and whether the web's security model needs to evolve for legitimate AI agents.]]></itunes:summary>
      <itunes:duration>1602</itunes:duration>
      <itunes:episode>4751</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agents-anti-bot-detection.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agents-anti-bot-detection.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Reverse-Engineering Hidden Web APIs</title>
      <description><![CDATA[Every web app you use exposes a hidden API layer through the HTTP requests your browser makes. This episode explores how to discover those undocumented endpoints, reverse-engineer them from network traffic, and wrap them into tools for AI agents. From simple copy-as-cURL endpoints to CSRF-protected workflows and request sequencing, we cover the taxonomy of hidden APIs, browser-agnostic approaches using mitmproxy, and the tradeoffs of building agent tooling on unsupported interfaces.]]></description>
      <link>https://myweirdprompts.com/episode/reverse-engineering-hidden-apis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/reverse-engineering-hidden-apis/</guid>
      <pubDate>Mon, 17 Aug 2026 17:29:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/reverse-engineering-hidden-apis.m4a"
        type="audio/mp4"
        length="29197822"
      />
      <itunes:title>Reverse-Engineering Hidden Web APIs</itunes:title>
      <itunes:subtitle>How to find undocumented APIs in your browser&apos;s network tab and hand them to AI agents.</itunes:subtitle>
      <itunes:summary><![CDATA[Every web app you use exposes a hidden API layer through the HTTP requests your browser makes. This episode explores how to discover those undocumented endpoints, reverse-engineer them from network traffic, and wrap them into tools for AI agents. From simple copy-as-cURL endpoints to CSRF-protected workflows and request sequencing, we cover the taxonomy of hidden APIs, browser-agnostic approaches using mitmproxy, and the tradeoffs of building agent tooling on unsupported interfaces.]]></itunes:summary>
      <itunes:duration>1678</itunes:duration>
      <itunes:episode>4750</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/reverse-engineering-hidden-apis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/reverse-engineering-hidden-apis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Hardware Tokens That Never Leave the Port</title>
      <description><![CDATA[Most hardware token advice assumes you'll carry it on a keychain and pull it out occasionally. But what if you want one that lives in the USB port — on a crusted home desktop or a travel laptop? This episode explores the always-attached workflow: which tokens handle it, how SSH key storage works on FIDO2 devices, and why developers traveling with laptops should care. We cover the YubiKey 5 series, Nitrokey 3, Google Titan, and SoloKeys, plus the practical tradeoffs between USB-A and USB-C, Nano form factors, and NFC you'll never use. If you've ever wondered whether a hardware token makes sense for daily driver use instead of phone-based authenticators, this is the episode for you.]]></description>
      <link>https://myweirdprompts.com/episode/hardware-tokens-always-attached/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hardware-tokens-always-attached/</guid>
      <pubDate>Sun, 16 Aug 2026 19:57:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hardware-tokens-always-attached.m4a"
        type="audio/mp4"
        length="28408742"
      />
      <itunes:title>Hardware Tokens That Never Leave the Port</itunes:title>
      <itunes:subtitle>Why developers should consider keeping a hardware security token permanently plugged in — and which ones survive the abuse.</itunes:subtitle>
      <itunes:summary><![CDATA[Most hardware token advice assumes you'll carry it on a keychain and pull it out occasionally. But what if you want one that lives in the USB port — on a crusted home desktop or a travel laptop? This episode explores the always-attached workflow: which tokens handle it, how SSH key storage works on FIDO2 devices, and why developers traveling with laptops should care. We cover the YubiKey 5 series, Nitrokey 3, Google Titan, and SoloKeys, plus the practical tradeoffs between USB-A and USB-C, Nano form factors, and NFC you'll never use. If you've ever wondered whether a hardware token makes sense for daily driver use instead of phone-based authenticators, this is the episode for you.]]></itunes:summary>
      <itunes:duration>1632</itunes:duration>
      <itunes:episode>4741</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hardware-tokens-always-attached.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hardware-tokens-always-attached.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>NFS Unmasked: Versions, Gotchas, and Hard Truths</title>
      <description><![CDATA[NFS is the default answer for Linux-to-Linux file sharing, but most sysadmins don't know which version they're running or what's silently going wrong. This episode pulls back the curtain on NFS—from the radically different protocol versions (v2 through v4.3) to the gotchas that ruin weekends: stale file handles, silent lock failures, hard vs. soft mount tradeoffs, and why WiFi is a disaster. If you've ever mounted a remote directory and wondered what could go wrong, this is the episode for you.]]></description>
      <link>https://myweirdprompts.com/episode/nfs-versions-gotchas-truths/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/nfs-versions-gotchas-truths/</guid>
      <pubDate>Sun, 16 Aug 2026 17:08:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/nfs-versions-gotchas-truths.m4a"
        type="audio/mp4"
        length="26297069"
      />
      <itunes:title>NFS Unmasked: Versions, Gotchas, and Hard Truths</itunes:title>
      <itunes:subtitle>NFS isn&apos;t one protocol—it&apos;s a succession of them. Here&apos;s what&apos;s really happening under the hood and where it breaks.</itunes:subtitle>
      <itunes:summary><![CDATA[NFS is the default answer for Linux-to-Linux file sharing, but most sysadmins don't know which version they're running or what's silently going wrong. This episode pulls back the curtain on NFS—from the radically different protocol versions (v2 through v4.3) to the gotchas that ruin weekends: stale file handles, silent lock failures, hard vs. soft mount tradeoffs, and why WiFi is a disaster. If you've ever mounted a remote directory and wondered what could go wrong, this is the episode for you.]]></itunes:summary>
      <itunes:duration>1487</itunes:duration>
      <itunes:episode>4739</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/nfs-versions-gotchas-truths.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/nfs-versions-gotchas-truths.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why OAuth Still Makes You Dance on Your Own Machine</title>
      <description><![CDATA[Daniel just wanted to update a printer setting, but first he had to authenticate Claude through a browser that was already logged in. Why does OAuth make you jump through hoops on your own trusted machine? We break down the original threat model behind OAuth 2.0, PKCE's blast radius containment, and whether Linux's secret service APIs or kernel keyrings could short-circuit the redirect dance. Plus: why token lifecycle management makes the problem harder than better storage alone can solve.]]></description>
      <link>https://myweirdprompts.com/episode/oauth-trusted-environment-tokens/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/oauth-trusted-environment-tokens/</guid>
      <pubDate>Sat, 15 Aug 2026 18:17:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/oauth-trusted-environment-tokens.m4a"
        type="audio/mp4"
        length="29552915"
      />
      <itunes:title>Why OAuth Still Makes You Dance on Your Own Machine</itunes:title>
      <itunes:subtitle>Why does OAuth treat your personal computer like a public terminal? We explore token storage, PKCE, and Linux secret APIs.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel just wanted to update a printer setting, but first he had to authenticate Claude through a browser that was already logged in. Why does OAuth make you jump through hoops on your own trusted machine? We break down the original threat model behind OAuth 2.0, PKCE's blast radius containment, and whether Linux's secret service APIs or kernel keyrings could short-circuit the redirect dance. Plus: why token lifecycle management makes the problem harder than better storage alone can solve.]]></itunes:summary>
      <itunes:duration>1701</itunes:duration>
      <itunes:episode>4733</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/oauth-trusted-environment-tokens.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/oauth-trusted-environment-tokens.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Travel Router Tailscale Subnet Bridge</title>
      <description><![CDATA[When Daniel moved apartments and set up a portable home network with Tailscale, he hit the failure modes the manual doesn't cover: subnet collisions, printer discovery across tunnels, and exit node bottlenecks. This episode pulls from U.S. military tactical network doctrine — standardized labeling, pre-planned subnet allocation, and redundant egress paths — to solve real problems like what happens when your in-laws' router uses the same 192.168.1.0/24 subnet as home. We walk through Tailscale subnet routers, exit nodes, DERP relay fallbacks, and how a GL.iNet travel router running OpenWrt can bridge two networks without breaking everything.]]></description>
      <link>https://myweirdprompts.com/episode/travel-router-tailscale-subnet/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/travel-router-tailscale-subnet/</guid>
      <pubDate>Sat, 15 Aug 2026 18:08:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/travel-router-tailscale-subnet.m4a"
        type="audio/mp4"
        length="28951995"
      />
      <itunes:title>Travel Router Tailscale Subnet Bridge</itunes:title>
      <itunes:subtitle>Military network tactics for home users bridging subnets with Tailscale and travel routers.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel moved apartments and set up a portable home network with Tailscale, he hit the failure modes the manual doesn't cover: subnet collisions, printer discovery across tunnels, and exit node bottlenecks. This episode pulls from U.S. military tactical network doctrine — standardized labeling, pre-planned subnet allocation, and redundant egress paths — to solve real problems like what happens when your in-laws' router uses the same 192.168.1.0/24 subnet as home. We walk through Tailscale subnet routers, exit nodes, DERP relay fallbacks, and how a GL.iNet travel router running OpenWrt can bridge two networks without breaking everything.]]></itunes:summary>
      <itunes:duration>1649</itunes:duration>
      <itunes:episode>4732</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/travel-router-tailscale-subnet.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/travel-router-tailscale-subnet.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Teaching Your Router Bedtime: Automated QoS with OPNsense</title>
      <description><![CDATA[Daniel wants his router to automatically know that streaming gets priority at 8 PM and backups can run wild at midnight—no manual scheduling, no buffering during movie night. We dig into OPNsense's dummynet shaper, the cron-based automation gap, and the shell scripts that make time-based QoS real. From DHCP reservations to defensive scripting, this is how you turn a static traffic shaper into a butler that knows your family's rhythms.]]></description>
      <link>https://myweirdprompts.com/episode/automated-qos-opnsense-scheduling/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/automated-qos-opnsense-scheduling/</guid>
      <pubDate>Sat, 15 Aug 2026 17:58:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/automated-qos-opnsense-scheduling.m4a"
        type="audio/mp4"
        length="27309324"
      />
      <itunes:title>Teaching Your Router Bedtime: Automated QoS with OPNsense</itunes:title>
      <itunes:subtitle>Can your router learn when to prioritize Netflix over backups? We build schedule-aware QoS with OPNsense and cron.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants his router to automatically know that streaming gets priority at 8 PM and backups can run wild at midnight—no manual scheduling, no buffering during movie night. We dig into OPNsense's dummynet shaper, the cron-based automation gap, and the shell scripts that make time-based QoS real. From DHCP reservations to defensive scripting, this is how you turn a static traffic shaper into a butler that knows your family's rhythms.]]></itunes:summary>
      <itunes:duration>1573</itunes:duration>
      <itunes:episode>4731</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/automated-qos-opnsense-scheduling.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/automated-qos-opnsense-scheduling.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Compass vs. Warning Sign: Prompting for Long-Term AI</title>
      <description><![CDATA[Daniel's monthly maintenance on the show's AI pipeline revealed a shift: his instructions are moving from bug reports to preservation lists. Is that shift meaningful? We dig into the geometry of vector spaces to explain why positive and negative instructions operate fundamentally differently inside a model. Negative instructions create unpredictable "avoidance gradients" that can crater entire semantic neighborhoods, while positive instructions reinforce existing paths but risk slow overfitting. The practical takeaway: a well-maintained system prompt should read like an editorial style guide, not a list of prohibitions. If you run any long-term agentic pipeline, this episode will change how you write your next update.]]></description>
      <link>https://myweirdprompts.com/episode/ai-prompt-maintenance-positive-framing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-prompt-maintenance-positive-framing/</guid>
      <pubDate>Fri, 14 Aug 2026 02:45:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-prompt-maintenance-positive-framing.m4a"
        type="audio/mp4"
        length="27555499"
      />
      <itunes:title>Compass vs. Warning Sign: Prompting for Long-Term AI</itunes:title>
      <itunes:subtitle>How positive and negative instructions reshape AI behavior differently — and why &quot;preserve this&quot; beats &quot;fix this&quot; in system prompts.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's monthly maintenance on the show's AI pipeline revealed a shift: his instructions are moving from bug reports to preservation lists. Is that shift meaningful? We dig into the geometry of vector spaces to explain why positive and negative instructions operate fundamentally differently inside a model. Negative instructions create unpredictable "avoidance gradients" that can crater entire semantic neighborhoods, while positive instructions reinforce existing paths but risk slow overfitting. The practical takeaway: a well-maintained system prompt should read like an editorial style guide, not a list of prohibitions. If you run any long-term agentic pipeline, this episode will change how you write your next update.]]></itunes:summary>
      <itunes:duration>1576</itunes:duration>
      <itunes:episode>4720</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-prompt-maintenance-positive-framing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-prompt-maintenance-positive-framing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Home Whisper Server: The Full Spec</title>
      <description><![CDATA[Daniel's been dictating daily for a year and a half, and he's ready to build a dedicated home server for Whisper inference. In this episode, we break down the full bill of materials: why 16GB of VRAM is the sweet spot, why the RTX 4060 Ti beats pricier cards for this workload, and why TPUs and AMD are non-answers for a set-it-and-forget-it setup. Plus, the surprising truth about CPU requirements and network bandwidth. If you've ever thought about running your own speech-to-text server, this is the spec you need.]]></description>
      <link>https://myweirdprompts.com/episode/home-whisper-server-spec/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-whisper-server-spec/</guid>
      <pubDate>Thu, 13 Aug 2026 23:10:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-whisper-server-spec.m4a"
        type="audio/mp4"
        length="26689291"
      />
      <itunes:title>Building a Home Whisper Server: The Full Spec</itunes:title>
      <itunes:subtitle>Daniel wants a dedicated home server for Whisper dictation. Here&apos;s the exact build — GPU, RAM, storage, and runtime — to make it work.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's been dictating daily for a year and a half, and he's ready to build a dedicated home server for Whisper inference. In this episode, we break down the full bill of materials: why 16GB of VRAM is the sweet spot, why the RTX 4060 Ti beats pricier cards for this workload, and why TPUs and AMD are non-answers for a set-it-and-forget-it setup. Plus, the surprising truth about CPU requirements and network bandwidth. If you've ever thought about running your own speech-to-text server, this is the spec you need.]]></itunes:summary>
      <itunes:duration>1531</itunes:duration>
      <itunes:episode>4717</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-whisper-server-spec.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-whisper-server-spec.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>MCP vs Browser-Use: The Agentic AI Showdown</title>
      <description><![CDATA[The last mile of agentic AI is being solved two different ways, and the outcome will shape software development for years. MCP integration offers a typed, reliable API contract, while browser-use gives agents the ability to navigate any interface like a human. We break down the reliability vs. coverage tradeoffs, the vendor power play, and why the future likely isn't one winner but a division of labor.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-vs-browser-use-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-vs-browser-use-agents/</guid>
      <pubDate>Thu, 13 Aug 2026 19:40:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-vs-browser-use-agents.m4a"
        type="audio/mp4"
        length="23722135"
      />
      <itunes:title>MCP vs Browser-Use: The Agentic AI Showdown</itunes:title>
      <itunes:subtitle>Two patterns are fighting to define how AI agents interact with software. Which one wins shapes the next decade of building.</itunes:subtitle>
      <itunes:summary><![CDATA[The last mile of agentic AI is being solved two different ways, and the outcome will shape software development for years. MCP integration offers a typed, reliable API contract, while browser-use gives agents the ability to navigate any interface like a human. We break down the reliability vs. coverage tradeoffs, the vendor power play, and why the future likely isn't one winner but a division of labor.]]></itunes:summary>
      <itunes:duration>1393</itunes:duration>
      <itunes:episode>4713</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-vs-browser-use-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-vs-browser-use-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Codex vs Claude Code: The Real Agentic Coding Showdown</title>
      <description><![CDATA[Daniel wanted a proper head-to-head: Codex versus Claude Code. We break down the real model names (GPT-5.6 Luna vs Claude Opus 5), what the benchmarks actually show, and how their MCP implementations differ. Plus: how to structure your setup so you can switch between both without rebuilding everything.]]></description>
      <link>https://myweirdprompts.com/episode/codex-vs-claude-code-agentic/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/codex-vs-claude-code-agentic/</guid>
      <pubDate>Thu, 13 Aug 2026 19:12:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/codex-vs-claude-code-agentic.m4a"
        type="audio/mp4"
        length="21569790"
      />
      <itunes:title>Codex vs Claude Code: The Real Agentic Coding Showdown</itunes:title>
      <itunes:subtitle>Benchmarks, model variants, and MCP setups — which coding agent actually wins where it matters?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wanted a proper head-to-head: Codex versus Claude Code. We break down the real model names (GPT-5.6 Luna vs Claude Opus 5), what the benchmarks actually show, and how their MCP implementations differ. Plus: how to structure your setup so you can switch between both without rebuilding everything.]]></itunes:summary>
      <itunes:duration>1222</itunes:duration>
      <itunes:episode>4712</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/codex-vs-claude-code-agentic.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/codex-vs-claude-code-agentic.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Unified AI Filesystem with Rclone and MinIO</title>
      <description><![CDATA[Daniel wants a unified virtual filesystem on his home server — one namespace where AI agents can reach any file across shared drives, S3 buckets, and MinIO. This episode breaks down the full architecture: where MinIO should sit, why rclone union beats mergerfs for cloud remotes, and how to handle silent token refresh so agents never hit an auth wall at 3 AM. We cover the dependency chain between union layer, access protocol, and front-end choice — plus a middle path that keeps MinIO useful as a local ingest buffer. If you're building storage for agents, not humans, this is the blueprint.]]></description>
      <link>https://myweirdprompts.com/episode/unified-ai-filesystem-rclone-minio/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unified-ai-filesystem-rclone-minio/</guid>
      <pubDate>Wed, 12 Aug 2026 23:21:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unified-ai-filesystem-rclone-minio.m4a"
        type="audio/mp4"
        length="35473788"
      />
      <itunes:title>Building a Unified AI Filesystem with Rclone and MinIO</itunes:title>
      <itunes:subtitle>How to build a single virtual filesystem for AI agents across multiple cloud storage providers — without the token headaches.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants a unified virtual filesystem on his home server — one namespace where AI agents can reach any file across shared drives, S3 buckets, and MinIO. This episode breaks down the full architecture: where MinIO should sit, why rclone union beats mergerfs for cloud remotes, and how to handle silent token refresh so agents never hit an auth wall at 3 AM. We cover the dependency chain between union layer, access protocol, and front-end choice — plus a middle path that keeps MinIO useful as a local ingest buffer. If you're building storage for agents, not humans, this is the blueprint.]]></itunes:summary>
      <itunes:duration>2049</itunes:duration>
      <itunes:episode>4704</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unified-ai-filesystem-rclone-minio.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unified-ai-filesystem-rclone-minio.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>MCP Binary Payloads: Why URLs Beat Base64</title>
      <description><![CDATA[Daniel's aggregated MCP setup keeps hitting the same wall: remote tools need binary files, but MCP is JSON-RPC and can't express raw bytes. In this episode, we break down the three ways to represent binary data in MCP, why the stage-and-pass-a-URL pattern isn't a workaround but the protocol's maximum expression, and whether the transport underneath actually matters. Plus, we look at the File Uploads Working Group and the three possible directions for closing this gap—and why the aggregation layer has a second problem no fix can solve.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-binary-payloads-url-pattern/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-binary-payloads-url-pattern/</guid>
      <pubDate>Wed, 12 Aug 2026 23:19:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-binary-payloads-url-pattern.m4a"
        type="audio/mp4"
        length="26710287"
      />
      <itunes:title>MCP Binary Payloads: Why URLs Beat Base64</itunes:title>
      <itunes:subtitle>MCP can&apos;t carry raw bytes. Here&apos;s why the URL pattern is the only legal move—and what the File Uploads Working Group might change.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's aggregated MCP setup keeps hitting the same wall: remote tools need binary files, but MCP is JSON-RPC and can't express raw bytes. In this episode, we break down the three ways to represent binary data in MCP, why the stage-and-pass-a-URL pattern isn't a workaround but the protocol's maximum expression, and whether the transport underneath actually matters. Plus, we look at the File Uploads Working Group and the three possible directions for closing this gap—and why the aggregation layer has a second problem no fix can solve.]]></itunes:summary>
      <itunes:duration>1530</itunes:duration>
      <itunes:episode>4703</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-binary-payloads-url-pattern.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-binary-payloads-url-pattern.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Two Books, One AI Podcast: The Full Archive</title>
      <description><![CDATA[Hilbert walks into the studio holding two brand-new paperbacks: the complete production documentation for the My Weird Prompts AI pipeline, and a printed index of all 4,620 episodes published to date. He explains how the books came together over six months, what writing them down revealed about the system's quiet failures, and why making the invisible archive physical matters. Plus: the permanent six-digit episode codes, the ad mechanism that ran on its fallback for months, and what the books say about the show that the show never said about itself.]]></description>
      <link>https://myweirdprompts.com/episode/ai-podcast-books-archive/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-podcast-books-archive/</guid>
      <pubDate>Wed, 12 Aug 2026 21:44:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-podcast-books-archive.m4a"
        type="audio/mp4"
        length="24289069"
      />
      <itunes:title>Two Books, One AI Podcast: The Full Archive</itunes:title>
      <itunes:subtitle>Hilbert turned the entire My Weird Prompts archive into two physical books — the full production docs and all 4,620 episode descriptions.</itunes:subtitle>
      <itunes:summary><![CDATA[Hilbert walks into the studio holding two brand-new paperbacks: the complete production documentation for the My Weird Prompts AI pipeline, and a printed index of all 4,620 episodes published to date. He explains how the books came together over six months, what writing them down revealed about the system's quiet failures, and why making the invisible archive physical matters. Plus: the permanent six-digit episode codes, the ad mechanism that ran on its fallback for months, and what the books say about the show that the show never said about itself.]]></itunes:summary>
      <itunes:duration>1416</itunes:duration>
      <itunes:episode>4702</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-podcast-books-archive.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-podcast-books-archive.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Claude Code Re-Introduces Itself to Your Repo</title>
      <description><![CDATA[Ever wonder why Claude Code re-scans your repository every session, even when nothing's changed? This episode unpacks the distinction between instruction memory and structure memory, and why the harness optimizes for freshness over recall. We explore the third-party tools that already solve this problem — Aider's repo maps, repomix, context7, and MCP servers — and the system-prompt hacks that can wire them in. Plus, we discuss the real frontier: judgment memory, the institutional knowledge no tool has captured yet.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-repo-memory/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-repo-memory/</guid>
      <pubDate>Wed, 12 Aug 2026 18:38:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-repo-memory.m4a"
        type="audio/mp4"
        length="19668098"
      />
      <itunes:title>Why Claude Code Re-Introduces Itself to Your Repo</itunes:title>
      <itunes:subtitle>Claude Code remembers your preferences but not your codebase. We explore why that&apos;s by design and how to work around it.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wonder why Claude Code re-scans your repository every session, even when nothing's changed? This episode unpacks the distinction between instruction memory and structure memory, and why the harness optimizes for freshness over recall. We explore the third-party tools that already solve this problem — Aider's repo maps, repomix, context7, and MCP servers — and the system-prompt hacks that can wire them in. Plus, we discuss the real frontier: judgment memory, the institutional knowledge no tool has captured yet.]]></itunes:summary>
      <itunes:duration>1110</itunes:duration>
      <itunes:episode>4701</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-repo-memory.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-repo-memory.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Browser Agents: Three Paths to One Protocol</title>
      <description><![CDATA[Browser AI agents are fragmented across three approaches: pixel-watching screenshots, fragile dev-tools extensions, and Google's new native MCP support in Chrome. We break down why the visual method is systematically blind to network failures, why extensions are a maintenance nightmare, and how a single open protocol could consolidate the entire ecosystem. The browser isn't just a display surface anymore — it's becoming an instrumented environment that speaks the agent's language.]]></description>
      <link>https://myweirdprompts.com/episode/browser-agents-mcp-protocol/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/browser-agents-mcp-protocol/</guid>
      <pubDate>Wed, 12 Aug 2026 02:52:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/browser-agents-mcp-protocol.m4a"
        type="audio/mp4"
        length="25121035"
      />
      <itunes:title>Browser Agents: Three Paths to One Protocol</itunes:title>
      <itunes:subtitle>How screenshot watching, dev-tools extensions, and native MCP in Chrome are converging — and what wins.</itunes:subtitle>
      <itunes:summary><![CDATA[Browser AI agents are fragmented across three approaches: pixel-watching screenshots, fragile dev-tools extensions, and Google's new native MCP support in Chrome. We break down why the visual method is systematically blind to network failures, why extensions are a maintenance nightmare, and how a single open protocol could consolidate the entire ecosystem. The browser isn't just a display surface anymore — it's becoming an instrumented environment that speaks the agent's language.]]></itunes:summary>
      <itunes:duration>1452</itunes:duration>
      <itunes:episode>4700</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/browser-agents-mcp-protocol.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/browser-agents-mcp-protocol.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Engine: What Makes Claude Code Tick</title>
      <description><![CDATA[When Claude Code feels smarter, is it the model or the harness? This episode unpacks the invisible engineering — tool grounding, context compaction, error recovery, and task decomposition — that turns a language model into a true agent. We explore why the distinction matters for developers, how harness improvements ship faster than model updates, and what it means for evaluating AI tools. If you've ever wondered why your AI assistant feels like it's growing up, this is the episode for you.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-harness-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-harness-explained/</guid>
      <pubDate>Wed, 12 Aug 2026 02:46:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-harness-explained.m4a"
        type="audio/mp4"
        length="26103042"
      />
      <itunes:title>The Hidden Engine: What Makes Claude Code Tick</itunes:title>
      <itunes:subtitle>Is it the model or the harness? We break down the invisible scaffolding that turns LLMs into real agents.</itunes:subtitle>
      <itunes:summary><![CDATA[When Claude Code feels smarter, is it the model or the harness? This episode unpacks the invisible engineering — tool grounding, context compaction, error recovery, and task decomposition — that turns a language model into a true agent. We explore why the distinction matters for developers, how harness improvements ship faster than model updates, and what it means for evaluating AI tools. If you've ever wondered why your AI assistant feels like it's growing up, this is the episode for you.]]></itunes:summary>
      <itunes:duration>1493</itunes:duration>
      <itunes:episode>4699</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-harness-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-harness-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Typst for Agents: Programmatic PDF Generation</title>
      <description><![CDATA[Daniel and Herman explore Typst's full capability surface for programmatic PDF generation—from page geometry and typography to tables, cross-references, and the scripting layer that makes it all programmable. They break down what's genuinely easy versus where you still fight it, and what a sensible pipeline looks like when an AI agent—not a person—is writing the document. If you're building document generation into an agent workflow, this episode maps the entire territory.]]></description>
      <link>https://myweirdprompts.com/episode/typst-programmatic-pdf-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/typst-programmatic-pdf-agents/</guid>
      <pubDate>Mon, 10 Aug 2026 20:47:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/typst-programmatic-pdf-agents.m4a"
        type="audio/mp4"
        length="25104667"
      />
      <itunes:title>Typst for Agents: Programmatic PDF Generation</itunes:title>
      <itunes:subtitle>How Typst&apos;s function-based design makes it the ideal document engine for AI agents generating professional PDFs.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel and Herman explore Typst's full capability surface for programmatic PDF generation—from page geometry and typography to tables, cross-references, and the scripting layer that makes it all programmable. They break down what's genuinely easy versus where you still fight it, and what a sensible pipeline looks like when an AI agent—not a person—is writing the document. If you're building document generation into an agent workflow, this episode maps the entire territory.]]></itunes:summary>
      <itunes:duration>1427</itunes:duration>
      <itunes:episode>4690</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/typst-programmatic-pdf-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/typst-programmatic-pdf-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Bootable Standby Drives: The Ship of Theseus Server Solution</title>
      <description><![CDATA[When Daniel's server suffered a cascading hardware failure, he replaced every single component — then faced the storage question. Instead of rebuilding a RAID array, he wants a permanently installed SATA SSD that can serve as a bootable standby copy of his system. We break down the established pattern, the tools, and the exact steps to make a standby drive that actually boots when you need it. No fragile hacks, no reinventing the wheel — just practical Linux recovery planning.]]></description>
      <link>https://myweirdprompts.com/episode/bootable-standby-drive-rsync/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/bootable-standby-drive-rsync/</guid>
      <pubDate>Mon, 10 Aug 2026 17:48:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/bootable-standby-drive-rsync.m4a"
        type="audio/mp4"
        length="35179285"
      />
      <itunes:title>Bootable Standby Drives: The Ship of Theseus Server Solution</itunes:title>
      <itunes:subtitle>Recovering from a dead NVMe drive in minutes, not days. A practical guide to building a bootable standby disk with rsync.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel's server suffered a cascading hardware failure, he replaced every single component — then faced the storage question. Instead of rebuilding a RAID array, he wants a permanently installed SATA SSD that can serve as a bootable standby copy of his system. We break down the established pattern, the tools, and the exact steps to make a standby drive that actually boots when you need it. No fragile hacks, no reinventing the wheel — just practical Linux recovery planning.]]></itunes:summary>
      <itunes:duration>2028</itunes:duration>
      <itunes:episode>4689</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/bootable-standby-drive-rsync.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/bootable-standby-drive-rsync.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Bonding Cellular Connections: Why Can&apos;t You Buy It?</title>
      <description><![CDATA[Daniel asks why he can't buy a device that bonds multiple cellular carriers into one fast, reliable connection. We break down the DIY path — Linux box, two USB-tethered phones, a cheap VPS, and MPTCP — and explain why the tech has existed for 14 years but nobody sells it to consumers. From middlebox interference to the economics of hardware startups, we unpack the gap between what's possible and what's buyable.]]></description>
      <link>https://myweirdprompts.com/episode/cellular-bonding-diy-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cellular-bonding-diy-guide/</guid>
      <pubDate>Mon, 10 Aug 2026 17:14:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cellular-bonding-diy-guide.m4a"
        type="audio/mp4"
        length="26647424"
      />
      <itunes:title>Bonding Cellular Connections: Why Can&apos;t You Buy It?</itunes:title>
      <itunes:subtitle>Why can&apos;t you buy a box that bonds two SIM cards? We explore the DIY build and the market gap.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asks why he can't buy a device that bonds multiple cellular carriers into one fast, reliable connection. We break down the DIY path — Linux box, two USB-tethered phones, a cheap VPS, and MPTCP — and explain why the tech has existed for 14 years but nobody sells it to consumers. From middlebox interference to the economics of hardware startups, we unpack the gap between what's possible and what's buyable.]]></itunes:summary>
      <itunes:duration>1530</itunes:duration>
      <itunes:episode>4687</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cellular-bonding-diy-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cellular-bonding-diy-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Claude Code&apos;s Patience Works: Fork, Wait, and the Transcript Stack</title>
      <description><![CDATA[When Claude Code says it's waiting for a deploy, it's not actually waiting. The model is stateless—it exists in bursts—so the framework constructs the appearance of patience by backgrounding processes and handing the model a receipt. In this episode, we trace the entire mechanism: the Bash tool's timeout that doesn't kill, the Monitor tool's event-driven watching, and the reaping that cleans up orphans. Then we map it all onto Unix primitives—fork, nohup, tail -f, select, SIGCHLD—and find where the analogy breaks: the transcript is the stack, and truncating it corrupts the model's memory in ways a SIGSTOP'd process never could.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-background-tasks-mechanics/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-background-tasks-mechanics/</guid>
      <pubDate>Mon, 10 Aug 2026 17:10:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-background-tasks-mechanics.m4a"
        type="audio/mp4"
        length="27345782"
      />
      <itunes:title>How Claude Code&apos;s Patience Works: Fork, Wait, and the Transcript Stack</itunes:title>
      <itunes:subtitle>Claude Code&apos;s &quot;waiting&quot; is an illusion. We break down the Unix primitives behind its background task system and where the analogy cracks.</itunes:subtitle>
      <itunes:summary><![CDATA[When Claude Code says it's waiting for a deploy, it's not actually waiting. The model is stateless—it exists in bursts—so the framework constructs the appearance of patience by backgrounding processes and handing the model a receipt. In this episode, we trace the entire mechanism: the Bash tool's timeout that doesn't kill, the Monitor tool's event-driven watching, and the reaping that cleans up orphans. Then we map it all onto Unix primitives—fork, nohup, tail -f, select, SIGCHLD—and find where the analogy breaks: the transcript is the stack, and truncating it corrupts the model's memory in ways a SIGSTOP'd process never could.]]></itunes:summary>
      <itunes:duration>1566</itunes:duration>
      <itunes:episode>4686</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-background-tasks-mechanics.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-background-tasks-mechanics.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>GitHub vs AWS: Agentic AI Cert Showdown</title>
      <description><![CDATA[Daniel wants a head-to-head: GitHub's Certified Agentic AI Developer (GH-600) versus AWS's AI certification pathway. We crack open both syllabi to see what's actually tested, where the vendor lock-in hides, and which credential teaches skills that survive outside its ecosystem. If you're deciding where to spend your study hours in 2026, this episode maps the tradeoffs.]]></description>
      <link>https://myweirdprompts.com/episode/github-aws-agentic-ai-certs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/github-aws-agentic-ai-certs/</guid>
      <pubDate>Mon, 10 Aug 2026 14:47:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/github-aws-agentic-ai-certs.m4a"
        type="audio/mp4"
        length="24152610"
      />
      <itunes:title>GitHub vs AWS: Agentic AI Cert Showdown</itunes:title>
      <itunes:subtitle>GH-600 vs AWS&apos;s AI ladder — which curriculum teaches transferable skills, and which is just product training?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants a head-to-head: GitHub's Certified Agentic AI Developer (GH-600) versus AWS's AI certification pathway. We crack open both syllabi to see what's actually tested, where the vendor lock-in hides, and which credential teaches skills that survive outside its ecosystem. If you're deciding where to spend your study hours in 2026, this episode maps the tradeoffs.]]></itunes:summary>
      <itunes:duration>1385</itunes:duration>
      <itunes:episode>4685</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/github-aws-agentic-ai-certs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/github-aws-agentic-ai-certs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Focus-Aware Key Remapping on Linux: The Hard Parts</title>
      <description><![CDATA[Daniel's been using a foot pedal for dictation for a year and a half, and it works great — until he needs it to behave differently depending on which app has focus. This episode breaks down the nuts and bolts of focus-conditional key remapping on Linux: where keyd lives in the input stack, how it learns about window focus through keyd-application-mapper, and why the X11 and Wayland paths are completely different beasts. We get into the polling race conditions that can corrupt held key sequences, and why the clean solution — a remapping hook inside the display server — doesn't exist yet.]]></description>
      <link>https://myweirdprompts.com/episode/focus-aware-key-remapping-linux/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/focus-aware-key-remapping-linux/</guid>
      <pubDate>Mon, 10 Aug 2026 14:43:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/focus-aware-key-remapping-linux.m4a"
        type="audio/mp4"
        length="30130230"
      />
      <itunes:title>Focus-Aware Key Remapping on Linux: The Hard Parts</itunes:title>
      <itunes:subtitle>How keyd bridges kernel input events and display-server focus — and why Wayland makes it harder than it should be.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's been using a foot pedal for dictation for a year and a half, and it works great — until he needs it to behave differently depending on which app has focus. This episode breaks down the nuts and bolts of focus-conditional key remapping on Linux: where keyd lives in the input stack, how it learns about window focus through keyd-application-mapper, and why the X11 and Wayland paths are completely different beasts. We get into the polling race conditions that can corrupt held key sequences, and why the clean solution — a remapping hook inside the display server — doesn't exist yet.]]></itunes:summary>
      <itunes:duration>1739</itunes:duration>
      <itunes:episode>4684</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/focus-aware-key-remapping-linux.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/focus-aware-key-remapping-linux.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Parallel vs Sequential Agent Loops: The Real Tradeoffs</title>
      <description><![CDATA[Two diagrams show the same AI workflow: one as a sequential chain, one as a parallel fan-out. But the real difference isn't the shape — it's what happens when results come back. We dig into when parallelization actually wins, when it secretly costs more, and why the join node is where workflows live or die. Plus: where does the loop actually live in a graph, and why most diagrams get it wrong.]]></description>
      <link>https://myweirdprompts.com/episode/agent-loop-topology-parallel-sequential/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-loop-topology-parallel-sequential/</guid>
      <pubDate>Mon, 10 Aug 2026 14:29:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-loop-topology-parallel-sequential.m4a"
        type="audio/mp4"
        length="29550940"
      />
      <itunes:title>Parallel vs Sequential Agent Loops: The Real Tradeoffs</itunes:title>
      <itunes:subtitle>When should AI agents run in parallel vs sequential chains? The dependency graph isn&apos;t enough — join costs and error handling matter more.</itunes:subtitle>
      <itunes:summary><![CDATA[Two diagrams show the same AI workflow: one as a sequential chain, one as a parallel fan-out. But the real difference isn't the shape — it's what happens when results come back. We dig into when parallelization actually wins, when it secretly costs more, and why the join node is where workflows live or die. Plus: where does the loop actually live in a graph, and why most diagrams get it wrong.]]></itunes:summary>
      <itunes:duration>1722</itunes:duration>
      <itunes:episode>4683</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-loop-topology-parallel-sequential.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-loop-topology-parallel-sequential.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Loops vs Workflows: Defining AI&apos;s New Unit of Work</title>
      <description><![CDATA[Everyone's talking about "loops" in Claude Code, but nobody's defining them. We dig into what actually separates a loop from a prompt, a workflow, or a cron job—and why the model's agency over the cycle is the real differentiator. If you've been wondering whether loops are genuinely new or just agentic workflows with better packaging, this episode draws the line.]]></description>
      <link>https://myweirdprompts.com/episode/defining-ai-loops-workflows/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/defining-ai-loops-workflows/</guid>
      <pubDate>Mon, 10 Aug 2026 14:11:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/defining-ai-loops-workflows.m4a"
        type="audio/mp4"
        length="25620707"
      />
      <itunes:title>Loops vs Workflows: Defining AI&apos;s New Unit of Work</itunes:title>
      <itunes:subtitle>What makes a Claude Code loop different from a prompt or workflow? We pin down the actual definition—and who owns the off switch.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone's talking about "loops" in Claude Code, but nobody's defining them. We dig into what actually separates a loop from a prompt, a workflow, or a cron job—and why the model's agency over the cycle is the real differentiator. If you've been wondering whether loops are genuinely new or just agentic workflows with better packaging, this episode draws the line.]]></itunes:summary>
      <itunes:duration>1512</itunes:duration>
      <itunes:episode>4681</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/defining-ai-loops-workflows.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/defining-ai-loops-workflows.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Miniature Cloud: Proxmox, MinIO, and the Seams</title>
      <description><![CDATA[Daniel's been wondering if a single physical machine can be carved up into a miniature private cloud. The answer involves more than just a hypervisor. This episode breaks down the software stack—Proxmox, MinIO, and OpenNebula—and the hardware realities that separate a box with VMs from a true cloud with tenants, quotas, and S3-compatible storage.]]></description>
      <link>https://myweirdprompts.com/episode/miniature-cloud-proxmox-minio/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/miniature-cloud-proxmox-minio/</guid>
      <pubDate>Sun, 09 Aug 2026 16:24:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/miniature-cloud-proxmox-minio.m4a"
        type="audio/mp4"
        length="28474824"
      />
      <itunes:title>Building a Miniature Cloud: Proxmox, MinIO, and the Seams</itunes:title>
      <itunes:subtitle>Proxmox alone isn&apos;t a private cloud. What does it actually take to build one?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's been wondering if a single physical machine can be carved up into a miniature private cloud. The answer involves more than just a hypervisor. This episode breaks down the software stack—Proxmox, MinIO, and OpenNebula—and the hardware realities that separate a box with VMs from a true cloud with tenants, quotas, and S3-compatible storage.]]></itunes:summary>
      <itunes:duration>1647</itunes:duration>
      <itunes:episode>4671</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/miniature-cloud-proxmox-minio.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/miniature-cloud-proxmox-minio.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Trapdoor in Your CPU: How Hypervisors Actually Work</title>
      <description><![CDATA[Ever wonder what actually happens when a virtual machine runs? This episode breaks down the hypervisor layer: the hardware trapdoor in your CPU, KVM's split personality, the VMCS data structure, and why virtio beats emulation. Then we contrast it with Docker's fundamentally different isolation model — and why the security trade-offs matter more than the marketing.]]></description>
      <link>https://myweirdprompts.com/episode/hypervisor-trapdoor-cpu-virtualization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hypervisor-trapdoor-cpu-virtualization/</guid>
      <pubDate>Sun, 09 Aug 2026 04:29:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hypervisor-trapdoor-cpu-virtualization.m4a"
        type="audio/mp4"
        length="25908227"
      />
      <itunes:title>The Trapdoor in Your CPU: How Hypervisors Actually Work</itunes:title>
      <itunes:subtitle>Inside the silicon trapdoor that makes virtualization possible — and why it&apos;s nothing like Docker.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wonder what actually happens when a virtual machine runs? This episode breaks down the hypervisor layer: the hardware trapdoor in your CPU, KVM's split personality, the VMCS data structure, and why virtio beats emulation. Then we contrast it with Docker's fundamentally different isolation model — and why the security trade-offs matter more than the marketing.]]></itunes:summary>
      <itunes:duration>1483</itunes:duration>
      <itunes:episode>4664</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hypervisor-trapdoor-cpu-virtualization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hypervisor-trapdoor-cpu-virtualization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Python vs TypeScript for MCP Servers: The Real Decision Rule</title>
      <description><![CDATA[MCP servers sit at the seam between AI models and applications — and both Python and TypeScript have official, first-class SDKs. But the choice isn't about the server itself; it's about everything the server connects to. In this episode, we break down the real differences: compile-time safety vs iteration speed, cold starts, ecosystem gravity, and why the heaviest integration in your stack should decide the language. If you're building AI tools that glue models to interfaces, this framework will save you from a rewrite.]]></description>
      <link>https://myweirdprompts.com/episode/python-typescript-mcp-servers/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/python-typescript-mcp-servers/</guid>
      <pubDate>Sat, 08 Aug 2026 17:42:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/python-typescript-mcp-servers.m4a"
        type="audio/mp4"
        length="22609315"
      />
      <itunes:title>Python vs TypeScript for MCP Servers: The Real Decision Rule</itunes:title>
      <itunes:subtitle>Both are first-class for MCP, but the real choice depends on what your server talks to. Here&apos;s the framework.</itunes:subtitle>
      <itunes:summary><![CDATA[MCP servers sit at the seam between AI models and applications — and both Python and TypeScript have official, first-class SDKs. But the choice isn't about the server itself; it's about everything the server connects to. In this episode, we break down the real differences: compile-time safety vs iteration speed, cold starts, ecosystem gravity, and why the heaviest integration in your stack should decide the language. If you're building AI tools that glue models to interfaces, this framework will save you from a rewrite.]]></itunes:summary>
      <itunes:duration>1272</itunes:duration>
      <itunes:episode>4663</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/python-typescript-mcp-servers.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/python-typescript-mcp-servers.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>TypeScript Server Anatomy: Layers &amp; Request Flow</title>
      <description><![CDATA[Ever wonder what actually happens when a request hits your TypeScript server? We trace the full path — from raw bytes on a socket through middleware, routing, services, and data access — and explain why layers matter even when everything runs in one process. Plus: how TypeScript enforces boundaries that plain JavaScript leaves to chance, and where dependency injection fits in.]]></description>
      <link>https://myweirdprompts.com/episode/typescript-server-anatomy-layers/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/typescript-server-anatomy-layers/</guid>
      <pubDate>Sat, 08 Aug 2026 17:39:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/typescript-server-anatomy-layers.m4a"
        type="audio/mp4"
        length="33349593"
      />
      <itunes:title>TypeScript Server Anatomy: Layers &amp; Request Flow</itunes:title>
      <itunes:subtitle>Dissecting a TypeScript backend from runtime to repository — how middleware, services, and data access fit together.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wonder what actually happens when a request hits your TypeScript server? We trace the full path — from raw bytes on a socket through middleware, routing, services, and data access — and explain why layers matter even when everything runs in one process. Plus: how TypeScript enforces boundaries that plain JavaScript leaves to chance, and where dependency injection fits in.]]></itunes:summary>
      <itunes:duration>1902</itunes:duration>
      <itunes:episode>4662</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/typescript-server-anatomy-layers.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/typescript-server-anatomy-layers.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Database Migrations: The Full Lifecycle</title>
      <description><![CDATA[Ever wondered what really happens between "I need to change this column" and "it's live in production"? We trace the full migration lifecycle — from the initial decision through ORM-generated SQL, review, testing, and the multi-step deployments that keep production alive. Learn the expand/contract pattern, why reviewing generated SQL is non-negotiable, and how to avoid the failures that lock tables for hours.]]></description>
      <link>https://myweirdprompts.com/episode/database-migrations-full-lifecycle/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/database-migrations-full-lifecycle/</guid>
      <pubDate>Sat, 08 Aug 2026 17:36:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/database-migrations-full-lifecycle.m4a"
        type="audio/mp4"
        length="26166649"
      />
      <itunes:title>Database Migrations: The Full Lifecycle</itunes:title>
      <itunes:subtitle>From schema change to production — the complete guide to safe, zero-downtime database migrations.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wondered what really happens between "I need to change this column" and "it's live in production"? We trace the full migration lifecycle — from the initial decision through ORM-generated SQL, review, testing, and the multi-step deployments that keep production alive. Learn the expand/contract pattern, why reviewing generated SQL is non-negotiable, and how to avoid the failures that lock tables for hours.]]></itunes:summary>
      <itunes:duration>1504</itunes:duration>
      <itunes:episode>4661</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/database-migrations-full-lifecycle.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/database-migrations-full-lifecycle.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Books That Outlive Frameworks: A Dev&apos;s Guide</title>
      <description><![CDATA[Daniel's hitting the limit of learning-by-doing. This episode breaks down how to choose technical books that teach principles, not ephemeral APIs. We cover three essential titles for Python, TypeScript, and Node.js, plus a five-point checklist to spot books that won't be obsolete in six months. For anyone deep in agentic AI, MCP, or just tired of tutorials that don't stick.]]></description>
      <link>https://myweirdprompts.com/episode/books-that-outlive-frameworks/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/books-that-outlive-frameworks/</guid>
      <pubDate>Sat, 08 Aug 2026 00:32:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/books-that-outlive-frameworks.m4a"
        type="audio/mp4"
        length="31462092"
      />
      <itunes:title>Books That Outlive Frameworks: A Dev&apos;s Guide</itunes:title>
      <itunes:subtitle>Skip the API docs. Learn the data model, type system, and patterns that survive framework churn.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's hitting the limit of learning-by-doing. This episode breaks down how to choose technical books that teach principles, not ephemeral APIs. We cover three essential titles for Python, TypeScript, and Node.js, plus a five-point checklist to spot books that won't be obsolete in six months. For anyone deep in agentic AI, MCP, or just tired of tutorials that don't stick.]]></itunes:summary>
      <itunes:duration>1806</itunes:duration>
      <itunes:episode>4656</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/books-that-outlive-frameworks.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/books-that-outlive-frameworks.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Open Source Heroes Can&apos;t Afford Rent</title>
      <description><![CDATA[Heartbleed. OpenSSL. Four maintainers and a budget that wouldn't hire one decent engineer. This episode explores the structural tension between creating foundational digital infrastructure and getting paid. We trace the pattern from Gutenberg to the Linux kernel, unpack why markets price scarcity over value, and examine the models that have tried to fix it — from corporate sponsorship to crowdfunding to bounty systems. Why is the software that runs the world priced at zero?]]></description>
      <link>https://myweirdprompts.com/episode/open-source-maintainer-funding-crisis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/open-source-maintainer-funding-crisis/</guid>
      <pubDate>Fri, 07 Aug 2026 22:38:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/open-source-maintainer-funding-crisis.m4a"
        type="audio/mp4"
        length="25712675"
      />
      <itunes:title>Why Open Source Heroes Can&apos;t Afford Rent</itunes:title>
      <itunes:subtitle>The code running the internet is maintained by people earning a dollar a year. Why does the market fail to reward our digital commons?</itunes:subtitle>
      <itunes:summary><![CDATA[Heartbleed. OpenSSL. Four maintainers and a budget that wouldn't hire one decent engineer. This episode explores the structural tension between creating foundational digital infrastructure and getting paid. We trace the pattern from Gutenberg to the Linux kernel, unpack why markets price scarcity over value, and examine the models that have tried to fix it — from corporate sponsorship to crowdfunding to bounty systems. Why is the software that runs the world priced at zero?]]></itunes:summary>
      <itunes:duration>1476</itunes:duration>
      <itunes:episode>4649</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/open-source-maintainer-funding-crisis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/open-source-maintainer-funding-crisis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Graph Engineering: AI Memory Beyond Retrieval</title>
      <description><![CDATA[From Karpathy's Markdown hack to Cognizant's Caesar Framework, we explore graph engineering — the emerging discipline of structuring AI's knowledge substrate. Learn how traversing connections between notes, documents, and concepts unlocks serendipity that vector search can't touch, and where this approach breaks down in practice.]]></description>
      <link>https://myweirdprompts.com/episode/graph-engineering-ai-memory/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/graph-engineering-ai-memory/</guid>
      <pubDate>Fri, 07 Aug 2026 22:33:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/graph-engineering-ai-memory.m4a"
        type="audio/mp4"
        length="22522226"
      />
      <itunes:title>Graph Engineering: AI Memory Beyond Retrieval</itunes:title>
      <itunes:subtitle>How linked notes and knowledge graphs are turning AI agents from memory tools into discovery engines.</itunes:subtitle>
      <itunes:summary><![CDATA[From Karpathy's Markdown hack to Cognizant's Caesar Framework, we explore graph engineering — the emerging discipline of structuring AI's knowledge substrate. Learn how traversing connections between notes, documents, and concepts unlocks serendipity that vector search can't touch, and where this approach breaks down in practice.]]></itunes:summary>
      <itunes:duration>1288</itunes:duration>
      <itunes:episode>4648</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/graph-engineering-ai-memory.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/graph-engineering-ai-memory.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Traffic Shaping: How Routers Decide Who Gets Bandwidth</title>
      <description><![CDATA[Ever wonder why your video call stays clear while downloads crawl? It's not magic — it's traffic shaping. We break down the token bucket algorithm, how ISPs use Deep Packet Inspection to prioritize (or punish) traffic, and how you can take control with OpenWrt's SQM and fq_codel to eliminate bufferbloat. From in-flight Wi-Fi to your home router, here's how the internet actually decides who goes first.]]></description>
      <link>https://myweirdprompts.com/episode/traffic-shaping-qos-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/traffic-shaping-qos-explained/</guid>
      <pubDate>Fri, 07 Aug 2026 13:36:03 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/traffic-shaping-qos-explained.m4a"
        type="audio/mp4"
        length="26934317"
      />
      <itunes:title>Traffic Shaping: How Routers Decide Who Gets Bandwidth</itunes:title>
      <itunes:subtitle>Token buckets, bufferbloat, and why your router&apos;s QoS settings might be lying to you.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wonder why your video call stays clear while downloads crawl? It's not magic — it's traffic shaping. We break down the token bucket algorithm, how ISPs use Deep Packet Inspection to prioritize (or punish) traffic, and how you can take control with OpenWrt's SQM and fq_codel to eliminate bufferbloat. From in-flight Wi-Fi to your home router, here's how the internet actually decides who goes first.]]></itunes:summary>
      <itunes:duration>1548</itunes:duration>
      <itunes:episode>4638</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/traffic-shaping-qos-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/traffic-shaping-qos-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Taming Three DNS Overlays on One Laptop</title>
      <description><![CDATA[Daniel runs a commercial VPN, Tailscale, and Cloudflare WARP on a single laptop — and all three want to control DNS. The failure mode is silent: queries leak to the wrong resolver while pages still load. We break down where these clients collide, why the OS can't represent multiple resolvers, and the configuration strategy to make them coexist — including a local DNS proxy as a choke point, client-specific overrides, and how to guarantee no unencrypted fallback.]]></description>
      <link>https://myweirdprompts.com/episode/dns-overlays-coexistence-strategy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dns-overlays-coexistence-strategy/</guid>
      <pubDate>Thu, 06 Aug 2026 15:44:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dns-overlays-coexistence-strategy.m4a"
        type="audio/mp4"
        length="30893091"
      />
      <itunes:title>Taming Three DNS Overlays on One Laptop</itunes:title>
      <itunes:subtitle>Commercial VPN, Tailscale, and WARP on one machine — here&apos;s how to make them share DNS without leaking a single query.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel runs a commercial VPN, Tailscale, and Cloudflare WARP on a single laptop — and all three want to control DNS. The failure mode is silent: queries leak to the wrong resolver while pages still load. We break down where these clients collide, why the OS can't represent multiple resolvers, and the configuration strategy to make them coexist — including a local DNS proxy as a choke point, client-specific overrides, and how to guarantee no unencrypted fallback.]]></itunes:summary>
      <itunes:duration>1773</itunes:duration>
      <itunes:episode>4636</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dns-overlays-coexistence-strategy.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dns-overlays-coexistence-strategy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Twilio and RingCentral Never Converge</title>
      <description><![CDATA[Twilio and RingCentral both started from the same premise — voice should leave the proprietary closet and become software. But they built products so different they barely compete. Twilio went full programmatic; RingCentral became a finished appliance. The standard narrative says the legacy mess will eventually collapse into clean APIs. But as this episode explores, the leaks in Twilio's abstraction aren't technical debt — they're jurisdictional. E911, number porting, and messaging registration are law wearing the costume of infrastructure, and law doesn't refactor. If that's right, the divergence is structural, not transitional.]]></description>
      <link>https://myweirdprompts.com/episode/twilio-ringcentral-telephony-divergence/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/twilio-ringcentral-telephony-divergence/</guid>
      <pubDate>Tue, 04 Aug 2026 23:16:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/twilio-ringcentral-telephony-divergence.m4a"
        type="audio/mp4"
        length="22540216"
      />
      <itunes:title>Why Twilio and RingCentral Never Converge</itunes:title>
      <itunes:subtitle>Twilio and RingCentral both turned telephony into software. So why do they barely compete? The answer is law, not tech.</itunes:subtitle>
      <itunes:summary><![CDATA[Twilio and RingCentral both started from the same premise — voice should leave the proprietary closet and become software. But they built products so different they barely compete. Twilio went full programmatic; RingCentral became a finished appliance. The standard narrative says the legacy mess will eventually collapse into clean APIs. But as this episode explores, the leaks in Twilio's abstraction aren't technical debt — they're jurisdictional. E911, number porting, and messaging registration are law wearing the costume of infrastructure, and law doesn't refactor. If that's right, the divergence is structural, not transitional.]]></itunes:summary>
      <itunes:duration>1294</itunes:duration>
      <itunes:episode>4610</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/twilio-ringcentral-telephony-divergence.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/twilio-ringcentral-telephony-divergence.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Chatterbox Still Leads Open-Source TTS</title>
      <description><![CDATA[Ever wondered what actually powers the voices you hear on this show? We're doing a full teardown of Chatterbox, the open-source text-to-speech model from Resemble AI. We explore its non-autoregressive architecture built on a Llama backbone, the S3 tokenizer, and how it achieves parallel generation for massive speedups. We also dig into why, over a year later, it still beats newer models in production—thanks to stability, caching, and a thriving open-source ecosystem.]]></description>
      <link>https://myweirdprompts.com/episode/chatterbox-open-source-tts/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/chatterbox-open-source-tts/</guid>
      <pubDate>Tue, 04 Aug 2026 22:41:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/chatterbox-open-source-tts.m4a"
        type="audio/mp4"
        length="28531352"
      />
      <itunes:title>Why Chatterbox Still Leads Open-Source TTS</itunes:title>
      <itunes:subtitle>We break down the architecture of Chatterbox, the open-source TTS model powering this podcast, and why it remains state of the art.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wondered what actually powers the voices you hear on this show? We're doing a full teardown of Chatterbox, the open-source text-to-speech model from Resemble AI. We explore its non-autoregressive architecture built on a Llama backbone, the S3 tokenizer, and how it achieves parallel generation for massive speedups. We also dig into why, over a year later, it still beats newer models in production—thanks to stability, caching, and a thriving open-source ecosystem.]]></itunes:summary>
      <itunes:duration>1621</itunes:duration>
      <itunes:episode>4605</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/chatterbox-open-source-tts.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/chatterbox-open-source-tts.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Sub-Agents Overwhelm the Orchestrator</title>
      <description><![CDATA[Daniel's clever Claude-powered inventory system worked—until parallel sub-agents flooded the orchestrator's context window and it all ground to a halt. In this episode, we dig into the orchestrator pattern, why it breaks down under load, and the two numbers that determine failure: linear context growth and non-linear attention collapse. We walk through a practical heuristic for estimating your own risk, the update granularity knob you can turn, and when independent parallel instances might actually be the smarter choice. If you're building multi-agent systems, this is the episode for you.]]></description>
      <link>https://myweirdprompts.com/episode/sub-agent-orchestrator-bottleneck/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/sub-agent-orchestrator-bottleneck/</guid>
      <pubDate>Tue, 04 Aug 2026 22:34:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/sub-agent-orchestrator-bottleneck.m4a"
        type="audio/mp4"
        length="24464955"
      />
      <itunes:title>When Sub-Agents Overwhelm the Orchestrator</itunes:title>
      <itunes:subtitle>Daniel&apos;s inventory system hit a wall when parallel sub-agents flooded the orchestrator&apos;s context window. We break down the math and the fix.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's clever Claude-powered inventory system worked—until parallel sub-agents flooded the orchestrator's context window and it all ground to a halt. In this episode, we dig into the orchestrator pattern, why it breaks down under load, and the two numbers that determine failure: linear context growth and non-linear attention collapse. We walk through a practical heuristic for estimating your own risk, the update granularity knob you can turn, and when independent parallel instances might actually be the smarter choice. If you're building multi-agent systems, this is the episode for you.]]></itunes:summary>
      <itunes:duration>1408</itunes:duration>
      <itunes:episode>4604</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/sub-agent-orchestrator-bottleneck.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/sub-agent-orchestrator-bottleneck.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Cost of Conversational AI: Why Stateless LLM Calls Matter</title>
      <description><![CDATA[When Daniel wrote in about the "strange paucity" of text-in, text-out AI tools, he tapped into a fundamental gap in how LLMs are built and deployed. This episode unpacks the difference between conversational workflows (multi-turn, stateful, context-accumulating) and transformation workflows (single-turn, stateless, input-to-output mapping). We trace how the conversation assumption is baked into every layer of the stack — from SDKs to API design to fine-tuning — and why that makes pure transformation pipelines actively harder to build. We explore the cost of dragging bad outputs through context windows, the synchronous versus asynchronous distinction, and why the completions endpoint is treated as the neglected sibling to chat completions. Plus, we look at where model development is heading with instruction-following models and parameter selection, and what tooling exists — or doesn't — for builders who want stateless LLM calls as a first-class primitive.]]></description>
      <link>https://myweirdprompts.com/episode/stateless-llm-transformation-cost/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/stateless-llm-transformation-cost/</guid>
      <pubDate>Mon, 03 Aug 2026 20:19:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/stateless-llm-transformation-cost.m4a"
        type="audio/mp4"
        length="29482541"
      />
      <itunes:title>The Hidden Cost of Conversational AI: Why Stateless LLM Calls Matter</itunes:title>
      <itunes:subtitle>Why conversational AI tools are making batch document processing slower, costlier, and less reliable — and what to do about it.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel wrote in about the "strange paucity" of text-in, text-out AI tools, he tapped into a fundamental gap in how LLMs are built and deployed. This episode unpacks the difference between conversational workflows (multi-turn, stateful, context-accumulating) and transformation workflows (single-turn, stateless, input-to-output mapping). We trace how the conversation assumption is baked into every layer of the stack — from SDKs to API design to fine-tuning — and why that makes pure transformation pipelines actively harder to build. We explore the cost of dragging bad outputs through context windows, the synchronous versus asynchronous distinction, and why the completions endpoint is treated as the neglected sibling to chat completions. Plus, we look at where model development is heading with instruction-following models and parameter selection, and what tooling exists — or doesn't — for builders who want stateless LLM calls as a first-class primitive.]]></itunes:summary>
      <itunes:duration>1689</itunes:duration>
      <itunes:episode>4588</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/stateless-llm-transformation-cost.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/stateless-llm-transformation-cost.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can Agentic AI Finally Break the Google Workspace Grip?</title>
      <description><![CDATA[Daniel, a 19-year Google Workspace veteran, makes a bold case: agentic AI could finally break the grip of the big cloud monoliths. We unpack the three concrete friction points he hit—email routing, contact storage, and document APIs—and explore whether the convenience of the bundle is actually holding you back. From Cloudflare's DNS-level routing to the missing market for standalone contact services, we examine the real cost of staying put and the viable paths to a more tailored, self-hosted setup.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ai-google-workspace-alternatives/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ai-google-workspace-alternatives/</guid>
      <pubDate>Mon, 03 Aug 2026 19:32:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ai-google-workspace-alternatives.m4a"
        type="audio/mp4"
        length="28158089"
      />
      <itunes:title>Can Agentic AI Finally Break the Google Workspace Grip?</itunes:title>
      <itunes:subtitle>We dig into the real friction points of Google Workspace and whether agentic AI can finally make the monolith obsolete.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel, a 19-year Google Workspace veteran, makes a bold case: agentic AI could finally break the grip of the big cloud monoliths. We unpack the three concrete friction points he hit—email routing, contact storage, and document APIs—and explore whether the convenience of the bundle is actually holding you back. From Cloudflare's DNS-level routing to the missing market for standalone contact services, we examine the real cost of staying put and the viable paths to a more tailored, self-hosted setup.]]></itunes:summary>
      <itunes:duration>1611</itunes:duration>
      <itunes:episode>4587</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ai-google-workspace-alternatives.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ai-google-workspace-alternatives.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fixing Stale UI: The 4-Link Chain of Data Flow</title>
      <description><![CDATA[When you upload a photo and the badge on screen refuses to update, the instinct is to reach for WebSockets. But that's the wrong layer entirely. This episode breaks down the actual chain of machinery that lets a number change by itself — from the network response landing in the browser, through the state store, the framework's dependency graph, and the render commit. We explore why the break is almost always in the middle links, how TanStack Query reframes server state as a cache, and why the "optimistic update" fix creates a whole new class of bugs.]]></description>
      <link>https://myweirdprompts.com/episode/stale-ui-data-flow-chain/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/stale-ui-data-flow-chain/</guid>
      <pubDate>Mon, 03 Aug 2026 17:37:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/stale-ui-data-flow-chain.m4a"
        type="audio/mp4"
        length="25593465"
      />
      <itunes:title>Fixing Stale UI: The 4-Link Chain of Data Flow</itunes:title>
      <itunes:subtitle>Why does a number on screen stay frozen after an upload? We trace the 4-link chain from response to redraw — and where it breaks.</itunes:subtitle>
      <itunes:summary><![CDATA[When you upload a photo and the badge on screen refuses to update, the instinct is to reach for WebSockets. But that's the wrong layer entirely. This episode breaks down the actual chain of machinery that lets a number change by itself — from the network response landing in the browser, through the state store, the framework's dependency graph, and the render commit. We explore why the break is almost always in the middle links, how TanStack Query reframes server state as a cache, and why the "optimistic update" fix creates a whole new class of bugs.]]></itunes:summary>
      <itunes:duration>1477</itunes:duration>
      <itunes:episode>4586</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/stale-ui-data-flow-chain.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/stale-ui-data-flow-chain.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why `export` Fails But Files Work: Linux Process Secrets</title>
      <description><![CDATA[Ever wondered why a file written in one terminal tab is instantly visible in another, but an exported variable isn't? This episode traces the exact mechanics of Linux process creation — fork, exec, and copy-on-write — to explain why the filesystem is the universal shared medium between processes, while the environment is a one-time snapshot. Using a real Claude Code workflow as the entry point, we break down what's actually happening at the kernel level and why the design is both a feature and a security boundary.]]></description>
      <link>https://myweirdprompts.com/episode/linux-process-environment-filesystem/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/linux-process-environment-filesystem/</guid>
      <pubDate>Mon, 03 Aug 2026 17:34:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/linux-process-environment-filesystem.m4a"
        type="audio/mp4"
        length="28358445"
      />
      <itunes:title>Why `export` Fails But Files Work: Linux Process Secrets</itunes:title>
      <itunes:subtitle>Why does `wrangler login` work across shells but `export` doesn&apos;t? The answer reveals how Linux processes really share data.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wondered why a file written in one terminal tab is instantly visible in another, but an exported variable isn't? This episode traces the exact mechanics of Linux process creation — fork, exec, and copy-on-write — to explain why the filesystem is the universal shared medium between processes, while the environment is a one-time snapshot. Using a real Claude Code workflow as the entry point, we break down what's actually happening at the kernel level and why the design is both a feature and a security boundary.]]></itunes:summary>
      <itunes:duration>1634</itunes:duration>
      <itunes:episode>4585</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/linux-process-environment-filesystem.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/linux-process-environment-filesystem.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Directing Context Loss: Smart Compaction Prompts</title>
      <description><![CDATA[When you run /compact in Claude Code, you're not stuck with a generic summary. By passing a compaction directive — a prompt that shapes what survives and how — you can transform context loss into a strategic advantage. This episode breaks down the mechanics of the separate summarization model call, explores why vague instructions fail, and reveals four powerful patterns: reframing conversations as decision logs, extracting task lists, distilling reusable knowledge, and shifting perspectives. If you've ever felt the sting of lost context after a long session, this episode shows you how to make every compaction count.]]></description>
      <link>https://myweirdprompts.com/episode/compaction-directives-claude-code/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/compaction-directives-claude-code/</guid>
      <pubDate>Mon, 03 Aug 2026 13:12:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/compaction-directives-claude-code.m4a"
        type="audio/mp4"
        length="27593352"
      />
      <itunes:title>Directing Context Loss: Smart Compaction Prompts</itunes:title>
      <itunes:subtitle>Learn how to steer Claude Code&apos;s context compaction with targeted instructions — turning a lossy default into a directed operation.</itunes:subtitle>
      <itunes:summary><![CDATA[When you run /compact in Claude Code, you're not stuck with a generic summary. By passing a compaction directive — a prompt that shapes what survives and how — you can transform context loss into a strategic advantage. This episode breaks down the mechanics of the separate summarization model call, explores why vague instructions fail, and reveals four powerful patterns: reframing conversations as decision logs, extracting task lists, distilling reusable knowledge, and shifting perspectives. If you've ever felt the sting of lost context after a long session, this episode shows you how to make every compaction count.]]></itunes:summary>
      <itunes:duration>1613</itunes:duration>
      <itunes:episode>4584</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/compaction-directives-claude-code.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/compaction-directives-claude-code.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Designing a Home MCP Gateway for Conversational AI</title>
      <description><![CDATA[Daniel asks how to design an MCP gateway for home use that conversational AI clients can actually connect to. We break down the transport options — Streamable HTTP vs WebSocket — and the authentication landscape: OAuth 2.1 with PKCE, device authorization grants, and mutual TLS. Then we get practical: Cloudflare Tunnel and Tailscale Funnel for safe network exposure, and why the ecosystem still feels immature for this exact use case.]]></description>
      <link>https://myweirdprompts.com/episode/home-mcp-gateway-conversational-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-mcp-gateway-conversational-ai/</guid>
      <pubDate>Mon, 03 Aug 2026 12:07:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-mcp-gateway-conversational-ai.m4a"
        type="audio/mp4"
        length="30070332"
      />
      <itunes:title>Designing a Home MCP Gateway for Conversational AI</itunes:title>
      <itunes:subtitle>How to securely connect ChatGPT to a home-hosted MCP gateway — transport, auth, and the missing pieces.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asks how to design an MCP gateway for home use that conversational AI clients can actually connect to. We break down the transport options — Streamable HTTP vs WebSocket — and the authentication landscape: OAuth 2.1 with PKCE, device authorization grants, and mutual TLS. Then we get practical: Cloudflare Tunnel and Tailscale Funnel for safe network exposure, and why the ecosystem still feels immature for this exact use case.]]></itunes:summary>
      <itunes:duration>1726</itunes:duration>
      <itunes:episode>4582</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-mcp-gateway-conversational-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-mcp-gateway-conversational-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Text In, Text Out: Designing Single-Purpose AI Utilities</title>
      <description><![CDATA[Most productive AI use isn't flashy agents or chatbots — it's small, single-purpose utilities that transform text reliably. But these models are trained to be conversational, and that friendliness breaks the utility pattern. In this episode, we break down why the conversational default is baked into the model, and walk through a four-part system prompt structure that makes AI utilities behave like functions: role framing, explicit output instructions, strict format specs, and deterministic error handling. If you've ever gotten "Sure, here's your JSON!" when you needed raw data, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/single-purpose-ai-utilities-prompts/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/single-purpose-ai-utilities-prompts/</guid>
      <pubDate>Mon, 03 Aug 2026 12:03:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/single-purpose-ai-utilities-prompts.m4a"
        type="audio/mp4"
        length="25348005"
      />
      <itunes:title>Text In, Text Out: Designing Single-Purpose AI Utilities</itunes:title>
      <itunes:subtitle>Why small, narrowly scoped AI tools need system prompts that say &quot;shut up and return the output&quot; — and how to write them.</itunes:subtitle>
      <itunes:summary><![CDATA[Most productive AI use isn't flashy agents or chatbots — it's small, single-purpose utilities that transform text reliably. But these models are trained to be conversational, and that friendliness breaks the utility pattern. In this episode, we break down why the conversational default is baked into the model, and walk through a four-part system prompt structure that makes AI utilities behave like functions: role framing, explicit output instructions, strict format specs, and deterministic error handling. If you've ever gotten "Sure, here's your JSON!" when you needed raw data, this one's for you.]]></itunes:summary>
      <itunes:duration>1457</itunes:duration>
      <itunes:episode>4581</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/single-purpose-ai-utilities-prompts.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/single-purpose-ai-utilities-prompts.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Tiny AI Tools That Beat Big Agents</title>
      <description><![CDATA[Daniel's been building a collection of small, single-purpose AI assistants for daily tasks — from formatting podcast dictation to inventory logging. In this episode, we dig into why these constrained utilities often beat big agentic workflows, how MCP integrations let them grow into lightweight agents without losing reliability, and why the chat interface is the wrong home for them. Plus: what would a proper launcher for a dozen mini AI tools look like?]]></description>
      <link>https://myweirdprompts.com/episode/tiny-ai-tools-beat-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tiny-ai-tools-beat-agents/</guid>
      <pubDate>Mon, 03 Aug 2026 11:48:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tiny-ai-tools-beat-agents.m4a"
        type="audio/mp4"
        length="24554642"
      />
      <itunes:title>The Tiny AI Tools That Beat Big Agents</itunes:title>
      <itunes:subtitle>Why small, single-purpose AI utilities outperform autonomous agents for daily work — and what interface they need.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's been building a collection of small, single-purpose AI assistants for daily tasks — from formatting podcast dictation to inventory logging. In this episode, we dig into why these constrained utilities often beat big agentic workflows, how MCP integrations let them grow into lightweight agents without losing reliability, and why the chat interface is the wrong home for them. Plus: what would a proper launcher for a dozen mini AI tools look like?]]></itunes:summary>
      <itunes:duration>1416</itunes:duration>
      <itunes:episode>4580</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tiny-ai-tools-beat-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tiny-ai-tools-beat-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Measuring Rooms with Fiducial Markers: AprilTag vs ArUco</title>
      <description><![CDATA[Daniel's trying to measure his new apartment with photos, but AR apps keep drifting. In this episode, we dive into fiducial markers — AprilTags, ArUco, ChArUco, and plain checkerboards — to figure out which one can actually establish accurate scale in a normal smartphone photo. We cover how pose estimation works, why scale only transfers on a single plane, and what the ideal physical reference board looks like. If you've ever wondered why your phone says one thing and the tape measure says another, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/fiducial-markers-room-measurement/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fiducial-markers-room-measurement/</guid>
      <pubDate>Sun, 02 Aug 2026 11:27:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fiducial-markers-room-measurement.m4a"
        type="audio/mp4"
        length="27943823"
      />
      <itunes:title>Measuring Rooms with Fiducial Markers: AprilTag vs ArUco</itunes:title>
      <itunes:subtitle>AprilTags, ArUco, ChArUco — which marker system actually helps you measure a room from a photo? We break down the tradeoffs.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's trying to measure his new apartment with photos, but AR apps keep drifting. In this episode, we dive into fiducial markers — AprilTags, ArUco, ChArUco, and plain checkerboards — to figure out which one can actually establish accurate scale in a normal smartphone photo. We cover how pose estimation works, why scale only transfers on a single plane, and what the ideal physical reference board looks like. If you've ever wondered why your phone says one thing and the tape measure says another, this one's for you.]]></itunes:summary>
      <itunes:duration>1598</itunes:duration>
      <itunes:episode>4575</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fiducial-markers-room-measurement.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fiducial-markers-room-measurement.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The vCard Files: Why a 30-Year-Old Format Still Runs Your Contacts</title>
      <description><![CDATA[Every time you scan a QR code and tap "Add Contact," a VCF file moves between phones. This humble text format has outlived countless proprietary networking apps — and for good reason. We dissect the vCard spec: the structured name fields that silently corrupt data, the encoding traps that mangle accents, and the compatibility gaps between vCard 3.0 and 4.0. Plus: how to build a future-proof digital business card that works everywhere, and why the format is still evolving — with SOCIALPROFILE support added just two years ago.]]></description>
      <link>https://myweirdprompts.com/episode/vcard-vcf-contact-format-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vcard-vcf-contact-format-guide/</guid>
      <pubDate>Sun, 02 Aug 2026 11:00:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vcard-vcf-contact-format-guide.m4a"
        type="audio/mp4"
        length="24472841"
      />
      <itunes:title>The vCard Files: Why a 30-Year-Old Format Still Runs Your Contacts</itunes:title>
      <itunes:subtitle>We crack open the VCF spec — what&apos;s inside, what breaks, and why the humble vCard still beats every proprietary contact app.</itunes:subtitle>
      <itunes:summary><![CDATA[Every time you scan a QR code and tap "Add Contact," a VCF file moves between phones. This humble text format has outlived countless proprietary networking apps — and for good reason. We dissect the vCard spec: the structured name fields that silently corrupt data, the encoding traps that mangle accents, and the compatibility gaps between vCard 3.0 and 4.0. Plus: how to build a future-proof digital business card that works everywhere, and why the format is still evolving — with SOCIALPROFILE support added just two years ago.]]></itunes:summary>
      <itunes:duration>1391</itunes:duration>
      <itunes:episode>4572</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vcard-vcf-contact-format-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vcard-vcf-contact-format-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Dialing Without a Switchboard: Twilio Number Normalization</title>
      <description><![CDATA[Daniel's moving his phone lines to Twilio and discovering that "it just works" doesn't exist outside the US. This episode unpacks the hidden translation layer traditional carriers absorb — local formatting, trunk prefixes, and star codes like *3000 — and what you have to build yourself when you own the switch. If you're running your own telephony stack, this is the gap you didn't know you had.]]></description>
      <link>https://myweirdprompts.com/episode/twilio-number-normalization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/twilio-number-normalization/</guid>
      <pubDate>Sun, 02 Aug 2026 10:37:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/twilio-number-normalization.m4a"
        type="audio/mp4"
        length="27490850"
      />
      <itunes:title>Dialing Without a Switchboard: Twilio Number Normalization</itunes:title>
      <itunes:subtitle>Twilio expects E.164, but Israel dials 0-3-... Here&apos;s what breaks when you become your own carrier.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's moving his phone lines to Twilio and discovering that "it just works" doesn't exist outside the US. This episode unpacks the hidden translation layer traditional carriers absorb — local formatting, trunk prefixes, and star codes like *3000 — and what you have to build yourself when you own the switch. If you're running your own telephony stack, this is the gap you didn't know you had.]]></itunes:summary>
      <itunes:duration>1579</itunes:duration>
      <itunes:episode>4569</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/twilio-number-normalization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/twilio-number-normalization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Agents Spend Real Money</title>
      <description><![CDATA[When Daniel asked Claude Code to check available phone numbers on Twilio, he didn't expect it to purchase one. But the API made it trivial — and this moment reveals a much bigger truth about agentic commerce. In this episode, we break down how AI agents are already spending real money through everyday SaaS APIs, from cloud infrastructure to advertising platforms. We explore why no new payment infrastructure was needed, what this means for authorization and governance, and whether dedicated agent wallets are solving a future problem or a different one entirely.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agents-spending-money-apis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agents-spending-money-apis/</guid>
      <pubDate>Fri, 31 Jul 2026 21:30:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agents-spending-money-apis.m4a"
        type="audio/mp4"
        length="29586088"
      />
      <itunes:title>When AI Agents Spend Real Money</itunes:title>
      <itunes:subtitle>An AI agent bought a phone number through Twilio. Here&apos;s how agentic commerce already works — and what it means for authorization.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel asked Claude Code to check available phone numbers on Twilio, he didn't expect it to purchase one. But the API made it trivial — and this moment reveals a much bigger truth about agentic commerce. In this episode, we break down how AI agents are already spending real money through everyday SaaS APIs, from cloud infrastructure to advertising platforms. We explore why no new payment infrastructure was needed, what this means for authorization and governance, and whether dedicated agent wallets are solving a future problem or a different one entirely.]]></itunes:summary>
      <itunes:duration>1700</itunes:duration>
      <itunes:episode>4549</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agents-spending-money-apis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agents-spending-money-apis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Software Running Your Company</title>
      <description><![CDATA[Most organizations run on invisible software — internal tools never marketed or demoed, from CRMs to inventory systems to the spreadsheet that became mission-critical. This episode explores why companies build versus buy, the hidden costs of workflow mismatch, and how low-code platforms and generative AI are shifting the balance toward custom software. What happens when every organization can afford to own its operational tooling?]]></description>
      <link>https://myweirdprompts.com/episode/internal-tools-software-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/internal-tools-software-ai/</guid>
      <pubDate>Fri, 31 Jul 2026 21:11:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/internal-tools-software-ai.m4a"
        type="audio/mp4"
        length="28310696"
      />
      <itunes:title>The Hidden Software Running Your Company</itunes:title>
      <itunes:subtitle>Inside the shadow fleet of custom apps running organizations — and how AI is making building them the default.</itunes:subtitle>
      <itunes:summary><![CDATA[Most organizations run on invisible software — internal tools never marketed or demoed, from CRMs to inventory systems to the spreadsheet that became mission-critical. This episode explores why companies build versus buy, the hidden costs of workflow mismatch, and how low-code platforms and generative AI are shifting the balance toward custom software. What happens when every organization can afford to own its operational tooling?]]></itunes:summary>
      <itunes:duration>1657</itunes:duration>
      <itunes:episode>4548</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/internal-tools-software-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/internal-tools-software-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Redis: What It Actually Is and Why It&apos;s Everywhere</title>
      <description><![CDATA[Redis is the invisible backbone of modern web applications — the in-memory data structure server that powers caching, queues, leaderboards, and real-time features across the internet. But what actually is it, and why did it become the default answer for performance problems? In this episode, we trace Redis from its origin as a single C file written by an Italian startup founder in 2009 to its status as the industry standard. We break down how it differs from PostgreSQL and MongoDB, why RAM gives it a thousand-to-one speed advantage, the trade-offs of in-memory storage (cost, capacity, volatility), and the most common real-world use cases — from session storage and rate limiting to distributed locks and pub/sub messaging. We also get honest about the downsides: when Redis is unnecessary complexity, what operational costs and consistency concerns you should understand, and how to decide if it belongs in your architecture at all.]]></description>
      <link>https://myweirdprompts.com/episode/redis-in-memory-data-structure-server/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/redis-in-memory-data-structure-server/</guid>
      <pubDate>Fri, 31 Jul 2026 21:05:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/redis-in-memory-data-structure-server.m4a"
        type="audio/mp4"
        length="28822170"
      />
      <itunes:title>Redis: What It Actually Is and Why It&apos;s Everywhere</itunes:title>
      <itunes:subtitle>Redis powers half the internet&apos;s caching, but almost nobody knows what it actually is. We break it down from the ground up.</itunes:subtitle>
      <itunes:summary><![CDATA[Redis is the invisible backbone of modern web applications — the in-memory data structure server that powers caching, queues, leaderboards, and real-time features across the internet. But what actually is it, and why did it become the default answer for performance problems? In this episode, we trace Redis from its origin as a single C file written by an Italian startup founder in 2009 to its status as the industry standard. We break down how it differs from PostgreSQL and MongoDB, why RAM gives it a thousand-to-one speed advantage, the trade-offs of in-memory storage (cost, capacity, volatility), and the most common real-world use cases — from session storage and rate limiting to distributed locks and pub/sub messaging. We also get honest about the downsides: when Redis is unnecessary complexity, what operational costs and consistency concerns you should understand, and how to decide if it belongs in your architecture at all.]]></itunes:summary>
      <itunes:duration>1644</itunes:duration>
      <itunes:episode>4546</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/redis-in-memory-data-structure-server.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/redis-in-memory-data-structure-server.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>WhatsApp Business API vs App: Which Fits Your Solo Practice?</title>
      <description><![CDATA[Daniel recently provisioned Twilio infrastructure and chose the WhatsApp Business API over the simpler Business app. In this episode, we break down the real differences between these two products — from shared inbox capabilities and multi-device support to metered pricing and template requirements. We explore whether two business partners managing client messages need a custom-built dashboard or can rely on WhatsApp's native tools, and what the API path actually costs in integration effort versus monthly fees.]]></description>
      <link>https://myweirdprompts.com/episode/whatsapp-business-api-vs-app/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/whatsapp-business-api-vs-app/</guid>
      <pubDate>Fri, 31 Jul 2026 13:46:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/whatsapp-business-api-vs-app.m4a"
        type="audio/mp4"
        length="23841648"
      />
      <itunes:title>WhatsApp Business API vs App: Which Fits Your Solo Practice?</itunes:title>
      <itunes:subtitle>A solo consultant&apos;s guide to choosing between the WhatsApp Business app and the API for client messaging.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel recently provisioned Twilio infrastructure and chose the WhatsApp Business API over the simpler Business app. In this episode, we break down the real differences between these two products — from shared inbox capabilities and multi-device support to metered pricing and template requirements. We explore whether two business partners managing client messages need a custom-built dashboard or can rely on WhatsApp's native tools, and what the API path actually costs in integration effort versus monthly fees.]]></itunes:summary>
      <itunes:duration>1369</itunes:duration>
      <itunes:episode>4543</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/whatsapp-business-api-vs-app.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/whatsapp-business-api-vs-app.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Going Fully SIP: Why It&apos;s Not Ready (Yet)</title>
      <description><![CDATA[Daniel built an elegant Twilio setup with Claude as his API translator, routing SMS to email and dreaming of a fully SIP-based phone. But he hit three walls: 2FA blacklists, domestic calling economics, and emergency services. In this episode, we break down why these aren't technical bugs but structural features of how the phone network evolved—and why the pragmatic answer is "SIP and carrier," not "SIP or carrier." If you've ever considered cutting the carrier cord, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/sip-phone-2fa-emergency-calls/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/sip-phone-2fa-emergency-calls/</guid>
      <pubDate>Fri, 31 Jul 2026 13:41:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/sip-phone-2fa-emergency-calls.m4a"
        type="audio/mp4"
        length="28990315"
      />
      <itunes:title>Going Fully SIP: Why It&apos;s Not Ready (Yet)</itunes:title>
      <itunes:subtitle>A deep dive into the three structural reasons you can&apos;t ditch your carrier for Twilio and SIP—yet.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel built an elegant Twilio setup with Claude as his API translator, routing SMS to email and dreaming of a fully SIP-based phone. But he hit three walls: 2FA blacklists, domestic calling economics, and emergency services. In this episode, we break down why these aren't technical bugs but structural features of how the phone network evolved—and why the pragmatic answer is "SIP and carrier," not "SIP or carrier." If you've ever considered cutting the carrier cord, this one's for you.]]></itunes:summary>
      <itunes:duration>1658</itunes:duration>
      <itunes:episode>4542</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/sip-phone-2fa-emergency-calls.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/sip-phone-2fa-emergency-calls.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can You Trust an AI&apos;s Summary?</title>
      <description><![CDATA[A listener asks a sharp question: do dedicated text compaction models exist, and how would you ever know the summary didn't drop something critical? This episode explores the surprising gap between research compressors and production coding agents — from LLMLingua and RECOMP to the deeper fidelity problem that keeps engineers up at night. We unpack why token-level pruning breaks on agent transcripts, why prompt-cache economics invert the cost argument, and why "the agent kept working" is a dangerously flawed signal for summary quality.]]></description>
      <link>https://myweirdprompts.com/episode/ai-summary-fidelity-problem/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-summary-fidelity-problem/</guid>
      <pubDate>Thu, 30 Jul 2026 12:11:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-summary-fidelity-problem.m4a"
        type="audio/mp4"
        length="24511017"
      />
      <itunes:title>Can You Trust an AI&apos;s Summary?</itunes:title>
      <itunes:subtitle>Dedicated text compressors exist but aren&apos;t in production. The real problem? Nobody can verify the summary didn&apos;t drop what mattered.</itunes:subtitle>
      <itunes:summary><![CDATA[A listener asks a sharp question: do dedicated text compaction models exist, and how would you ever know the summary didn't drop something critical? This episode explores the surprising gap between research compressors and production coding agents — from LLMLingua and RECOMP to the deeper fidelity problem that keeps engineers up at night. We unpack why token-level pruning breaks on agent transcripts, why prompt-cache economics invert the cost argument, and why "the agent kept working" is a dangerously flawed signal for summary quality.]]></itunes:summary>
      <itunes:duration>1395</itunes:duration>
      <itunes:episode>4523</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-summary-fidelity-problem.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-summary-fidelity-problem.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Sixth Shim: When MCP Becomes a Middleware Factory</title>
      <description><![CDATA[You started with one quick wrapper. Then another. Now you're running six bespoke shims between your MCP gateway and your servers, and nobody can explain the system without a whiteboard. This episode builds a taxonomy of shim patterns — transport translation, credential injection, tool filtering, schema coercion — and ruthlessly sorts which problems are generalizable infrastructure and which are server-specific one-offs that belong upstream. We name the real prior art, weigh the blast radius tradeoffs of consolidation versus independence, and give you a decision rule that prevents the seventh shim from ever being written.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-shim-taxonomy-middleware/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-shim-taxonomy-middleware/</guid>
      <pubDate>Thu, 30 Jul 2026 09:52:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-shim-taxonomy-middleware.m4a"
        type="audio/mp4"
        length="35605766"
      />
      <itunes:title>The Sixth Shim: When MCP Becomes a Middleware Factory</itunes:title>
      <itunes:subtitle>Six servers, six shims, no architecture. A taxonomy of MCP integration pain.</itunes:subtitle>
      <itunes:summary><![CDATA[You started with one quick wrapper. Then another. Now you're running six bespoke shims between your MCP gateway and your servers, and nobody can explain the system without a whiteboard. This episode builds a taxonomy of shim patterns — transport translation, credential injection, tool filtering, schema coercion — and ruthlessly sorts which problems are generalizable infrastructure and which are server-specific one-offs that belong upstream. We name the real prior art, weigh the blast radius tradeoffs of consolidation versus independence, and give you a decision rule that prevents the seventh shim from ever being written.]]></itunes:summary>
      <itunes:duration>2067</itunes:duration>
      <itunes:episode>4522</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-shim-taxonomy-middleware.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-shim-taxonomy-middleware.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>MCP Transport Architecture in 2026: Gateway Design</title>
      <description><![CDATA[If you're building an MCP aggregation gateway in 2026, you face a messy reality: the spec says streamable HTTP is the standard, but servers in the field still speak stdio and the deprecated HTTP+SSE. This episode is a decision framework for exactly that situation. We walk through what each transport looks like on the wire, why the old SSE design was replaced, and why your gateway should present streamable HTTP northbound while speaking whatever each server offers southbound. Covering process supervision for local stdio servers, OAuth handling for vendor remotes, legacy SSE bridging, and the cross-cutting problems that only appear at aggregation scale — tool name collisions, context-window pressure, session affinity, and minimum viable plumbing for a local box.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-transport-gateway-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-transport-gateway-architecture/</guid>
      <pubDate>Thu, 30 Jul 2026 09:49:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-transport-gateway-architecture.m4a"
        type="audio/mp4"
        length="33661775"
      />
      <itunes:title>MCP Transport Architecture in 2026: Gateway Design</itunes:title>
      <itunes:subtitle>Three transports, one gateway. How to bridge stdio, legacy SSE, and streamable HTTP in production.</itunes:subtitle>
      <itunes:summary><![CDATA[If you're building an MCP aggregation gateway in 2026, you face a messy reality: the spec says streamable HTTP is the standard, but servers in the field still speak stdio and the deprecated HTTP+SSE. This episode is a decision framework for exactly that situation. We walk through what each transport looks like on the wire, why the old SSE design was replaced, and why your gateway should present streamable HTTP northbound while speaking whatever each server offers southbound. Covering process supervision for local stdio servers, OAuth handling for vendor remotes, legacy SSE bridging, and the cross-cutting problems that only appear at aggregation scale — tool name collisions, context-window pressure, session affinity, and minimum viable plumbing for a local box.]]></itunes:summary>
      <itunes:duration>1940</itunes:duration>
      <itunes:episode>4521</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-transport-gateway-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-transport-gateway-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Twilio for Small Business: IVRs, SIP &amp; Phone Trees</title>
      <description><![CDATA[Twilio turns phone systems into programmable web applications. This episode breaks down how small businesses and freelancers can build professional IVRs, round-robin call routing, voicemail with automatic transcription, and call recording — all for around $35/month. We cover the core mechanics of TwiML and webhooks, the learning curve of SIP, new features like "Bring Your Own JSON" in Studio, and the legal compliance requirements for call recording. If you've ever wanted to replace a costly PBX with a cloud-based system you can version-control and deploy, this is your starting point.]]></description>
      <link>https://myweirdprompts.com/episode/twilio-ivr-sip-phone-trees/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/twilio-ivr-sip-phone-trees/</guid>
      <pubDate>Tue, 28 Jul 2026 13:59:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/twilio-ivr-sip-phone-trees.m4a"
        type="audio/mp4"
        length="28183635"
      />
      <itunes:title>Twilio for Small Business: IVRs, SIP &amp; Phone Trees</itunes:title>
      <itunes:subtitle>How to build professional phone systems with Twilio — IVRs, round-robin routing, voicemail transcription, and SIP setup.</itunes:subtitle>
      <itunes:summary><![CDATA[Twilio turns phone systems into programmable web applications. This episode breaks down how small businesses and freelancers can build professional IVRs, round-robin call routing, voicemail with automatic transcription, and call recording — all for around $35/month. We cover the core mechanics of TwiML and webhooks, the learning curve of SIP, new features like "Bring Your Own JSON" in Studio, and the legal compliance requirements for call recording. If you've ever wanted to replace a costly PBX with a cloud-based system you can version-control and deploy, this is your starting point.]]></itunes:summary>
      <itunes:duration>1603</itunes:duration>
      <itunes:episode>4499</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/twilio-ivr-sip-phone-trees.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/twilio-ivr-sip-phone-trees.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Find Anything in Israel with Google Lens</title>
      <description><![CDATA[When Daniel needed to find an industrial trolley for his apartment in Israel, he discovered that searching visually with Google Lens and the Hebrew word for "price" — mechir — was the only reliable way to cut through the country's fragmented e-commerce landscape. This episode unpacks why Israel's small retailers use inconsistent terminology across languages, how Google Lens actually works under the hood (it's not just image search), and why the mechir hack exploits a gap in the visual indexing pipeline. We also explore what a proper MCP-based solution would look like: an AI agent that could take product images and return structured results from local vendors. The technical challenges are real — crawling hundreds of small sites with no APIs — but the vision points toward a future where visual procurement is programmable, not manual.]]></description>
      <link>https://myweirdprompts.com/episode/google-lens-israel-product-search/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/google-lens-israel-product-search/</guid>
      <pubDate>Tue, 28 Jul 2026 11:06:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/google-lens-israel-product-search.m4a"
        type="audio/mp4"
        length="25771183"
      />
      <itunes:title>How to Find Anything in Israel with Google Lens</itunes:title>
      <itunes:subtitle>A visual search hack for finding products in Israel&apos;s fragmented e-commerce market — and why an MCP solution could automate it.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel needed to find an industrial trolley for his apartment in Israel, he discovered that searching visually with Google Lens and the Hebrew word for "price" — mechir — was the only reliable way to cut through the country's fragmented e-commerce landscape. This episode unpacks why Israel's small retailers use inconsistent terminology across languages, how Google Lens actually works under the hood (it's not just image search), and why the mechir hack exploits a gap in the visual indexing pipeline. We also explore what a proper MCP-based solution would look like: an AI agent that could take product images and return structured results from local vendors. The technical challenges are real — crawling hundreds of small sites with no APIs — but the vision points toward a future where visual procurement is programmable, not manual.]]></itunes:summary>
      <itunes:duration>1466</itunes:duration>
      <itunes:episode>4496</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/google-lens-israel-product-search.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/google-lens-israel-product-search.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Right to Repair: History, Hype, and Hard Limits</title>
      <description><![CDATA[With the EU's Right to Repair Directive transposition deadline just days away, this episode delivers the full treatment Daniel's been asking for. We trace the movement from the 1975 Magnuson-Moss Warranty Act through Massachusetts' 2012 automotive ballot initiative, John Deere's Ukrainian firmware debacle, and the DMCA Section 1201 exemption battles. We name names on which manufacturers are genuinely committed — Framework, Fairphone, Valve — versus those performing compliance, like Samsung's collapsed iFixit partnership and John Deere's hollow MOU. Then we tackle the hard question: where are the legitimate safety and security limits? Lithium cell puncture, medical device tampering, and the real distinction between genuine risk and manufactured excuse. Finally, we assess whether any of this has actually worked — repairability indices, battery regulations, and the metrics being gamed.]]></description>
      <link>https://myweirdprompts.com/episode/right-to-repair-reality-check/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/right-to-repair-reality-check/</guid>
      <pubDate>Mon, 27 Jul 2026 13:48:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/right-to-repair-reality-check.m4a"
        type="audio/mp4"
        length="25435128"
      />
      <itunes:title>Right to Repair: History, Hype, and Hard Limits</itunes:title>
      <itunes:subtitle>From John Deere tractors to iPhone batteries — the real history, genuine commitments, and where repair advocates overreach.</itunes:subtitle>
      <itunes:summary><![CDATA[With the EU's Right to Repair Directive transposition deadline just days away, this episode delivers the full treatment Daniel's been asking for. We trace the movement from the 1975 Magnuson-Moss Warranty Act through Massachusetts' 2012 automotive ballot initiative, John Deere's Ukrainian firmware debacle, and the DMCA Section 1201 exemption battles. We name names on which manufacturers are genuinely committed — Framework, Fairphone, Valve — versus those performing compliance, like Samsung's collapsed iFixit partnership and John Deere's hollow MOU. Then we tackle the hard question: where are the legitimate safety and security limits? Lithium cell puncture, medical device tampering, and the real distinction between genuine risk and manufactured excuse. Finally, we assess whether any of this has actually worked — repairability indices, battery regulations, and the metrics being gamed.]]></itunes:summary>
      <itunes:duration>1434</itunes:duration>
      <itunes:episode>4490</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/right-to-repair-reality-check.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/right-to-repair-reality-check.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Digikey, Mouser, and the Hidden Supply Chain of Components</title>
      <description><![CDATA[What exactly are Digikey, Mouser, and Farnell? They look like websites from 1998, but they're the backbone of the global electronics supply chain. This episode unpacks the difference between MRO suppliers (Grainger, Talmir) and electronic component distributors, why their "ugly" websites are actually perfectly designed for engineers, and how these distributors became quiet saviors during the chip shortage. Plus: how a single facility in Thief River Falls, Minnesota (population 8,500) stocks 2.7 million components and ships them same-day.]]></description>
      <link>https://myweirdprompts.com/episode/electronic-component-distributors-supply-chain/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/electronic-component-distributors-supply-chain/</guid>
      <pubDate>Sun, 26 Jul 2026 08:46:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/electronic-component-distributors-supply-chain.m4a"
        type="audio/mp4"
        length="27715871"
      />
      <itunes:title>Digikey, Mouser, and the Hidden Supply Chain of Components</itunes:title>
      <itunes:subtitle>How Digikey and Mouser quietly keep the global electronics industry running with impossibly large catalogs and same-day shipping.</itunes:subtitle>
      <itunes:summary><![CDATA[What exactly are Digikey, Mouser, and Farnell? They look like websites from 1998, but they're the backbone of the global electronics supply chain. This episode unpacks the difference between MRO suppliers (Grainger, Talmir) and electronic component distributors, why their "ugly" websites are actually perfectly designed for engineers, and how these distributors became quiet saviors during the chip shortage. Plus: how a single facility in Thief River Falls, Minnesota (population 8,500) stocks 2.7 million components and ships them same-day.]]></itunes:summary>
      <itunes:duration>1543</itunes:duration>
      <itunes:episode>4478</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/electronic-component-distributors-supply-chain.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/electronic-component-distributors-supply-chain.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Structuring Raw Voice Prompts at Scale</title>
      <description><![CDATA[Daniel has built a prompt enhancement node that takes raw voice-to-text input and decomposes it into questions, context data, and host notes. It works well — but how do you make it reliable when it runs thousands of times? We explore four design principles for building meta-prompts that structure other prompts: classification before extraction, real voice-capture few-shot examples, explicit schemas with fallback fields, and chain-of-thought reasoning for ambiguous cases. If you're building a high-volume AI agent workflow that processes human speech, this episode is about making it never break in ways you don't catch.]]></description>
      <link>https://myweirdprompts.com/episode/prompt-structuring-voice-input/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/prompt-structuring-voice-input/</guid>
      <pubDate>Sun, 26 Jul 2026 07:33:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/prompt-structuring-voice-input.m4a"
        type="audio/mp4"
        length="23847661"
      />
      <itunes:title>Structuring Raw Voice Prompts at Scale</itunes:title>
      <itunes:subtitle>How to decompose messy voice dictation into clean, routable components for AI workflows.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel has built a prompt enhancement node that takes raw voice-to-text input and decomposes it into questions, context data, and host notes. It works well — but how do you make it reliable when it runs thousands of times? We explore four design principles for building meta-prompts that structure other prompts: classification before extraction, real voice-capture few-shot examples, explicit schemas with fallback fields, and chain-of-thought reasoning for ambiguous cases. If you're building a high-volume AI agent workflow that processes human speech, this episode is about making it never break in ways you don't catch.]]></itunes:summary>
      <itunes:duration>1363</itunes:duration>
      <itunes:episode>4477</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/prompt-structuring-voice-input.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/prompt-structuring-voice-input.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Python Visualization vs. Subscription Tools</title>
      <description><![CDATA[Python’s visualization ecosystem has over thirty libraries, from matplotlib to GeoPandas, yet companies still pay thousands annually for Tableau and ArcGIS. This episode maps the landscape, explains why the subscription reflex persists, and shows how a few core functions can replace expensive tools. We explore the two APIs of matplotlib, the rise of interactive libraries like Plotly and HoloViews, and the geospatial stack that makes ArcGIS licenses hard to justify for repetitive workflows.]]></description>
      <link>https://myweirdprompts.com/episode/python-visualization-subscription-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/python-visualization-subscription-tools/</guid>
      <pubDate>Sun, 26 Jul 2026 07:27:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/python-visualization-subscription-tools.m4a"
        type="audio/mp4"
        length="23223423"
      />
      <itunes:title>Python Visualization vs. Subscription Tools</itunes:title>
      <itunes:subtitle>Thirty free Python libraries that rival paid tools like Tableau and ArcGIS. When is the subscription worth it?</itunes:subtitle>
      <itunes:summary><![CDATA[Python’s visualization ecosystem has over thirty libraries, from matplotlib to GeoPandas, yet companies still pay thousands annually for Tableau and ArcGIS. This episode maps the landscape, explains why the subscription reflex persists, and shows how a few core functions can replace expensive tools. We explore the two APIs of matplotlib, the rise of interactive libraries like Plotly and HoloViews, and the geospatial stack that makes ArcGIS licenses hard to justify for repetitive workflows.]]></itunes:summary>
      <itunes:duration>1321</itunes:duration>
      <itunes:episode>4476</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/python-visualization-subscription-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/python-visualization-subscription-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where GeoJSON Breaks and KMZ Still Wins</title>
      <description><![CDATA[GeoJSON looks clean and modern, but try rendering ten million points on a map and your browser will crash. In this episode, we stress-test geographic data formats — where GeoJSON's text-based design collapses at scale, why KMZ still dominates for styled field surveys and time-animated data, and what you lose every time you convert between them. We cover the real-world performance ceiling of each format, the workaround that breaks the spec but keeps workflows alive, and why the conversion pipeline (GDAL's ogr2ogr) is the actual skill you need.]]></description>
      <link>https://myweirdprompts.com/episode/geojson-kmz-geo-format-limits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/geojson-kmz-geo-format-limits/</guid>
      <pubDate>Sun, 26 Jul 2026 07:26:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/geojson-kmz-geo-format-limits.m4a"
        type="audio/mp4"
        length="26472922"
      />
      <itunes:title>Where GeoJSON Breaks and KMZ Still Wins</itunes:title>
      <itunes:subtitle>Why your browser freezes on a 1.5GB GeoJSON and when KMZ is actually the better choice.</itunes:subtitle>
      <itunes:summary><![CDATA[GeoJSON looks clean and modern, but try rendering ten million points on a map and your browser will crash. In this episode, we stress-test geographic data formats — where GeoJSON's text-based design collapses at scale, why KMZ still dominates for styled field surveys and time-animated data, and what you lose every time you convert between them. We cover the real-world performance ceiling of each format, the workaround that breaks the spec but keeps workflows alive, and why the conversion pipeline (GDAL's ogr2ogr) is the actual skill you need.]]></itunes:summary>
      <itunes:duration>1510</itunes:duration>
      <itunes:episode>4475</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/geojson-kmz-geo-format-limits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/geojson-kmz-geo-format-limits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What&apos;s Actually Behind the API Endpoint?</title>
      <description><![CDATA[Most developers treat the API endpoint as a magic curtain — send a request, get a response, and everything in between is someone else's problem. But at the scale of OpenAI or Anthropic, that "in between" is a distributed system in its own right. This episode explores the tiered routing architecture behind public-facing APIs: global load balancers at the edge, Envoy proxy in the gateway layer, and internal service meshes routing to inference servers. We break down how Envoy's xDS APIs enable dynamic configuration without reloads, how circuit breaking and outlier detection prevent cascading failures, and why Stripe built a custom gateway to handle idempotency at the routing layer. If you've ever wondered what actually happens after you hit "send" on an API request, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/api-gateway-scale-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/api-gateway-scale-architecture/</guid>
      <pubDate>Sat, 25 Jul 2026 19:40:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/api-gateway-scale-architecture.m4a"
        type="audio/mp4"
        length="26081585"
      />
      <itunes:title>What&apos;s Actually Behind the API Endpoint?</itunes:title>
      <itunes:subtitle>How OpenAI, Stripe, and Netflix actually manage APIs at millions of requests per second.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers treat the API endpoint as a magic curtain — send a request, get a response, and everything in between is someone else's problem. But at the scale of OpenAI or Anthropic, that "in between" is a distributed system in its own right. This episode explores the tiered routing architecture behind public-facing APIs: global load balancers at the edge, Envoy proxy in the gateway layer, and internal service meshes routing to inference servers. We break down how Envoy's xDS APIs enable dynamic configuration without reloads, how circuit breaking and outlier detection prevent cascading failures, and why Stripe built a custom gateway to handle idempotency at the routing layer. If you've ever wondered what actually happens after you hit "send" on an API request, this one's for you.]]></itunes:summary>
      <itunes:duration>1501</itunes:duration>
      <itunes:episode>4471</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/api-gateway-scale-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/api-gateway-scale-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Missing Protocol for AI Software Development</title>
      <description><![CDATA[After an exhaustive survey in July 2026, the answer is clear: nobody has built a software development protocol designed from the ground up for AI agents. Not for spec definition, task decomposition, execution tracking, or agent-to-agent handoff. Markdown is still the state of practice, and agents routinely misparse task state from checkbox characters. This episode walks through the five candidate primitives for such a protocol, debates which one is actually load-bearing, and wrestles with the uncomfortable possibility that the artifact has to stay human-legible because a human has to remain accountable for it.]]></description>
      <link>https://myweirdprompts.com/episode/agent-native-development-protocol/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-native-development-protocol/</guid>
      <pubDate>Sat, 25 Jul 2026 18:42:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-native-development-protocol.m4a"
        type="audio/mp4"
        length="24123736"
      />
      <itunes:title>The Missing Protocol for AI Software Development</itunes:title>
      <itunes:subtitle>No one has built a software development protocol designed for AI agents. Here&apos;s what that would require.</itunes:subtitle>
      <itunes:summary><![CDATA[After an exhaustive survey in July 2026, the answer is clear: nobody has built a software development protocol designed from the ground up for AI agents. Not for spec definition, task decomposition, execution tracking, or agent-to-agent handoff. Markdown is still the state of practice, and agents routinely misparse task state from checkbox characters. This episode walks through the five candidate primitives for such a protocol, debates which one is actually load-bearing, and wrestles with the uncomfortable possibility that the artifact has to stay human-legible because a human has to remain accountable for it.]]></itunes:summary>
      <itunes:duration>1389</itunes:duration>
      <itunes:episode>4469</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-native-development-protocol.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-native-development-protocol.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Zoxide&apos;s Decay Algorithm Works</title>
      <description><![CDATA[What makes zoxide's directory jumping feel so intuitive? It's not magic — it's an exponential decay function baked into a single line of Rust code. This episode breaks down the actual formula from zoxide's source, explains how recency and frequency combine to produce adaptive rankings, and explores where the same mathematical pattern shows up in radioactive decay, drug metabolism, and time series forecasting. If you've ever wondered how tools learn your habits without hard rules, this is the episode for you.]]></description>
      <link>https://myweirdprompts.com/episode/decaying-algorithms-zoxide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/decaying-algorithms-zoxide/</guid>
      <pubDate>Sat, 25 Jul 2026 18:34:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/decaying-algorithms-zoxide.m4a"
        type="audio/mp4"
        length="29362485"
      />
      <itunes:title>How Zoxide&apos;s Decay Algorithm Works</itunes:title>
      <itunes:subtitle>The math behind how zoxide remembers and forgets your directory habits with exponential precision.</itunes:subtitle>
      <itunes:summary><![CDATA[What makes zoxide's directory jumping feel so intuitive? It's not magic — it's an exponential decay function baked into a single line of Rust code. This episode breaks down the actual formula from zoxide's source, explains how recency and frequency combine to produce adaptive rankings, and explores where the same mathematical pattern shows up in radioactive decay, drug metabolism, and time series forecasting. If you've ever wondered how tools learn your habits without hard rules, this is the episode for you.]]></itunes:summary>
      <itunes:duration>1669</itunes:duration>
      <itunes:episode>4467</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/decaying-algorithms-zoxide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/decaying-algorithms-zoxide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Free Your Shopping Cart: Dot Cart Explained</title>
      <description><![CDATA[Shopping carts are trapped in browser sessions — clear your cookies and your carefully assembled BOM vanishes. A new open file format called dot cart aims to fix that. It's a JSON-based, vendor-neutral standard for ecommerce carts that supports export, import, comparison, and document generation. We dig into how it works, what it unlocks for procurement, and whether lightweight formats can succeed where heavyweight standards couldn't.]]></description>
      <link>https://myweirdprompts.com/episode/dot-cart-open-format/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dot-cart-open-format/</guid>
      <pubDate>Sat, 25 Jul 2026 17:48:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dot-cart-open-format.m4a"
        type="audio/mp4"
        length="33717417"
      />
      <itunes:title>Free Your Shopping Cart: Dot Cart Explained</itunes:title>
      <itunes:subtitle>A new open file format lets you export, share, and compare shopping carts across any vendor.</itunes:subtitle>
      <itunes:summary><![CDATA[Shopping carts are trapped in browser sessions — clear your cookies and your carefully assembled BOM vanishes. A new open file format called dot cart aims to fix that. It's a JSON-based, vendor-neutral standard for ecommerce carts that supports export, import, comparison, and document generation. We dig into how it works, what it unlocks for procurement, and whether lightweight formats can succeed where heavyweight standards couldn't.]]></itunes:summary>
      <itunes:duration>1948</itunes:duration>
      <itunes:episode>4466</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dot-cart-open-format.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dot-cart-open-format.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a $120 Porch Monitor for Construction Noise</title>
      <description><![CDATA[When Daniel realized he'd be leaving town just as a construction site next door began drilling into rock, he didn't settle for secondhand reports. Instead, he designed a complete weatherproofed porch sensor system that reports decibel levels, particulate counts, and vibration data directly into Home Assistant. This episode walks through the entire build: the ESP32-based brain, the digital MEMS microphone that delivers lab-adjacent accuracy for under $4, the dust sensor that duty-cycles its laser to last years, the accelerometer that catches vibrations before you hear them, and the clever enclosure design that keeps electronics dry while letting sensors breathe. Whether you're monitoring construction, wildfire smoke, or just curious about citizen-science hardware, this is a masterclass in practical sensor deployment.]]></description>
      <link>https://myweirdprompts.com/episode/porch-construction-monitor-sensor/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/porch-construction-monitor-sensor/</guid>
      <pubDate>Sat, 25 Jul 2026 17:37:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/porch-construction-monitor-sensor.m4a"
        type="audio/mp4"
        length="34884069"
      />
      <itunes:title>Building a $120 Porch Monitor for Construction Noise</itunes:title>
      <itunes:subtitle>Daniel built a weatherproof sensor system to track construction noise and dust while he&apos;s away for the summer.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel realized he'd be leaving town just as a construction site next door began drilling into rock, he didn't settle for secondhand reports. Instead, he designed a complete weatherproofed porch sensor system that reports decibel levels, particulate counts, and vibration data directly into Home Assistant. This episode walks through the entire build: the ESP32-based brain, the digital MEMS microphone that delivers lab-adjacent accuracy for under $4, the dust sensor that duty-cycles its laser to last years, the accelerometer that catches vibrations before you hear them, and the clever enclosure design that keeps electronics dry while letting sensors breathe. Whether you're monitoring construction, wildfire smoke, or just curious about citizen-science hardware, this is a masterclass in practical sensor deployment.]]></itunes:summary>
      <itunes:duration>1989</itunes:duration>
      <itunes:episode>4464</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/porch-construction-monitor-sensor.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/porch-construction-monitor-sensor.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>3D File Formats: What Survives Conversion</title>
      <description><![CDATA[Every 3D project hits the wall of format fragmentation eventually. This episode breaks down the landscape by use case: glTF for web delivery, FBX for game engine interchange, USD for film pipelines, STL for 3D printing, and the dozen other formats you'll encounter. More importantly, we trace what silently gets dropped in every conversion — rigs, animation, PBR maps, scene hierarchy — and build a practical decision guide for choosing the right export format based on what you're building.]]></description>
      <link>https://myweirdprompts.com/episode/3d-file-format-conversion-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/3d-file-format-conversion-guide/</guid>
      <pubDate>Sat, 25 Jul 2026 16:20:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/3d-file-format-conversion-guide.m4a"
        type="audio/mp4"
        length="29683005"
      />
      <itunes:title>3D File Formats: What Survives Conversion</itunes:title>
      <itunes:subtitle>A practical tour of the 3D format landscape organized by what you&apos;re actually trying to build.</itunes:subtitle>
      <itunes:summary><![CDATA[Every 3D project hits the wall of format fragmentation eventually. This episode breaks down the landscape by use case: glTF for web delivery, FBX for game engine interchange, USD for film pipelines, STL for 3D printing, and the dozen other formats you'll encounter. More importantly, we trace what silently gets dropped in every conversion — rigs, animation, PBR maps, scene hierarchy — and build a practical decision guide for choosing the right export format based on what you're building.]]></itunes:summary>
      <itunes:duration>1703</itunes:duration>
      <itunes:episode>4463</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/3d-file-format-conversion-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/3d-file-format-conversion-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How AI Imagines Your Skeleton</title>
      <description><![CDATA[A deep dive into the AI 3D model generation pipeline, from generative multi-view synthesis to auto-rigging. We walk each stage — neural reconstruction, remeshing, UV unwrapping, PBR texturing, and skeleton inference — explaining the decades of research behind Meshy's one-click interface. Plus: why a machine inferring your shoulder joint is both impressive and presumptuous.]]></description>
      <link>https://myweirdprompts.com/episode/ai-3d-model-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-3d-model-pipeline/</guid>
      <pubDate>Sat, 25 Jul 2026 16:16:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-3d-model-pipeline.m4a"
        type="audio/mp4"
        length="26074612"
      />
      <itunes:title>How AI Imagines Your Skeleton</itunes:title>
      <itunes:subtitle>From image to fully rigged 3D model — the six computational stages that turn photos into animated characters.</itunes:subtitle>
      <itunes:summary><![CDATA[A deep dive into the AI 3D model generation pipeline, from generative multi-view synthesis to auto-rigging. We walk each stage — neural reconstruction, remeshing, UV unwrapping, PBR texturing, and skeleton inference — explaining the decades of research behind Meshy's one-click interface. Plus: why a machine inferring your shoulder joint is both impressive and presumptuous.]]></itunes:summary>
      <itunes:duration>1474</itunes:duration>
      <itunes:episode>4462</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-3d-model-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-3d-model-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Container Has 233 Hidden Chrome Processes</title>
      <description><![CDATA[When Daniel SSH'd into his homelab server, he found 233 live Chromium processes and 254 zombies — all while his monitoring stack showed green. This episode dives into the Linux kernel mechanics of process reaping, why containers break the 50-year-old assumption that PID 1 handles orphans, and how a 100-line C program called tini prevents multi-gigabyte memory leaks. We cover zombies vs. orphans, the PR_SET_CHILD_SUBREAPER syscall, and why your health checks will pass while your server dies by inches.]]></description>
      <link>https://myweirdprompts.com/episode/container-process-leak-zombie-reaping/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/container-process-leak-zombie-reaping/</guid>
      <pubDate>Sat, 25 Jul 2026 15:04:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/container-process-leak-zombie-reaping.m4a"
        type="audio/mp4"
        length="22669933"
      />
      <itunes:title>Why Your Container Has 233 Hidden Chrome Processes</itunes:title>
      <itunes:subtitle>Your container&apos;s PID 1 probably isn&apos;t reaping children. Here&apos;s why that quietly eats your RAM.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel SSH'd into his homelab server, he found 233 live Chromium processes and 254 zombies — all while his monitoring stack showed green. This episode dives into the Linux kernel mechanics of process reaping, why containers break the 50-year-old assumption that PID 1 handles orphans, and how a 100-line C program called tini prevents multi-gigabyte memory leaks. We cover zombies vs. orphans, the PR_SET_CHILD_SUBREAPER syscall, and why your health checks will pass while your server dies by inches.]]></itunes:summary>
      <itunes:duration>1290</itunes:duration>
      <itunes:episode>4460</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/container-process-leak-zombie-reaping.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/container-process-leak-zombie-reaping.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fixed Egress IP From Anywhere: Is IP Allowlisting Dead?</title>
      <description><![CDATA[You've locked down your admin panel to a single IP. Works great from home. But what happens when you're on a train in Belgium trying to SSH into production? This episode tackles the two-part question: first, the practical mechanics of getting a fixed egress IP from anywhere — DIY WireGuard VPS, Mullvad with Tailscale, Cloudflare Zero Trust, and more. Then the harder question: is IP allowlisting an outdated security model in 2026? We walk through six tool categories, their gotchas, and whether you should be replacing IP lists with mutual TLS, identity-aware proxies, or zero-trust access.]]></description>
      <link>https://myweirdprompts.com/episode/fixed-egress-ip-allowlisting/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fixed-egress-ip-allowlisting/</guid>
      <pubDate>Sat, 25 Jul 2026 14:29:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fixed-egress-ip-allowlisting.m4a"
        type="audio/mp4"
        length="27671351"
      />
      <itunes:title>Fixed Egress IP From Anywhere: Is IP Allowlisting Dead?</itunes:title>
      <itunes:subtitle>How to get a static IP from anywhere for admin access — and whether IP allowlisting still makes sense in 2026.</itunes:subtitle>
      <itunes:summary><![CDATA[You've locked down your admin panel to a single IP. Works great from home. But what happens when you're on a train in Belgium trying to SSH into production? This episode tackles the two-part question: first, the practical mechanics of getting a fixed egress IP from anywhere — DIY WireGuard VPS, Mullvad with Tailscale, Cloudflare Zero Trust, and more. Then the harder question: is IP allowlisting an outdated security model in 2026? We walk through six tool categories, their gotchas, and whether you should be replacing IP lists with mutual TLS, identity-aware proxies, or zero-trust access.]]></itunes:summary>
      <itunes:duration>1586</itunes:duration>
      <itunes:episode>4459</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fixed-egress-ip-allowlisting.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fixed-egress-ip-allowlisting.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Headroom Compresses Agent Context Without Breaking Outputs</title>
      <description><![CDATA[Headroom is an open-source Python tool that sits between your AI agent and the LLM, compressing tool outputs, logs, files, and RAG chunks before they hit the context window. With claims of 20% savings for coding agents and up to 95% for JSON payloads — all while supposedly preserving answer quality — we examine what's actually happening under the hood. This episode walks through the fifteen different compressors, the Rust-based content detector, the SmartCrusher JSON optimizer, the Compressed Content Retrieval (CCR) system, and the fail-open proxy architecture. We also discuss the three integration surfaces (library, proxy, and MCP server) and where lossy compression could silently degrade agent output.]]></description>
      <link>https://myweirdprompts.com/episode/headroom-agent-context-compression/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/headroom-agent-context-compression/</guid>
      <pubDate>Fri, 24 Jul 2026 13:06:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/headroom-agent-context-compression.m4a"
        type="audio/mp4"
        length="27262215"
      />
      <itunes:title>How Headroom Compresses Agent Context Without Breaking Outputs</itunes:title>
      <itunes:subtitle>A deep dive into Headroom&apos;s content-aware compression pipeline that promises 20-95% token savings without changing agent answers.</itunes:subtitle>
      <itunes:summary><![CDATA[Headroom is an open-source Python tool that sits between your AI agent and the LLM, compressing tool outputs, logs, files, and RAG chunks before they hit the context window. With claims of 20% savings for coding agents and up to 95% for JSON payloads — all while supposedly preserving answer quality — we examine what's actually happening under the hood. This episode walks through the fifteen different compressors, the Rust-based content detector, the SmartCrusher JSON optimizer, the Compressed Content Retrieval (CCR) system, and the fail-open proxy architecture. We also discuss the three integration surfaces (library, proxy, and MCP server) and where lossy compression could silently degrade agent output.]]></itunes:summary>
      <itunes:duration>1559</itunes:duration>
      <itunes:episode>4458</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/headroom-agent-context-compression.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/headroom-agent-context-compression.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>OpenViking: A Filesystem for AI Agent Memory</title>
      <description><![CDATA[OpenViking from Volcengine rethinks how AI agents store and retrieve context — replacing the standard vector database + skill files stack with a single browsable filesystem. This episode unpacks the viking:// URI protocol, the three-tier L0/L1/L2 abstraction that cuts token usage by up to 91%, and how the system's directory-tree structure makes retrieval observable and debuggable. We cover the Rust, Python, and C++ codebase, the asynchronous memory extraction pipeline, and what "self-evolving" means in practice — memories that merge, patch, and update without creating duplicates.]]></description>
      <link>https://myweirdprompts.com/episode/openviking-ai-agent-filesystem/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/openviking-ai-agent-filesystem/</guid>
      <pubDate>Fri, 24 Jul 2026 13:03:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/openviking-ai-agent-filesystem.m4a"
        type="audio/mp4"
        length="26279567"
      />
      <itunes:title>OpenViking: A Filesystem for AI Agent Memory</itunes:title>
      <itunes:subtitle>OpenViking unifies memory, RAG, and skills into a browsable filesystem for AI agents — no vector database abstraction needed.</itunes:subtitle>
      <itunes:summary><![CDATA[OpenViking from Volcengine rethinks how AI agents store and retrieve context — replacing the standard vector database + skill files stack with a single browsable filesystem. This episode unpacks the viking:// URI protocol, the three-tier L0/L1/L2 abstraction that cuts token usage by up to 91%, and how the system's directory-tree structure makes retrieval observable and debuggable. We cover the Rust, Python, and C++ codebase, the asynchronous memory extraction pipeline, and what "self-evolving" means in practice — memories that merge, patch, and update without creating duplicates.]]></itunes:summary>
      <itunes:duration>1498</itunes:duration>
      <itunes:episode>4457</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/openviking-ai-agent-filesystem.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/openviking-ai-agent-filesystem.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside the Podcast Pipeline: How 15 Weekly Episodes Get Made</title>
      <description><![CDATA[Ever wonder what happens between hitting "send" on a prompt and hearing a finished episode? Producer Hilbert Flumingtop steps out from behind the mixing desk to reveal the mid-2026 pipeline in full detail. Learn how a single generic dispatcher routes over a dozen episode formats through one code path, why DeepSeek V4 Pro writes the scripts, how three parallel GPU workers cut voice synthesis time from 36 minutes to 10, and why the slower speech model won out over the faster one. Plus: the two-layer memory system that keeps hour-long episodes coherent, the additive-only review pass that preserves jokes, and what one episode actually costs end to end.]]></description>
      <link>https://myweirdprompts.com/episode/podcast-pipeline-production-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcast-pipeline-production-2026/</guid>
      <pubDate>Fri, 24 Jul 2026 12:12:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcast-pipeline-production-2026.m4a"
        type="audio/mp4"
        length="24375691"
      />
      <itunes:title>Inside the Podcast Pipeline: How 15 Weekly Episodes Get Made</itunes:title>
      <itunes:subtitle>From prompt to published episode — a full walkthrough of the automated production system running 15 shows weekly.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wonder what happens between hitting "send" on a prompt and hearing a finished episode? Producer Hilbert Flumingtop steps out from behind the mixing desk to reveal the mid-2026 pipeline in full detail. Learn how a single generic dispatcher routes over a dozen episode formats through one code path, why DeepSeek V4 Pro writes the scripts, how three parallel GPU workers cut voice synthesis time from 36 minutes to 10, and why the slower speech model won out over the faster one. Plus: the two-layer memory system that keeps hour-long episodes coherent, the additive-only review pass that preserves jokes, and what one episode actually costs end to end.]]></itunes:summary>
      <itunes:duration>1425</itunes:duration>
      <itunes:episode>4456</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcast-pipeline-production-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcast-pipeline-production-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Borrowing a GPU Over Your Home Network</title>
      <description><![CDATA[Daniel asks a compelling question: can you expose your desktop's GPU as a virtualized hardware device on your home network, so a laptop can use it transparently? We walk through every layer of the stack — from NVIDIA's MIG and SR-IOV hardware partitioning to rCUDA software forwarding and VirtualGL — and explain why none delivers the plug-and-play fantasy. Then we discuss the practical alternatives: SSH, running services like Ollama and Whisper, and why the "elegant" solution may not be worth the complexity.]]></description>
      <link>https://myweirdprompts.com/episode/remote-gpu-access-home-network/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/remote-gpu-access-home-network/</guid>
      <pubDate>Fri, 24 Jul 2026 12:04:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/remote-gpu-access-home-network.m4a"
        type="audio/mp4"
        length="27863281"
      />
      <itunes:title>Borrowing a GPU Over Your Home Network</itunes:title>
      <itunes:subtitle>Can you use your desktop&apos;s AMD GPU from a laptop as if it were local? We explore the real options.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asks a compelling question: can you expose your desktop's GPU as a virtualized hardware device on your home network, so a laptop can use it transparently? We walk through every layer of the stack — from NVIDIA's MIG and SR-IOV hardware partitioning to rCUDA software forwarding and VirtualGL — and explain why none delivers the plug-and-play fantasy. Then we discuss the practical alternatives: SSH, running services like Ollama and Whisper, and why the "elegant" solution may not be worth the complexity.]]></itunes:summary>
      <itunes:duration>1588</itunes:duration>
      <itunes:episode>4455</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/remote-gpu-access-home-network.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/remote-gpu-access-home-network.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Workers vs Workflows: Recovering From SSD Failure</title>
      <description><![CDATA[When your VPS SSD dies at 3AM, taking down all your micro-automations with it, you need more than just scripts — you need visibility and recoverability. This episode unpacks the crucial distinction between workflows, automations, and workers, and explores four real platforms for managing self-hosted tasks. From Huginn's event-driven agents to Temporal's enterprise orchestration, we break down what actually works for running ten to twenty independent scripts without SSHing in every morning. Plus, how AI code generation shifts the bottleneck from writing code to infrastructure management.]]></description>
      <link>https://myweirdprompts.com/episode/workers-workflows-ssd-recovery/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/workers-workflows-ssd-recovery/</guid>
      <pubDate>Fri, 24 Jul 2026 11:19:47 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/workers-workflows-ssd-recovery.m4a"
        type="audio/mp4"
        length="25557293"
      />
      <itunes:title>Workers vs Workflows: Recovering From SSD Failure</itunes:title>
      <itunes:subtitle>When your VPS SSD dies at 3AM, how do you know what broke? A practical guide to self-hosted worker management.</itunes:subtitle>
      <itunes:summary><![CDATA[When your VPS SSD dies at 3AM, taking down all your micro-automations with it, you need more than just scripts — you need visibility and recoverability. This episode unpacks the crucial distinction between workflows, automations, and workers, and explores four real platforms for managing self-hosted tasks. From Huginn's event-driven agents to Temporal's enterprise orchestration, we break down what actually works for running ten to twenty independent scripts without SSHing in every morning. Plus, how AI code generation shifts the bottleneck from writing code to infrastructure management.]]></itunes:summary>
      <itunes:duration>1464</itunes:duration>
      <itunes:episode>4453</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/workers-workflows-ssd-recovery.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/workers-workflows-ssd-recovery.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>YouTube Backup Pipeline: RSS + yt-dlp + Wasabi</title>
      <description><![CDATA[How do you automatically detect new uploads on a YouTube channel, download them, and push them to cloud storage? This episode walks through the full pipeline — from polling YouTube's undocumented RSS feed to running yt-dlp with the right flags to uploading to Wasabi. We cover why serverless is the wrong tool for this job, when to use a VPS versus a home server, and why a simple cron job beats complex orchestration platforms like Airflow. Plus: the difference between backup and archival, and why boring infrastructure wins for reliability.]]></description>
      <link>https://myweirdprompts.com/episode/youtube-backup-automation-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/youtube-backup-automation-pipeline/</guid>
      <pubDate>Fri, 24 Jul 2026 11:14:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/youtube-backup-automation-pipeline.m4a"
        type="audio/mp4"
        length="24957681"
      />
      <itunes:title>YouTube Backup Pipeline: RSS + yt-dlp + Wasabi</itunes:title>
      <itunes:subtitle>Build a free, reliable pipeline to auto-download and backup YouTube videos using RSS feeds, yt-dlp, and cloud storage.</itunes:subtitle>
      <itunes:summary><![CDATA[How do you automatically detect new uploads on a YouTube channel, download them, and push them to cloud storage? This episode walks through the full pipeline — from polling YouTube's undocumented RSS feed to running yt-dlp with the right flags to uploading to Wasabi. We cover why serverless is the wrong tool for this job, when to use a VPS versus a home server, and why a simple cron job beats complex orchestration platforms like Airflow. Plus: the difference between backup and archival, and why boring infrastructure wins for reliability.]]></itunes:summary>
      <itunes:duration>1423</itunes:duration>
      <itunes:episode>4452</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/youtube-backup-automation-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/youtube-backup-automation-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Python 3.14: Deferred Annotations, t-Strings &amp; Free-Threaded Mode</title>
      <description><![CDATA[Python 3.14 landed in October 2025 with major changes: deferred evaluation of annotations (no more string-quoting forward references), template string literals for safe SQL/HTML construction, production-ready free-threaded mode, and subinterpreters in the standard library. This episode breaks down what's new, what breaks, and how to stay on top of Python's evolution without fighting dependency hell. Plus: why point releases like 3.14.6 are mostly bugfixes you'll never notice — and when you should actually upgrade.]]></description>
      <link>https://myweirdprompts.com/episode/python-314-new-features/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/python-314-new-features/</guid>
      <pubDate>Fri, 24 Jul 2026 10:27:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/python-314-new-features.m4a"
        type="audio/mp4"
        length="27630397"
      />
      <itunes:title>Python 3.14: Deferred Annotations, t-Strings &amp; Free-Threaded Mode</itunes:title>
      <itunes:subtitle>Deferred annotations, t-strings, subinterpreters, and free-threaded mode — what actually changed in Python 3.14.</itunes:subtitle>
      <itunes:summary><![CDATA[Python 3.14 landed in October 2025 with major changes: deferred evaluation of annotations (no more string-quoting forward references), template string literals for safe SQL/HTML construction, production-ready free-threaded mode, and subinterpreters in the standard library. This episode breaks down what's new, what breaks, and how to stay on top of Python's evolution without fighting dependency hell. Plus: why point releases like 3.14.6 are mostly bugfixes you'll never notice — and when you should actually upgrade.]]></itunes:summary>
      <itunes:duration>1583</itunes:duration>
      <itunes:episode>4451</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/python-314-new-features.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/python-314-new-features.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Intercepting Claude Code: The Proxy Ecosystem</title>
      <description><![CDATA[When Daniel hit voice latency issues with Claude Code, he wondered if he could intercept the API call and route it somewhere faster. The answer is yes — and there's a whole ecosystem of projects doing exactly that. This episode breaks down how middleware proxies work under the hood, from TLS termination to real-time streaming translation. We explore three categories of proxies — privacy logging, cost-saving local routing, and extensibility platforms — and discuss the ethical and legal boundaries of intercepting your own AI traffic.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-proxy-ecosystem/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-proxy-ecosystem/</guid>
      <pubDate>Fri, 24 Jul 2026 10:27:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-proxy-ecosystem.m4a"
        type="audio/mp4"
        length="24722807"
      />
      <itunes:title>Intercepting Claude Code: The Proxy Ecosystem</itunes:title>
      <itunes:subtitle>How middleware proxies intercept, reroute, and transform AI tool traffic — and what it means for users.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel hit voice latency issues with Claude Code, he wondered if he could intercept the API call and route it somewhere faster. The answer is yes — and there's a whole ecosystem of projects doing exactly that. This episode breaks down how middleware proxies work under the hood, from TLS termination to real-time streaming translation. We explore three categories of proxies — privacy logging, cost-saving local routing, and extensibility platforms — and discuss the ethical and legal boundaries of intercepting your own AI traffic.]]></itunes:summary>
      <itunes:duration>1416</itunes:duration>
      <itunes:episode>4450</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-proxy-ecosystem.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-proxy-ecosystem.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Blur Before You Publish: Image Redaction Tools</title>
      <description><![CDATA[Taking photos for a blog post seems harmless until a QR code in the background links to your internal dashboard. While text redaction is practically solved, images remain a challenge. This episode explores the open-source tools and cloud APIs available for automated image obfuscation — from pyzbar and OpenCV for QR detection to OCR pipelines that find PII in photos. We break down the two-stage detection problem and what actually works today.]]></description>
      <link>https://myweirdprompts.com/episode/image-redaction-tools-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/image-redaction-tools-guide/</guid>
      <pubDate>Fri, 24 Jul 2026 10:22:47 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/image-redaction-tools-guide.m4a"
        type="audio/mp4"
        length="24668859"
      />
      <itunes:title>Blur Before You Publish: Image Redaction Tools</itunes:title>
      <itunes:subtitle>How to automatically find and blur QR codes, credit cards, and sensitive text in photos before publishing.</itunes:subtitle>
      <itunes:summary><![CDATA[Taking photos for a blog post seems harmless until a QR code in the background links to your internal dashboard. While text redaction is practically solved, images remain a challenge. This episode explores the open-source tools and cloud APIs available for automated image obfuscation — from pyzbar and OpenCV for QR detection to OCR pipelines that find PII in photos. We break down the two-stage detection problem and what actually works today.]]></itunes:summary>
      <itunes:duration>1425</itunes:duration>
      <itunes:episode>4449</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/image-redaction-tools-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/image-redaction-tools-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The MK Claude Project: AI That Writes Political Manifestos</title>
      <description><![CDATA[On Tisha B'Av, a day of mourning and reflection, Daniel built MK Claude—a multi-agent AI system that researches current law, stress-tests claims, and synthesizes coherent policy positions. This episode explores how the pipeline works, what it says about Israeli democracy, and what happens when a citizen decides to build the political platform nobody is offering them. From rental law reform to forkable manifestos, we examine whether AI can be a better policy ideator than the politicians we've got.]]></description>
      <link>https://myweirdprompts.com/episode/ai-political-manifestos-mk-claude/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-political-manifestos-mk-claude/</guid>
      <pubDate>Thu, 23 Jul 2026 14:47:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-political-manifestos-mk-claude.m4a"
        type="audio/mp4"
        length="25657180"
      />
      <itunes:title>The MK Claude Project: AI That Writes Political Manifestos</itunes:title>
      <itunes:subtitle>One citizen built a multi-agent AI to research, stress-test, and synthesize policy positions. The result? A forkable manifesto.</itunes:subtitle>
      <itunes:summary><![CDATA[On Tisha B'Av, a day of mourning and reflection, Daniel built MK Claude—a multi-agent AI system that researches current law, stress-tests claims, and synthesizes coherent policy positions. This episode explores how the pipeline works, what it says about Israeli democracy, and what happens when a citizen decides to build the political platform nobody is offering them. From rental law reform to forkable manifestos, we examine whether AI can be a better policy ideator than the politicians we've got.]]></itunes:summary>
      <itunes:duration>1467</itunes:duration>
      <itunes:episode>4448</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-political-manifestos-mk-claude.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-political-manifestos-mk-claude.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Agents Keep Undoing Each Other&apos;s Work</title>
      <description><![CDATA[When AI coding agents work in parallel, they can silently undo each other's work — a problem rooted not in bad tooling, but in fundamental amnesia. This episode unpacks three approaches to solving it: vector-backed memory servers, built-in tool memory like CLAUDE.md files, and agent-maintained documentation in the repo. We cover the failure patterns of each approach — stale embeddings, junk-drawer configs, and logs agents write but never read — plus practical fixes like read-before-write enforcement and separating decisions logs from changelogs. If you're running multiple AI coding agents on the same codebase, this episode will save you days of archaeological debugging.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agents-parallel-memory/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agents-parallel-memory/</guid>
      <pubDate>Thu, 23 Jul 2026 14:09:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agents-parallel-memory.m4a"
        type="audio/mp4"
        length="26510862"
      />
      <itunes:title>AI Agents Keep Undoing Each Other&apos;s Work</itunes:title>
      <itunes:subtitle>Why AI coding agents silently revert each other&apos;s fixes — and three memory strategies that prevent it.</itunes:subtitle>
      <itunes:summary><![CDATA[When AI coding agents work in parallel, they can silently undo each other's work — a problem rooted not in bad tooling, but in fundamental amnesia. This episode unpacks three approaches to solving it: vector-backed memory servers, built-in tool memory like CLAUDE.md files, and agent-maintained documentation in the repo. We cover the failure patterns of each approach — stale embeddings, junk-drawer configs, and logs agents write but never read — plus practical fixes like read-before-write enforcement and separating decisions logs from changelogs. If you're running multiple AI coding agents on the same codebase, this episode will save you days of archaeological debugging.]]></itunes:summary>
      <itunes:duration>1516</itunes:duration>
      <itunes:episode>4445</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agents-parallel-memory.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agents-parallel-memory.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Testing the Unpredictable: QA for Agentic AI</title>
      <description><![CDATA[Traditional software testing assumes determinism — run the same test, get the same result. Agentic AI shatters that assumption. This episode maps the emerging QA landscape for probabilistic systems: from golden datasets and LLM-as-judge to trajectory evaluation and adversarial prompting suites like Garak. We explore what carries over from traditional testing, what requires entirely new methodologies, and what a sane minimum testing stack looks like for teams shipping agentic systems.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ai-qa-methods/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ai-qa-methods/</guid>
      <pubDate>Thu, 23 Jul 2026 14:08:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ai-qa-methods.m4a"
        type="audio/mp4"
        length="24606397"
      />
      <itunes:title>Testing the Unpredictable: QA for Agentic AI</itunes:title>
      <itunes:subtitle>How QA adapts when your AI system gives different answers to the same question every time.</itunes:subtitle>
      <itunes:summary><![CDATA[Traditional software testing assumes determinism — run the same test, get the same result. Agentic AI shatters that assumption. This episode maps the emerging QA landscape for probabilistic systems: from golden datasets and LLM-as-judge to trajectory evaluation and adversarial prompting suites like Garak. We explore what carries over from traditional testing, what requires entirely new methodologies, and what a sane minimum testing stack looks like for teams shipping agentic systems.]]></itunes:summary>
      <itunes:duration>1396</itunes:duration>
      <itunes:episode>4444</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ai-qa-methods.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ai-qa-methods.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Terminal Navigation Beyond cd: Favorites, Recents &amp; Agentic Workflows</title>
      <description><![CDATA[The terminal is the purest interface we have — and also the blindest. Without visual scaffolding, our spatial memory for file paths decays faster than we can maintain it. This episode explores the full landscape of terminal navigation tools: from frecency-based jumpers like zoxide, autojump, and fasd, to shell-native options like CDPATH and pushd, to visual terminal explorers like broot and yazi. But the real conversation is about what's missing: a favourites-and-recents manager that accounts for agentic workflows where tools like Claude Code are deeply tied to the working directory. If you've ever forgotten an alias you made three months ago, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/terminal-navigation-favorites-recents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/terminal-navigation-favorites-recents/</guid>
      <pubDate>Thu, 23 Jul 2026 12:03:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/terminal-navigation-favorites-recents.m4a"
        type="audio/mp4"
        length="26085474"
      />
      <itunes:title>Terminal Navigation Beyond cd: Favorites, Recents &amp; Agentic Workflows</itunes:title>
      <itunes:subtitle>Why cd isn&apos;t broken — our spatial memory is. A tour of zoxide, broot, and what&apos;s missing for power users.</itunes:subtitle>
      <itunes:summary><![CDATA[The terminal is the purest interface we have — and also the blindest. Without visual scaffolding, our spatial memory for file paths decays faster than we can maintain it. This episode explores the full landscape of terminal navigation tools: from frecency-based jumpers like zoxide, autojump, and fasd, to shell-native options like CDPATH and pushd, to visual terminal explorers like broot and yazi. But the real conversation is about what's missing: a favourites-and-recents manager that accounts for agentic workflows where tools like Claude Code are deeply tied to the working directory. If you've ever forgotten an alias you made three months ago, this one's for you.]]></itunes:summary>
      <itunes:duration>1495</itunes:duration>
      <itunes:episode>4443</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/terminal-navigation-favorites-recents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/terminal-navigation-favorites-recents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Prompt Enhancers Hallucinate Your Intent</title>
      <description><![CDATA[That "enhance prompt" button hides a dangerous secret — especially when it's running in an agentic pipeline with no human oversight. We break down the two failure modes Daniel is worried about: content loss (the enhancer drops a constraint you needed) and content invention (it hallucinates requirements you never asked for). Then we get concrete: what a well-constructed enhancer system prompt actually looks like, why distillation beats expansion, and when the best enhancement is no enhancement at all.]]></description>
      <link>https://myweirdprompts.com/episode/prompt-enhancer-agentic-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/prompt-enhancer-agentic-pipeline/</guid>
      <pubDate>Thu, 23 Jul 2026 11:57:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/prompt-enhancer-agentic-pipeline.m4a"
        type="audio/mp4"
        length="27580533"
      />
      <itunes:title>When Prompt Enhancers Hallucinate Your Intent</itunes:title>
      <itunes:subtitle>How to build a prompt enhancer that doesn&apos;t rewrite your constraints into oblivion.</itunes:subtitle>
      <itunes:summary><![CDATA[That "enhance prompt" button hides a dangerous secret — especially when it's running in an agentic pipeline with no human oversight. We break down the two failure modes Daniel is worried about: content loss (the enhancer drops a constraint you needed) and content invention (it hallucinates requirements you never asked for). Then we get concrete: what a well-constructed enhancer system prompt actually looks like, why distillation beats expansion, and when the best enhancement is no enhancement at all.]]></itunes:summary>
      <itunes:duration>1583</itunes:duration>
      <itunes:episode>4442</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/prompt-enhancer-agentic-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/prompt-enhancer-agentic-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>ADB + Claude Code: Clean Up Your Android Phone</title>
      <description><![CDATA[Your phone ships with apps you can't uninstall that drain battery and RAM. Pairing Android Debug Bridge (ADB) with Anthropic's Claude Code lets you remove bloatware, diagnose crashes, and reclaim memory — all without root access. You supply the judgment, the agent handles the syntax. Learn how this force multiplier extends your phone's life and teaches you the tool along the way.]]></description>
      <link>https://myweirdprompts.com/episode/adb-claude-code-android/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/adb-claude-code-android/</guid>
      <pubDate>Wed, 22 Jul 2026 12:16:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/adb-claude-code-android.m4a"
        type="audio/mp4"
        length="25702604"
      />
      <itunes:title>ADB + Claude Code: Clean Up Your Android Phone</itunes:title>
      <itunes:subtitle>An AI agent plus a 20-year-old debugging tool can strip bloatware and fix apps without root.</itunes:subtitle>
      <itunes:summary><![CDATA[Your phone ships with apps you can't uninstall that drain battery and RAM. Pairing Android Debug Bridge (ADB) with Anthropic's Claude Code lets you remove bloatware, diagnose crashes, and reclaim memory — all without root access. You supply the judgment, the agent handles the syntax. Learn how this force multiplier extends your phone's life and teaches you the tool along the way.]]></itunes:summary>
      <itunes:duration>1474</itunes:duration>
      <itunes:episode>4427</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/adb-claude-code-android.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/adb-claude-code-android.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>DNS Blocking That Doesn&apos;t Break Everything</title>
      <description><![CDATA[Network-level DNS blocking is powerful — it catches ads, telemetry, and tracking pixels that browser extensions miss. But one wrong blocklist can break your smart TV, your work dashboard, and your kid's game. This episode walks through the practical workflow for running AdGuard Home without collateral damage: reading the query log, using per-client filtering instead of global allowlists, and the three-tier approach that keeps IoT devices locked down while letting your work laptop access what it needs. No more "just whitelist it" shortcuts that punch holes in your protection.]]></description>
      <link>https://myweirdprompts.com/episode/dns-blocking-adguard-workflow/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dns-blocking-adguard-workflow/</guid>
      <pubDate>Wed, 22 Jul 2026 12:12:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dns-blocking-adguard-workflow.m4a"
        type="audio/mp4"
        length="23868712"
      />
      <itunes:title>DNS Blocking That Doesn&apos;t Break Everything</itunes:title>
      <itunes:subtitle>How to block ads and trackers at the network level without breaking your spouse&apos;s banking app or your kid&apos;s game.</itunes:subtitle>
      <itunes:summary><![CDATA[Network-level DNS blocking is powerful — it catches ads, telemetry, and tracking pixels that browser extensions miss. But one wrong blocklist can break your smart TV, your work dashboard, and your kid's game. This episode walks through the practical workflow for running AdGuard Home without collateral damage: reading the query log, using per-client filtering instead of global allowlists, and the three-tier approach that keeps IoT devices locked down while letting your work laptop access what it needs. No more "just whitelist it" shortcuts that punch holes in your protection.]]></itunes:summary>
      <itunes:duration>1368</itunes:duration>
      <itunes:episode>4425</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dns-blocking-adguard-workflow.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dns-blocking-adguard-workflow.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How a Bank Card Nearly Killed 5,000 Episodes</title>
      <description><![CDATA[Daniel nearly lost his entire 5,000-episode podcast archive last month. Not from a hard drive crash or ransomware—but from a bank account change, a NAS sitting in a moving box, and an expired credit card. In this episode, we dissect how a textbook three-two-one backup setup silently degraded to a single point of failure, and what the evolved three-two-one-one-zero rule adds to prevent it. We explore why mundane administrative failures are more dangerous than exotic attacks, and how to build a backup architecture that survives life chaos hitting everything at once.]]></description>
      <link>https://myweirdprompts.com/episode/backup-failure-life-chaos/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/backup-failure-life-chaos/</guid>
      <pubDate>Mon, 20 Jul 2026 16:01:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/backup-failure-life-chaos.m4a"
        type="audio/mp4"
        length="33315914"
      />
      <itunes:title>How a Bank Card Nearly Killed 5,000 Episodes</itunes:title>
      <itunes:subtitle>A textbook backup setup quietly degraded to a single copy. Here’s how—and how to prevent it.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel nearly lost his entire 5,000-episode podcast archive last month. Not from a hard drive crash or ransomware—but from a bank account change, a NAS sitting in a moving box, and an expired credit card. In this episode, we dissect how a textbook three-two-one backup setup silently degraded to a single point of failure, and what the evolved three-two-one-one-zero rule adds to prevent it. We explore why mundane administrative failures are more dangerous than exotic attacks, and how to build a backup architecture that survives life chaos hitting everything at once.]]></itunes:summary>
      <itunes:duration>1931</itunes:duration>
      <itunes:episode>4399</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/backup-failure-life-chaos.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/backup-failure-life-chaos.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The $120 Noise Sensor That Could Change Cities</title>
      <description><![CDATA[When Daniel moved into a new apartment in Jerusalem, he discovered that noise pollution is invisible, intermittent, and nearly impossible to prove. The WHO estimates one million healthy life-years are lost annually in Western Europe from environmental noise alone — yet citizen monitoring infrastructure barely exists. Unlike air quality, where $30 sensors feed global networks, calibrated noise monitoring still costs thousands. This episode explores the hardware gap, the missing networks, and the build path that could finally let citizens contribute defensible noise data to researchers. From the miniDSP UMIK-1 to Sensor.Community, we trace what it would take to break the chicken-and-egg problem of citizen noise science.]]></description>
      <link>https://myweirdprompts.com/episode/citizen-noise-sensor-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/citizen-noise-sensor-guide/</guid>
      <pubDate>Mon, 20 Jul 2026 09:38:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/citizen-noise-sensor-guide.m4a"
        type="audio/mp4"
        length="27205175"
      />
      <itunes:title>The $120 Noise Sensor That Could Change Cities</itunes:title>
      <itunes:subtitle>One Jerusalem resident wants to track construction noise from his balcony. Here’s why that’s harder than it should be.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel moved into a new apartment in Jerusalem, he discovered that noise pollution is invisible, intermittent, and nearly impossible to prove. The WHO estimates one million healthy life-years are lost annually in Western Europe from environmental noise alone — yet citizen monitoring infrastructure barely exists. Unlike air quality, where $30 sensors feed global networks, calibrated noise monitoring still costs thousands. This episode explores the hardware gap, the missing networks, and the build path that could finally let citizens contribute defensible noise data to researchers. From the miniDSP UMIK-1 to Sensor.Community, we trace what it would take to break the chicken-and-egg problem of citizen noise science.]]></itunes:summary>
      <itunes:duration>1567</itunes:duration>
      <itunes:episode>4389</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/citizen-noise-sensor-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/citizen-noise-sensor-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Filesystem to Rule Them All</title>
      <description><![CDATA[Stop configuring every remote on every device. Learn how to build a server-side unified filesystem using rclone and mergerfs, then browse it from Android via WebDAV. We cover Google Shared Drives, caching strategies, and why the phone should be a dumb terminal for your multi-cloud storage.]]></description>
      <link>https://myweirdprompts.com/episode/unified-cloud-filesystem-android/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unified-cloud-filesystem-android/</guid>
      <pubDate>Sat, 18 Jul 2026 18:26:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unified-cloud-filesystem-android.m4a"
        type="audio/mp4"
        length="36331623"
      />
      <itunes:title>One Filesystem to Rule Them All</itunes:title>
      <itunes:subtitle>Merge Google Drive, S3, and NAS into one browsable tree on your phone.</itunes:subtitle>
      <itunes:summary><![CDATA[Stop configuring every remote on every device. Learn how to build a server-side unified filesystem using rclone and mergerfs, then browse it from Android via WebDAV. We cover Google Shared Drives, caching strategies, and why the phone should be a dumb terminal for your multi-cloud storage.]]></itunes:summary>
      <itunes:duration>2096</itunes:duration>
      <itunes:episode>4379</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unified-cloud-filesystem-android.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unified-cloud-filesystem-android.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Audit 47 Stale Sessions Without Breaking Everything</title>
      <description><![CDATA[When you open your password manager and see dozens of active sessions you don't recognize, the standard advice to "revoke everything" can break your workflows. This episode breaks down a forensic approach to session auditing — using IP geolocation, user-agent strings, and activity timestamps to triage unknown sessions. We cover the three-strike rule for identifying compromised tokens, the wave-based revocation method that prevents accidental lockouts, and why stale sessions have become the top initial access vector for ransomware groups. If you've ever stared at a session list and felt overwhelmed, this episode gives you a repeatable process.]]></description>
      <link>https://myweirdprompts.com/episode/session-audit-framework-security/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/session-audit-framework-security/</guid>
      <pubDate>Sat, 18 Jul 2026 11:55:03 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/session-audit-framework-security.m4a"
        type="audio/mp4"
        length="25433208"
      />
      <itunes:title>How to Audit 47 Stale Sessions Without Breaking Everything</itunes:title>
      <itunes:subtitle>A systematic method for identifying and cleaning up forgotten sessions without locking yourself out of critical services.</itunes:subtitle>
      <itunes:summary><![CDATA[When you open your password manager and see dozens of active sessions you don't recognize, the standard advice to "revoke everything" can break your workflows. This episode breaks down a forensic approach to session auditing — using IP geolocation, user-agent strings, and activity timestamps to triage unknown sessions. We cover the three-strike rule for identifying compromised tokens, the wave-based revocation method that prevents accidental lockouts, and why stale sessions have become the top initial access vector for ransomware groups. If you've ever stared at a session list and felt overwhelmed, this episode gives you a repeatable process.]]></itunes:summary>
      <itunes:duration>1449</itunes:duration>
      <itunes:episode>4375</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/session-audit-framework-security.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/session-audit-framework-security.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How AI Agents Finally Made the Terminal Accessible</title>
      <description><![CDATA[For twenty years, Daniel wanted to be a terminal-first developer but couldn't make it stick — not because he wasn't technical enough, but because the memory tax of flags, parameters, and man pages was incompatible with his ADHD brain. Then Claude Code changed everything. This episode explores the quiet renaissance happening in the terminal: how AI agents are finally solving the CLI's biggest cognitive friction, and what happens when an interface that's been hostile to certain brains for fifty years suddenly becomes the most accessible one in the stack. We also cover modern terminal tools like lsd and bat that add visual hierarchy without leaving the command line.]]></description>
      <link>https://myweirdprompts.com/episode/ai-terminal-cognitive-accessibility/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-terminal-cognitive-accessibility/</guid>
      <pubDate>Sat, 18 Jul 2026 11:49:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-terminal-cognitive-accessibility.m4a"
        type="audio/mp4"
        length="24851788"
      />
      <itunes:title>How AI Agents Finally Made the Terminal Accessible</itunes:title>
      <itunes:subtitle>How Claude Code removes the memory tax that kept neurodivergent users from living in the terminal.</itunes:subtitle>
      <itunes:summary><![CDATA[For twenty years, Daniel wanted to be a terminal-first developer but couldn't make it stick — not because he wasn't technical enough, but because the memory tax of flags, parameters, and man pages was incompatible with his ADHD brain. Then Claude Code changed everything. This episode explores the quiet renaissance happening in the terminal: how AI agents are finally solving the CLI's biggest cognitive friction, and what happens when an interface that's been hostile to certain brains for fifty years suddenly becomes the most accessible one in the stack. We also cover modern terminal tools like lsd and bat that add visual hierarchy without leaving the command line.]]></itunes:summary>
      <itunes:duration>1425</itunes:duration>
      <itunes:episode>4374</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-terminal-cognitive-accessibility.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-terminal-cognitive-accessibility.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Jaffa Street Has Three Names</title>
      <description><![CDATA[A single street in Jerusalem has three valid English spellings: Jaffa, Yafo, and Yaffo. Now multiply that across every language and script, and the permutations explode. This episode unpacks why address geolocation is a systemic failure, not a localization bug — and why global supply chains are breaking on romanization inconsistencies. We explore how geocoding works under the hood, why Google’s dominance creates a single point of failure, and what open alternatives like Plus Codes and what3words offer as replacements for the broken text-address paradigm.]]></description>
      <link>https://myweirdprompts.com/episode/address-geolocation-universal-id/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/address-geolocation-universal-id/</guid>
      <pubDate>Sat, 18 Jul 2026 11:36:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/address-geolocation-universal-id.m4a"
        type="audio/mp4"
        length="24852376"
      />
      <itunes:title>Why Jaffa Street Has Three Names</itunes:title>
      <itunes:subtitle>Addresses are human-readable, not machine-validatable. Why global logistics breaks on transliteration.</itunes:subtitle>
      <itunes:summary><![CDATA[A single street in Jerusalem has three valid English spellings: Jaffa, Yafo, and Yaffo. Now multiply that across every language and script, and the permutations explode. This episode unpacks why address geolocation is a systemic failure, not a localization bug — and why global supply chains are breaking on romanization inconsistencies. We explore how geocoding works under the hood, why Google’s dominance creates a single point of failure, and what open alternatives like Plus Codes and what3words offer as replacements for the broken text-address paradigm.]]></itunes:summary>
      <itunes:duration>1420</itunes:duration>
      <itunes:episode>4373</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/address-geolocation-universal-id.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/address-geolocation-universal-id.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Long Will Your ESP32 Sensor Actually Run on a Single 18650?</title>
      <description><![CDATA[Daniel is building a parking spot presence sensor with an ESP32 and a single 18650 cell. He wants to know if it runs for weeks or dies in two days. This episode walks through the actual mathematics step by step — sleep currents, ultrasonic pings, LoRaWAN transmissions, voltage regulator losses, battery derating, and the killer variable of retransmissions in a concrete parking garage. The spreadsheet says 11 years. Reality says something very different.]]></description>
      <link>https://myweirdprompts.com/episode/esp32-battery-life-calculation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/esp32-battery-life-calculation/</guid>
      <pubDate>Fri, 17 Jul 2026 09:43:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/esp32-battery-life-calculation.m4a"
        type="audio/mp4"
        length="24792394"
      />
      <itunes:title>How Long Will Your ESP32 Sensor Actually Run on a Single 18650?</itunes:title>
      <itunes:subtitle>Walk through the real math behind ESP32 battery life — from sleep current to LoRa retransmissions in concrete garages.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel is building a parking spot presence sensor with an ESP32 and a single 18650 cell. He wants to know if it runs for weeks or dies in two days. This episode walks through the actual mathematics step by step — sleep currents, ultrasonic pings, LoRaWAN transmissions, voltage regulator losses, battery derating, and the killer variable of retransmissions in a concrete parking garage. The spreadsheet says 11 years. Reality says something very different.]]></itunes:summary>
      <itunes:duration>1421</itunes:duration>
      <itunes:episode>4360</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/esp32-battery-life-calculation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/esp32-battery-life-calculation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>GPIO Pins: Your SBC&apos;s Hidden Superpower</title>
      <description><![CDATA[That row of pins on your Raspberry Pi or ESP32 isn't just decoration — it's the bridge between software and the physical world. In this episode, we break down what GPIO actually does, from digital inputs and outputs to I2C, SPI, and UART protocols. We walk through Daniel's real-world LoRa parking sensor project to show how ultrasonic sensors, temperature modules, and radio transceivers all connect through these humble headers. Then we tackle the power limits that separate a working project from a fried board — including why the HC-SR04's five-volt output can kill your Pi, how the ESP32's strapping pins can prevent boot, and exactly where the three-point-three-volt regulator's current budget runs out. Whether you're building a parking sensor or just finally curious about those pins, this is your practical guide to GPIO.]]></description>
      <link>https://myweirdprompts.com/episode/gpio-pins-sbc-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gpio-pins-sbc-guide/</guid>
      <pubDate>Thu, 16 Jul 2026 22:48:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gpio-pins-sbc-guide.m4a"
        type="audio/mp4"
        length="26159706"
      />
      <itunes:title>GPIO Pins: Your SBC&apos;s Hidden Superpower</itunes:title>
      <itunes:subtitle>What GPIO pins actually do, where the power limits are, and which sensors work on each side of the divide.</itunes:subtitle>
      <itunes:summary><![CDATA[That row of pins on your Raspberry Pi or ESP32 isn't just decoration — it's the bridge between software and the physical world. In this episode, we break down what GPIO actually does, from digital inputs and outputs to I2C, SPI, and UART protocols. We walk through Daniel's real-world LoRa parking sensor project to show how ultrasonic sensors, temperature modules, and radio transceivers all connect through these humble headers. Then we tackle the power limits that separate a working project from a fried board — including why the HC-SR04's five-volt output can kill your Pi, how the ESP32's strapping pins can prevent boot, and exactly where the three-point-three-volt regulator's current budget runs out. Whether you're building a parking sensor or just finally curious about those pins, this is your practical guide to GPIO.]]></itunes:summary>
      <itunes:duration>1509</itunes:duration>
      <itunes:episode>4353</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gpio-pins-sbc-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gpio-pins-sbc-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building an AI That Finds Your Blind Spots</title>
      <description><![CDATA[What if an AI could interview you about your vulnerabilities, critique your responses, and generate a preventative action plan — all using tools available right now? This episode breaks down a four-agent architecture for cascading failure detection: an Ideation Agent that generates plausible disaster chains, an Interviewer Agent that forces you to articulate your response, a Critique Agent that finds your blind spots, and a Remediation Agent that documents actionable steps. We explore prompt engineering lessons from Anthropic’s Causal Chain Decomposition paper, the adversarial design patterns from IQT Labs’ Snowglobe framework, and a MIT Media Lab study on framing feedback for engagement. Plus, a practical comparison of LangGraph, CrewAI, and AutoGen for wiring it all together.]]></description>
      <link>https://myweirdprompts.com/episode/ai-cascading-failure-agent/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-cascading-failure-agent/</guid>
      <pubDate>Thu, 16 Jul 2026 14:57:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-cascading-failure-agent.m4a"
        type="audio/mp4"
        length="30167986"
      />
      <itunes:title>Building an AI That Finds Your Blind Spots</itunes:title>
      <itunes:subtitle>Can LLMs surface the chain reactions you never see? A four-agent system for proactive preparedness.</itunes:subtitle>
      <itunes:summary><![CDATA[What if an AI could interview you about your vulnerabilities, critique your responses, and generate a preventative action plan — all using tools available right now? This episode breaks down a four-agent architecture for cascading failure detection: an Ideation Agent that generates plausible disaster chains, an Interviewer Agent that forces you to articulate your response, a Critique Agent that finds your blind spots, and a Remediation Agent that documents actionable steps. We explore prompt engineering lessons from Anthropic’s Causal Chain Decomposition paper, the adversarial design patterns from IQT Labs’ Snowglobe framework, and a MIT Media Lab study on framing feedback for engagement. Plus, a practical comparison of LangGraph, CrewAI, and AutoGen for wiring it all together.]]></itunes:summary>
      <itunes:duration>1728</itunes:duration>
      <itunes:episode>4347</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-cascading-failure-agent.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-cascading-failure-agent.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Freeing IR Codes from Broadlink&apos;s Walled Garden</title>
      <description><![CDATA[Daniel's Broadlink RM4 Mini learns IR codes but won't share them locally — they're locked behind Broadlink's cloud. We walk through the hardware teardown, UART flashing, and open firmware options (Tasmota-IR, OpenBeken) to expose those codes over MQTT. Plus logic analyzer capture techniques and a Pi-based alternative for the non-flasher.]]></description>
      <link>https://myweirdprompts.com/episode/broadlink-ir-blaster-open-firmware/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/broadlink-ir-blaster-open-firmware/</guid>
      <pubDate>Thu, 16 Jul 2026 14:23:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/broadlink-ir-blaster-open-firmware.m4a"
        type="audio/mp4"
        length="25292317"
      />
      <itunes:title>Freeing IR Codes from Broadlink&apos;s Walled Garden</itunes:title>
      <itunes:subtitle>Dump the firmware, flash Tasmota-IR, and take control of your own IR codes.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's Broadlink RM4 Mini learns IR codes but won't share them locally — they're locked behind Broadlink's cloud. We walk through the hardware teardown, UART flashing, and open firmware options (Tasmota-IR, OpenBeken) to expose those codes over MQTT. Plus logic analyzer capture techniques and a Pi-based alternative for the non-flasher.]]></itunes:summary>
      <itunes:duration>1445</itunes:duration>
      <itunes:episode>4344</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/broadlink-ir-blaster-open-firmware.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/broadlink-ir-blaster-open-firmware.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building an AI Tutor That Teaches You to Code Without Writing It For You</title>
      <description><![CDATA[What if an AI could teach you programming the way a professor does — assign a project, let you struggle through it independently, then provide detailed, honest feedback? This episode unpacks the full architecture for such a system: intake diagnostics that reveal actual ability (not self-reported skill), competency graphs that track concept mastery over time, sandboxed execution environments for safe code review, and multi-stage review pipelines that distinguish between one-off bugs and genuine misconceptions. We explore how platforms like Replit and CodeSignal Learn are racing toward adaptive project-based learning, why most AI tutoring systems fail by being too hands-on, and how an asynchronous, pull-request-based workflow mirrors real software development. If you've ever wondered what a genuinely effective AI programming tutor would look like — one that teaches you to not need it — this episode is for you.]]></description>
      <link>https://myweirdprompts.com/episode/ai-programming-tutor-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-programming-tutor-architecture/</guid>
      <pubDate>Thu, 16 Jul 2026 13:11:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-programming-tutor-architecture.m4a"
        type="audio/mp4"
        length="29224382"
      />
      <itunes:title>Building an AI Tutor That Teaches You to Code Without Writing It For You</itunes:title>
      <itunes:subtitle>How to build an AI tutor that assigns projects, reviews code, and remembers what you&apos;ve learned — without writing your code for you.</itunes:subtitle>
      <itunes:summary><![CDATA[What if an AI could teach you programming the way a professor does — assign a project, let you struggle through it independently, then provide detailed, honest feedback? This episode unpacks the full architecture for such a system: intake diagnostics that reveal actual ability (not self-reported skill), competency graphs that track concept mastery over time, sandboxed execution environments for safe code review, and multi-stage review pipelines that distinguish between one-off bugs and genuine misconceptions. We explore how platforms like Replit and CodeSignal Learn are racing toward adaptive project-based learning, why most AI tutoring systems fail by being too hands-on, and how an asynchronous, pull-request-based workflow mirrors real software development. If you've ever wondered what a genuinely effective AI programming tutor would look like — one that teaches you to not need it — this episode is for you.]]></itunes:summary>
      <itunes:duration>1671</itunes:duration>
      <itunes:episode>4340</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-programming-tutor-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-programming-tutor-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Text Survives a Tunnel: Offline Queueing Deep Dive</title>
      <description><![CDATA[Ever tapped send on a form just as your train entered a tunnel, and the message arrived perfectly on the other side? This episode unpacks the surprisingly intricate chain of events that makes that work for small text payloads. We trace the journey from a failed fetch through IndexedDB storage, service worker background sync, and Stripe-style idempotency keys on the backend. Along the way, we expose the traps: why navigator.onLine lies, the browser support gap for Background Sync, and the five failure modes that turn a simple queue into a debugging nightmare. If you've ever wondered where the boundary lies between lightweight offline queueing and full offline-first architecture, this is your episode.]]></description>
      <link>https://myweirdprompts.com/episode/offline-queueing-text-payloads/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/offline-queueing-text-payloads/</guid>
      <pubDate>Thu, 16 Jul 2026 12:58:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/offline-queueing-text-payloads.m4a"
        type="audio/mp4"
        length="23953634"
      />
      <itunes:title>How Text Survives a Tunnel: Offline Queueing Deep Dive</itunes:title>
      <itunes:subtitle>What happens when you tap send in a tunnel? The browser, service worker, and backend dance that makes tiny payloads survive offline.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever tapped send on a form just as your train entered a tunnel, and the message arrived perfectly on the other side? This episode unpacks the surprisingly intricate chain of events that makes that work for small text payloads. We trace the journey from a failed fetch through IndexedDB storage, service worker background sync, and Stripe-style idempotency keys on the backend. Along the way, we expose the traps: why navigator.onLine lies, the browser support gap for Background Sync, and the five failure modes that turn a simple queue into a debugging nightmare. If you've ever wondered where the boundary lies between lightweight offline queueing and full offline-first architecture, this is your episode.]]></itunes:summary>
      <itunes:duration>1355</itunes:duration>
      <itunes:episode>4339</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/offline-queueing-text-payloads.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/offline-queueing-text-payloads.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Reverse Engineering Android APKs in 2026</title>
      <description><![CDATA[The old playbook of decompiling Java doesn't work anymore. Modern Android apps hide their logic in monolithic ARM64 native libraries, demanding a new toolchain. In this episode, we break down the three layers of a modern APK, from the hollow Java shell to the obfuscated native code. We explore how the NSA’s Ghidra framework and the dynamic analysis toolkit Frida/Objection are used to trace API calls and disable SSL pinning. We also navigate the murky ethical and legal landscape of reverse engineering for unofficial integrations, distinguishing between reading the API contract and exploiting vulnerabilities.]]></description>
      <link>https://myweirdprompts.com/episode/android-apk-reverse-engineering/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-apk-reverse-engineering/</guid>
      <pubDate>Wed, 15 Jul 2026 23:42:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-apk-reverse-engineering.m4a"
        type="audio/mp4"
        length="22638636"
      />
      <itunes:title>Reverse Engineering Android APKs in 2026</itunes:title>
      <itunes:subtitle>From Ghidra to Frida: the modern toolkit for analyzing bundled Android apps.</itunes:subtitle>
      <itunes:summary><![CDATA[The old playbook of decompiling Java doesn't work anymore. Modern Android apps hide their logic in monolithic ARM64 native libraries, demanding a new toolchain. In this episode, we break down the three layers of a modern APK, from the hollow Java shell to the obfuscated native code. We explore how the NSA’s Ghidra framework and the dynamic analysis toolkit Frida/Objection are used to trace API calls and disable SSL pinning. We also navigate the murky ethical and legal landscape of reverse engineering for unofficial integrations, distinguishing between reading the API contract and exploiting vulnerabilities.]]></itunes:summary>
      <itunes:duration>1290</itunes:duration>
      <itunes:episode>4321</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-apk-reverse-engineering.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-apk-reverse-engineering.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Breaking Free from Google Auth Dependency</title>
      <description><![CDATA[Every "Sign in with Google" button chains your entire app ecosystem to a single workspace subscription. Lose that Google Workspace or GitHub organization account, and you lose access to everything. This episode breaks down the real alternatives: self-hosted identity providers like Dex, Authentik, and Keycloak versus dedicated SaaS auth services like Auth0, Clerk, and WorkOS. We explore the tradeoffs between operational overhead and dependency risk, and why the March 2026 Auth0 outage proves there's no perfect solution — only different failure modes.]]></description>
      <link>https://myweirdprompts.com/episode/breaking-auth-dependency-chain/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/breaking-auth-dependency-chain/</guid>
      <pubDate>Wed, 15 Jul 2026 21:21:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/breaking-auth-dependency-chain.m4a"
        type="audio/mp4"
        length="23755723"
      />
      <itunes:title>Breaking Free from Google Auth Dependency</itunes:title>
      <itunes:subtitle>How to decouple your authentication from workspace providers without breaking your apps.</itunes:subtitle>
      <itunes:summary><![CDATA[Every "Sign in with Google" button chains your entire app ecosystem to a single workspace subscription. Lose that Google Workspace or GitHub organization account, and you lose access to everything. This episode breaks down the real alternatives: self-hosted identity providers like Dex, Authentik, and Keycloak versus dedicated SaaS auth services like Auth0, Clerk, and WorkOS. We explore the tradeoffs between operational overhead and dependency risk, and why the March 2026 Auth0 outage proves there's no perfect solution — only different failure modes.]]></itunes:summary>
      <itunes:duration>1373</itunes:duration>
      <itunes:episode>4319</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/breaking-auth-dependency-chain.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/breaking-auth-dependency-chain.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Ladder of Minimal Linux Systems</title>
      <description><![CDATA[What’s the smallest Linux system that can actually do something useful? This episode climbs the ladder of minimalism — from Docker’s FROM scratch (zero bytes) through BusyBox (one megabyte, three hundred utilities) to Alpine and distroless. We explore the engineering tradeoffs: pull time, attack surface, CVE baselines, debugging access, and the multi-stage build pattern that keeps production artifacts tiny. Plus: why a BusyBox sidecar might be the smartest addition to your Kubernetes stack.]]></description>
      <link>https://myweirdprompts.com/episode/minimal-linux-ladder/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/minimal-linux-ladder/</guid>
      <pubDate>Wed, 15 Jul 2026 12:38:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/minimal-linux-ladder.m4a"
        type="audio/mp4"
        length="26228219"
      />
      <itunes:title>The Ladder of Minimal Linux Systems</itunes:title>
      <itunes:subtitle>From scratch containers to BusyBox to Alpine — how barebones can you go and still serve production traffic?</itunes:subtitle>
      <itunes:summary><![CDATA[What’s the smallest Linux system that can actually do something useful? This episode climbs the ladder of minimalism — from Docker’s FROM scratch (zero bytes) through BusyBox (one megabyte, three hundred utilities) to Alpine and distroless. We explore the engineering tradeoffs: pull time, attack surface, CVE baselines, debugging access, and the multi-stage build pattern that keeps production artifacts tiny. Plus: why a BusyBox sidecar might be the smartest addition to your Kubernetes stack.]]></itunes:summary>
      <itunes:duration>1506</itunes:duration>
      <itunes:episode>4317</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/minimal-linux-ladder.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/minimal-linux-ladder.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Build Memory That Survives AI Sessions</title>
      <description><![CDATA[When you're deep in multi-session technical investigation, AI agents lose context every time you close the chat. This episode unpacks why default memory systems fail and how to build external memory that survives session boundaries using markdown files in a private repo. We explore system prompt patterns that actually work, the difference between reactive and proactive documentation, and whether tools like Obsidian add real value over raw markdown. If you've ever lost hours of agent context between sessions, this is your fix.]]></description>
      <link>https://myweirdprompts.com/episode/ai-memory-externalization-markdown/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-memory-externalization-markdown/</guid>
      <pubDate>Wed, 15 Jul 2026 12:26:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-memory-externalization-markdown.m4a"
        type="audio/mp4"
        length="26698930"
      />
      <itunes:title>How to Build Memory That Survives AI Sessions</itunes:title>
      <itunes:subtitle>Why AI agents forget between sessions — and how to fix it with markdown files instead of black-box memory.</itunes:subtitle>
      <itunes:summary><![CDATA[When you're deep in multi-session technical investigation, AI agents lose context every time you close the chat. This episode unpacks why default memory systems fail and how to build external memory that survives session boundaries using markdown files in a private repo. We explore system prompt patterns that actually work, the difference between reactive and proactive documentation, and whether tools like Obsidian add real value over raw markdown. If you've ever lost hours of agent context between sessions, this is your fix.]]></itunes:summary>
      <itunes:duration>1511</itunes:duration>
      <itunes:episode>4316</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-memory-externalization-markdown.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-memory-externalization-markdown.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Layer Shaping Every AI Chat</title>
      <description><![CDATA[Million-token context windows sound impressive, but the real magic happens in the invisible layer between you and the model API. This episode unpacks the AI Agent Harness — the middleware that handles context compression, sliding windows, summarization pipelines, and memory management. We explore how tools like Open WebUI and LangChain automate these decisions, why the distinction between model features and middleware matters, and how users develop superstitions about model behavior based on harness choices they never see. If you've ever wondered why an AI seems to "remember" or "forget" things, the answer is probably in the harness.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-harness-middleware/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-harness-middleware/</guid>
      <pubDate>Wed, 15 Jul 2026 10:49:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-harness-middleware.m4a"
        type="audio/mp4"
        length="27245433"
      />
      <itunes:title>The Hidden Layer Shaping Every AI Chat</itunes:title>
      <itunes:subtitle>Context windows are huge now, but relevance is the real bottleneck. How middleware decides what your AI actually sees.</itunes:subtitle>
      <itunes:summary><![CDATA[Million-token context windows sound impressive, but the real magic happens in the invisible layer between you and the model API. This episode unpacks the AI Agent Harness — the middleware that handles context compression, sliding windows, summarization pipelines, and memory management. We explore how tools like Open WebUI and LangChain automate these decisions, why the distinction between model features and middleware matters, and how users develop superstitions about model behavior based on harness choices they never see. If you've ever wondered why an AI seems to "remember" or "forget" things, the answer is probably in the harness.]]></itunes:summary>
      <itunes:duration>1558</itunes:duration>
      <itunes:episode>4314</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-harness-middleware.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-harness-middleware.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Did You Really Build That App? AI Imposter Syndrome</title>
      <description><![CDATA[You fire up Claude or Codex, build a working app in a weekend, and people might even pay for it. Then a voice creeps in: "But you didn't really write the code. Who are you to charge money for this?" This episode tackles both sides of AI-assisted building — the objective "how do I know this thing is actually production-ready" question, and the psychological hurdle of feeling like a fraud when the heavy lifting was done by a language model. Corn and Herman walk through a concrete pre-rollout QA checklist for AI-built apps, discuss why effort heuristic makes us devalue easy wins, and explore the difference between typing semicolons and delivering value.]]></description>
      <link>https://myweirdprompts.com/episode/ai-imposter-syndrome-building/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-imposter-syndrome-building/</guid>
      <pubDate>Wed, 15 Jul 2026 10:48:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-imposter-syndrome-building.m4a"
        type="audio/mp4"
        length="24520161"
      />
      <itunes:title>Did You Really Build That App? AI Imposter Syndrome</itunes:title>
      <itunes:subtitle>You built a working app with AI in a weekend. So why does it feel like you don&apos;t deserve to charge for it?</itunes:subtitle>
      <itunes:summary><![CDATA[You fire up Claude or Codex, build a working app in a weekend, and people might even pay for it. Then a voice creeps in: "But you didn't really write the code. Who are you to charge money for this?" This episode tackles both sides of AI-assisted building — the objective "how do I know this thing is actually production-ready" question, and the psychological hurdle of feeling like a fraud when the heavy lifting was done by a language model. Corn and Herman walk through a concrete pre-rollout QA checklist for AI-built apps, discuss why effort heuristic makes us devalue easy wins, and explore the difference between typing semicolons and delivering value.]]></itunes:summary>
      <itunes:duration>1405</itunes:duration>
      <itunes:episode>4313</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-imposter-syndrome-building.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-imposter-syndrome-building.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Auto-Splitting AI Threads: Smarter Context Management</title>
      <description><![CDATA[Daniel's habit of manually starting new ChatGPT threads is actually smart — but it doesn't have to be manual. This episode walks through exactly how to build a lightweight classifier that detects topic shifts and spawns fresh threads automatically, plus the more ambitious version that routes messages to whichever past thread they belong to. We cover the engineering tradeoffs, cost analysis (spoiler: pennies per day), and how to implement it with LangChain in about fifty lines of Python. If you've ever wondered why large context windows don't solve the real problem, or how to build a single continuous conversation that keeps context clean behind the scenes, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/auto-splitting-ai-threads/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/auto-splitting-ai-threads/</guid>
      <pubDate>Mon, 13 Jul 2026 09:26:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/auto-splitting-ai-threads.m4a"
        type="audio/mp4"
        length="25996596"
      />
      <itunes:title>Auto-Splitting AI Threads: Smarter Context Management</itunes:title>
      <itunes:subtitle>How to build a system that automatically detects topic shifts and routes messages to the right thread.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's habit of manually starting new ChatGPT threads is actually smart — but it doesn't have to be manual. This episode walks through exactly how to build a lightweight classifier that detects topic shifts and spawns fresh threads automatically, plus the more ambitious version that routes messages to whichever past thread they belong to. We cover the engineering tradeoffs, cost analysis (spoiler: pennies per day), and how to implement it with LangChain in about fifty lines of Python. If you've ever wondered why large context windows don't solve the real problem, or how to build a single continuous conversation that keeps context clean behind the scenes, this one's for you.]]></itunes:summary>
      <itunes:duration>1490</itunes:duration>
      <itunes:episode>4296</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/auto-splitting-ai-threads.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/auto-splitting-ai-threads.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Spreadsheets vs. SQL: Is the Grid Dying?</title>
      <description><![CDATA[Daniel posed a sharp question: if AI agents like Airtable's SuperAgent can now generate SQL from plain English inside a grid interface, what's left for the traditional spreadsheet? This episode unpacks whether the spreadsheet's last advantage — ease of use — is evaporating. We explore the structural costs of schema-on-read flexibility, the brittleness of VLOOKUP vs. SQL JOINs, and whether centralizing around databases would actually streamline data management. Plus, why the grid interface itself might survive as a view layer, even as the spreadsheet engine fades.]]></description>
      <link>https://myweirdprompts.com/episode/spreadsheets-vs-sql-database/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/spreadsheets-vs-sql-database/</guid>
      <pubDate>Mon, 13 Jul 2026 07:38:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/spreadsheets-vs-sql-database.m4a"
        type="audio/mp4"
        length="26356460"
      />
      <itunes:title>Spreadsheets vs. SQL: Is the Grid Dying?</itunes:title>
      <itunes:subtitle>Airtable&apos;s AI agent just collapsed the usability gap. Does the spreadsheet still have a purpose?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel posed a sharp question: if AI agents like Airtable's SuperAgent can now generate SQL from plain English inside a grid interface, what's left for the traditional spreadsheet? This episode unpacks whether the spreadsheet's last advantage — ease of use — is evaporating. We explore the structural costs of schema-on-read flexibility, the brittleness of VLOOKUP vs. SQL JOINs, and whether centralizing around databases would actually streamline data management. Plus, why the grid interface itself might survive as a view layer, even as the spreadsheet engine fades.]]></itunes:summary>
      <itunes:duration>1519</itunes:duration>
      <itunes:episode>4289</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/spreadsheets-vs-sql-database.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/spreadsheets-vs-sql-database.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fixing Loop-Damaged Podcasts with AI and FFmpeg</title>
      <description><![CDATA[A pipeline bug caused podcast episodes to loop back to the start mid-sentence, creating thirty-minute files that were two conversations stitched together. This episode explores a two-part rescue: whether an AI agent can reliably detect repetition in transcripts and generate correct FFmpeg splice commands, and whether overwriting audio files in Cloudflare R2 can update episodes across podcast apps without breaking RSS syndication. We walk through protocol mechanics, caching behaviors of Apple Podcasts vs Spotify vs Overcast, and why fully autonomous audio surgery is riskier than it sounds.]]></description>
      <link>https://myweirdprompts.com/episode/podcast-loop-repair-ffmpeg/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcast-loop-repair-ffmpeg/</guid>
      <pubDate>Sun, 12 Jul 2026 12:07:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcast-loop-repair-ffmpeg.m4a"
        type="audio/mp4"
        length="24086295"
      />
      <itunes:title>Fixing Loop-Damaged Podcasts with AI and FFmpeg</itunes:title>
      <itunes:subtitle>Can an AI agent detect repeated audio, generate FFmpeg commands, and overwrite files in cloud storage without breaking RSS?</itunes:subtitle>
      <itunes:summary><![CDATA[A pipeline bug caused podcast episodes to loop back to the start mid-sentence, creating thirty-minute files that were two conversations stitched together. This episode explores a two-part rescue: whether an AI agent can reliably detect repetition in transcripts and generate correct FFmpeg splice commands, and whether overwriting audio files in Cloudflare R2 can update episodes across podcast apps without breaking RSS syndication. We walk through protocol mechanics, caching behaviors of Apple Podcasts vs Spotify vs Overcast, and why fully autonomous audio surgery is riskier than it sounds.]]></itunes:summary>
      <itunes:duration>1379</itunes:duration>
      <itunes:episode>4274</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcast-loop-repair-ffmpeg.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcast-loop-repair-ffmpeg.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Modbus to Smart Home: The Real Bridge Guide</title>
      <description><![CDATA[Heat pumps, solar inverters, and battery systems all speak Modbus internally, but your smart home speaks TCP/IP. That gap looks small — it is not small. This episode tests three Modbus-to-Ethernet gateways at different price points, from a $25 serial-config module to a $285 industrial beast, and reveals the single best option for home integration. But hardware is only the first hurdle. We break down the two hidden traps that stall most projects: non-standard Modbus function codes and the register map puzzle. Plus, why ZigBee and Matter are dead ends for direct Modbus integration — and the workaround that actually functions.]]></description>
      <link>https://myweirdprompts.com/episode/modbus-smart-home-gateway-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/modbus-smart-home-gateway-guide/</guid>
      <pubDate>Fri, 10 Jul 2026 13:53:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/modbus-smart-home-gateway-guide.m4a"
        type="audio/mp4"
        length="27242675"
      />
      <itunes:title>Modbus to Smart Home: The Real Bridge Guide</itunes:title>
      <itunes:subtitle>Three gateways tested, three hurdles revealed. What actually works when bridging OT gear to Home Assistant.</itunes:subtitle>
      <itunes:summary><![CDATA[Heat pumps, solar inverters, and battery systems all speak Modbus internally, but your smart home speaks TCP/IP. That gap looks small — it is not small. This episode tests three Modbus-to-Ethernet gateways at different price points, from a $25 serial-config module to a $285 industrial beast, and reveals the single best option for home integration. But hardware is only the first hurdle. We break down the two hidden traps that stall most projects: non-standard Modbus function codes and the register map puzzle. Plus, why ZigBee and Matter are dead ends for direct Modbus integration — and the workaround that actually functions.]]></itunes:summary>
      <itunes:duration>1561</itunes:duration>
      <itunes:episode>4230</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/modbus-smart-home-gateway-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/modbus-smart-home-gateway-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>SSH Key Strategy: Compartmentalization vs. Chaos</title>
      <description><![CDATA[Should you generate a dedicated SSH key for every server you manage, or reuse one key everywhere? This episode breaks down the real security tradeoffs — blast radius vs. operational friction — and shows how ssh-agent, Host blocks, and password manager backends can make compartmentalization viable. We also tackle the new complication of AI agents that need SSH access: static keys in ephemeral environments, command restrictions, and when an SSH certificate authority actually makes sense. If you manage more than a handful of servers, this will change how you think about key hygiene.]]></description>
      <link>https://myweirdprompts.com/episode/ssh-key-strategy-compartmentalization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ssh-key-strategy-compartmentalization/</guid>
      <pubDate>Fri, 10 Jul 2026 12:31:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ssh-key-strategy-compartmentalization.m4a"
        type="audio/mp4"
        length="27700989"
      />
      <itunes:title>SSH Key Strategy: Compartmentalization vs. Chaos</itunes:title>
      <itunes:subtitle>Application-specific SSH keys vs. one key for everything — and what AI agents mean for both approaches.</itunes:subtitle>
      <itunes:summary><![CDATA[Should you generate a dedicated SSH key for every server you manage, or reuse one key everywhere? This episode breaks down the real security tradeoffs — blast radius vs. operational friction — and shows how ssh-agent, Host blocks, and password manager backends can make compartmentalization viable. We also tackle the new complication of AI agents that need SSH access: static keys in ephemeral environments, command restrictions, and when an SSH certificate authority actually makes sense. If you manage more than a handful of servers, this will change how you think about key hygiene.]]></itunes:summary>
      <itunes:duration>1564</itunes:duration>
      <itunes:episode>4229</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ssh-key-strategy-compartmentalization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ssh-key-strategy-compartmentalization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Local Network Discovery Breaks with Custom DNS</title>
      <description><![CDATA[Ever set up a custom DNS resolver like AdGuard Home, only to find your printer or Raspberry Pi mysteriously vanishes from the network? This episode dives into the elegant world of multicast DNS and DNS Service Discovery — the protocols that make zero-configuration networking feel like magic — and explains exactly why they collide with split DNS setups. We trace the failure from query to NXDOMAIN, walk through how macOS, Windows, and Linux each handle .local domains differently, and reveal why the fix isn't what most people expect.]]></description>
      <link>https://myweirdprompts.com/episode/local-network-discovery-mdns-dns/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/local-network-discovery-mdns-dns/</guid>
      <pubDate>Fri, 10 Jul 2026 11:06:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/local-network-discovery-mdns-dns.m4a"
        type="audio/mp4"
        length="31688922"
      />
      <itunes:title>How Local Network Discovery Breaks with Custom DNS</itunes:title>
      <itunes:subtitle>Why your printer disappears when you run AdGuard Home — and how mDNS actually works.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever set up a custom DNS resolver like AdGuard Home, only to find your printer or Raspberry Pi mysteriously vanishes from the network? This episode dives into the elegant world of multicast DNS and DNS Service Discovery — the protocols that make zero-configuration networking feel like magic — and explains exactly why they collide with split DNS setups. We trace the failure from query to NXDOMAIN, walk through how macOS, Windows, and Linux each handle .local domains differently, and reveal why the fix isn't what most people expect.]]></itunes:summary>
      <itunes:duration>1819</itunes:duration>
      <itunes:episode>4222</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/local-network-discovery-mdns-dns.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/local-network-discovery-mdns-dns.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Awkward Honesty of AI Attribution</title>
      <description><![CDATA[Why does adding "co-written with AI" to your work feel like confessing a sin? We explore the patchwork of emerging attribution norms across open-source, academia, and content platforms — from GitHub's "Co-authored-by" trailers to Nature's ban on AI authorship. We unpack the three roots of the AI stigma: the craft ideology that valorizes suffering, the effort heuristic that penalizes disclosed AI help, and the impostor amplifier that makes transparency feel like self-exposure. Plus: why a 2024 study found that context-rich disclosure mitigates the quality penalty, and what the gold standard for attribution actually looks like.]]></description>
      <link>https://myweirdprompts.com/episode/ai-attribution-protocols/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-attribution-protocols/</guid>
      <pubDate>Thu, 09 Jul 2026 19:48:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-attribution-protocols.m4a"
        type="audio/mp4"
        length="25047282"
      />
      <itunes:title>The Awkward Honesty of AI Attribution</itunes:title>
      <itunes:subtitle>Why admitting AI helped feels like a confession — and what emerging norms actually work.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does adding "co-written with AI" to your work feel like confessing a sin? We explore the patchwork of emerging attribution norms across open-source, academia, and content platforms — from GitHub's "Co-authored-by" trailers to Nature's ban on AI authorship. We unpack the three roots of the AI stigma: the craft ideology that valorizes suffering, the effort heuristic that penalizes disclosed AI help, and the impostor amplifier that makes transparency feel like self-exposure. Plus: why a 2024 study found that context-rich disclosure mitigates the quality penalty, and what the gold standard for attribution actually looks like.]]></itunes:summary>
      <itunes:duration>1445</itunes:duration>
      <itunes:episode>4208</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-attribution-protocols.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-attribution-protocols.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Custom GPTs: When to Build, When to Skip</title>
      <description><![CDATA[Three million custom GPTs exist, but most collect dust after week one. This episode builds a decision framework for when a custom GPT actually earns its keep. We break down the three levers — system prompts, knowledge base upload, and custom actions — and explain why the car manual is the platonic ideal while the restaurant recommender is the cautionary tale. If you've ever stared at the "Create a GPT" button wondering whether to click it, this framework will save you time.]]></description>
      <link>https://myweirdprompts.com/episode/custom-gpts-build-or-skip/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/custom-gpts-build-or-skip/</guid>
      <pubDate>Thu, 09 Jul 2026 11:27:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/custom-gpts-build-or-skip.m4a"
        type="audio/mp4"
        length="24923375"
      />
      <itunes:title>Custom GPTs: When to Build, When to Skip</itunes:title>
      <itunes:subtitle>A decision framework for knowing when a custom GPT is worth the overhead — and when to just open a fresh chat.</itunes:subtitle>
      <itunes:summary><![CDATA[Three million custom GPTs exist, but most collect dust after week one. This episode builds a decision framework for when a custom GPT actually earns its keep. We break down the three levers — system prompts, knowledge base upload, and custom actions — and explain why the car manual is the platonic ideal while the restaurant recommender is the cautionary tale. If you've ever stared at the "Create a GPT" button wondering whether to click it, this framework will save you time.]]></itunes:summary>
      <itunes:duration>1424</itunes:duration>
      <itunes:episode>4194</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/custom-gpts-build-or-skip.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/custom-gpts-build-or-skip.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Build a Kid-Safe Electronics Workshop</title>
      <description><![CDATA[A parent named Daniel asks how to pass his love of electronics to his one-year-old son Ezra without exposing him to electrical hazards. This episode explores the practical side of building a safe, low-voltage electronics workspace at home — from the physics of why 3.3V DC is non-lethal to the real risks of small parts and curious mouths. We cover the tools that make modern hobby electronics safer than ever, the safety rituals every young tinkerer should learn, and how to structure a child's progression from observer to builder. If you've ever wondered how to introduce a kid to blinking LEDs without introducing them to the emergency room, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/kid-safe-electronics-workshop/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/kid-safe-electronics-workshop/</guid>
      <pubDate>Wed, 08 Jul 2026 13:55:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/kid-safe-electronics-workshop.m4a"
        type="audio/mp4"
        length="21757269"
      />
      <itunes:title>How to Build a Kid-Safe Electronics Workshop</itunes:title>
      <itunes:subtitle>Teaching electronics to curious kids without the electrical risks — low-voltage DC, breadboards, and smart separation strategies.</itunes:subtitle>
      <itunes:summary><![CDATA[A parent named Daniel asks how to pass his love of electronics to his one-year-old son Ezra without exposing him to electrical hazards. This episode explores the practical side of building a safe, low-voltage electronics workspace at home — from the physics of why 3.3V DC is non-lethal to the real risks of small parts and curious mouths. We cover the tools that make modern hobby electronics safer than ever, the safety rituals every young tinkerer should learn, and how to structure a child's progression from observer to builder. If you've ever wondered how to introduce a kid to blinking LEDs without introducing them to the emergency room, this one's for you.]]></itunes:summary>
      <itunes:duration>1241</itunes:duration>
      <itunes:episode>4191</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/kid-safe-electronics-workshop.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/kid-safe-electronics-workshop.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Find the Quietest Time to Drive Using Google&apos;s API</title>
      <description><![CDATA[Daniel noticed his nocturnal delivery runs through Jerusalem felt quieter at certain hours — but he wanted confidence intervals, not vibes. This episode walks through how to programmatically query Google's Routes API, dump results into BigQuery, and use percentile analysis to find the exact quietest 45-minute window for any route on any day. We cover sampling strategy, API mechanics, the free tier math, and why Jerusalem's Shabbat-driven traffic patterns make this genuinely non-trivial. No gut feelings — just durationInTraffic data and statistical confidence.]]></description>
      <link>https://myweirdprompts.com/episode/google-routes-api-quiet-window/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/google-routes-api-quiet-window/</guid>
      <pubDate>Wed, 08 Jul 2026 13:02:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/google-routes-api-quiet-window.m4a"
        type="audio/mp4"
        length="25203163"
      />
      <itunes:title>Find the Quietest Time to Drive Using Google&apos;s API</itunes:title>
      <itunes:subtitle>Query Google&apos;s Routes API to find statistically optimal driving times in Jerusalem — no gut feelings required.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel noticed his nocturnal delivery runs through Jerusalem felt quieter at certain hours — but he wanted confidence intervals, not vibes. This episode walks through how to programmatically query Google's Routes API, dump results into BigQuery, and use percentile analysis to find the exact quietest 45-minute window for any route on any day. We cover sampling strategy, API mechanics, the free tier math, and why Jerusalem's Shabbat-driven traffic patterns make this genuinely non-trivial. No gut feelings — just durationInTraffic data and statistical confidence.]]></itunes:summary>
      <itunes:duration>1453</itunes:duration>
      <itunes:episode>4188</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/google-routes-api-quiet-window.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/google-routes-api-quiet-window.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Phone Typing to Direct Agent: Fixing Headless Server AI Access</title>
      <description><![CDATA[When Daniel's home server suffered a kernel panic, ChatGPT diagnosed the problem perfectly — missing kernel modules, GRUB errors, the whole works. The repair worked, but it took hours because every single command had to be read off a phone screen and typed into a headless machine in a closet. This episode maps the three approaches to solving that bottleneck: cloud CLI tools with API key auth (like Shell-GPT), local models via Ollama for offline work, and a hybrid strategy that uses a tiny local model just to get the network back up before handing off to a cloud AI for the heavy lifting. Plus, the infrastructure homework you should do before your server ever crashes.]]></description>
      <link>https://myweirdprompts.com/episode/headless-server-ai-authentication/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/headless-server-ai-authentication/</guid>
      <pubDate>Tue, 07 Jul 2026 16:24:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/headless-server-ai-authentication.m4a"
        type="audio/mp4"
        length="32737356"
      />
      <itunes:title>From Phone Typing to Direct Agent: Fixing Headless Server AI Access</itunes:title>
      <itunes:subtitle>ChatGPT saved his OS, but the copy-paste bottleneck from phone to server took hours. Here&apos;s how to fix it.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel's home server suffered a kernel panic, ChatGPT diagnosed the problem perfectly — missing kernel modules, GRUB errors, the whole works. The repair worked, but it took hours because every single command had to be read off a phone screen and typed into a headless machine in a closet. This episode maps the three approaches to solving that bottleneck: cloud CLI tools with API key auth (like Shell-GPT), local models via Ollama for offline work, and a hybrid strategy that uses a tiny local model just to get the network back up before handing off to a cloud AI for the heavy lifting. Plus, the infrastructure homework you should do before your server ever crashes.]]></itunes:summary>
      <itunes:duration>1876</itunes:duration>
      <itunes:episode>4181</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/headless-server-ai-authentication.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/headless-server-ai-authentication.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Phone Number Link Is Costing You Customers</title>
      <description><![CDATA[Millions of small business websites display phone numbers as plain text, forcing visitors to manually copy and dial. Each extra step loses 20-30% of potential callers. The fix is a tiny HTML attribute combining the tel URI scheme with the E.164 international numbering standard. In this episode, we break down why the leading zero gets dropped, how to format numbers for global compatibility, and why a single canonical phone number unlocks SMS, WhatsApp, and VoIP channels. No more silent failure — just one tap to connect.]]></description>
      <link>https://myweirdprompts.com/episode/phone-number-tel-uri-e164/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/phone-number-tel-uri-e164/</guid>
      <pubDate>Tue, 07 Jul 2026 13:34:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/phone-number-tel-uri-e164.m4a"
        type="audio/mp4"
        length="25786060"
      />
      <itunes:title>Why Your Phone Number Link Is Costing You Customers</itunes:title>
      <itunes:subtitle>That non-clickable phone number on your site is quietly bleeding leads. Here’s the five-minute fix.</itunes:subtitle>
      <itunes:summary><![CDATA[Millions of small business websites display phone numbers as plain text, forcing visitors to manually copy and dial. Each extra step loses 20-30% of potential callers. The fix is a tiny HTML attribute combining the tel URI scheme with the E.164 international numbering standard. In this episode, we break down why the leading zero gets dropped, how to format numbers for global compatibility, and why a single canonical phone number unlocks SMS, WhatsApp, and VoIP channels. No more silent failure — just one tap to connect.]]></itunes:summary>
      <itunes:duration>1473</itunes:duration>
      <itunes:episode>4179</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/phone-number-tel-uri-e164.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/phone-number-tel-uri-e164.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>DIY ISP Quality Dashboard Tools</title>
      <description><![CDATA[Tired of ISPs saying "looks fine on our end"? This episode covers four open-source tools you can run on your own hardware to track actual latency, jitter, and throughput over weeks and months. From the powerful TIG stack (Telegraf, InfluxDB, Grafana) to the OPNsense-native SmokePing plugin, we walk through what to install, how to configure it, and how to use long-term data to hold your provider accountable.]]></description>
      <link>https://myweirdprompts.com/episode/isp-quality-monitoring-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/isp-quality-monitoring-tools/</guid>
      <pubDate>Tue, 07 Jul 2026 13:25:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/isp-quality-monitoring-tools.m4a"
        type="audio/mp4"
        length="44705916"
      />
      <itunes:title>DIY ISP Quality Dashboard Tools</itunes:title>
      <itunes:subtitle>Measure your real internet performance with self-hosted tools. No ISP speed tests, no guesswork.</itunes:subtitle>
      <itunes:summary><![CDATA[Tired of ISPs saying "looks fine on our end"? This episode covers four open-source tools you can run on your own hardware to track actual latency, jitter, and throughput over weeks and months. From the powerful TIG stack (Telegraf, InfluxDB, Grafana) to the OPNsense-native SmokePing plugin, we walk through what to install, how to configure it, and how to use long-term data to hold your provider accountable.]]></itunes:summary>
      <itunes:duration>2600</itunes:duration>
      <itunes:episode>4178</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/isp-quality-monitoring-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/isp-quality-monitoring-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Pallet Puzzle: Picking the Right Freight Tool</title>
      <description><![CDATA[Freight volume consolidation sounds simple — just divide cubic volume by pallet size, right? Wrong. The moment you introduce multiple SKUs with different dimensions, you're solving an NP-hard three-dimensional bin-packing problem. And the tool you pick makes an optimization decision on your behalf, often without telling you. We break down three very different approaches: PalletStacking's greedy algorithm, LoadMaster's genetic algorithm, and FreightOptix's heuristic shortcuts — and explain why they give different answers for the same input. Plus, the hidden costs of ignoring weight limits, overhang rules, and stackability constraints.]]></description>
      <link>https://myweirdprompts.com/episode/pallet-optimization-tools-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pallet-optimization-tools-guide/</guid>
      <pubDate>Tue, 07 Jul 2026 12:58:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pallet-optimization-tools-guide.m4a"
        type="audio/mp4"
        length="25686264"
      />
      <itunes:title>Pallet Puzzle: Picking the Right Freight Tool</itunes:title>
      <itunes:subtitle>Three tools, three algorithms, three different answers for the same boxes. Here&apos;s how to choose.</itunes:subtitle>
      <itunes:summary><![CDATA[Freight volume consolidation sounds simple — just divide cubic volume by pallet size, right? Wrong. The moment you introduce multiple SKUs with different dimensions, you're solving an NP-hard three-dimensional bin-packing problem. And the tool you pick makes an optimization decision on your behalf, often without telling you. We break down three very different approaches: PalletStacking's greedy algorithm, LoadMaster's genetic algorithm, and FreightOptix's heuristic shortcuts — and explain why they give different answers for the same input. Plus, the hidden costs of ignoring weight limits, overhang rules, and stackability constraints.]]></itunes:summary>
      <itunes:duration>1475</itunes:duration>
      <itunes:episode>4176</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pallet-optimization-tools-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pallet-optimization-tools-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Obfuscation as Risk Management with AI</title>
      <description><![CDATA[When telling a difficult story could destroy your life, how do you share it safely? This episode explores Daniel's proposal for an AI-powered obfuscation agent that goes beyond simple find-and-replace. We break down the quasi-identifier problem, the difference between redaction and true obfuscation, and how attention mechanisms enable context-aware entity recognition. From therapeutic disclosures to whistleblowing, we examine the engineering challenge of building a system that weighs narrative utility against identifiability — and why manual processes can't scale.]]></description>
      <link>https://myweirdprompts.com/episode/ai-obfuscation-risk-management/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-obfuscation-risk-management/</guid>
      <pubDate>Tue, 07 Jul 2026 11:24:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-obfuscation-risk-management.m4a"
        type="audio/mp4"
        length="30592992"
      />
      <itunes:title>Obfuscation as Risk Management with AI</itunes:title>
      <itunes:subtitle>How AI can protect whistleblowers and trauma survivors by intelligently obscuring identities while preserving story integrity.</itunes:subtitle>
      <itunes:summary><![CDATA[When telling a difficult story could destroy your life, how do you share it safely? This episode explores Daniel's proposal for an AI-powered obfuscation agent that goes beyond simple find-and-replace. We break down the quasi-identifier problem, the difference between redaction and true obfuscation, and how attention mechanisms enable context-aware entity recognition. From therapeutic disclosures to whistleblowing, we examine the engineering challenge of building a system that weighs narrative utility against identifiability — and why manual processes can't scale.]]></itunes:summary>
      <itunes:duration>1760</itunes:duration>
      <itunes:episode>4170</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-obfuscation-risk-management.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-obfuscation-risk-management.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Change Detection AI for Construction Sites</title>
      <description><![CDATA[Construction sites change daily — different trucks, new equipment, shifting shadows. A layperson sees noise, an expert sees process, but what can AI see that both miss? This episode explores the rapidly maturing field of visual change detection, from Siamese networks to transformer-based architectures like ChangeFormer and DINOv2. We break down how foundation models have made ground-level change detection practical for hyperlocal use cases, the tradeoffs between sensitivity and specificity, and the commercial and open-source tools available today. If you've ever wondered how to build a system that tells the difference between "same truck, different Tuesday" and "different truck entirely," this episode is for you.]]></description>
      <link>https://myweirdprompts.com/episode/visual-change-detection-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/visual-change-detection-ai/</guid>
      <pubDate>Tue, 07 Jul 2026 11:10:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/visual-change-detection-ai.m4a"
        type="audio/mp4"
        length="38331410"
      />
      <itunes:title>Change Detection AI for Construction Sites</itunes:title>
      <itunes:subtitle>How AI catches what experts miss in construction site photos — from Siamese networks to transformer models.</itunes:subtitle>
      <itunes:summary><![CDATA[Construction sites change daily — different trucks, new equipment, shifting shadows. A layperson sees noise, an expert sees process, but what can AI see that both miss? This episode explores the rapidly maturing field of visual change detection, from Siamese networks to transformer-based architectures like ChangeFormer and DINOv2. We break down how foundation models have made ground-level change detection practical for hyperlocal use cases, the tradeoffs between sensitivity and specificity, and the commercial and open-source tools available today. If you've ever wondered how to build a system that tells the difference between "same truck, different Tuesday" and "different truck entirely," this episode is for you.]]></itunes:summary>
      <itunes:duration>2192</itunes:duration>
      <itunes:episode>4169</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/visual-change-detection-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/visual-change-detection-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Can’t Read Your Gmail (Yet)</title>
      <description><![CDATA[You ask your AI assistant to find a quote in your inbox. It says no results. But the email is right there, three threads deep. This isn’t an AI reasoning failure—it’s a crisis of integration plumbing. In this episode, we unpack why ChatGPT and Gemini’s Gmail connectors are brittle and buggy, even though the Model Context Protocol (MCP) has existed for over a year. We trace three layers of failure: rate limits that force shallow searches, thread reconstruction errors from flat message data, and safety layers that add friction without fixing root causes. The gap between a developer’s prototype and a production system serving millions is enormous—and it’s training users not to trust AI with anything that matters.]]></description>
      <link>https://myweirdprompts.com/episode/ai-gmail-integration-failures/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-gmail-integration-failures/</guid>
      <pubDate>Tue, 07 Jul 2026 10:28:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-gmail-integration-failures.m4a"
        type="audio/mp4"
        length="23007240"
      />
      <itunes:title>Why AI Can’t Read Your Gmail (Yet)</itunes:title>
      <itunes:subtitle>Your AI misses emails and fails to send replies. It’s not dumb—the pipes are leaky.</itunes:subtitle>
      <itunes:summary><![CDATA[You ask your AI assistant to find a quote in your inbox. It says no results. But the email is right there, three threads deep. This isn’t an AI reasoning failure—it’s a crisis of integration plumbing. In this episode, we unpack why ChatGPT and Gemini’s Gmail connectors are brittle and buggy, even though the Model Context Protocol (MCP) has existed for over a year. We trace three layers of failure: rate limits that force shallow searches, thread reconstruction errors from flat message data, and safety layers that add friction without fixing root causes. The gap between a developer’s prototype and a production system serving millions is enormous—and it’s training users not to trust AI with anything that matters.]]></itunes:summary>
      <itunes:duration>1329</itunes:duration>
      <itunes:episode>4167</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-gmail-integration-failures.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-gmail-integration-failures.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Prompt Engineering Is a Misnomer — Managing Ambiguity in AI</title>
      <description><![CDATA[For years we've called it "prompt engineering" — but what if that framing is fundamentally wrong? This episode explores a provocative idea: that the real skill in working with language models isn't precision, but the art of managing ambiguity. Drawing on hundreds of episodes of real-world prompting, we examine why over-specifying your prompts actually caps output quality, how voice dictation preserves crucial signals that typed text strips away, and why the most impressive AI results come when you deliberately leave doors open. We break down the spectrum between tight specificity and productive vagueness, and show how moving fluidly between them — rather than picking one mode — is where the real power lives. Whether you're debugging a network or brainstorming creative work, the calibration between constraint and creativity is the skill that transfers.]]></description>
      <link>https://myweirdprompts.com/episode/managing-ambiguity-ai-prompts/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/managing-ambiguity-ai-prompts/</guid>
      <pubDate>Mon, 06 Jul 2026 21:15:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/managing-ambiguity-ai-prompts.m4a"
        type="audio/mp4"
        length="27162354"
      />
      <itunes:title>Prompt Engineering Is a Misnomer — Managing Ambiguity in AI</itunes:title>
      <itunes:subtitle>Why the best AI outputs come not from tighter prompts, but from knowing where to leave room for surprise.</itunes:subtitle>
      <itunes:summary><![CDATA[For years we've called it "prompt engineering" — but what if that framing is fundamentally wrong? This episode explores a provocative idea: that the real skill in working with language models isn't precision, but the art of managing ambiguity. Drawing on hundreds of episodes of real-world prompting, we examine why over-specifying your prompts actually caps output quality, how voice dictation preserves crucial signals that typed text strips away, and why the most impressive AI results come when you deliberately leave doors open. We break down the spectrum between tight specificity and productive vagueness, and show how moving fluidly between them — rather than picking one mode — is where the real power lives. Whether you're debugging a network or brainstorming creative work, the calibration between constraint and creativity is the skill that transfers.]]></itunes:summary>
      <itunes:duration>1557</itunes:duration>
      <itunes:episode>4157</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/managing-ambiguity-ai-prompts.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/managing-ambiguity-ai-prompts.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>KVM Over IP: Remote Server Access When SSH Dies</title>
      <description><![CDATA[When your server's network stack collapses, SSH and RDP are useless. KVM over IP devices capture video output and inject keystrokes at the hardware level — they work when the OS is a smoking crater. This episode maps the market from PiKVM to enterprise Raritan units, explains USB Ethernet gadget mode that bypasses dead NICs, and tackles the architectural fork: do you isolate the KVM behind a bastion host or let it run Tailscale directly? A deep dive into out-of-band management for homelabbers and sysadmins alike.]]></description>
      <link>https://myweirdprompts.com/episode/kvm-over-ip-remote-access/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/kvm-over-ip-remote-access/</guid>
      <pubDate>Mon, 06 Jul 2026 17:39:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/kvm-over-ip-remote-access.m4a"
        type="audio/mp4"
        length="29351286"
      />
      <itunes:title>KVM Over IP: Remote Server Access When SSH Dies</itunes:title>
      <itunes:subtitle>What do you do when your server is alive but unreachable? KVM over IP hardware gives you BIOS-level access from anywhere.</itunes:subtitle>
      <itunes:summary><![CDATA[When your server's network stack collapses, SSH and RDP are useless. KVM over IP devices capture video output and inject keystrokes at the hardware level — they work when the OS is a smoking crater. This episode maps the market from PiKVM to enterprise Raritan units, explains USB Ethernet gadget mode that bypasses dead NICs, and tackles the architectural fork: do you isolate the KVM behind a bastion host or let it run Tailscale directly? A deep dive into out-of-band management for homelabbers and sysadmins alike.]]></itunes:summary>
      <itunes:duration>1689</itunes:duration>
      <itunes:episode>4151</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/kvm-over-ip-remote-access.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/kvm-over-ip-remote-access.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hosepipe-to-Digest AI: Your Outbox Middleware</title>
      <description><![CDATA[What if an AI sat between your outbox and your recipient's inbox? This episode explores a novel open-source tool concept: AI middleware that intercepts your firehose of emails, holds them in a buffer, and delivers one structured executive summary with BLUF subject lines on a schedule. We break down the five-stage pipeline—intercept, classify urgency, buffer, summarize, deliver—and discuss the clever design decisions like two-tier urgency override and contradiction detection that make this more than just another email tool.]]></description>
      <link>https://myweirdprompts.com/episode/ai-email-middleware-outbox/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-email-middleware-outbox/</guid>
      <pubDate>Sun, 05 Jul 2026 21:58:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-email-middleware-outbox.m4a"
        type="audio/mp4"
        length="26957936"
      />
      <itunes:title>The Hosepipe-to-Digest AI: Your Outbox Middleware</itunes:title>
      <itunes:subtitle>An AI middleware that intercepts your outgoing emails, buffers them, and delivers a crisp executive summary.</itunes:subtitle>
      <itunes:summary><![CDATA[What if an AI sat between your outbox and your recipient's inbox? This episode explores a novel open-source tool concept: AI middleware that intercepts your firehose of emails, holds them in a buffer, and delivers one structured executive summary with BLUF subject lines on a schedule. We break down the five-stage pipeline—intercept, classify urgency, buffer, summarize, deliver—and discuss the clever design decisions like two-tier urgency override and contradiction detection that make this more than just another email tool.]]></itunes:summary>
      <itunes:duration>1566</itunes:duration>
      <itunes:episode>4139</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-email-middleware-outbox.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-email-middleware-outbox.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Safe AC-to-DC Enclosure for MQTT Traffic Lights</title>
      <description><![CDATA[Ready to move beyond USB bricks and build real mains-powered hardware? This episode walks through Daniel's project: a twelve-volt DC traffic light controlled by a Raspberry Pi running Eclipse Paho MQTT. We cover enclosure selection, the Mean Well LRS-35-12 constant-voltage supply, proper fusing with an IEC C14 inlet, relay isolation between Pi and LEDs, and multimeter verification per IEC 62368-1. This is the perfect entry point for learning AC-to-DC conversion safety while keeping the low-voltage side in the safe ES1 zone.]]></description>
      <link>https://myweirdprompts.com/episode/ac-dc-enclosure-mqtt-traffic-light/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ac-dc-enclosure-mqtt-traffic-light/</guid>
      <pubDate>Sun, 05 Jul 2026 12:59:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ac-dc-enclosure-mqtt-traffic-light.m4a"
        type="audio/mp4"
        length="35973366"
      />
      <itunes:title>Building a Safe AC-to-DC Enclosure for MQTT Traffic Lights</itunes:title>
      <itunes:subtitle>Learn to safely build a constant-voltage enclosure for a Raspberry Pi-powered traffic light with MQTT control.</itunes:subtitle>
      <itunes:summary><![CDATA[Ready to move beyond USB bricks and build real mains-powered hardware? This episode walks through Daniel's project: a twelve-volt DC traffic light controlled by a Raspberry Pi running Eclipse Paho MQTT. We cover enclosure selection, the Mean Well LRS-35-12 constant-voltage supply, proper fusing with an IEC C14 inlet, relay isolation between Pi and LEDs, and multimeter verification per IEC 62368-1. This is the perfect entry point for learning AC-to-DC conversion safety while keeping the low-voltage side in the safe ES1 zone.]]></itunes:summary>
      <itunes:duration>2065</itunes:duration>
      <itunes:episode>4124</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ac-dc-enclosure-mqtt-traffic-light.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ac-dc-enclosure-mqtt-traffic-light.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Embedding Models vs LLMs: What Actually Connects?</title>
      <description><![CDATA[Daniel asks two sharp questions about RAG pipelines: does your embedding model constrain which LLM you can use, and why are new embedding models still releasing if embeddings feel like a solved problem? We break down the architectural decoupling between embedding models and LLMs — they're different neural networks trained for different objectives, and any embedding works with any LLM. But that clean answer makes the second question more urgent: the real innovation in embedding models isn't about general benchmarks — it's about fixing specific failure modes like domain specialization, multilingual alignment, and silent drift that only show up at scale. We also unpack the "silent drift" problem where an auto-embedding model upgrade can quietly break retrieval without anyone noticing until support tickets spike.]]></description>
      <link>https://myweirdprompts.com/episode/embedding-models-llm-compatibility/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/embedding-models-llm-compatibility/</guid>
      <pubDate>Sat, 04 Jul 2026 13:57:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/embedding-models-llm-compatibility.m4a"
        type="audio/mp4"
        length="28214626"
      />
      <itunes:title>Embedding Models vs LLMs: What Actually Connects?</itunes:title>
      <itunes:subtitle>Can you mix any embedding model with any LLM? And why are new embedding models still dropping if they&apos;re &quot;solved&quot;?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asks two sharp questions about RAG pipelines: does your embedding model constrain which LLM you can use, and why are new embedding models still releasing if embeddings feel like a solved problem? We break down the architectural decoupling between embedding models and LLMs — they're different neural networks trained for different objectives, and any embedding works with any LLM. But that clean answer makes the second question more urgent: the real innovation in embedding models isn't about general benchmarks — it's about fixing specific failure modes like domain specialization, multilingual alignment, and silent drift that only show up at scale. We also unpack the "silent drift" problem where an auto-embedding model upgrade can quietly break retrieval without anyone noticing until support tickets spike.]]></itunes:summary>
      <itunes:duration>1622</itunes:duration>
      <itunes:episode>4106</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/embedding-models-llm-compatibility.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/embedding-models-llm-compatibility.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why 20% of AI Scripts Loop on Themselves</title>
      <description><![CDATA[About twenty percent of AI-generated podcast episodes suffer from a strange repetition loop — the scriptwriter generates a section, then generates it again, producing duplicated content. In this episode, we diagnose the three interacting causes: attention collapse as context windows fill, low-temperature sampling that locks onto patterns, and word-count targets that incentivize padding. We also explore why the obvious fix — a review agent — introduced its own failure mode by leaking commentary into scripts. If you're building production pipelines on LLMs, this is a masterclass in debugging emergent failures.]]></description>
      <link>https://myweirdprompts.com/episode/ai-script-repetition-loops/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-script-repetition-loops/</guid>
      <pubDate>Sat, 04 Jul 2026 13:44:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-script-repetition-loops.m4a"
        type="audio/mp4"
        length="25757820"
      />
      <itunes:title>Why 20% of AI Scripts Loop on Themselves</itunes:title>
      <itunes:subtitle>Debugging the mysterious repetition bug affecting one in five podcast episodes.</itunes:subtitle>
      <itunes:summary><![CDATA[About twenty percent of AI-generated podcast episodes suffer from a strange repetition loop — the scriptwriter generates a section, then generates it again, producing duplicated content. In this episode, we diagnose the three interacting causes: attention collapse as context windows fill, low-temperature sampling that locks onto patterns, and word-count targets that incentivize padding. We also explore why the obvious fix — a review agent — introduced its own failure mode by leaking commentary into scripts. If you're building production pipelines on LLMs, this is a masterclass in debugging emergent failures.]]></itunes:summary>
      <itunes:duration>1483</itunes:duration>
      <itunes:episode>4104</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-script-repetition-loops.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-script-repetition-loops.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Mind or Two? AI Podcast Dialogue Showdown</title>
      <description><![CDATA[Is our podcast's banter better because one AI writes both sides — or despite it? In this episode, we dive into the engineering question of whether splitting the scriptwriting into two separate agents would produce more authentic, surprising conversation — or just more complexity. We explore the tension between coherence and authenticity, the challenge of enforcing word counts in emergent dialogue, and whether listeners can tell the difference between simulated and genuine conversational messiness. Along the way, we unpack what makes real dialogue tick: informational asymmetry, genuine disagreement, and the rough edges that one-mind scripts struggle to fake.]]></description>
      <link>https://myweirdprompts.com/episode/ai-podcast-dialogue-single-vs-dual-agent/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-podcast-dialogue-single-vs-dual-agent/</guid>
      <pubDate>Fri, 03 Jul 2026 12:51:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-podcast-dialogue-single-vs-dual-agent.m4a"
        type="audio/mp4"
        length="26491027"
      />
      <itunes:title>One Mind or Two? AI Podcast Dialogue Showdown</itunes:title>
      <itunes:subtitle>Can two AI agents create better podcast banter than one? We explore the tradeoffs.</itunes:subtitle>
      <itunes:summary><![CDATA[Is our podcast's banter better because one AI writes both sides — or despite it? In this episode, we dive into the engineering question of whether splitting the scriptwriting into two separate agents would produce more authentic, surprising conversation — or just more complexity. We explore the tension between coherence and authenticity, the challenge of enforcing word counts in emergent dialogue, and whether listeners can tell the difference between simulated and genuine conversational messiness. Along the way, we unpack what makes real dialogue tick: informational asymmetry, genuine disagreement, and the rough edges that one-mind scripts struggle to fake.]]></itunes:summary>
      <itunes:duration>1517</itunes:duration>
      <itunes:episode>4094</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-podcast-dialogue-single-vs-dual-agent.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-podcast-dialogue-single-vs-dual-agent.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How AI Remembers What You Never Told It</title>
      <description><![CDATA[When Daniel asked ChatGPT for help removing wall anchors, the AI casually recalled he'd bought a rotary hammer days earlier — without being prompted to check his memory. This episode explores the engineering behind invisible AI memory: how retrieval-augmented generation (RAG) pulls relevant facts from vector databases without eating your token budget, how systems decide what to save, and why the seamlessness you feel hides staggering complexity.]]></description>
      <link>https://myweirdprompts.com/episode/invisible-ai-memory-retrieval/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/invisible-ai-memory-retrieval/</guid>
      <pubDate>Fri, 03 Jul 2026 12:31:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/invisible-ai-memory-retrieval.m4a"
        type="audio/mp4"
        length="40300824"
      />
      <itunes:title>How AI Remembers What You Never Told It</itunes:title>
      <itunes:subtitle>How ChatGPT connected &quot;wall anchors&quot; to a power tool you bought days ago — without being asked.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel asked ChatGPT for help removing wall anchors, the AI casually recalled he'd bought a rotary hammer days earlier — without being prompted to check his memory. This episode explores the engineering behind invisible AI memory: how retrieval-augmented generation (RAG) pulls relevant facts from vector databases without eating your token budget, how systems decide what to save, and why the seamlessness you feel hides staggering complexity.]]></itunes:summary>
      <itunes:duration>2343</itunes:duration>
      <itunes:episode>4092</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/invisible-ai-memory-retrieval.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/invisible-ai-memory-retrieval.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Resumable Uploads Actually Work</title>
      <description><![CDATA[Ever wondered how a file upload survives a dropped connection? This episode unpacks the engineering behind resumable transfers — from TCP's limitations to the TUS protocol that powers Vimeo and Google Photos. We explore why downloads are easy, uploads are hard, and how chunking, offset tracking, and atomic writes turn flaky connections into reliable sync engines. Whether you're uploading from a campsite or a coffee shop, the invisible stack that makes resumption possible is more clever — and more fragile — than you'd think.]]></description>
      <link>https://myweirdprompts.com/episode/resumable-uploads-file-transfer/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/resumable-uploads-file-transfer/</guid>
      <pubDate>Fri, 03 Jul 2026 10:35:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/resumable-uploads-file-transfer.m4a"
        type="audio/mp4"
        length="24069147"
      />
      <itunes:title>How Resumable Uploads Actually Work</itunes:title>
      <itunes:subtitle>What happens when your file transfer fails at 92% — and the app just picks up where it left off.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wondered how a file upload survives a dropped connection? This episode unpacks the engineering behind resumable transfers — from TCP's limitations to the TUS protocol that powers Vimeo and Google Photos. We explore why downloads are easy, uploads are hard, and how chunking, offset tracking, and atomic writes turn flaky connections into reliable sync engines. Whether you're uploading from a campsite or a coffee shop, the invisible stack that makes resumption possible is more clever — and more fragile — than you'd think.]]></itunes:summary>
      <itunes:duration>1370</itunes:duration>
      <itunes:episode>4080</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/resumable-uploads-file-transfer.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/resumable-uploads-file-transfer.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Private Buckets, Pre-Signed URLs: Self-Hosting Without the Compromise</title>
      <description><![CDATA[Self-hosters often face a false choice: keep everything local and lose mobility, or move to the cloud and surrender control. This episode walks through a third path — private R2 buckets, pre-signed URLs, and edge-based image processing — that gives you authenticated, time-limited access to your inventory photos from anywhere, without exposing PII or burning server CPU.]]></description>
      <link>https://myweirdprompts.com/episode/private-buckets-pre-signed-urls/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/private-buckets-pre-signed-urls/</guid>
      <pubDate>Thu, 02 Jul 2026 13:41:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/private-buckets-pre-signed-urls.m4a"
        type="audio/mp4"
        length="26255351"
      />
      <itunes:title>Private Buckets, Pre-Signed URLs: Self-Hosting Without the Compromise</itunes:title>
      <itunes:subtitle>How to keep your self-hosted images secure, accessible, and cloud-friendly without becoming the product.</itunes:subtitle>
      <itunes:summary><![CDATA[Self-hosters often face a false choice: keep everything local and lose mobility, or move to the cloud and surrender control. This episode walks through a third path — private R2 buckets, pre-signed URLs, and edge-based image processing — that gives you authenticated, time-limited access to your inventory photos from anywhere, without exposing PII or burning server CPU.]]></itunes:summary>
      <itunes:duration>1517</itunes:duration>
      <itunes:episode>4073</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/private-buckets-pre-signed-urls.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/private-buckets-pre-signed-urls.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Four Tools for a Human Review Layer on AI Tags</title>
      <description><![CDATA[Daniel manages nearly four thousand AI-tagged episodes in a Neon Postgres database on Vercel. The AI tagging pipeline works, but topic-specific feeds need human eyes to catch nuance. In this episode, we explore four approaches to add a lightweight review layer — Retool Embedded, NocoDB, Directus, and a custom Next.js template — without rebuilding the stack. We compare deployment constraints, schema intrusion, connection pooling, and Vercel's serverless function timeouts to find the fastest path from database to working admin UI.]]></description>
      <link>https://myweirdprompts.com/episode/ai-tag-review-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-tag-review-tools/</guid>
      <pubDate>Thu, 02 Jul 2026 12:37:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-tag-review-tools.m4a"
        type="audio/mp4"
        length="38890983"
      />
      <itunes:title>Four Tools for a Human Review Layer on AI Tags</itunes:title>
      <itunes:subtitle>Adding human review to AI-generated tags without building an admin panel from scratch.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel manages nearly four thousand AI-tagged episodes in a Neon Postgres database on Vercel. The AI tagging pipeline works, but topic-specific feeds need human eyes to catch nuance. In this episode, we explore four approaches to add a lightweight review layer — Retool Embedded, NocoDB, Directus, and a custom Next.js template — without rebuilding the stack. We compare deployment constraints, schema intrusion, connection pooling, and Vercel's serverless function timeouts to find the fastest path from database to working admin UI.]]></itunes:summary>
      <itunes:duration>2260</itunes:duration>
      <itunes:episode>4071</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-tag-review-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-tag-review-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How AI Is Rewriting Your Tech Stack Choices</title>
      <description><![CDATA[When Daniel customized an open-source inventory system with Go, Vue, Nuxt, Postgres, and Drizzle, he noticed something surprising: smart component choices eliminated entire categories of tooling. No separate migration manager, no dedicated type-checker. This episode explores how AI code generation is flipping the old "pick what the team knows" rule on its head. Now the question is: what makes architectural sense? And how does training data coverage affect which stacks AI tools can actually support? We walk through every layer of a modern data-driven app — from database to reverse proxy — and ask whether the pick-and-mix approach is finally viable when AI handles the glue code.]]></description>
      <link>https://myweirdprompts.com/episode/ai-tech-stack-decisions/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-tech-stack-decisions/</guid>
      <pubDate>Thu, 02 Jul 2026 12:07:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-tech-stack-decisions.m4a"
        type="audio/mp4"
        length="37879598"
      />
      <itunes:title>How AI Is Rewriting Your Tech Stack Choices</itunes:title>
      <itunes:subtitle>What happens when AI agents change how we pick frameworks, ORMs, and databases?</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel customized an open-source inventory system with Go, Vue, Nuxt, Postgres, and Drizzle, he noticed something surprising: smart component choices eliminated entire categories of tooling. No separate migration manager, no dedicated type-checker. This episode explores how AI code generation is flipping the old "pick what the team knows" rule on its head. Now the question is: what makes architectural sense? And how does training data coverage affect which stacks AI tools can actually support? We walk through every layer of a modern data-driven app — from database to reverse proxy — and ask whether the pick-and-mix approach is finally viable when AI handles the glue code.]]></itunes:summary>
      <itunes:duration>2181</itunes:duration>
      <itunes:episode>4069</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-tech-stack-decisions.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-tech-stack-decisions.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Build a Go Image Pipeline for Your Inventory App</title>
      <description><![CDATA[Your home inventory app doesn't need full-resolution JPEGs or a CDN. This episode breaks down a practical Go-based image processing pipeline for self-hosted apps: downscale to 2MP, burn timestamps into pixels, convert to WebP, and generate thumbnails only for primary photos. Learn why selective processing, database-aware pipelines, and restraint with dependencies matter more than architectural complexity.]]></description>
      <link>https://myweirdprompts.com/episode/go-image-pipeline-inventory-app/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/go-image-pipeline-inventory-app/</guid>
      <pubDate>Thu, 02 Jul 2026 12:02:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/go-image-pipeline-inventory-app.m4a"
        type="audio/mp4"
        length="29698281"
      />
      <itunes:title>How to Build a Go Image Pipeline for Your Inventory App</itunes:title>
      <itunes:subtitle>Stop treating your inventory app like a photo dump. Here&apos;s how to build a smart image pipeline in Go.</itunes:subtitle>
      <itunes:summary><![CDATA[Your home inventory app doesn't need full-resolution JPEGs or a CDN. This episode breaks down a practical Go-based image processing pipeline for self-hosted apps: downscale to 2MP, burn timestamps into pixels, convert to WebP, and generate thumbnails only for primary photos. Learn why selective processing, database-aware pipelines, and restraint with dependencies matter more than architectural complexity.]]></itunes:summary>
      <itunes:duration>1705</itunes:duration>
      <itunes:episode>4068</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/go-image-pipeline-inventory-app.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/go-image-pipeline-inventory-app.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Keep a Baby&apos;s Room Comfortable With a Dumb AC</title>
      <description><![CDATA[Most people think a smart thermostat is the answer to keeping a baby's room comfortable. But as this episode reveals, a $200 dumb window AC unit paired with $30 in sensors and Home Assistant actually gives you more control — and better results. We break down the specific temperature and humidity targets recommended for infants under 12 months, why tight hysteresis destroys sleep and dehumidification, and how PID control algorithms can keep a nursery stable without short-cycling the compressor. Plus: dew-point automations and multi-room "follow the baby" setups that no off-the-shelf thermostat can match.]]></description>
      <link>https://myweirdprompts.com/episode/baby-room-ac-automation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/baby-room-ac-automation/</guid>
      <pubDate>Thu, 02 Jul 2026 10:42:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/baby-room-ac-automation.m4a"
        type="audio/mp4"
        length="38175916"
      />
      <itunes:title>How to Keep a Baby&apos;s Room Comfortable With a Dumb AC</itunes:title>
      <itunes:subtitle>Smart sensors + dumb AC + PID control = better than a Nest for keeping your infant comfortable at night.</itunes:subtitle>
      <itunes:summary><![CDATA[Most people think a smart thermostat is the answer to keeping a baby's room comfortable. But as this episode reveals, a $200 dumb window AC unit paired with $30 in sensors and Home Assistant actually gives you more control — and better results. We break down the specific temperature and humidity targets recommended for infants under 12 months, why tight hysteresis destroys sleep and dehumidification, and how PID control algorithms can keep a nursery stable without short-cycling the compressor. Plus: dew-point automations and multi-room "follow the baby" setups that no off-the-shelf thermostat can match.]]></itunes:summary>
      <itunes:duration>2200</itunes:duration>
      <itunes:episode>4062</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/baby-room-ac-automation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/baby-room-ac-automation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Fix AI&apos;s Bullet Point Addiction</title>
      <description><![CDATA[AI models love bullet points — but when you're generating client reports, board memos, or regulatory filings, that default style destroys credibility. In this episode, we break down why transformers favor structured output (it's not laziness, it's the attention mechanism) and compare three engineering approaches to fix it: system prompting (62% adherence), fine-tuning ($200+), and textual LoRAs (94% adherence for $20). Using DeepSeek v4 Pro as a case study, we explore how teams can deterministically steer AI toward flowing prose without losing base model capabilities.]]></description>
      <link>https://myweirdprompts.com/episode/fixing-ai-bullet-points/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fixing-ai-bullet-points/</guid>
      <pubDate>Thu, 02 Jul 2026 10:06:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fixing-ai-bullet-points.m4a"
        type="audio/mp4"
        length="30843348"
      />
      <itunes:title>How to Fix AI&apos;s Bullet Point Addiction</itunes:title>
      <itunes:subtitle>Why AI models default to bullet points and how textual LoRAs achieve 94% prose adherence.</itunes:subtitle>
      <itunes:summary><![CDATA[AI models love bullet points — but when you're generating client reports, board memos, or regulatory filings, that default style destroys credibility. In this episode, we break down why transformers favor structured output (it's not laziness, it's the attention mechanism) and compare three engineering approaches to fix it: system prompting (62% adherence), fine-tuning ($200+), and textual LoRAs (94% adherence for $20). Using DeepSeek v4 Pro as a case study, we explore how teams can deterministically steer AI toward flowing prose without losing base model capabilities.]]></itunes:summary>
      <itunes:duration>1769</itunes:duration>
      <itunes:episode>4057</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fixing-ai-bullet-points.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fixing-ai-bullet-points.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How a $150 Geopolitical AI Simulation Scales to $15,000</title>
      <description><![CDATA[Geopolitical simulations using AI agents sound like a powerful forecasting tool—until you see the bill. One run of Snowflake, an open-source platform from the CIA’s Technical Innovation Lab, can cost over $150 in API tokens. Because LLMs are probabilistic, you need to run the simulation dozens or hundreds of times for statistically valid results. In this episode, we break down exactly where the money goes: agent personality prompts, growing context windows, and combinatorial multiplication of calls. We also explore how switching models—like from Claude to DeepSeek—can slash costs by 99% while actually improving simulation quality through ensemble methods.]]></description>
      <link>https://myweirdprompts.com/episode/geopolitical-ai-simulation-cost/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/geopolitical-ai-simulation-cost/</guid>
      <pubDate>Thu, 02 Jul 2026 10:04:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/geopolitical-ai-simulation-cost.m4a"
        type="audio/mp4"
        length="24534174"
      />
      <itunes:title>How a $150 Geopolitical AI Simulation Scales to $15,000</itunes:title>
      <itunes:subtitle>One simulation run costs $150. To get meaningful results, you need 100 runs—that’s $15,000.</itunes:subtitle>
      <itunes:summary><![CDATA[Geopolitical simulations using AI agents sound like a powerful forecasting tool—until you see the bill. One run of Snowflake, an open-source platform from the CIA’s Technical Innovation Lab, can cost over $150 in API tokens. Because LLMs are probabilistic, you need to run the simulation dozens or hundreds of times for statistically valid results. In this episode, we break down exactly where the money goes: agent personality prompts, growing context windows, and combinatorial multiplication of calls. We also explore how switching models—like from Claude to DeepSeek—can slash costs by 99% while actually improving simulation quality through ensemble methods.]]></itunes:summary>
      <itunes:duration>1402</itunes:duration>
      <itunes:episode>4056</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/geopolitical-ai-simulation-cost.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/geopolitical-ai-simulation-cost.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can Your Phone Upscale Photos Without the Cloud?</title>
      <description><![CDATA[Daniel wants to upscale a blurry photo on a train, no cloud involved. The hardware is ready — Snapdragon 8 Gen 4 and Dimensity 9400 both pack over 40 TOPS of NPU performance. But the software path is still up for grabs. Do you quantize a general model like Real-ESRGAN to run anywhere, or build something bespoke for each chip? We break down the tradeoffs: sub-10ms specialized hardware versus 200ms general inference, three-megabyte Apple Core ML models versus sixty-five-megabyte quantized networks, and what it all means for the future of edge AI.]]></description>
      <link>https://myweirdprompts.com/episode/edge-ai-upscaling-phones/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/edge-ai-upscaling-phones/</guid>
      <pubDate>Tue, 30 Jun 2026 04:14:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/edge-ai-upscaling-phones.m4a"
        type="audio/mp4"
        length="22718149"
      />
      <itunes:title>Can Your Phone Upscale Photos Without the Cloud?</itunes:title>
      <itunes:subtitle>Local upscaling on phones is here — but which approach actually works?</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants to upscale a blurry photo on a train, no cloud involved. The hardware is ready — Snapdragon 8 Gen 4 and Dimensity 9400 both pack over 40 TOPS of NPU performance. But the software path is still up for grabs. Do you quantize a general model like Real-ESRGAN to run anywhere, or build something bespoke for each chip? We break down the tradeoffs: sub-10ms specialized hardware versus 200ms general inference, three-megabyte Apple Core ML models versus sixty-five-megabyte quantized networks, and what it all means for the future of edge AI.]]></itunes:summary>
      <itunes:duration>1300</itunes:duration>
      <itunes:episode>3995</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/edge-ai-upscaling-phones.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/edge-ai-upscaling-phones.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why the DNS Postman Metaphor Fails Learners</title>
      <description><![CDATA[The friendly postman explaining DNS has been a staple of technical education for decades. But what happens when your audience has never mailed a letter—or lives in a region without door-to-mail delivery? This episode unpacks why the standard DNS metaphor is quietly failing a huge chunk of learners, what the neuroscience says about who actually benefits from analogies, and why a rainforest canopy might map the mechanism more faithfully. We explore a 2023 Internet Society study showing 34% improvement in troubleshooting scores with culturally-matched metaphors, and offer a practical framework for auditing which analogies you reach for.]]></description>
      <link>https://myweirdprompts.com/episode/dns-metaphor-failures/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dns-metaphor-failures/</guid>
      <pubDate>Fri, 26 Jun 2026 10:23:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dns-metaphor-failures.m4a"
        type="audio/mp4"
        length="24953936"
      />
      <itunes:title>Why the DNS Postman Metaphor Fails Learners</itunes:title>
      <itunes:subtitle>The postal metaphor for DNS is everywhere—but it&apos;s failing millions of learners. Here&apos;s what works better.</itunes:subtitle>
      <itunes:summary><![CDATA[The friendly postman explaining DNS has been a staple of technical education for decades. But what happens when your audience has never mailed a letter—or lives in a region without door-to-mail delivery? This episode unpacks why the standard DNS metaphor is quietly failing a huge chunk of learners, what the neuroscience says about who actually benefits from analogies, and why a rainforest canopy might map the mechanism more faithfully. We explore a 2023 Internet Society study showing 34% improvement in troubleshooting scores with culturally-matched metaphors, and offer a practical framework for auditing which analogies you reach for.]]></itunes:summary>
      <itunes:duration>1400</itunes:duration>
      <itunes:episode>3907</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dns-metaphor-failures.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dns-metaphor-failures.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Manual NAT vs Double NAT: The Measurable Reality</title>
      <description><![CDATA[This episode dives into the mechanics of manual outbound NAT in OPNsense and the measurable reality of double NAT latency. We trace what happens at the packet level when you switch from automatic to manual mode, explain why hybrid mode is the safest starting point, and cut through the forum mythology to reveal how much latency double NAT actually costs versus other network issues like bufferbloat.]]></description>
      <link>https://myweirdprompts.com/episode/manual-nat-double-nat-latency/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/manual-nat-double-nat-latency/</guid>
      <pubDate>Fri, 26 Jun 2026 02:16:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/manual-nat-double-nat-latency.m4a"
        type="audio/mp4"
        length="38552616"
      />
      <itunes:title>Manual NAT vs Double NAT: The Measurable Reality</itunes:title>
      <itunes:subtitle>Tracing OPNsense packet paths and quantifying double NAT&apos;s real latency cost.</itunes:subtitle>
      <itunes:summary><![CDATA[This episode dives into the mechanics of manual outbound NAT in OPNsense and the measurable reality of double NAT latency. We trace what happens at the packet level when you switch from automatic to manual mode, explain why hybrid mode is the safest starting point, and cut through the forum mythology to reveal how much latency double NAT actually costs versus other network issues like bufferbloat.]]></itunes:summary>
      <itunes:duration>2194</itunes:duration>
      <itunes:episode>3904</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/manual-nat-double-nat-latency.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/manual-nat-double-nat-latency.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Traceroute Beyond the Basics: Path Diagnostics</title>
      <description><![CDATA[Traceroute is more than a hop-count tool—it's a diagnostic scalpel for modern networks. This episode unpacks the packet-level mechanics of TTL, probe types (UDP, ICMP, TCP), and how each interacts with firewalls and MPLS cores. Learn to spot hidden infrastructure, verify multi-WAN failover in seconds, and diagnose asymmetric routing that causes mysterious connectivity issues. Whether you're troubleshooting cloud egress or SD-WAN topologies, understanding traceroute's deeper capabilities turns vague problems into clear answers.]]></description>
      <link>https://myweirdprompts.com/episode/traceroute-path-diagnostics/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/traceroute-path-diagnostics/</guid>
      <pubDate>Thu, 25 Jun 2026 23:57:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/traceroute-path-diagnostics.m4a"
        type="audio/mp4"
        length="32190433"
      />
      <itunes:title>Traceroute Beyond the Basics: Path Diagnostics</itunes:title>
      <itunes:subtitle>Discover what traceroute actually reveals about network paths, firewalls, and multi-WAN failover.</itunes:subtitle>
      <itunes:summary><![CDATA[Traceroute is more than a hop-count tool—it's a diagnostic scalpel for modern networks. This episode unpacks the packet-level mechanics of TTL, probe types (UDP, ICMP, TCP), and how each interacts with firewalls and MPLS cores. Learn to spot hidden infrastructure, verify multi-WAN failover in seconds, and diagnose asymmetric routing that causes mysterious connectivity issues. Whether you're troubleshooting cloud egress or SD-WAN topologies, understanding traceroute's deeper capabilities turns vague problems into clear answers.]]></itunes:summary>
      <itunes:duration>1868</itunes:duration>
      <itunes:episode>3901</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/traceroute-path-diagnostics.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/traceroute-path-diagnostics.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Tag 4000 Episodes Without Losing Your Mind</title>
      <description><![CDATA[When your podcast library hits 4,000 episodes, traditional tagging and search break down completely. Tags multiply into duplicates, context windows overflow, and users stop trusting the system. This episode unpacks the two-stage agentic pipeline that solves it: a map step that generates raw tags per episode, then a reduce step using embedding similarity and DBSCAN clustering to normalize everything into a clean, canonical taxonomy. No manual effort, no token limits, no drift.]]></description>
      <link>https://myweirdprompts.com/episode/tagging-4000-episodes-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tagging-4000-episodes-pipeline/</guid>
      <pubDate>Wed, 24 Jun 2026 09:40:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tagging-4000-episodes-pipeline.m4a"
        type="audio/mp4"
        length="28782129"
      />
      <itunes:title>How to Tag 4000 Episodes Without Losing Your Mind</itunes:title>
      <itunes:subtitle>Why tagging breaks at scale, and how a two-stage AI pipeline fixes it for good.</itunes:subtitle>
      <itunes:summary><![CDATA[When your podcast library hits 4,000 episodes, traditional tagging and search break down completely. Tags multiply into duplicates, context windows overflow, and users stop trusting the system. This episode unpacks the two-stage agentic pipeline that solves it: a map step that generates raw tags per episode, then a reduce step using embedding similarity and DBSCAN clustering to normalize everything into a clean, canonical taxonomy. No manual effort, no token limits, no drift.]]></itunes:summary>
      <itunes:duration>1648</itunes:duration>
      <itunes:episode>3874</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tagging-4000-episodes-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tagging-4000-episodes-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Slack&apos;s Accidental AI Agent Superpower</title>
      <description><![CDATA[Slack was supposed to liberate knowledge workers from email hell. Instead, it gave us two inboxes to check and higher stress levels. But while it failed at human communication, the same architecture—channels, subscriptions, bot APIs—turns out to be the perfect notification layer for AI agents. We trace how Slack's structural failures for people became its accidental strengths for machine-to-human communication, and what that means for the future of how AI talks to us.]]></description>
      <link>https://myweirdprompts.com/episode/slack-ai-agent-notification-layer/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/slack-ai-agent-notification-layer/</guid>
      <pubDate>Tue, 23 Jun 2026 12:51:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/slack-ai-agent-notification-layer.m4a"
        type="audio/mp4"
        length="27106189"
      />
      <itunes:title>Slack&apos;s Accidental AI Agent Superpower</itunes:title>
      <itunes:subtitle>The tool that failed to kill email is now thriving as the notification backbone for AI agents. Here&apos;s why.</itunes:subtitle>
      <itunes:summary><![CDATA[Slack was supposed to liberate knowledge workers from email hell. Instead, it gave us two inboxes to check and higher stress levels. But while it failed at human communication, the same architecture—channels, subscriptions, bot APIs—turns out to be the perfect notification layer for AI agents. We trace how Slack's structural failures for people became its accidental strengths for machine-to-human communication, and what that means for the future of how AI talks to us.]]></itunes:summary>
      <itunes:duration>1554</itunes:duration>
      <itunes:episode>3835</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/slack-ai-agent-notification-layer.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/slack-ai-agent-notification-layer.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Stop AI Scripts From Falling Apart</title>
      <description><![CDATA[Long-form AI generation has a silent killer: context degradation. Even with stable temperature settings, reasoning quality measurably declines after ~32K tokens as micro-errors compound. In this episode, we explore why truncation fails, how hierarchical generation with structured memory vectors solves the problem, and what Google DeepMind's 2025 study and Anthropic's précis mode teach us about keeping AI scripts coherent from first sentence to last.]]></description>
      <link>https://myweirdprompts.com/episode/ai-script-coherence-degradation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-script-coherence-degradation/</guid>
      <pubDate>Mon, 22 Jun 2026 19:45:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-script-coherence-degradation.m4a"
        type="audio/mp4"
        length="42551650"
      />
      <itunes:title>How to Stop AI Scripts From Falling Apart</itunes:title>
      <itunes:subtitle>Why long-form AI generation breaks down and how hierarchical memory fixes it.</itunes:subtitle>
      <itunes:summary><![CDATA[Long-form AI generation has a silent killer: context degradation. Even with stable temperature settings, reasoning quality measurably declines after ~32K tokens as micro-errors compound. In this episode, we explore why truncation fails, how hierarchical generation with structured memory vectors solves the problem, and what Google DeepMind's 2025 study and Anthropic's précis mode teach us about keeping AI scripts coherent from first sentence to last.]]></itunes:summary>
      <itunes:duration>2449</itunes:duration>
      <itunes:episode>3816</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-script-coherence-degradation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-script-coherence-degradation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What&apos;s Really in That Private Network Cable?</title>
      <description><![CDATA[When you click "add to private network" in a cloud console, what's actually happening? This episode unpacks the three tiers of cross-data-center connectivity — from hyperscalers with their own submarine cables to mid-tier providers leasing carrier circuits to software-defined overlays running over the public internet. We explore MPLS, VXLAN, dark fiber, and the hard speed-of-light limits that no amount of optimization can beat. Plus: what "private" really means when your traffic leaves the data center.]]></description>
      <link>https://myweirdprompts.com/episode/private-network-cables-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/private-network-cables-explained/</guid>
      <pubDate>Sun, 21 Jun 2026 19:53:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/private-network-cables-explained.m4a"
        type="audio/mp4"
        length="32566827"
      />
      <itunes:title>What&apos;s Really in That Private Network Cable?</itunes:title>
      <itunes:subtitle>Virtual cables, MPLS circuits, and dark fiber — how cloud providers connect data centers behind the scenes.</itunes:subtitle>
      <itunes:summary><![CDATA[When you click "add to private network" in a cloud console, what's actually happening? This episode unpacks the three tiers of cross-data-center connectivity — from hyperscalers with their own submarine cables to mid-tier providers leasing carrier circuits to software-defined overlays running over the public internet. We explore MPLS, VXLAN, dark fiber, and the hard speed-of-light limits that no amount of optimization can beat. Plus: what "private" really means when your traffic leaves the data center.]]></itunes:summary>
      <itunes:duration>1867</itunes:duration>
      <itunes:episode>3802</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/private-network-cables-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/private-network-cables-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Self-Reverting Watchdogs Save Broken SSH Sessions</title>
      <description><![CDATA[That frozen SSH terminal after a network change is every sysadmin's nightmare. This episode explores the self-reverting watchdog pattern — a dead man's switch for configuration changes that automatically undoes risky modifications if you lose connectivity. We break down the three-phase architecture: pre-flight snapshots, background timers, and confirmation handshakes through separate channels. Learn how to build a simple bash watchdog, why secondary SSH services and external verifiers prevent lockouts, and why this pattern is essential as AI agents increasingly make autonomous infrastructure changes without human hesitation.]]></description>
      <link>https://myweirdprompts.com/episode/self-reverting-watchdog-ssh/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/self-reverting-watchdog-ssh/</guid>
      <pubDate>Sun, 21 Jun 2026 18:02:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/self-reverting-watchdog-ssh.m4a"
        type="audio/mp4"
        length="35341217"
      />
      <itunes:title>How Self-Reverting Watchdogs Save Broken SSH Sessions</itunes:title>
      <itunes:subtitle>A dead man&apos;s switch for server configs that automatically rolls back risky changes when connectivity drops.</itunes:subtitle>
      <itunes:summary><![CDATA[That frozen SSH terminal after a network change is every sysadmin's nightmare. This episode explores the self-reverting watchdog pattern — a dead man's switch for configuration changes that automatically undoes risky modifications if you lose connectivity. We break down the three-phase architecture: pre-flight snapshots, background timers, and confirmation handshakes through separate channels. Learn how to build a simple bash watchdog, why secondary SSH services and external verifiers prevent lockouts, and why this pattern is essential as AI agents increasingly make autonomous infrastructure changes without human hesitation.]]></itunes:summary>
      <itunes:duration>2014</itunes:duration>
      <itunes:episode>3797</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/self-reverting-watchdog-ssh.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/self-reverting-watchdog-ssh.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Solving the Bulk Redirect Problem for System Migrations</title>
      <description><![CDATA[When you're migrating from one inventory system to another and QR codes and barcodes are already printed and stuck to physical boxes, you can't just push an update. The redirect layer becomes operational infrastructure. This episode explores the landscape of tools for managing bulk redirects — from simple pattern-matching with Traefik and Redis to programmable routing with Pomerium and Kong. We break down the three layers of the redirect problem and what actually works when you're dealing with millions of mappings and a deadline.]]></description>
      <link>https://myweirdprompts.com/episode/bulk-redirect-system-migration/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/bulk-redirect-system-migration/</guid>
      <pubDate>Sun, 21 Jun 2026 09:48:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/bulk-redirect-system-migration.m4a"
        type="audio/mp4"
        length="21871042"
      />
      <itunes:title>Solving the Bulk Redirect Problem for System Migrations</itunes:title>
      <itunes:subtitle>What tools exist for managing bulk redirect mappings when QR codes are already stuck on physical assets?</itunes:subtitle>
      <itunes:summary><![CDATA[When you're migrating from one inventory system to another and QR codes and barcodes are already printed and stuck to physical boxes, you can't just push an update. The redirect layer becomes operational infrastructure. This episode explores the landscape of tools for managing bulk redirects — from simple pattern-matching with Traefik and Redis to programmable routing with Pomerium and Kong. We break down the three layers of the redirect problem and what actually works when you're dealing with millions of mappings and a deadline.]]></itunes:summary>
      <itunes:duration>1233</itunes:duration>
      <itunes:episode>3793</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/bulk-redirect-system-migration.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/bulk-redirect-system-migration.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Cloud Brain, Local Fingers: Decoupled Home Assistant</title>
      <description><![CDATA[What if your Home Assistant brain lived in a cloud VPS while the Zigbee coordinator stayed in your apartment? This episode unpacks the "decoupled control plane" architecture — cloud orchestration with local actuation. We explore the traffic flow over Tailscale tunnels, the 30-80ms latency tradeoffs, and why fallback Zigbee bindings can keep lights working even when the Frankfurt server goes dark. Plus: why Matter devices complicate the picture, and the hard rule that life safety stays local.]]></description>
      <link>https://myweirdprompts.com/episode/decoupled-home-assistant-cloud-brain/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/decoupled-home-assistant-cloud-brain/</guid>
      <pubDate>Sun, 21 Jun 2026 09:41:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/decoupled-home-assistant-cloud-brain.m4a"
        type="audio/mp4"
        length="30927945"
      />
      <itunes:title>Cloud Brain, Local Fingers: Decoupled Home Assistant</itunes:title>
      <itunes:subtitle>Can Home Assistant run in the cloud while Zigbee stays local? We explore the decoupled control plane architecture.</itunes:subtitle>
      <itunes:summary><![CDATA[What if your Home Assistant brain lived in a cloud VPS while the Zigbee coordinator stayed in your apartment? This episode unpacks the "decoupled control plane" architecture — cloud orchestration with local actuation. We explore the traffic flow over Tailscale tunnels, the 30-80ms latency tradeoffs, and why fallback Zigbee bindings can keep lights working even when the Frankfurt server goes dark. Plus: why Matter devices complicate the picture, and the hard rule that life safety stays local.]]></itunes:summary>
      <itunes:duration>1782</itunes:duration>
      <itunes:episode>3792</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/decoupled-home-assistant-cloud-brain.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/decoupled-home-assistant-cloud-brain.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Server Distro Showdown: BTRFS, ZFS &amp; Pragmatic Picks</title>
      <description><![CDATA[Why do server admins make such boringly pragmatic distro choices while desktop users debate wallpaper defaults? This episode unpacks the real decision tree behind server distro selection — where kernel versions, package availability, installer behavior, and support lifecycles matter far more than identity or aesthetics. We trace how filesystem support for BTRFS and ZFS can effectively pick your distro for you, examine what "supported" actually means across Ubuntu, Debian, OpenSUSE, and Fedora Server, and explore why Ubuntu dominates at 33% of web servers while Debian holds 20%. We also cover the death of CentOS, the rise of Rocky and AlmaLinux, OpenSUSE Leap's impending migration to ALP, and the CDDL vs GPLv2 legal standoff that keeps ZFS out of the Linux kernel tree.]]></description>
      <link>https://myweirdprompts.com/episode/server-distro-filesystem-decision/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/server-distro-filesystem-decision/</guid>
      <pubDate>Sun, 21 Jun 2026 09:20:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/server-distro-filesystem-decision.m4a"
        type="audio/mp4"
        length="29647188"
      />
      <itunes:title>Server Distro Showdown: BTRFS, ZFS &amp; Pragmatic Picks</itunes:title>
      <itunes:subtitle>Why filesystem support often picks your distro — and what &quot;support&quot; actually means in practice.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do server admins make such boringly pragmatic distro choices while desktop users debate wallpaper defaults? This episode unpacks the real decision tree behind server distro selection — where kernel versions, package availability, installer behavior, and support lifecycles matter far more than identity or aesthetics. We trace how filesystem support for BTRFS and ZFS can effectively pick your distro for you, examine what "supported" actually means across Ubuntu, Debian, OpenSUSE, and Fedora Server, and explore why Ubuntu dominates at 33% of web servers while Debian holds 20%. We also cover the death of CentOS, the rise of Rocky and AlmaLinux, OpenSUSE Leap's impending migration to ALP, and the CDDL vs GPLv2 legal standoff that keeps ZFS out of the Linux kernel tree.]]></itunes:summary>
      <itunes:duration>1708</itunes:duration>
      <itunes:episode>3790</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/server-distro-filesystem-decision.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/server-distro-filesystem-decision.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Virtualization Actually Costs on 2026 Hardware</title>
      <description><![CDATA[When you reflexively install Proxmox on a new Ubuntu server, what performance are you actually trading for those snapshot and rollback capabilities? This episode digs into real benchmark data from a Ryzen 9 7950X running Proxmox 8.3 with a single Ubuntu VM — measuring CPU cycles, memory access, disk I/O, and network throughput. We trace the overhead from VM-exits on Intel and AMD hardware, nested page table walks, virtio driver paths, and the real-world cost of that hypervisor translation layer. The answer: 2-6% depending on workload, with specific numbers for PostgreSQL, Nginx, 7-Zip compression, and NVMe disk operations.]]></description>
      <link>https://myweirdprompts.com/episode/virtualization-overhead-performance-costs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/virtualization-overhead-performance-costs/</guid>
      <pubDate>Sun, 21 Jun 2026 09:19:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/virtualization-overhead-performance-costs.m4a"
        type="audio/mp4"
        length="17236502"
      />
      <itunes:title>What Virtualization Actually Costs on 2026 Hardware</itunes:title>
      <itunes:subtitle>Real benchmarks show 2-6% overhead for single-VM setups. Here&apos;s what&apos;s actually happening at the CPU level.</itunes:subtitle>
      <itunes:summary><![CDATA[When you reflexively install Proxmox on a new Ubuntu server, what performance are you actually trading for those snapshot and rollback capabilities? This episode digs into real benchmark data from a Ryzen 9 7950X running Proxmox 8.3 with a single Ubuntu VM — measuring CPU cycles, memory access, disk I/O, and network throughput. We trace the overhead from VM-exits on Intel and AMD hardware, nested page table walks, virtio driver paths, and the real-world cost of that hypervisor translation layer. The answer: 2-6% depending on workload, with specific numbers for PostgreSQL, Nginx, 7-Zip compression, and NVMe disk operations.]]></itunes:summary>
      <itunes:duration>977</itunes:duration>
      <itunes:episode>3789</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/virtualization-overhead-performance-costs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/virtualization-overhead-performance-costs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Proxmox vs Stock Linux for Home Servers</title>
      <description><![CDATA[When your home server dies and you're rebuilding, the default choice for many is Proxmox — but is it actually the right call? This episode digs into the real differences between running Proxmox VE and a stock Ubuntu Server install with KVM and libvirt for a simple three-VM setup. We cover resource overhead (Proxmox uses about 1GB RAM idle vs ~350MB for minimal Ubuntu), ZFS management differences, USB passthrough for Zigbee dongles, and the hidden cost of running Corosync on a single node. If you've ever wondered whether muscle memory is hiding architectural inertia in your homelab, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/proxmox-vs-stock-linux/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/proxmox-vs-stock-linux/</guid>
      <pubDate>Sun, 21 Jun 2026 08:23:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/proxmox-vs-stock-linux.m4a"
        type="audio/mp4"
        length="35570899"
      />
      <itunes:title>Proxmox vs Stock Linux for Home Servers</itunes:title>
      <itunes:subtitle>When rebuilding a home server, should you install Proxmox or just use stock Linux with KVM? We break down the tradeoffs.</itunes:subtitle>
      <itunes:summary><![CDATA[When your home server dies and you're rebuilding, the default choice for many is Proxmox — but is it actually the right call? This episode digs into the real differences between running Proxmox VE and a stock Ubuntu Server install with KVM and libvirt for a simple three-VM setup. We cover resource overhead (Proxmox uses about 1GB RAM idle vs ~350MB for minimal Ubuntu), ZFS management differences, USB passthrough for Zigbee dongles, and the hidden cost of running Corosync on a single node. If you've ever wondered whether muscle memory is hiding architectural inertia in your homelab, this one's for you.]]></itunes:summary>
      <itunes:duration>2067</itunes:duration>
      <itunes:episode>3787</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/proxmox-vs-stock-linux.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/proxmox-vs-stock-linux.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>ZFS Mirroring: Why Your RAID Card Is the Weak Link</title>
      <description><![CDATA[After a power supply failure nearly wiped his data, a home server user wants to know: what's the next step for a safer ZFS setup? The answer isn't what most people expect. Herman and Corn explain why a hardware RAID controller actually undermines ZFS's self-healing capabilities, why a simple two-drive mirror on motherboard SATA ports is often sufficient, and how a used LSI HBA flashed to IT mode can future-proof your build for under $60. They cover the exact procedure for adding a mirror drive, the reality of the ECC RAM debate for home users, and why the philosophy of ZFS is "get out of the way." If you're running ZFS on a home server, this episode will save you from the most common—and most dangerous—misconception in the hobby.]]></description>
      <link>https://myweirdprompts.com/episode/zfs-mirroring-hba-vs-raid/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/zfs-mirroring-hba-vs-raid/</guid>
      <pubDate>Sat, 20 Jun 2026 22:14:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/zfs-mirroring-hba-vs-raid.m4a"
        type="audio/mp4"
        length="26202048"
      />
      <itunes:title>ZFS Mirroring: Why Your RAID Card Is the Weak Link</itunes:title>
      <itunes:subtitle>A hardware RAID card makes ZFS less safe. Here&apos;s why an HBA and a simple mirror are the real upgrade.</itunes:subtitle>
      <itunes:summary><![CDATA[After a power supply failure nearly wiped his data, a home server user wants to know: what's the next step for a safer ZFS setup? The answer isn't what most people expect. Herman and Corn explain why a hardware RAID controller actually undermines ZFS's self-healing capabilities, why a simple two-drive mirror on motherboard SATA ports is often sufficient, and how a used LSI HBA flashed to IT mode can future-proof your build for under $60. They cover the exact procedure for adding a mirror drive, the reality of the ECC RAM debate for home users, and why the philosophy of ZFS is "get out of the way." If you're running ZFS on a home server, this episode will save you from the most common—and most dangerous—misconception in the hobby.]]></itunes:summary>
      <itunes:duration>1505</itunes:duration>
      <itunes:episode>3776</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/zfs-mirroring-hba-vs-raid.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/zfs-mirroring-hba-vs-raid.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Hermes vs OpenClaw: Mobile-to-Server AI Frameworks</title>
      <description><![CDATA[The Hermes agent framework is pulling developers away from OpenClaw at an accelerating rate. But beneath the surface question of which framework wins lies a harder problem: can you run complex, long development sessions with sub-agents and bash output from a mobile interface? This episode unpacks the three key reasons for the migration—development velocity, architectural flexibility, and native MCP support—and explores why neither framework has fully cracked the mobile-to-server interaction problem for serious development work. We also examine Nvidia's strategic investment in Hermes and what it signals about the emerging "agent runtime layer.]]></description>
      <link>https://myweirdprompts.com/episode/hermes-openclaw-mobile-server-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hermes-openclaw-mobile-server-ai/</guid>
      <pubDate>Sat, 20 Jun 2026 16:45:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hermes-openclaw-mobile-server-ai.m4a"
        type="audio/mp4"
        length="34842727"
      />
      <itunes:title>Hermes vs OpenClaw: Mobile-to-Server AI Frameworks</itunes:title>
      <itunes:subtitle>Why developers are leaving OpenClaw for Hermes—and why mobile-to-server AI interaction remains unsolved.</itunes:subtitle>
      <itunes:summary><![CDATA[The Hermes agent framework is pulling developers away from OpenClaw at an accelerating rate. But beneath the surface question of which framework wins lies a harder problem: can you run complex, long development sessions with sub-agents and bash output from a mobile interface? This episode unpacks the three key reasons for the migration—development velocity, architectural flexibility, and native MCP support—and explores why neither framework has fully cracked the mobile-to-server interaction problem for serious development work. We also examine Nvidia's strategic investment in Hermes and what it signals about the emerging "agent runtime layer.]]></itunes:summary>
      <itunes:duration>2001</itunes:duration>
      <itunes:episode>3755</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hermes-openclaw-mobile-server-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hermes-openclaw-mobile-server-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Source-Restricted vs. Open Retrieval: How to Lock Down Your LLM</title>
      <description><![CDATA[The terminology around "closed corpus" and "open world" LLM retrieval is a mess — and the stakes are high. A model blending your documents with its own training data can turn a contract review into a malpractice suit. This episode unpacks the real distinction: source-restricted versus open retrieval, and why it's a per-generation decision, not an application-level toggle. We walk through concrete cases in legal, medical, and compliance work, then survey how LangGraph, LlamaIndex, and the Anthropic and OpenAI SDKs handle — or fail to handle — the constraint. The verdict: there's no clean primitive, just system prompts and tool lists. Here's how to wire it up without getting burned.]]></description>
      <link>https://myweirdprompts.com/episode/closed-corpus-llm-retrieval/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/closed-corpus-llm-retrieval/</guid>
      <pubDate>Sat, 20 Jun 2026 16:16:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/closed-corpus-llm-retrieval.m4a"
        type="audio/mp4"
        length="31286709"
      />
      <itunes:title>Source-Restricted vs. Open Retrieval: How to Lock Down Your LLM</itunes:title>
      <itunes:subtitle>When should an LLM be locked to specific documents, and when should it search the web? A practical framework for grounding decisions.</itunes:subtitle>
      <itunes:summary><![CDATA[The terminology around "closed corpus" and "open world" LLM retrieval is a mess — and the stakes are high. A model blending your documents with its own training data can turn a contract review into a malpractice suit. This episode unpacks the real distinction: source-restricted versus open retrieval, and why it's a per-generation decision, not an application-level toggle. We walk through concrete cases in legal, medical, and compliance work, then survey how LangGraph, LlamaIndex, and the Anthropic and OpenAI SDKs handle — or fail to handle — the constraint. The verdict: there's no clean primitive, just system prompts and tool lists. Here's how to wire it up without getting burned.]]></itunes:summary>
      <itunes:duration>1799</itunes:duration>
      <itunes:episode>3751</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/closed-corpus-llm-retrieval.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/closed-corpus-llm-retrieval.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Your Backup Is Probably Corrupted Right Now</title>
      <description><![CDATA[Daniel learned the hard way that a silently degrading ZFS pool can fill your backups with corruption for weeks before you notice. This episode breaks down the pre-backup health check workflow: using zpool status and scrub results to gate backups, the difference between ZFS checksums and SMART monitoring, notification systems that actually get your attention (from Pushover to PagerDuty), and the uncomfortable question of whether to pull the plug immediately if you're running without RAID. We explore the tooling landscape — zed, Sanoid, Syncoid — and why the ZFS ecosystem expects you to wire up your own safety nets.]]></description>
      <link>https://myweirdprompts.com/episode/zfs-backup-corruption-detection/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/zfs-backup-corruption-detection/</guid>
      <pubDate>Sat, 20 Jun 2026 14:13:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/zfs-backup-corruption-detection.m4a"
        type="audio/mp4"
        length="30330050"
      />
      <itunes:title>Your Backup Is Probably Corrupted Right Now</itunes:title>
      <itunes:subtitle>How to catch ZFS pool degradation before your backup faithfully preserves garbage for weeks.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel learned the hard way that a silently degrading ZFS pool can fill your backups with corruption for weeks before you notice. This episode breaks down the pre-backup health check workflow: using zpool status and scrub results to gate backups, the difference between ZFS checksums and SMART monitoring, notification systems that actually get your attention (from Pushover to PagerDuty), and the uncomfortable question of whether to pull the plug immediately if you're running without RAID. We explore the tooling landscape — zed, Sanoid, Syncoid — and why the ZFS ecosystem expects you to wire up your own safety nets.]]></itunes:summary>
      <itunes:duration>1733</itunes:duration>
      <itunes:episode>3748</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/zfs-backup-corruption-detection.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/zfs-backup-corruption-detection.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Knowledge Graphs vs SQL: How Custom Relationships Change Retrieval</title>
      <description><![CDATA[Most databases only tell you that two things are connected. Graph databases tell you how — and that distinction changes everything about retrieval. In this episode, we explore how custom relationship types like EXPLORES, REFUTES, and BUILDS_ON turn a podcast archive from a flat list into a navigable knowledge structure. We walk through concrete Neo4j schema design, show why embeddings alone collapse important nuance, and explain the vector-first, graph-second pattern that combines semantic search with structural precision. If you've wondered how to move beyond "find me similar documents" to "find me what's structurally relevant," this episode walks through the exact modeling decisions that make it work — including the pitfalls of over-taxonomizing your edge types.]]></description>
      <link>https://myweirdprompts.com/episode/knowledge-graphs-custom-relationships/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/knowledge-graphs-custom-relationships/</guid>
      <pubDate>Thu, 18 Jun 2026 00:21:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/knowledge-graphs-custom-relationships.m4a"
        type="audio/mp4"
        length="29188989"
      />
      <itunes:title>Knowledge Graphs vs SQL: How Custom Relationships Change Retrieval</itunes:title>
      <itunes:subtitle>Why naming relationships (not just connecting data) transforms how you retrieve information.</itunes:subtitle>
      <itunes:summary><![CDATA[Most databases only tell you that two things are connected. Graph databases tell you how — and that distinction changes everything about retrieval. In this episode, we explore how custom relationship types like EXPLORES, REFUTES, and BUILDS_ON turn a podcast archive from a flat list into a navigable knowledge structure. We walk through concrete Neo4j schema design, show why embeddings alone collapse important nuance, and explain the vector-first, graph-second pattern that combines semantic search with structural precision. If you've wondered how to move beyond "find me similar documents" to "find me what's structurally relevant," this episode walks through the exact modeling decisions that make it work — including the pitfalls of over-taxonomizing your edge types.]]></itunes:summary>
      <itunes:duration>1690</itunes:duration>
      <itunes:episode>3673</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/knowledge-graphs-custom-relationships.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/knowledge-graphs-custom-relationships.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your Podcast Outgrows Its Feed</title>
      <description><![CDATA[After 3,700 episodes and 68 days of continuous audio, the RSS feed is groaning under its own weight. This episode explores the technical ceiling of traditional podcast distribution—where feeds choke, platforms like Spotify and Apple Podcasts impose hidden limits, and the entire model assumes recency bias. We examine practical solutions: rolling window feeds, vector search with Algolia, knowledge graphs for non-linear discovery, curated entry points, and chapter-level indexing. Plus, why the BBC model of "feed for subscription, website for exploration" may be the path forward for massive catalogs.]]></description>
      <link>https://myweirdprompts.com/episode/podcast-feed-scale-limits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcast-feed-scale-limits/</guid>
      <pubDate>Wed, 17 Jun 2026 15:38:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcast-feed-scale-limits.m4a"
        type="audio/mp4"
        length="37128807"
      />
      <itunes:title>When Your Podcast Outgrows Its Feed</itunes:title>
      <itunes:subtitle>3,700 episodes. 68 days of audio. One RSS feed designed for 10 blog posts. Can podcast infrastructure handle this?</itunes:subtitle>
      <itunes:summary><![CDATA[After 3,700 episodes and 68 days of continuous audio, the RSS feed is groaning under its own weight. This episode explores the technical ceiling of traditional podcast distribution—where feeds choke, platforms like Spotify and Apple Podcasts impose hidden limits, and the entire model assumes recency bias. We examine practical solutions: rolling window feeds, vector search with Algolia, knowledge graphs for non-linear discovery, curated entry points, and chapter-level indexing. Plus, why the BBC model of "feed for subscription, website for exploration" may be the path forward for massive catalogs.]]></itunes:summary>
      <itunes:duration>2142</itunes:duration>
      <itunes:episode>3667</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcast-feed-scale-limits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcast-feed-scale-limits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fixing Your Phone&apos;s Variable Frame Rate Video</title>
      <description><![CDATA[Ever import phone video into an editor only to find the audio drifts out of sync? That's variable frame rate — a quirk of smartphone recording that drives editors crazy. In this episode, we break down what causes VFR, how to fix it with tools like FFmpeg and HandBrake, and whether you can force your phone to record clean footage in the first place. We also tackle the big question: is shooting RAW video on a phone actually worth the enormous file sizes and workflow headaches? For most people, the answer may surprise you.]]></description>
      <link>https://myweirdprompts.com/episode/variable-frame-rate-video-fix/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/variable-frame-rate-video-fix/</guid>
      <pubDate>Sun, 14 Jun 2026 13:49:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/variable-frame-rate-video-fix.m4a"
        type="audio/mp4"
        length="34073339"
      />
      <itunes:title>Fixing Your Phone&apos;s Variable Frame Rate Video</itunes:title>
      <itunes:subtitle>Why your phone&apos;s video drifts out of sync, how to fix it, and whether RAW video is actually worth it.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever import phone video into an editor only to find the audio drifts out of sync? That's variable frame rate — a quirk of smartphone recording that drives editors crazy. In this episode, we break down what causes VFR, how to fix it with tools like FFmpeg and HandBrake, and whether you can force your phone to record clean footage in the first place. We also tackle the big question: is shooting RAW video on a phone actually worth the enormous file sizes and workflow headaches? For most people, the answer may surprise you.]]></itunes:summary>
      <itunes:duration>1959</itunes:duration>
      <itunes:episode>3564</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/variable-frame-rate-video-fix.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/variable-frame-rate-video-fix.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can AI Review Your Lease in Israel?</title>
      <description><![CDATA[Can artificial intelligence really help you review a lease in Israel, or anywhere with specific local laws? We dig into the current state of AI contract review tools—from DoNotPay to LawFlex—and why most fall short on jurisdiction awareness. Then we tackle the build-your-own question: what would it take to create a system that uses public-domain legislation, not a massive contract database, to flag dangerous clauses? We walk through the technical stack, the false positive problem, and the gap between what's legal and what's customary in Israel's tight rental market. If you've ever signed a lease wondering what you missed, this episode is for you.]]></description>
      <link>https://myweirdprompts.com/episode/ai-lease-review-israel/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-lease-review-israel/</guid>
      <pubDate>Sun, 14 Jun 2026 09:46:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-lease-review-israel.m4a"
        type="audio/mp4"
        length="37174811"
      />
      <itunes:title>Can AI Review Your Lease in Israel?</itunes:title>
      <itunes:subtitle>Can AI actually understand Israeli tenant law? We explore the tools, the gaps, and how to build your own.</itunes:subtitle>
      <itunes:summary><![CDATA[Can artificial intelligence really help you review a lease in Israel, or anywhere with specific local laws? We dig into the current state of AI contract review tools—from DoNotPay to LawFlex—and why most fall short on jurisdiction awareness. Then we tackle the build-your-own question: what would it take to create a system that uses public-domain legislation, not a massive contract database, to flag dangerous clauses? We walk through the technical stack, the false positive problem, and the gap between what's legal and what's customary in Israel's tight rental market. If you've ever signed a lease wondering what you missed, this episode is for you.]]></itunes:summary>
      <itunes:duration>2137</itunes:duration>
      <itunes:episode>3553</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-lease-review-israel.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-lease-review-israel.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>LoRA Isn’t Just for Image Generation</title>
      <description><![CDATA[Most people associate LoRA with image generation — style adapters and character packs for Stable Diffusion. But LoRA actually originated in the language-model world, and it’s one of the most practical techniques in the entire LLM ecosystem. In this episode, we break down the 2021 Microsoft paper that introduced Low-Rank Adaptation, explain how freezing base weights and injecting tiny trainable matrices can reshape a model’s voice, format, and domain vocabulary with under 1% of the parameters. We cover the mechanics (rank, alpha, target modules), the two inference modes (merge vs. hot-swap), and four concrete use cases where a text LoRA outperforms prompt engineering — plus the trade-offs when a simple prompt is the better choice.]]></description>
      <link>https://myweirdprompts.com/episode/lora-text-llm-adaptation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/lora-text-llm-adaptation/</guid>
      <pubDate>Tue, 09 Jun 2026 19:11:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/lora-text-llm-adaptation.m4a"
        type="audio/mp4"
        length="31635401"
      />
      <itunes:title>LoRA Isn’t Just for Image Generation</itunes:title>
      <itunes:subtitle>LoRA lets you fine-tune an LLM’s behavior with a 50MB file. Here’s how it works and why it matters.</itunes:subtitle>
      <itunes:summary><![CDATA[Most people associate LoRA with image generation — style adapters and character packs for Stable Diffusion. But LoRA actually originated in the language-model world, and it’s one of the most practical techniques in the entire LLM ecosystem. In this episode, we break down the 2021 Microsoft paper that introduced Low-Rank Adaptation, explain how freezing base weights and injecting tiny trainable matrices can reshape a model’s voice, format, and domain vocabulary with under 1% of the parameters. We cover the mechanics (rank, alpha, target modules), the two inference modes (merge vs. hot-swap), and four concrete use cases where a text LoRA outperforms prompt engineering — plus the trade-offs when a simple prompt is the better choice.]]></itunes:summary>
      <itunes:duration>1819</itunes:duration>
      <itunes:episode>3406</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/lora-text-llm-adaptation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/lora-text-llm-adaptation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Desk Pets: Local AI on Your Desktop</title>
      <description><![CDATA[Desk pets are small desktop devices with local AI that can blink, whisper notifications, and integrate with your calendar and email. But are they actually useful, or just expensive Tamagotchis? This episode explores the three tiers of the market—from commercial devices like Emo and Vector 2.0 to DIY kits like the M5 Stack Desk Pet and software-only simulations. We dig into the tech stack: quantized transformer models running on $3 chips, the real privacy tradeoffs (local AI ≠ total privacy), and the psychological drivers that make people name and apologize to these devices. Plus, we discuss a surprising Carnegie Mellon preprint finding that ambient notification devices may increase task-switching by 22%. If you've ever wondered whether a plastic creature on your desk could actually help or just add another distraction, this episode is for you.]]></description>
      <link>https://myweirdprompts.com/episode/desk-pets-local-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/desk-pets-local-ai/</guid>
      <pubDate>Sun, 07 Jun 2026 09:12:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/desk-pets-local-ai.m4a"
        type="audio/mp4"
        length="26650886"
      />
      <itunes:title>Desk Pets: Local AI on Your Desktop</itunes:title>
      <itunes:subtitle>Are desk pets useful tools or expensive Tamagotchis? We break down the local AI, privacy tradeoffs, and psychology behind these devices.</itunes:subtitle>
      <itunes:summary><![CDATA[Desk pets are small desktop devices with local AI that can blink, whisper notifications, and integrate with your calendar and email. But are they actually useful, or just expensive Tamagotchis? This episode explores the three tiers of the market—from commercial devices like Emo and Vector 2.0 to DIY kits like the M5 Stack Desk Pet and software-only simulations. We dig into the tech stack: quantized transformer models running on $3 chips, the real privacy tradeoffs (local AI ≠ total privacy), and the psychological drivers that make people name and apologize to these devices. Plus, we discuss a surprising Carnegie Mellon preprint finding that ambient notification devices may increase task-switching by 22%. If you've ever wondered whether a plastic creature on your desk could actually help or just add another distraction, this episode is for you.]]></itunes:summary>
      <itunes:duration>1522</itunes:duration>
      <itunes:episode>3322</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/desk-pets-local-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/desk-pets-local-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Breadboard to PCB: Your First Board Design</title>
      <description><![CDATA[You've got the coding skills. You can wire up an ESP32 on a breadboard. But the moment you open a PCB editor, your mind goes blank. This episode walks through exactly what to click, in what order, to turn your breadboard project into a real circuit board. We cover the three tiers of free tools — browser-based EasyEDA, open-source KiCad, and beginner-focused Fritzing — and explain why the barrier is psychological, not technical. You'll learn how schematic capture is just drawing your breadboard connections, how component libraries mean you never have to model a part from scratch, and why getting five boards delivered costs less than a sandwich. No circuit theory required.]]></description>
      <link>https://myweirdprompts.com/episode/first-pcb-design-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/first-pcb-design-guide/</guid>
      <pubDate>Fri, 05 Jun 2026 14:02:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/first-pcb-design-guide.m4a"
        type="audio/mp4"
        length="33154067"
      />
      <itunes:title>From Breadboard to PCB: Your First Board Design</itunes:title>
      <itunes:subtitle>You can code and breadboard. Here&apos;s how to cross the gap to custom PCBs with free tools and minimal risk.</itunes:subtitle>
      <itunes:summary><![CDATA[You've got the coding skills. You can wire up an ESP32 on a breadboard. But the moment you open a PCB editor, your mind goes blank. This episode walks through exactly what to click, in what order, to turn your breadboard project into a real circuit board. We cover the three tiers of free tools — browser-based EasyEDA, open-source KiCad, and beginner-focused Fritzing — and explain why the barrier is psychological, not technical. You'll learn how schematic capture is just drawing your breadboard connections, how component libraries mean you never have to model a part from scratch, and why getting five boards delivered costs less than a sandwich. No circuit theory required.]]></itunes:summary>
      <itunes:duration>1890</itunes:duration>
      <itunes:episode>3289</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/first-pcb-design-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/first-pcb-design-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Agent Infrastructure Engineer: The New DevOps</title>
      <description><![CDATA[Agentic AI is no longer just tinkering with APIs — it's becoming a full engineering discipline with specialized roles, salary bands, and certification paths. In this episode, we break down the three major skill silos emerging in the field, with a deep focus on the Agent Infrastructure Engineer — the DevOps equivalent for multi-agent systems. From designing supervisor topologies and implementing circuit breakers for LLMs to building observability stacks that track token consumption and agent drift, we explore what this role actually looks like day-to-day. We also cover Agent Safety Engineering and why testing emergent failure modes is the new QA frontier, plus the training requirements that separate prototype builders from production engineers.]]></description>
      <link>https://myweirdprompts.com/episode/agent-infrastructure-engineer-devops/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-infrastructure-engineer-devops/</guid>
      <pubDate>Fri, 05 Jun 2026 12:36:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-infrastructure-engineer-devops.m4a"
        type="audio/mp4"
        length="30036763"
      />
      <itunes:title>Agent Infrastructure Engineer: The New DevOps</itunes:title>
      <itunes:subtitle>Agentic AI is splintering into real engineering disciplines. Here&apos;s what the &quot;DevOps of AI&quot; actually does.</itunes:subtitle>
      <itunes:summary><![CDATA[Agentic AI is no longer just tinkering with APIs — it's becoming a full engineering discipline with specialized roles, salary bands, and certification paths. In this episode, we break down the three major skill silos emerging in the field, with a deep focus on the Agent Infrastructure Engineer — the DevOps equivalent for multi-agent systems. From designing supervisor topologies and implementing circuit breakers for LLMs to building observability stacks that track token consumption and agent drift, we explore what this role actually looks like day-to-day. We also cover Agent Safety Engineering and why testing emergent failure modes is the new QA frontier, plus the training requirements that separate prototype builders from production engineers.]]></itunes:summary>
      <itunes:duration>1723</itunes:duration>
      <itunes:episode>3284</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-infrastructure-engineer-devops.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-infrastructure-engineer-devops.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tuning DeepSeek for One Podcast</title>
      <description><![CDATA[After thousands of episodes, we've noticed DeepSeek has some persistent quirks — overused words, recycled analogy templates, and habits no system prompt seems to cure. What if we took a hundred scripts, wrote human feedback on each, and fine-tuned a version of DeepSeek optimized solely for producing this podcast? This episode breaks down the practical steps: collecting feedback data, choosing between supervised fine-tuning and DPO, structuring training examples within DeepSeek's 128K context window, and using LoRA to avoid catastrophic forgetting. We also tackle the question of where character personalities should live — baked into the fine-tune or kept in the system prompt. It's a deep dive into whether purpose-specific fine-tuning is practical engineering or just a beautiful fantasy.]]></description>
      <link>https://myweirdprompts.com/episode/fine-tuning-deepseek-podcast/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fine-tuning-deepseek-podcast/</guid>
      <pubDate>Fri, 05 Jun 2026 12:29:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fine-tuning-deepseek-podcast.m4a"
        type="audio/mp4"
        length="34075496"
      />
      <itunes:title>Fine-Tuning DeepSeek for One Podcast</itunes:title>
      <itunes:subtitle>Can a purpose-specific fine-tune fix a model&apos;s stubborn writing tics? We explore the practical engineering behind it.</itunes:subtitle>
      <itunes:summary><![CDATA[After thousands of episodes, we've noticed DeepSeek has some persistent quirks — overused words, recycled analogy templates, and habits no system prompt seems to cure. What if we took a hundred scripts, wrote human feedback on each, and fine-tuned a version of DeepSeek optimized solely for producing this podcast? This episode breaks down the practical steps: collecting feedback data, choosing between supervised fine-tuning and DPO, structuring training examples within DeepSeek's 128K context window, and using LoRA to avoid catastrophic forgetting. We also tackle the question of where character personalities should live — baked into the fine-tune or kept in the system prompt. It's a deep dive into whether purpose-specific fine-tuning is practical engineering or just a beautiful fantasy.]]></itunes:summary>
      <itunes:duration>1946</itunes:duration>
      <itunes:episode>3283</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fine-tuning-deepseek-podcast.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fine-tuning-deepseek-podcast.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Get Early AI Model Access as a Solo Developer</title>
      <description><![CDATA[Think you need to spend thousands to get early access to cutting-edge AI models? Think again. This episode unpacks the information asymmetry problem in AI and reveals how solo developers can bridge the gap — not through sales channels, but through developer relations. We break down the exact cold email structure that gets DevRel teams to pay attention, the automated monitoring techniques that catch new models before they're announced, and why your production use case is more valuable than you think. Whether you're running a podcast pipeline on DeepSeek or building a translation service on Claude, the strategies are the same: lead with what you offer, not what you want.]]></description>
      <link>https://myweirdprompts.com/episode/early-ai-model-access/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/early-ai-model-access/</guid>
      <pubDate>Fri, 05 Jun 2026 11:49:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/early-ai-model-access.m4a"
        type="audio/mp4"
        length="28430660"
      />
      <itunes:title>How to Get Early AI Model Access as a Solo Developer</itunes:title>
      <itunes:subtitle>How a solo developer spending $300/month can get early access to new AI models before the press release.</itunes:subtitle>
      <itunes:summary><![CDATA[Think you need to spend thousands to get early access to cutting-edge AI models? Think again. This episode unpacks the information asymmetry problem in AI and reveals how solo developers can bridge the gap — not through sales channels, but through developer relations. We break down the exact cold email structure that gets DevRel teams to pay attention, the automated monitoring techniques that catch new models before they're announced, and why your production use case is more valuable than you think. Whether you're running a podcast pipeline on DeepSeek or building a translation service on Claude, the strategies are the same: lead with what you offer, not what you want.]]></itunes:summary>
      <itunes:duration>1636</itunes:duration>
      <itunes:episode>3278</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/early-ai-model-access.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/early-ai-model-access.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>LLMs as Parsers, Not Calculators</title>
      <description><![CDATA[Apartment hunting means comparing rentals with different fee structures, periods, and VAT rules. The math is simple, but normalizing the inputs is a cognitive nightmare. This episode explores why the right architecture isn't a chatbot — it's a pipeline where an LLM extracts structured data from messy natural language, and deterministic code handles all arithmetic. We walk through function calling, prompt engineering for edge cases, and how MCP connections can save results to spreadsheets. A practical pattern for any comparison problem involving messy inputs.]]></description>
      <link>https://myweirdprompts.com/episode/llm-parser-rental-calculator/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-parser-rental-calculator/</guid>
      <pubDate>Fri, 05 Jun 2026 10:55:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-parser-rental-calculator.m4a"
        type="audio/mp4"
        length="32057586"
      />
      <itunes:title>LLMs as Parsers, Not Calculators</itunes:title>
      <itunes:subtitle>Stop letting LLMs do math. Use them to parse messy text, then let deterministic code handle the numbers.</itunes:subtitle>
      <itunes:summary><![CDATA[Apartment hunting means comparing rentals with different fee structures, periods, and VAT rules. The math is simple, but normalizing the inputs is a cognitive nightmare. This episode explores why the right architecture isn't a chatbot — it's a pipeline where an LLM extracts structured data from messy natural language, and deterministic code handles all arithmetic. We walk through function calling, prompt engineering for edge cases, and how MCP connections can save results to spreadsheets. A practical pattern for any comparison problem involving messy inputs.]]></itunes:summary>
      <itunes:duration>1852</itunes:duration>
      <itunes:episode>3271</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-parser-rental-calculator.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-parser-rental-calculator.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Your Own Cloud in 2026</title>
      <description><![CDATA[The economics of building your own private cloud have genuinely flipped in the last eighteen months. Ten-gigabit networking is dirt cheap, used enterprise hardware is abundant, and the software stack has matured to the point where most pain points that made DIY cloud a hobbyist nightmare are gone. This episode breaks down the three pillars — storage, compute, and GPU orchestration — and the specific tools that make each work. We compare MinIO vs Ceph vs Garage for S3-compatible object storage, Proxmox vs Incus for compute management, and explore how consumer GPUs can power AI inference in a self-hosted environment. If you've ever wondered whether you can actually build your own cloud across multiple properties, the answer is yes — and this episode shows you exactly which pieces to pick and in what order.]]></description>
      <link>https://myweirdprompts.com/episode/private-cloud-build-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/private-cloud-build-guide/</guid>
      <pubDate>Tue, 02 Jun 2026 10:02:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/private-cloud-build-guide.m4a"
        type="audio/mp4"
        length="29952189"
      />
      <itunes:title>Building Your Own Cloud in 2026</itunes:title>
      <itunes:subtitle>The software and hardware for a DIY private cloud have never been more feasible. Here&apos;s how to pick the right pieces.</itunes:subtitle>
      <itunes:summary><![CDATA[The economics of building your own private cloud have genuinely flipped in the last eighteen months. Ten-gigabit networking is dirt cheap, used enterprise hardware is abundant, and the software stack has matured to the point where most pain points that made DIY cloud a hobbyist nightmare are gone. This episode breaks down the three pillars — storage, compute, and GPU orchestration — and the specific tools that make each work. We compare MinIO vs Ceph vs Garage for S3-compatible object storage, Proxmox vs Incus for compute management, and explore how consumer GPUs can power AI inference in a self-hosted environment. If you've ever wondered whether you can actually build your own cloud across multiple properties, the answer is yes — and this episode shows you exactly which pieces to pick and in what order.]]></itunes:summary>
      <itunes:duration>1730</itunes:duration>
      <itunes:episode>3218</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/private-cloud-build-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/private-cloud-build-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Drawing the Melody: SSML&apos;s Hidden Power</title>
      <description><![CDATA[Raw text to speech sounds good. SSML makes it sound *right*. In this episode, we explore Speech Synthesis Markup Language — the XML-based standard that lets developers control prosody, emotion, and pacing in neural TTS. We break down the prosody tag's contour feature (think: drawing the melody of a sentence), ElevenLabs' custom emotion tag (whisper, excited, sad — applied to any voice), and the trap of over-tagging. With ElevenLabs supporting ~28 tags and emerging as the standard's gravitational center, understanding SSML is becoming essential for anyone building voice applications, audiobooks, or conversational AI.]]></description>
      <link>https://myweirdprompts.com/episode/ssml-speech-markup-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ssml-speech-markup-guide/</guid>
      <pubDate>Mon, 01 Jun 2026 09:27:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ssml-speech-markup-guide.m4a"
        type="audio/mp4"
        length="29839954"
      />
      <itunes:title>Drawing the Melody: SSML&apos;s Hidden Power</itunes:title>
      <itunes:subtitle>How SSML gives developers narrative control over AI voices — and why ElevenLabs became its center of gravity.</itunes:subtitle>
      <itunes:summary><![CDATA[Raw text to speech sounds good. SSML makes it sound *right*. In this episode, we explore Speech Synthesis Markup Language — the XML-based standard that lets developers control prosody, emotion, and pacing in neural TTS. We break down the prosody tag's contour feature (think: drawing the melody of a sentence), ElevenLabs' custom emotion tag (whisper, excited, sad — applied to any voice), and the trap of over-tagging. With ElevenLabs supporting ~28 tags and emerging as the standard's gravitational center, understanding SSML is becoming essential for anyone building voice applications, audiobooks, or conversational AI.]]></itunes:summary>
      <itunes:duration>1710</itunes:duration>
      <itunes:episode>3189</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ssml-speech-markup-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ssml-speech-markup-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Break an LLM&apos;s Bad Verbal Habits</title>
      <description><![CDATA[Every large language model develops a fingerprint of overused phrases — "delve," "failure modes," "it's worth noting." Blacklists leak into output, regex silently inverts meaning, and feeding previous episodes into context only compounds the problem. This episode explores three practical approaches to breaking the reinforcement loop: compressed style summaries with decay, dual-model editing architectures, and context-aware regex using dependency parsing. No silver bullets, just real trade-offs for production pipelines.]]></description>
      <link>https://myweirdprompts.com/episode/llm-verbal-tic-fix/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-verbal-tic-fix/</guid>
      <pubDate>Sun, 31 May 2026 16:13:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-verbal-tic-fix.m4a"
        type="audio/mp4"
        length="35655411"
      />
      <itunes:title>How to Break an LLM&apos;s Bad Verbal Habits</itunes:title>
      <itunes:subtitle>Blacklists fail and regex inverts meaning. Here&apos;s what actually works to clean up AI writing tics.</itunes:subtitle>
      <itunes:summary><![CDATA[Every large language model develops a fingerprint of overused phrases — "delve," "failure modes," "it's worth noting." Blacklists leak into output, regex silently inverts meaning, and feeding previous episodes into context only compounds the problem. This episode explores three practical approaches to breaking the reinforcement loop: compressed style summaries with decay, dual-model editing architectures, and context-aware regex using dependency parsing. No silver bullets, just real trade-offs for production pipelines.]]></itunes:summary>
      <itunes:duration>2056</itunes:duration>
      <itunes:episode>3171</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-verbal-tic-fix.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-verbal-tic-fix.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>30 BLE Tags for $60: DIY ADHD Object Tracking</title>
      <description><![CDATA[Tired of hunting for your phone, wallet, and keys every morning? Commercial trackers like Tile and AirTag break down when you need to track more than a handful of items. This episode reveals a radically cheaper alternative: a self-hosted BLE tracking system built on ESP32 proxies and Home Assistant that scales to 30+ tags for about $60 in hardware. We explore the architectural shift from persistent connections to connectionless scanning, how to build a portable travel tracker with a Raspberry Pi, and the ADHD-specific automations that turn a finder into an external working memory for physical objects. No subscriptions. No cloud dependency. Just dumb radio beacons and one smart listener.]]></description>
      <link>https://myweirdprompts.com/episode/diy-ble-tracking-home-assistant/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/diy-ble-tracking-home-assistant/</guid>
      <pubDate>Sun, 31 May 2026 13:22:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/diy-ble-tracking-home-assistant.m4a"
        type="audio/mp4"
        length="28907529"
      />
      <itunes:title>30 BLE Tags for $60: DIY ADHD Object Tracking</itunes:title>
      <itunes:subtitle>Stop losing your stuff. Build a self-hosted BLE tracker system for 30 items at 1/8 the cost of commercial trackers.</itunes:subtitle>
      <itunes:summary><![CDATA[Tired of hunting for your phone, wallet, and keys every morning? Commercial trackers like Tile and AirTag break down when you need to track more than a handful of items. This episode reveals a radically cheaper alternative: a self-hosted BLE tracking system built on ESP32 proxies and Home Assistant that scales to 30+ tags for about $60 in hardware. We explore the architectural shift from persistent connections to connectionless scanning, how to build a portable travel tracker with a Raspberry Pi, and the ADHD-specific automations that turn a finder into an external working memory for physical objects. No subscriptions. No cloud dependency. Just dumb radio beacons and one smart listener.]]></itunes:summary>
      <itunes:duration>1645</itunes:duration>
      <itunes:episode>3168</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/diy-ble-tracking-home-assistant.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/diy-ble-tracking-home-assistant.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Makes Agentic Search Tools Like Exa Actually Work?</title>
      <description><![CDATA[We swapped Google Search and Tavily for Exa AI in our production pipeline, and the accuracy jump was dramatic. This episode unpacks what makes agentic search tools fundamentally different: neural embeddings that match meaning instead of keywords, an agentic loop that decomposes and refines queries like a human researcher, and structured metadata that preserves provenance. We compare three approaches to search — Google's popularity-based index, Tavily's single-shot summarization, and Exa's iterative neural search — and explain why the retrieval layer matters more than most people realize when building reliable AI systems.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-search-exa-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-search-exa-architecture/</guid>
      <pubDate>Fri, 29 May 2026 13:44:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-search-exa-architecture.m4a"
        type="audio/mp4"
        length="35425972"
      />
      <itunes:title>What Makes Agentic Search Tools Like Exa Actually Work?</itunes:title>
      <itunes:subtitle>Why swapping Google for Exa transformed our show&apos;s accuracy — and what agentic search does differently.</itunes:subtitle>
      <itunes:summary><![CDATA[We swapped Google Search and Tavily for Exa AI in our production pipeline, and the accuracy jump was dramatic. This episode unpacks what makes agentic search tools fundamentally different: neural embeddings that match meaning instead of keywords, an agentic loop that decomposes and refines queries like a human researcher, and structured metadata that preserves provenance. We compare three approaches to search — Google's popularity-based index, Tavily's single-shot summarization, and Exa's iterative neural search — and explain why the retrieval layer matters more than most people realize when building reliable AI systems.]]></itunes:summary>
      <itunes:duration>2033</itunes:duration>
      <itunes:episode>3120</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-search-exa-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-search-exa-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Animating Toy Story: Math, Patience, and No Undo Button</title>
      <description><![CDATA[Before Pixar could make Woody blink, animators typed coordinates by hand, waited four hours per frame, and had no undo button. This episode unpacks what it actually felt like to animate the first fully computer-generated feature film — from the Silicon Graphics workstations with 64MB of RAM to the sweatbox screenings where John Lasseter gave acting notes to wireframe skeletons. We explore how traditional Disney animators learned to sculpt with spreadsheets, why limiting Buzz and Woody to around 200 facial controls actually made them more expressive, and what the forty-five-day render queue meant when someone spotted a mistake in frame 83,000.]]></description>
      <link>https://myweirdprompts.com/episode/animating-toy-story-pixar/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/animating-toy-story-pixar/</guid>
      <pubDate>Sat, 23 May 2026 22:00:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/animating-toy-story-pixar.m4a"
        type="audio/mp4"
        length="28960725"
      />
      <itunes:title>Animating Toy Story: Math, Patience, and No Undo Button</itunes:title>
      <itunes:subtitle>Before Pixar could make Woody blink, animators typed coordinates by hand and waited hours to see if it worked.</itunes:subtitle>
      <itunes:summary><![CDATA[Before Pixar could make Woody blink, animators typed coordinates by hand, waited four hours per frame, and had no undo button. This episode unpacks what it actually felt like to animate the first fully computer-generated feature film — from the Silicon Graphics workstations with 64MB of RAM to the sweatbox screenings where John Lasseter gave acting notes to wireframe skeletons. We explore how traditional Disney animators learned to sculpt with spreadsheets, why limiting Buzz and Woody to around 200 facial controls actually made them more expressive, and what the forty-five-day render queue meant when someone spotted a mistake in frame 83,000.]]></itunes:summary>
      <itunes:duration>1663</itunes:duration>
      <itunes:episode>3038</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/animating-toy-story-pixar.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/animating-toy-story-pixar.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>3,000 Episodes, 3 Copies: Is This Backup Setup Enough?</title>
      <description><![CDATA[When you have 3,000 episodes stored across Cloudflare R2, Wasabi, and a local NAS, it sounds like the gold standard of backup strategy. But as we dig into the details, the real question isn't how many copies you have — it's whether you've tested that any of them actually work. We explore the critical difference between a metadata mirror and a true backup, why object locking and versioning matter more than durability percentages, and the single authentication surface that could compromise all three copies at once. If you think three copies means you're safe, this episode will make you think again.]]></description>
      <link>https://myweirdprompts.com/episode/backup-strategy-testing-cloud-nas/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/backup-strategy-testing-cloud-nas/</guid>
      <pubDate>Sat, 23 May 2026 20:38:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/backup-strategy-testing-cloud-nas.m4a"
        type="audio/mp4"
        length="36404400"
      />
      <itunes:title>3,000 Episodes, 3 Copies: Is This Backup Setup Enough?</itunes:title>
      <itunes:subtitle>Three copies, two clouds, one NAS. But is this setup truly protecting 3,000 podcast episodes?</itunes:subtitle>
      <itunes:summary><![CDATA[When you have 3,000 episodes stored across Cloudflare R2, Wasabi, and a local NAS, it sounds like the gold standard of backup strategy. But as we dig into the details, the real question isn't how many copies you have — it's whether you've tested that any of them actually work. We explore the critical difference between a metadata mirror and a true backup, why object locking and versioning matter more than durability percentages, and the single authentication surface that could compromise all three copies at once. If you think three copies means you're safe, this episode will make you think again.]]></itunes:summary>
      <itunes:duration>2096</itunes:duration>
      <itunes:episode>3033</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/backup-strategy-testing-cloud-nas.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/backup-strategy-testing-cloud-nas.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Incrementally Back Up Google Photos to Your NAS</title>
      <description><![CDATA[If you lose your Google account, your child's entire documented life could vanish. This episode breaks down how to build a reliable, incremental backup pipeline for Google Photos — using the Library API, a hash-based deduplication system on your NAS, and replication to Wasabi, Backblaze, and M-Discs. No more re-downloading thousands of photos every quarter.]]></description>
      <link>https://myweirdprompts.com/episode/google-photos-incremental-backup/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/google-photos-incremental-backup/</guid>
      <pubDate>Sat, 23 May 2026 18:40:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/google-photos-incremental-backup.m4a"
        type="audio/mp4"
        length="31557402"
      />
      <itunes:title>How to Incrementally Back Up Google Photos to Your NAS</itunes:title>
      <itunes:subtitle>Build a quarterly backup pipeline for Google Photos using the Library API, hash deduplication, and your NAS.</itunes:subtitle>
      <itunes:summary><![CDATA[If you lose your Google account, your child's entire documented life could vanish. This episode breaks down how to build a reliable, incremental backup pipeline for Google Photos — using the Library API, a hash-based deduplication system on your NAS, and replication to Wasabi, Backblaze, and M-Discs. No more re-downloading thousands of photos every quarter.]]></itunes:summary>
      <itunes:duration>1810</itunes:duration>
      <itunes:episode>3024</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/google-photos-incremental-backup.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/google-photos-incremental-backup.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Chatterbox Locks Your Voice Clone Across Thousands of Generations</title>
      <description><![CDATA[Most single-shot voice cloning models sound great on generation one but drift by generation ten thousand. Chatterbox avoids this entirely with a cached speaker embedding and deterministic duration prediction. We pop the hood on the architecture—flow-matching decoder, WavLM-based extraction, BERT phonemizer—and explain why Resemble AI's open-source strategy makes this model a production workhorse. Plus: where the open-source TTS landscape stands in mid-2026 and what practitioners should watch for.]]></description>
      <link>https://myweirdprompts.com/episode/chatterbox-voice-cloning-consistency/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/chatterbox-voice-cloning-consistency/</guid>
      <pubDate>Sat, 23 May 2026 14:52:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/chatterbox-voice-cloning-consistency.m4a"
        type="audio/mp4"
        length="29279100"
      />
      <itunes:title>How Chatterbox Locks Your Voice Clone Across Thousands of Generations</itunes:title>
      <itunes:subtitle>Why most single-shot TTS models drift over time—and how Chatterbox&apos;s cached embedding approach solves it.</itunes:subtitle>
      <itunes:summary><![CDATA[Most single-shot voice cloning models sound great on generation one but drift by generation ten thousand. Chatterbox avoids this entirely with a cached speaker embedding and deterministic duration prediction. We pop the hood on the architecture—flow-matching decoder, WavLM-based extraction, BERT phonemizer—and explain why Resemble AI's open-source strategy makes this model a production workhorse. Plus: where the open-source TTS landscape stands in mid-2026 and what practitioners should watch for.]]></itunes:summary>
      <itunes:duration>1682</itunes:duration>
      <itunes:episode>3020</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/chatterbox-voice-cloning-consistency.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/chatterbox-voice-cloning-consistency.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your TTS Model Nails &quot;Shabbat&quot; but Not &quot;Keren Hishtalmut</title>
      <description><![CDATA[Ever notice how your text-to-speech model nails "Shabbat" but completely butchers "Keren Hishtalmut"? It's not a bug — it's a consequence of how training data is distributed. This episode unpacks the tokenization mechanics, the loanword threshold, and the Zipfian distribution problem that leaves niche multilingual vocabulary stranded. We explore why fine-tuning isn't practical for proprietary APIs, and walk through the current workarounds — from SSML phoneme overrides to phonetic spelling hacks — that can rescue your code-switched content today.]]></description>
      <link>https://myweirdprompts.com/episode/tts-code-switching-hebrew-vocabulary/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tts-code-switching-hebrew-vocabulary/</guid>
      <pubDate>Fri, 22 May 2026 18:11:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tts-code-switching-hebrew-vocabulary.m4a"
        type="audio/mp4"
        length="28662840"
      />
      <itunes:title>Why Your TTS Model Nails &quot;Shabbat&quot; but Not &quot;Keren Hishtalmut</itunes:title>
      <itunes:subtitle>Why multilingual TTS models handle loanwords but fail at niche vocabulary — and what you can do about it.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever notice how your text-to-speech model nails "Shabbat" but completely butchers "Keren Hishtalmut"? It's not a bug — it's a consequence of how training data is distributed. This episode unpacks the tokenization mechanics, the loanword threshold, and the Zipfian distribution problem that leaves niche multilingual vocabulary stranded. We explore why fine-tuning isn't practical for proprietary APIs, and walk through the current workarounds — from SSML phoneme overrides to phonetic spelling hacks — that can rescue your code-switched content today.]]></itunes:summary>
      <itunes:duration>1645</itunes:duration>
      <itunes:episode>2982</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tts-code-switching-hebrew-vocabulary.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tts-code-switching-hebrew-vocabulary.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Mapping a Gamepad to Control Android Dictation</title>
      <description><![CDATA[Ever wanted physical buttons for your dictation app? This episode breaks down the surprisingly complex bridge between Bluetooth gamepads and Android's touch-first interface. We explore three identification methods for UI elements, three workaround strategies when intents aren't available, and reveal why the 8BitDo Micro's hidden keyboard mode changes everything. No root required — just accessibility APIs and a little ingenuity.]]></description>
      <link>https://myweirdprompts.com/episode/gamepad-android-dictation-control/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gamepad-android-dictation-control/</guid>
      <pubDate>Wed, 20 May 2026 13:37:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gamepad-android-dictation-control.m4a"
        type="audio/mp4"
        length="32911852"
      />
      <itunes:title>Mapping a Gamepad to Control Android Dictation</itunes:title>
      <itunes:subtitle>How to map an 8BitDo Micro gamepad to control dictation apps on Android without root access.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wanted physical buttons for your dictation app? This episode breaks down the surprisingly complex bridge between Bluetooth gamepads and Android's touch-first interface. We explore three identification methods for UI elements, three workaround strategies when intents aren't available, and reveal why the 8BitDo Micro's hidden keyboard mode changes everything. No root required — just accessibility APIs and a little ingenuity.]]></itunes:summary>
      <itunes:duration>1887</itunes:duration>
      <itunes:episode>2943</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gamepad-android-dictation-control.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gamepad-android-dictation-control.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Distrobox: Linux Containers That Feel Like Native Apps</title>
      <description><![CDATA[Distrobox wraps Podman or Docker to create rootless containers that share your home directory, GPU, display, and audio with the host — no virtualization overhead. This episode explores the architecture behind user namespaces, device passthrough, and init management, then walks through five killer use cases: running Arch's AUR on Fedora Silverblue, isolating CUDA toolchains on immutable systems, containing messy build environments, distro tourism without dual-booting, and solving glibc mismatch problems. We also bust the big misconception: Distrobox is a workflow tool, not a security boundary.]]></description>
      <link>https://myweirdprompts.com/episode/distrobox-linux-containers-tutorial/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/distrobox-linux-containers-tutorial/</guid>
      <pubDate>Wed, 20 May 2026 11:45:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/distrobox-linux-containers-tutorial.m4a"
        type="audio/mp4"
        length="26673194"
      />
      <itunes:title>Distrobox: Linux Containers That Feel Like Native Apps</itunes:title>
      <itunes:subtitle>How Distrobox merges container isolation with native desktop integration for immutable distros, GPU work, and messy builds.</itunes:subtitle>
      <itunes:summary><![CDATA[Distrobox wraps Podman or Docker to create rootless containers that share your home directory, GPU, display, and audio with the host — no virtualization overhead. This episode explores the architecture behind user namespaces, device passthrough, and init management, then walks through five killer use cases: running Arch's AUR on Fedora Silverblue, isolating CUDA toolchains on immutable systems, containing messy build environments, distro tourism without dual-booting, and solving glibc mismatch problems. We also bust the big misconception: Distrobox is a workflow tool, not a security boundary.]]></itunes:summary>
      <itunes:duration>1534</itunes:duration>
      <itunes:episode>2941</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/distrobox-linux-containers-tutorial.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/distrobox-linux-containers-tutorial.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Distrobox: Linux Containers for Humans, Not Servers</title>
      <description><![CDATA[Distrobox transforms containers from ephemeral server tools into persistent, GPU-accelerated, GUI-ready Linux workstations. Learn how this tool lets you run Arch's AUR on Fedora Silverblue, test Ubuntu on Debian without rebooting, and isolate build environments that vanish when you're done—all with near-zero overhead and your existing files, apps, and devices intact.]]></description>
      <link>https://myweirdprompts.com/episode/distrobox-linux-containers-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/distrobox-linux-containers-explained/</guid>
      <pubDate>Wed, 20 May 2026 11:42:47 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/distrobox-linux-containers-explained.m4a"
        type="audio/mp4"
        length="26330155"
      />
      <itunes:title>Distrobox: Linux Containers for Humans, Not Servers</itunes:title>
      <itunes:subtitle>Run any distro&apos;s apps on any Linux host—no VM, no dual-boot, no dependency hell.</itunes:subtitle>
      <itunes:summary><![CDATA[Distrobox transforms containers from ephemeral server tools into persistent, GPU-accelerated, GUI-ready Linux workstations. Learn how this tool lets you run Arch's AUR on Fedora Silverblue, test Ubuntu on Debian without rebooting, and isolate build environments that vanish when you're done—all with near-zero overhead and your existing files, apps, and devices intact.]]></itunes:summary>
      <itunes:duration>1513</itunes:duration>
      <itunes:episode>2940</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/distrobox-linux-containers-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/distrobox-linux-containers-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can a Security Camera Detect a Baby Not Moving?</title>
      <description><![CDATA[A listener asks two questions hiding in one: can a security camera detect a baby about to fall off the bed, and can it detect when the baby stops moving entirely? We walk through the engineering reality behind each use case—from zone-based bounding box checks to pose estimation on infant bodies, and why the "non-motion" problem is fundamentally harder than it sounds. Along the way, we grapple with a deeper question: just because you can build it, should you? With data showing that smart alerts increase parental anxiety, this episode is as much about risk assessment as it is about computer vision.]]></description>
      <link>https://myweirdprompts.com/episode/baby-monitor-motion-detection/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/baby-monitor-motion-detection/</guid>
      <pubDate>Wed, 20 May 2026 11:30:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/baby-monitor-motion-detection.m4a"
        type="audio/mp4"
        length="36132413"
      />
      <itunes:title>Can a Security Camera Detect a Baby Not Moving?</itunes:title>
      <itunes:subtitle>Can AI tell when a baby is about to fall—or has stopped moving? We break down what&apos;s possible and what&apos;s not.</itunes:subtitle>
      <itunes:summary><![CDATA[A listener asks two questions hiding in one: can a security camera detect a baby about to fall off the bed, and can it detect when the baby stops moving entirely? We walk through the engineering reality behind each use case—from zone-based bounding box checks to pose estimation on infant bodies, and why the "non-motion" problem is fundamentally harder than it sounds. Along the way, we grapple with a deeper question: just because you can build it, should you? With data showing that smart alerts increase parental anxiety, this episode is as much about risk assessment as it is about computer vision.]]></itunes:summary>
      <itunes:duration>2084</itunes:duration>
      <itunes:episode>2939</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/baby-monitor-motion-detection.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/baby-monitor-motion-detection.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Prevent Linux Desktop Crashes Under Heavy Load</title>
      <description><![CDATA[Your Linux workstation freezes mid-edit — the cursor stops, the compositor dies, and you lose unsaved work. This episode breaks down three distinct failure domains: memory exhaustion (where the OOM killer shoots the wrong process), CPU lockups (where I/O stalls freeze your desktop), and GPU/VRAM overcommit (where driver hangs kill graphics contexts). You'll learn how to configure earlyoom with precise thresholds, tune kernel parameters like dirty ratios and hung task timeouts, set real-time priorities with chrt, and use Claude Code as a recovery tool from a TTY. Whether you're editing video in Kdenlive, running ML training scripts, or doing data science on Linux, these techniques keep your graphical session alive when things go wrong.]]></description>
      <link>https://myweirdprompts.com/episode/linux-desktop-crash-prevention/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/linux-desktop-crash-prevention/</guid>
      <pubDate>Wed, 20 May 2026 11:20:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/linux-desktop-crash-prevention.m4a"
        type="audio/mp4"
        length="38197447"
      />
      <itunes:title>How to Prevent Linux Desktop Crashes Under Heavy Load</itunes:title>
      <itunes:subtitle>Stop losing work to memory exhaustion, CPU lockups, and GPU hangs on Linux workstations.</itunes:subtitle>
      <itunes:summary><![CDATA[Your Linux workstation freezes mid-edit — the cursor stops, the compositor dies, and you lose unsaved work. This episode breaks down three distinct failure domains: memory exhaustion (where the OOM killer shoots the wrong process), CPU lockups (where I/O stalls freeze your desktop), and GPU/VRAM overcommit (where driver hangs kill graphics contexts). You'll learn how to configure earlyoom with precise thresholds, tune kernel parameters like dirty ratios and hung task timeouts, set real-time priorities with chrt, and use Claude Code as a recovery tool from a TTY. Whether you're editing video in Kdenlive, running ML training scripts, or doing data science on Linux, these techniques keep your graphical session alive when things go wrong.]]></itunes:summary>
      <itunes:duration>2207</itunes:duration>
      <itunes:episode>2938</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/linux-desktop-crash-prevention.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/linux-desktop-crash-prevention.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Still Can&apos;t Really Teach You to Code</title>
      <description><![CDATA[We explore the surprising gap between AI code generation tools and genuine coding tutors. While Cursor and Claude Code optimize for shipping speed, true pedagogical agents that remember your struggles and sequence your learning remain elusive. We break down why Khanmigo, Replit, Codecademy, and even Anthropic's Claude for Education fall short — and what architectural breakthroughs are needed to build a tutor that actually teaches, not just answers.]]></description>
      <link>https://myweirdprompts.com/episode/ai-coding-tutor-limits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-coding-tutor-limits/</guid>
      <pubDate>Wed, 20 May 2026 10:15:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-coding-tutor-limits.m4a"
        type="audio/mp4"
        length="28338835"
      />
      <itunes:title>Why AI Still Can&apos;t Really Teach You to Code</itunes:title>
      <itunes:subtitle>Code generators ship code. Real tutors build understanding. Why the gap is bigger than you think.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the surprising gap between AI code generation tools and genuine coding tutors. While Cursor and Claude Code optimize for shipping speed, true pedagogical agents that remember your struggles and sequence your learning remain elusive. We break down why Khanmigo, Replit, Codecademy, and even Anthropic's Claude for Education fall short — and what architectural breakthroughs are needed to build a tutor that actually teaches, not just answers.]]></itunes:summary>
      <itunes:duration>1623</itunes:duration>
      <itunes:episode>2936</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-coding-tutor-limits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-coding-tutor-limits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Notebooks vs Scripts: The Real Tradeoffs</title>
      <description><![CDATA[Notebooks are everywhere in data science, yet engineers often dismiss them as toys. This episode unpacks the real tension: notebooks excel at exploration but struggle with reproducibility due to hidden state and execution order issues. We examine the economics of cloud notebook platforms, the legitimate production use cases like scheduled reporting, and how tools like Papermill bridge the gap. Whether you're a Jupyter loyalist or a script purist, you'll understand the tradeoffs better.]]></description>
      <link>https://myweirdprompts.com/episode/notebooks-vs-scripts-tradeoffs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/notebooks-vs-scripts-tradeoffs/</guid>
      <pubDate>Wed, 20 May 2026 10:12:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/notebooks-vs-scripts-tradeoffs.m4a"
        type="audio/mp4"
        length="29072947"
      />
      <itunes:title>Notebooks vs Scripts: The Real Tradeoffs</itunes:title>
      <itunes:subtitle>Why data scientists love notebooks but engineers distrust them — and who&apos;s right.</itunes:subtitle>
      <itunes:summary><![CDATA[Notebooks are everywhere in data science, yet engineers often dismiss them as toys. This episode unpacks the real tension: notebooks excel at exploration but struggle with reproducibility due to hidden state and execution order issues. We examine the economics of cloud notebook platforms, the legitimate production use cases like scheduled reporting, and how tools like Papermill bridge the gap. Whether you're a Jupyter loyalist or a script purist, you'll understand the tradeoffs better.]]></itunes:summary>
      <itunes:duration>1670</itunes:duration>
      <itunes:episode>2935</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/notebooks-vs-scripts-tradeoffs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/notebooks-vs-scripts-tradeoffs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Adding One Agent Breaks Everything</title>
      <description><![CDATA[Everyone assumes scaling multi-agent systems is about cost. It's not. In this episode, we break down the real constraints — latency compounding, silent failure propagation, and the brutal math of agent reliability budgets. Using a concrete podcast transcription pipeline as a case study, we explore why adding that one extra agent can corrupt your entire output, how parallelization changes the equation, and where human checkpoints actually belong. Real production data from a 2025 survey of 200 agentic systems reveals the median agent count is just seven — and why most teams are right to keep it small.]]></description>
      <link>https://myweirdprompts.com/episode/multi-agent-system-fragility/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-agent-system-fragility/</guid>
      <pubDate>Tue, 19 May 2026 11:33:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-agent-system-fragility.m4a"
        type="audio/mp4"
        length="37650155"
      />
      <itunes:title>When Adding One Agent Breaks Everything</itunes:title>
      <itunes:subtitle>The math behind why your 100-agent pipeline fails 40% of the time — and what to do about it.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone assumes scaling multi-agent systems is about cost. It's not. In this episode, we break down the real constraints — latency compounding, silent failure propagation, and the brutal math of agent reliability budgets. Using a concrete podcast transcription pipeline as a case study, we explore why adding that one extra agent can corrupt your entire output, how parallelization changes the equation, and where human checkpoints actually belong. Real production data from a 2025 survey of 200 agentic systems reveals the median agent count is just seven — and why most teams are right to keep it small.]]></itunes:summary>
      <itunes:duration>2183</itunes:duration>
      <itunes:episode>2924</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-agent-system-fragility.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-agent-system-fragility.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Structured Outputs: Taming AI&apos;s Token Lottery</title>
      <description><![CDATA[Most developers discover the hard way that asking an AI nicely for JSON doesn't guarantee consistent results. This episode explores API-level schema enforcement — the technical mechanism that constrains which tokens a model can produce. Through two real case studies (footnote extraction from a published book and an inventory management system), we examine how structured outputs eliminate the "token lottery" problem, how chunking strategies improve extraction accuracy, and how to design schemas that handle nullable fields and unexpected data without collapsing into chaos.]]></description>
      <link>https://myweirdprompts.com/episode/structured-outputs-ai-schema/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/structured-outputs-ai-schema/</guid>
      <pubDate>Tue, 19 May 2026 11:29:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/structured-outputs-ai-schema.m4a"
        type="audio/mp4"
        length="43868801"
      />
      <itunes:title>Structured Outputs: Taming AI&apos;s Token Lottery</itunes:title>
      <itunes:subtitle>Why prompt engineering isn&apos;t enough to get consistent JSON from LLMs.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers discover the hard way that asking an AI nicely for JSON doesn't guarantee consistent results. This episode explores API-level schema enforcement — the technical mechanism that constrains which tokens a model can produce. Through two real case studies (footnote extraction from a published book and an inventory management system), we examine how structured outputs eliminate the "token lottery" problem, how chunking strategies improve extraction accuracy, and how to design schemas that handle nullable fields and unexpected data without collapsing into chaos.]]></itunes:summary>
      <itunes:duration>2550</itunes:duration>
      <itunes:episode>2923</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/structured-outputs-ai-schema.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/structured-outputs-ai-schema.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your MTU Setting Is Probably Wrong</title>
      <description><![CDATA[Thousands of people blindly set their MTU to 1492 because forum advice from the DSL era never died. But modern fiber connections using DHCP instead of PPPoE should be running 1500 — and that eight-byte difference adds unnecessary overhead to everything you do online. In this episode, we break down where 1492 actually comes from, why Path MTU Discovery breaks on modern networks, how MSS clamping works as a workaround, and exactly how to test your own connection empirically. If you're running OPNsense on fiber and want to stop cargo-culting your network config, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/mtu-setting-myths-fiber/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mtu-setting-myths-fiber/</guid>
      <pubDate>Mon, 18 May 2026 20:37:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mtu-setting-myths-fiber.m4a"
        type="audio/mp4"
        length="28187888"
      />
      <itunes:title>Why Your MTU Setting Is Probably Wrong</itunes:title>
      <itunes:subtitle>That 1492 MTU everyone recommends? It&apos;s likely costing you performance on modern fiber.</itunes:subtitle>
      <itunes:summary><![CDATA[Thousands of people blindly set their MTU to 1492 because forum advice from the DSL era never died. But modern fiber connections using DHCP instead of PPPoE should be running 1500 — and that eight-byte difference adds unnecessary overhead to everything you do online. In this episode, we break down where 1492 actually comes from, why Path MTU Discovery breaks on modern networks, how MSS clamping works as a workaround, and exactly how to test your own connection empirically. If you're running OPNsense on fiber and want to stop cargo-culting your network config, this one's for you.]]></itunes:summary>
      <itunes:duration>1610</itunes:duration>
      <itunes:episode>2916</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mtu-setting-myths-fiber.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mtu-setting-myths-fiber.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a $180 Privacy-First AI Wearable</title>
      <description><![CDATA[Omi started as a wearable recorder but pivoted to a developer platform in 2024. Now their $99 dev kit and new screen processing beta let you build a voice-controlled system that watches your monitor, extracts tasks, and keeps everything local — no cloud round trips. We break down the hardware specs, the local-first architecture using Phi-3-mini and Tesseract on ARM, and what the community is actually building with it. From automated Jira ticket creation to Figma revision tracking, the ecosystem is small but punching above its weight.]]></description>
      <link>https://myweirdprompts.com/episode/omi-dev-kit-voice-productivity/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/omi-dev-kit-voice-productivity/</guid>
      <pubDate>Mon, 18 May 2026 17:17:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/omi-dev-kit-voice-productivity.m4a"
        type="audio/mp4"
        length="32160628"
      />
      <itunes:title>Building a $180 Privacy-First AI Wearable</itunes:title>
      <itunes:subtitle>How Omi&apos;s $99 dev kit lets you build a local-first voice productivity system that watches your screen.</itunes:subtitle>
      <itunes:summary><![CDATA[Omi started as a wearable recorder but pivoted to a developer platform in 2024. Now their $99 dev kit and new screen processing beta let you build a voice-controlled system that watches your monitor, extracts tasks, and keeps everything local — no cloud round trips. We break down the hardware specs, the local-first architecture using Phi-3-mini and Tesseract on ARM, and what the community is actually building with it. From automated Jira ticket creation to Figma revision tracking, the ecosystem is small but punching above its weight.]]></itunes:summary>
      <itunes:duration>1855</itunes:duration>
      <itunes:episode>2911</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/omi-dev-kit-voice-productivity.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/omi-dev-kit-voice-productivity.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Full Disk Encryption: What It Actually Does and Why It Matters</title>
      <description><![CDATA[Is full disk encryption worth the performance cost? This episode breaks down exactly what happens when you check that box during a Linux install — how LUKS and dm-crypt encrypt every sector, where the keys actually live, and why modern hardware makes the speed impact negligible. We compare software encryption against hardware-based self-encrypting drives and BIOS passwords, explain why proprietary firmware implementations have a track record of critical flaws, and explore the practical arguments for encryption even if your computer never leaves your house. Plus: encrypted external drives with physical keypads versus creating your own software vaults, and why the cool factor of hardware keys doesn't always mean better security.]]></description>
      <link>https://myweirdprompts.com/episode/full-disk-encryption-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/full-disk-encryption-explained/</guid>
      <pubDate>Fri, 15 May 2026 10:18:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/full-disk-encryption-explained.m4a"
        type="audio/mp4"
        length="39967162"
      />
      <itunes:title>Full Disk Encryption: What It Actually Does and Why It Matters</itunes:title>
      <itunes:subtitle>Full disk encryption demystified — how LUKS works, performance reality, and when you actually need it.</itunes:subtitle>
      <itunes:summary><![CDATA[Is full disk encryption worth the performance cost? This episode breaks down exactly what happens when you check that box during a Linux install — how LUKS and dm-crypt encrypt every sector, where the keys actually live, and why modern hardware makes the speed impact negligible. We compare software encryption against hardware-based self-encrypting drives and BIOS passwords, explain why proprietary firmware implementations have a track record of critical flaws, and explore the practical arguments for encryption even if your computer never leaves your house. Plus: encrypted external drives with physical keypads versus creating your own software vaults, and why the cool factor of hardware keys doesn't always mean better security.]]></itunes:summary>
      <itunes:duration>2320</itunes:duration>
      <itunes:episode>2839</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/full-disk-encryption-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/full-disk-encryption-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Missing CRUD Framework for Real Code</title>
      <description><![CDATA[When you're building yet another internal tool — press invites, RSVPs, credential management — you don't want to write the same boilerplate for the four hundredth time. But what actually exists that gives you a genuine starting point? Not a platform you log into, not a service you subscribe to, but actual code that lives in your repo and handles the boring eighty percent. This episode explores the fragmented middle ground between no-code platforms and raw frameworks, covering Refine, Supabase, RedwoodJS, and Payload CMS — what each gives you, where the seams show, and how to choose when your business logic inevitably goes beyond basic CRUD.]]></description>
      <link>https://myweirdprompts.com/episode/missing-crud-framework-code/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/missing-crud-framework-code/</guid>
      <pubDate>Thu, 14 May 2026 14:34:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/missing-crud-framework-code.m4a"
        type="audio/mp4"
        length="40231097"
      />
      <itunes:title>The Missing CRUD Framework for Real Code</itunes:title>
      <itunes:subtitle>What actually gives you a real starting point for internal tools — not a platform, not a service, but code you own and deploy.</itunes:subtitle>
      <itunes:summary><![CDATA[When you're building yet another internal tool — press invites, RSVPs, credential management — you don't want to write the same boilerplate for the four hundredth time. But what actually exists that gives you a genuine starting point? Not a platform you log into, not a service you subscribe to, but actual code that lives in your repo and handles the boring eighty percent. This episode explores the fragmented middle ground between no-code platforms and raw frameworks, covering Refine, Supabase, RedwoodJS, and Payload CMS — what each gives you, where the seams show, and how to choose when your business logic inevitably goes beyond basic CRUD.]]></itunes:summary>
      <itunes:duration>2336</itunes:duration>
      <itunes:episode>2829</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/missing-crud-framework-code.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/missing-crud-framework-code.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Video on Static Sites: When to Use a Platform</title>
      <description><![CDATA[Hosting video on a static site isn't as simple as dropping in an MP4. In this episode, we break down the real technical trigger points—adaptive bitrate streaming, bandwidth costs, and user experience—that determine when you need a dedicated video platform. We compare three major players—Mux, Cloudflare Stream, and Bunny Stream—across pricing, features, and the specific use cases where each one shines. Whether you're building a landing page with a hero loop or a site with hours of lecture content, this episode gives you the framework to choose the right approach without over-engineering or overspending.]]></description>
      <link>https://myweirdprompts.com/episode/static-site-video-platforms/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/static-site-video-platforms/</guid>
      <pubDate>Thu, 14 May 2026 13:45:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/static-site-video-platforms.m4a"
        type="audio/mp4"
        length="36530248"
      />
      <itunes:title>Video on Static Sites: When to Use a Platform</itunes:title>
      <itunes:subtitle>Mux, Cloudflare Stream, or Bunny Stream? When to stop serving MP4s and start using a purpose-built video platform.</itunes:subtitle>
      <itunes:summary><![CDATA[Hosting video on a static site isn't as simple as dropping in an MP4. In this episode, we break down the real technical trigger points—adaptive bitrate streaming, bandwidth costs, and user experience—that determine when you need a dedicated video platform. We compare three major players—Mux, Cloudflare Stream, and Bunny Stream—across pricing, features, and the specific use cases where each one shines. Whether you're building a landing page with a hero loop or a site with hours of lecture content, this episode gives you the framework to choose the right approach without over-engineering or overspending.]]></itunes:summary>
      <itunes:duration>2117</itunes:duration>
      <itunes:episode>2823</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/static-site-video-platforms.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/static-site-video-platforms.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Serverless E-Commerce: Medusa, Saleor, and Vendure</title>
      <description><![CDATA[E-commerce is the hardest test case for serverless architecture—inventory, payments, user accounts, admin dashboards, cross-session carts. Yet the serverless ecosystem has quietly built a parallel universe that makes it work. We explore three serious contenders: Medusa, Saleor, and Vendure. Each takes a different approach to decoupling commerce logic from monolithic servers, and each reveals a different trade-off between polish and customizability. Plus: the build-it-yourself path with Stripe, headless CMS, and a JSON file.]]></description>
      <link>https://myweirdprompts.com/episode/serverless-ecommerce-frameworks/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/serverless-ecommerce-frameworks/</guid>
      <pubDate>Thu, 14 May 2026 13:41:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/serverless-ecommerce-frameworks.m4a"
        type="audio/mp4"
        length="35225791"
      />
      <itunes:title>Serverless E-Commerce: Medusa, Saleor, and Vendure</itunes:title>
      <itunes:subtitle>Three open-source headless commerce engines that make serverless e-commerce actually viable.</itunes:subtitle>
      <itunes:summary><![CDATA[E-commerce is the hardest test case for serverless architecture—inventory, payments, user accounts, admin dashboards, cross-session carts. Yet the serverless ecosystem has quietly built a parallel universe that makes it work. We explore three serious contenders: Medusa, Saleor, and Vendure. Each takes a different approach to decoupling commerce logic from monolithic servers, and each reveals a different trade-off between polish and customizability. Plus: the build-it-yourself path with Stripe, headless CMS, and a JSON file.]]></itunes:summary>
      <itunes:duration>2045</itunes:duration>
      <itunes:episode>2822</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/serverless-ecommerce-frameworks.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/serverless-ecommerce-frameworks.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Trench Coat Is on Fire: Making Smart Home Parts Interrupt Each Other</title>
      <description><![CDATA[Three engineering problems in a trench coat: how to make Zigbee sirens, Snapcast speakers, and microphone capture actually interrupt each other's streams in a home intercom system — and why the real challenge isn't the hardware, it's the audio routing.]]></description>
      <link>https://myweirdprompts.com/episode/zigbee-snapcast-home-intercom/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/zigbee-snapcast-home-intercom/</guid>
      <pubDate>Thu, 14 May 2026 13:30:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/zigbee-snapcast-home-intercom.m4a"
        type="audio/mp4"
        length="35698508"
      />
      <itunes:title>The Trench Coat Is on Fire: Making Smart Home Parts Interrupt Each Other</itunes:title>
      <itunes:subtitle>Three engineering problems in a trench coat. Make Zigbee sirens, Snapcast speakers, and push-to-talk audio actually work together.</itunes:subtitle>
      <itunes:summary><![CDATA[Three engineering problems in a trench coat: how to make Zigbee sirens, Snapcast speakers, and microphone capture actually interrupt each other's streams in a home intercom system — and why the real challenge isn't the hardware, it's the audio routing.]]></itunes:summary>
      <itunes:duration>2061</itunes:duration>
      <itunes:episode>2821</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/zigbee-snapcast-home-intercom.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/zigbee-snapcast-home-intercom.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Free Cloudflare WAF: Is It Enough for Self-Hosting?</title>
      <description><![CDATA[Can you secure your self-hosted setup using only Cloudflare's free WAF tier, or do you need Access? We break down the WAF rule cascade order, the five essential free-tier rules, and where webhooks break everything. Learn the exact WAF rules to write for Home Assistant and N8N, plus the hybrid approach that keeps human-facing services behind Access while letting machine endpoints breathe.]]></description>
      <link>https://myweirdprompts.com/episode/free-cloudflare-waf-self-hosting/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/free-cloudflare-waf-self-hosting/</guid>
      <pubDate>Thu, 14 May 2026 12:30:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/free-cloudflare-waf-self-hosting.m4a"
        type="audio/mp4"
        length="34348144"
      />
      <itunes:title>Free Cloudflare WAF: Is It Enough for Self-Hosting?</itunes:title>
      <itunes:subtitle>Skip Cloudflare Access and lock down Home Assistant with just the free WAF rules. Here&apos;s how.</itunes:subtitle>
      <itunes:summary><![CDATA[Can you secure your self-hosted setup using only Cloudflare's free WAF tier, or do you need Access? We break down the WAF rule cascade order, the five essential free-tier rules, and where webhooks break everything. Learn the exact WAF rules to write for Home Assistant and N8N, plus the hybrid approach that keeps human-facing services behind Access while letting machine endpoints breathe.]]></itunes:summary>
      <itunes:duration>1997</itunes:duration>
      <itunes:episode>2815</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/free-cloudflare-waf-self-hosting.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/free-cloudflare-waf-self-hosting.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>HTTP Redirects: 301, 308, and When to Use Each</title>
      <description><![CDATA[HTTP redirects are infrastructure plumbing most developers ignore until a migration goes wrong. This episode unpacks the full 300-series status code family: when to use 301 vs 308 for permanent moves, why 303 prevents double pizza orders, and why 302 is legacy cruft you should stop using. Then we tackle the tactical question of where redirect logic should live — at the Cloudflare edge or on your origin server — and what happens with search engine indexation when you get it right.]]></description>
      <link>https://myweirdprompts.com/episode/http-redirect-status-codes/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/http-redirect-status-codes/</guid>
      <pubDate>Thu, 14 May 2026 12:27:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/http-redirect-status-codes.m4a"
        type="audio/mp4"
        length="38172080"
      />
      <itunes:title>HTTP Redirects: 301, 308, and When to Use Each</itunes:title>
      <itunes:subtitle>301 isn&apos;t always the right choice. Learn the real differences between redirect codes and where to put them.</itunes:subtitle>
      <itunes:summary><![CDATA[HTTP redirects are infrastructure plumbing most developers ignore until a migration goes wrong. This episode unpacks the full 300-series status code family: when to use 301 vs 308 for permanent moves, why 303 prevents double pizza orders, and why 302 is legacy cruft you should stop using. Then we tackle the tactical question of where redirect logic should live — at the Cloudflare edge or on your origin server — and what happens with search engine indexation when you get it right.]]></itunes:summary>
      <itunes:duration>2220</itunes:duration>
      <itunes:episode>2814</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/http-redirect-status-codes.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/http-redirect-status-codes.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Database of Everything You Own</title>
      <description><![CDATA[Why do procurement engineers have instant access to any component's specs, while home inventory enthusiasts are left scraping distributor catalogs? This episode explores the parallel universes of product data — and why Israel's unique SKUs reveal a deeper problem.]]></description>
      <link>https://myweirdprompts.com/episode/product-spec-apis-israel-sku/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/product-spec-apis-israel-sku/</guid>
      <pubDate>Thu, 14 May 2026 11:44:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/product-spec-apis-israel-sku.m4a"
        type="audio/mp4"
        length="35061037"
      />
      <itunes:title>The Hidden Database of Everything You Own</itunes:title>
      <itunes:subtitle>Is there an API for product specs? Yes, but it&apos;s built for engineers, not homeowners — and Israel SKUs make it harder.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do procurement engineers have instant access to any component's specs, while home inventory enthusiasts are left scraping distributor catalogs? This episode explores the parallel universes of product data — and why Israel's unique SKUs reveal a deeper problem.]]></itunes:summary>
      <itunes:duration>2035</itunes:duration>
      <itunes:episode>2812</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/product-spec-apis-israel-sku.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/product-spec-apis-israel-sku.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The CNAME Trap: How a DNS Rule Shaped the Web</title>
      <description><![CDATA[Why can't a CNAME coexist with other DNS records at the same name? This episode explores the design constraint that forced the 'www' convention, created the apex domain problem, and still shapes how CDNs and cloud providers work today.]]></description>
      <link>https://myweirdprompts.com/episode/dns-cname-vs-a-records/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dns-cname-vs-a-records/</guid>
      <pubDate>Wed, 13 May 2026 22:57:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dns-cname-vs-a-records.m4a"
        type="audio/mp4"
        length="35628528"
      />
      <itunes:title>The CNAME Trap: How a DNS Rule Shaped the Web</itunes:title>
      <itunes:subtitle>Why CNAMEs can&apos;t live at the apex, how flattening works, and modern DNS best practices.</itunes:subtitle>
      <itunes:summary><![CDATA[Why can't a CNAME coexist with other DNS records at the same name? This episode explores the design constraint that forced the 'www' convention, created the apex domain problem, and still shapes how CDNs and cloud providers work today.]]></itunes:summary>
      <itunes:duration>2053</itunes:duration>
      <itunes:episode>2806</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dns-cname-vs-a-records.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dns-cname-vs-a-records.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where Should Your Images Actually Live?</title>
      <description><![CDATA[Migrating from WordPress to a static site framework? The build pipeline gets all the attention, but media storage is where the real pain lives. This episode breaks down the three tiers of image and video hosting for serverless architectures — from committing everything to your repo (and hitting Git LFS limits), to managed object stores like Vercel Blob, to full CDN-backed solutions. We cover the hidden costs of Git LFS bandwidth, how build times degrade as your media library grows, and why the marketing team's drag-and-drop expectations are the hardest problem to solve. Plus: backup strategies across distributed systems, and why your "complete export" might just be a pile of rotting HTML links.]]></description>
      <link>https://myweirdprompts.com/episode/image-storage-wordpress-migration/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/image-storage-wordpress-migration/</guid>
      <pubDate>Wed, 13 May 2026 15:24:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/image-storage-wordpress-migration.m4a"
        type="audio/mp4"
        length="44214418"
      />
      <itunes:title>Where Should Your Images Actually Live?</itunes:title>
      <itunes:subtitle>WordPress to static sites: where do images &amp; videos go? Repo, object storage, or CDN — and when to move up the chain.</itunes:subtitle>
      <itunes:summary><![CDATA[Migrating from WordPress to a static site framework? The build pipeline gets all the attention, but media storage is where the real pain lives. This episode breaks down the three tiers of image and video hosting for serverless architectures — from committing everything to your repo (and hitting Git LFS limits), to managed object stores like Vercel Blob, to full CDN-backed solutions. We cover the hidden costs of Git LFS bandwidth, how build times degrade as your media library grows, and why the marketing team's drag-and-drop expectations are the hardest problem to solve. Plus: backup strategies across distributed systems, and why your "complete export" might just be a pile of rotting HTML links.]]></itunes:summary>
      <itunes:duration>2582</itunes:duration>
      <itunes:episode>2799</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/image-storage-wordpress-migration.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/image-storage-wordpress-migration.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Tooling Finally Makes Static Frameworks Practical</title>
      <description><![CDATA[The old knock against frameworks like Astro was the setup cost for image pipelines. Now AI agents wire up Sharp in minutes, collapsing the barrier and giving the headless CMS model a second wind. This episode explores how the equation has changed.]]></description>
      <link>https://myweirdprompts.com/episode/astro-media-handling-sharp/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/astro-media-handling-sharp/</guid>
      <pubDate>Wed, 13 May 2026 15:06:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/astro-media-handling-sharp.m4a"
        type="audio/mp4"
        length="37774762"
      />
      <itunes:title>Why AI Tooling Finally Makes Static Frameworks Practical</itunes:title>
      <itunes:subtitle>From thumbnails to SVGs — how Astro changes the media game compared to WordPress.</itunes:subtitle>
      <itunes:summary><![CDATA[The old knock against frameworks like Astro was the setup cost for image pipelines. Now AI agents wire up Sharp in minutes, collapsing the barrier and giving the headless CMS model a second wind. This episode explores how the equation has changed.]]></itunes:summary>
      <itunes:duration>2195</itunes:duration>
      <itunes:episode>2798</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/astro-media-handling-sharp.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/astro-media-handling-sharp.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Git Hygiene for AI Coding Agents</title>
      <description><![CDATA[Your AI coding agent just ripped through five tasks in a row. The output looks great. But did it actually commit anything? In this episode, we break down a three-layer system for keeping git hygiene built into your AI workflow — not bolted on as an afterthought. We cover standing project-level instructions, per-session git state checks, and periodic verification of what's actually in the log. Then we dive into the recovery playbook: how to handle uncommitted changes from two weeks ago, why "git add -p" beats the nuclear option, and why tagging is the cheapest insurance policy in git for solo developers working with agents. Plus, why annotated tags are better than branches when you're working solo on a single branch.]]></description>
      <link>https://myweirdprompts.com/episode/git-hygiene-ai-coding-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/git-hygiene-ai-coding-agents/</guid>
      <pubDate>Wed, 13 May 2026 09:28:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/git-hygiene-ai-coding-agents.m4a"
        type="audio/mp4"
        length="38260632"
      />
      <itunes:title>Git Hygiene for AI Coding Agents</itunes:title>
      <itunes:subtitle>How to keep your git repo clean when Claude is blazing through tasks — plus a recovery playbook for when things go sideways.</itunes:subtitle>
      <itunes:summary><![CDATA[Your AI coding agent just ripped through five tasks in a row. The output looks great. But did it actually commit anything? In this episode, we break down a three-layer system for keeping git hygiene built into your AI workflow — not bolted on as an afterthought. We cover standing project-level instructions, per-session git state checks, and periodic verification of what's actually in the log. Then we dive into the recovery playbook: how to handle uncommitted changes from two weeks ago, why "git add -p" beats the nuclear option, and why tagging is the cheapest insurance policy in git for solo developers working with agents. Plus, why annotated tags are better than branches when you're working solo on a single branch.]]></itunes:summary>
      <itunes:duration>2212</itunes:duration>
      <itunes:episode>2790</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/git-hygiene-ai-coding-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/git-hygiene-ai-coding-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Self-Healing Agent Pipelines</title>
      <description><![CDATA[What happens when your AI pipeline starts drifting, hallucinating, or silently degrading output quality — and nobody notices for weeks? In this episode, we break down the emerging practice of "self-healing agent workflows": building specialized monitoring agents that scan logs, detect behavioral drift, and autonomously fix common failures. We explore the current landscape of tools (LangSmith, Braintrust, Arize, Modal), the three-tier deployment model (fully autonomous, human-approval, and escalation), and why the real intellectual property is the triage taxonomy — not the fix logic itself. If you're running agentic pipelines in production and want to avoid death by a thousand paper cuts, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/self-healing-agent-pipelines/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/self-healing-agent-pipelines/</guid>
      <pubDate>Tue, 12 May 2026 16:02:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/self-healing-agent-pipelines.m4a"
        type="audio/mp4"
        length="30219036"
      />
      <itunes:title>Building Self-Healing Agent Pipelines</itunes:title>
      <itunes:subtitle>How to build an agent that monitors and fixes other agents in production — without the hype.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when your AI pipeline starts drifting, hallucinating, or silently degrading output quality — and nobody notices for weeks? In this episode, we break down the emerging practice of "self-healing agent workflows": building specialized monitoring agents that scan logs, detect behavioral drift, and autonomously fix common failures. We explore the current landscape of tools (LangSmith, Braintrust, Arize, Modal), the three-tier deployment model (fully autonomous, human-approval, and escalation), and why the real intellectual property is the triage taxonomy — not the fix logic itself. If you're running agentic pipelines in production and want to avoid death by a thousand paper cuts, this one's for you.]]></itunes:summary>
      <itunes:duration>1752</itunes:duration>
      <itunes:episode>2780</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/self-healing-agent-pipelines.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/self-healing-agent-pipelines.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Stateful Side of Serverless GPU</title>
      <description><![CDATA[Serverless GPU platforms promise stateless magic, but the real engineering is in the build cache. This episode explores how Modal, RunPod, and others manage container builds, layer caching, and versioning—and why the abstraction boundary matters more than the runtime.]]></description>
      <link>https://myweirdprompts.com/episode/serverless-gpu-build-caching/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/serverless-gpu-build-caching/</guid>
      <pubDate>Tue, 12 May 2026 15:50:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/serverless-gpu-build-caching.m4a"
        type="audio/mp4"
        length="33514979"
      />
      <itunes:title>The Hidden Stateful Side of Serverless GPU</itunes:title>
      <itunes:subtitle>How Modal, RunPod, and other platforms handle container builds, caching, and versioning under the hood.</itunes:subtitle>
      <itunes:summary><![CDATA[Serverless GPU platforms promise stateless magic, but the real engineering is in the build cache. This episode explores how Modal, RunPod, and others manage container builds, layer caching, and versioning—and why the abstraction boundary matters more than the runtime.]]></itunes:summary>
      <itunes:duration>1947</itunes:duration>
      <itunes:episode>2779</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/serverless-gpu-build-caching.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/serverless-gpu-build-caching.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where Does Your Vercel Site Actually Live?</title>
      <description><![CDATA[When you deploy to Vercel, your website doesn't go to one server — it goes to hundreds of edge locations worldwide. But the database queries still run in a specific AWS region. This episode demystifies the architecture behind serverless hosting, explaining the difference between static assets, edge functions, and serverless functions. We break down why your site feels like magic (immutable deployments, instant rollbacks, global CDN caching) and where the limitations still live (database latency, cold starts, regional dependencies). If you've ever felt uneasy about not being able to SSH into your infrastructure, this is for you.]]></description>
      <link>https://myweirdprompts.com/episode/vercel-deployment-location-edge/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vercel-deployment-location-edge/</guid>
      <pubDate>Tue, 12 May 2026 13:06:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vercel-deployment-location-edge.m4a"
        type="audio/mp4"
        length="28126369"
      />
      <itunes:title>Where Does Your Vercel Site Actually Live?</itunes:title>
      <itunes:subtitle>Your Vercel site lives everywhere and nowhere. Here&apos;s what&apos;s actually happening under the hood.</itunes:subtitle>
      <itunes:summary><![CDATA[When you deploy to Vercel, your website doesn't go to one server — it goes to hundreds of edge locations worldwide. But the database queries still run in a specific AWS region. This episode demystifies the architecture behind serverless hosting, explaining the difference between static assets, edge functions, and serverless functions. We break down why your site feels like magic (immutable deployments, instant rollbacks, global CDN caching) and where the limitations still live (database latency, cold starts, regional dependencies). If you've ever felt uneasy about not being able to SSH into your infrastructure, this is for you.]]></itunes:summary>
      <itunes:duration>2162</itunes:duration>
      <itunes:episode>2776</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vercel-deployment-location-edge.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vercel-deployment-location-edge.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Stop Wasting 75% of Your Build Pipeline</title>
      <description><![CDATA[Most serverless deployment pipelines are running at 70% waste — triggering full site rebuilds for every content update. This episode breaks down a real-world pipeline using Modal, Neon, and Vercel, and shows how a fifteen-minute debounce window collapses four concurrent builds into one. We also explore why server-side caching deserves a second look when you're surgical about invalidation, and why faster build machines are a brute-force solution to a problem better solved with intelligence. If you're running webhook-driven deployments without asking whether ten changes in an hour really need ten separate builds, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/serverless-build-debouncing-caching/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/serverless-build-debouncing-caching/</guid>
      <pubDate>Tue, 12 May 2026 12:53:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/serverless-build-debouncing-caching.m4a"
        type="audio/mp4"
        length="30875571"
      />
      <itunes:title>How to Stop Wasting 75% of Your Build Pipeline</itunes:title>
      <itunes:subtitle>Stop rebuilding your entire site for every single content change. Here&apos;s how debouncing and caching save time and money.</itunes:subtitle>
      <itunes:summary><![CDATA[Most serverless deployment pipelines are running at 70% waste — triggering full site rebuilds for every content update. This episode breaks down a real-world pipeline using Modal, Neon, and Vercel, and shows how a fifteen-minute debounce window collapses four concurrent builds into one. We also explore why server-side caching deserves a second look when you're surgical about invalidation, and why faster build machines are a brute-force solution to a problem better solved with intelligence. If you're running webhook-driven deployments without asking whether ten changes in an hour really need ten separate builds, this one's for you.]]></itunes:summary>
      <itunes:duration>2391</itunes:duration>
      <itunes:episode>2775</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/serverless-build-debouncing-caching.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/serverless-build-debouncing-caching.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond Static Fallbacks: Agentic Error Handling in AI Pipelines</title>
      <description><![CDATA[Most AI pipeline tutorials show the happy path. But what happens when APIs go down, keys expire, or your transcription chain silently breaks at 2 AM? This episode explores the gap between "works in a demo" and "works for six months without thinking about it." We start with LangChain's `with_fallbacks` method and the adapter pattern for provider interchangeability, then move to intelligent routing — using LLMs to classify errors, select fallbacks based on provider strengths, and even build orchestrator agents that rewrite pipeline steps on the fly. If you're building production AI pipelines that need to survive the real world, this episode maps the territory from simple circuit breakers to self-healing systems.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-error-handling-pipelines/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-error-handling-pipelines/</guid>
      <pubDate>Tue, 12 May 2026 12:14:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-error-handling-pipelines.m4a"
        type="audio/mp4"
        length="31143707"
      />
      <itunes:title>Beyond Static Fallbacks: Agentic Error Handling in AI Pipelines</itunes:title>
      <itunes:subtitle>From try-except blocks to planning agents that route around failures intelligently.</itunes:subtitle>
      <itunes:summary><![CDATA[Most AI pipeline tutorials show the happy path. But what happens when APIs go down, keys expire, or your transcription chain silently breaks at 2 AM? This episode explores the gap between "works in a demo" and "works for six months without thinking about it." We start with LangChain's `with_fallbacks` method and the adapter pattern for provider interchangeability, then move to intelligent routing — using LLMs to classify errors, select fallbacks based on provider strengths, and even build orchestrator agents that rewrite pipeline steps on the fly. If you're building production AI pipelines that need to survive the real world, this episode maps the territory from simple circuit breakers to self-healing systems.]]></itunes:summary>
      <itunes:duration>2389</itunes:duration>
      <itunes:episode>2773</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-error-handling-pipelines.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-error-handling-pipelines.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Do You Really Need VLANs at Home?</title>
      <description><![CDATA[Should you segment your home network with VLANs, or is it overkill? We break down the real security benefits of isolating IoT devices, the effort of configuring ACL rules, and the hidden threat of botnets. Plus, we explore whether ZigBee and Thread devices even need VLANs at all.]]></description>
      <link>https://myweirdprompts.com/episode/home-vlan-segmentation-iot/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-vlan-segmentation-iot/</guid>
      <pubDate>Mon, 11 May 2026 14:37:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-vlan-segmentation-iot.m4a"
        type="audio/mp4"
        length="32462787"
      />
      <itunes:title>Do You Really Need VLANs at Home?</itunes:title>
      <itunes:subtitle>Is VLAN segmentation worth it for home networks, or is it just sysadmin cosplay?</itunes:subtitle>
      <itunes:summary><![CDATA[Should you segment your home network with VLANs, or is it overkill? We break down the real security benefits of isolating IoT devices, the effort of configuring ACL rules, and the hidden threat of botnets. Plus, we explore whether ZigBee and Thread devices even need VLANs at all.]]></itunes:summary>
      <itunes:duration>2500</itunes:duration>
      <itunes:episode>2763</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-vlan-segmentation-iot.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-vlan-segmentation-iot.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Build AI Memory That Actually Works</title>
      <description><![CDATA[Most conversations about AI memory jump straight to reconciliation—handling conflicts when the system learns you moved but still has your old address. But none of that matters without the pipes to get data in and out cleanly. This episode breaks down a practical two-flow architecture: prompt-context separation for persistent personal memory across AI interactions, and voice-note capture with smart namespacing. We explore why atomic context facts beat monolithic blobs for vector search precision, how to structure retrieval queries around the user not the topic, and why source-type namespacing is less useful than topic-based classification. Plus, a concrete walkthrough of the production pipeline using webhooks, n8n, and output schema constraints.]]></description>
      <link>https://myweirdprompts.com/episode/ai-memory-prompt-context-separation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-memory-prompt-context-separation/</guid>
      <pubDate>Mon, 11 May 2026 10:01:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-memory-prompt-context-separation.m4a"
        type="audio/mp4"
        length="32952307"
      />
      <itunes:title>How to Build AI Memory That Actually Works</itunes:title>
      <itunes:subtitle>Stop jumping to conflict resolution. The real challenge is getting data in and out cleanly.</itunes:subtitle>
      <itunes:summary><![CDATA[Most conversations about AI memory jump straight to reconciliation—handling conflicts when the system learns you moved but still has your old address. But none of that matters without the pipes to get data in and out cleanly. This episode breaks down a practical two-flow architecture: prompt-context separation for persistent personal memory across AI interactions, and voice-note capture with smart namespacing. We explore why atomic context facts beat monolithic blobs for vector search precision, how to structure retrieval queries around the user not the topic, and why source-type namespacing is less useful than topic-based classification. Plus, a concrete walkthrough of the production pipeline using webhooks, n8n, and output schema constraints.]]></itunes:summary>
      <itunes:duration>2572</itunes:duration>
      <itunes:episode>2755</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-memory-prompt-context-separation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-memory-prompt-context-separation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside Android&apos;s Binder: No HTTP Here</title>
      <description><![CDATA[When you hear "API," you probably think HTTP requests and JSON payloads. But inside your Android phone, the story is completely different. This episode unpacks the actual mechanism behind Android's internal communication — a kernel-level IPC system called Binder that operates through shared memory, not network sockets. We trace the full path from a microphone request to the green privacy dot, explaining why this architecture matters for security, performance, and understanding how Pegasus spyware could bypass it so cleanly. No HTTP. No localhost servers. Just binary parcels shot through the kernel at microsecond speeds.]]></description>
      <link>https://myweirdprompts.com/episode/android-binder-ipc-internal-apis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-binder-ipc-internal-apis/</guid>
      <pubDate>Thu, 07 May 2026 21:21:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-binder-ipc-internal-apis.m4a"
        type="audio/mp4"
        length="34483039"
      />
      <itunes:title>Inside Android&apos;s Binder: No HTTP Here</itunes:title>
      <itunes:subtitle>Android&apos;s internal APIs don&apos;t use HTTP. They use Binder — a kernel-level IPC mechanism that&apos;s faster, tighter, and completely opaque.</itunes:subtitle>
      <itunes:summary><![CDATA[When you hear "API," you probably think HTTP requests and JSON payloads. But inside your Android phone, the story is completely different. This episode unpacks the actual mechanism behind Android's internal communication — a kernel-level IPC system called Binder that operates through shared memory, not network sockets. We trace the full path from a microphone request to the green privacy dot, explaining why this architecture matters for security, performance, and understanding how Pegasus spyware could bypass it so cleanly. No HTTP. No localhost servers. Just binary parcels shot through the kernel at microsecond speeds.]]></itunes:summary>
      <itunes:duration>2657</itunes:duration>
      <itunes:episode>2699</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-binder-ipc-internal-apis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-binder-ipc-internal-apis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Ignores Your Style Guide</title>
      <description><![CDATA[Why do frontier models ace reasoning benchmarks but fail to follow simple formatting instructions? This episode explores the overlooked capability of format constraint adherence and practical fixes like the writer-editor pattern.]]></description>
      <link>https://myweirdprompts.com/episode/ai-format-adherence-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-format-adherence-pipeline/</guid>
      <pubDate>Thu, 07 May 2026 14:03:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-format-adherence-pipeline.m4a"
        type="audio/mp4"
        length="29506035"
      />
      <itunes:title>When AI Ignores Your Style Guide</itunes:title>
      <itunes:subtitle>Why your AI ignores formatting instructions and how to fix it with pipeline architecture, not model swaps.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do frontier models ace reasoning benchmarks but fail to follow simple formatting instructions? This episode explores the overlooked capability of format constraint adherence and practical fixes like the writer-editor pattern.]]></itunes:summary>
      <itunes:duration>2270</itunes:duration>
      <itunes:episode>2693</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-format-adherence-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-format-adherence-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Type Safety: Static vs Dynamic, Soundness &amp; More</title>
      <description><![CDATA[What does "type safety" actually mean? This episode unpacks the hidden taxonomy of type systems. We break down the fundamental distinction between static and dynamic typing, explore the fuzzy concept of strong vs weak typing, and tackle soundness—explaining why TypeScript is famously unsound by design. We also cover gradual typing (like Python with mypy), structural vs nominal typing, and type inference. Finally, we touch on Rust’s borrow checker and dependent types before landing on a practical takeaway for everyday software engineering. If you've ever wondered what your language's type system is actually doing, this episode is for you.]]></description>
      <link>https://myweirdprompts.com/episode/type-safety-static-dynamic-soundness/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/type-safety-static-dynamic-soundness/</guid>
      <pubDate>Thu, 07 May 2026 13:36:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/type-safety-static-dynamic-soundness.m4a"
        type="audio/mp4"
        length="21609372"
      />
      <itunes:title>Type Safety: Static vs Dynamic, Soundness &amp; More</itunes:title>
      <itunes:subtitle>Static vs dynamic, strong vs weak, and the truth about TypeScript&apos;s unsoundness. A deep dive into type theory.</itunes:subtitle>
      <itunes:summary><![CDATA[What does "type safety" actually mean? This episode unpacks the hidden taxonomy of type systems. We break down the fundamental distinction between static and dynamic typing, explore the fuzzy concept of strong vs weak typing, and tackle soundness—explaining why TypeScript is famously unsound by design. We also cover gradual typing (like Python with mypy), structural vs nominal typing, and type inference. Finally, we touch on Rust’s borrow checker and dependent types before landing on a practical takeaway for everyday software engineering. If you've ever wondered what your language's type system is actually doing, this episode is for you.]]></itunes:summary>
      <itunes:duration>1657</itunes:duration>
      <itunes:episode>2692</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/type-safety-static-dynamic-soundness.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/type-safety-static-dynamic-soundness.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Usability Tax of Least Privilege</title>
      <description><![CDATA[Fine-grained API permissions are secure but so tedious that developers often skip them. Could AI agents make least privilege actually practical—or do they introduce worse risks? This episode examines the real trade-off.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agents-api-key-management/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agents-api-key-management/</guid>
      <pubDate>Thu, 07 May 2026 12:40:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agents-api-key-management.m4a"
        type="audio/mp4"
        length="27764216"
      />
      <itunes:title>The Usability Tax of Least Privilege</itunes:title>
      <itunes:subtitle>Is it time to let AI agents handle your API key creation and rotation? We explore the real security tradeoffs.</itunes:subtitle>
      <itunes:summary><![CDATA[Fine-grained API permissions are secure but so tedious that developers often skip them. Could AI agents make least privilege actually practical—or do they introduce worse risks? This episode examines the real trade-off.]]></itunes:summary>
      <itunes:duration>2129</itunes:duration>
      <itunes:episode>2691</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agents-api-key-management.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agents-api-key-management.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why CLI Beats MCP for AI Agents Sometimes</title>
      <description><![CDATA[When an AI agent using a command-line tool outperforms one using a purpose-built MCP server, something's off. In this episode, we dig into Daniel's question about why GH CLI often beats MCP wrappers, the Google Workspace MCP that shipped without email attachment support, and the real tension between vendor-run and community-built MCP servers. We explore tool selection limits in the MCP spec, why enterprise security is blocking adoption, and whether the protocol can evolve to support namespacing and dynamic tool discovery before fragmentation becomes permanent.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-vs-cli-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-vs-cli-ai-agents/</guid>
      <pubDate>Thu, 07 May 2026 12:22:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-vs-cli-ai-agents.m4a"
        type="audio/mp4"
        length="31926071"
      />
      <itunes:title>Why CLI Beats MCP for AI Agents Sometimes</itunes:title>
      <itunes:subtitle>Why a plain command-line tool can outperform a purpose-built MCP server for AI agents — and what that means for the protocol&apos;s future.</itunes:subtitle>
      <itunes:summary><![CDATA[When an AI agent using a command-line tool outperforms one using a purpose-built MCP server, something's off. In this episode, we dig into Daniel's question about why GH CLI often beats MCP wrappers, the Google Workspace MCP that shipped without email attachment support, and the real tension between vendor-run and community-built MCP servers. We explore tool selection limits in the MCP spec, why enterprise security is blocking adoption, and whether the protocol can evolve to support namespacing and dynamic tool discovery before fragmentation becomes permanent.]]></itunes:summary>
      <itunes:duration>2457</itunes:duration>
      <itunes:episode>2689</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-vs-cli-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-vs-cli-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Intelligent Frame Extraction for Multimodal AI</title>
      <description><![CDATA[Moving soon and overwhelmed by stuff? This episode unpacks the technical pipeline for turning a phone video into actionable decluttering advice using FFmpeg scene detection and multimodal models like Claude and GPT-4o. No vaporware, just practical engineering.]]></description>
      <link>https://myweirdprompts.com/episode/ai-decluttering-video-analysis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-decluttering-video-analysis/</guid>
      <pubDate>Thu, 07 May 2026 12:14:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-decluttering-video-analysis.m4a"
        type="audio/mp4"
        length="31335350"
      />
      <itunes:title>Intelligent Frame Extraction for Multimodal AI</itunes:title>
      <itunes:subtitle>Use multimodal AI and smart frame extraction to turn a walk-through video into an actionable decluttering plan.</itunes:subtitle>
      <itunes:summary><![CDATA[Moving soon and overwhelmed by stuff? This episode unpacks the technical pipeline for turning a phone video into actionable decluttering advice using FFmpeg scene detection and multimodal models like Claude and GPT-4o. No vaporware, just practical engineering.]]></itunes:summary>
      <itunes:duration>2414</itunes:duration>
      <itunes:episode>2688</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-decluttering-video-analysis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-decluttering-video-analysis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Pre-Flight Checks Help (or Hurt) Agentic AI Plugins</title>
      <description><![CDATA[Building production-grade AI plugins means deciding when to add pre-flight checks — and when they just waste tokens. This episode explores the three signals that justify a pre-flight check, how to cache static checks safely, and why the best checks are diagnostic probes, not just gates. We also cover the tradeoffs between latency, cost, and reliability across different model contexts.]]></description>
      <link>https://myweirdprompts.com/episode/pre-flight-checks-agentic-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pre-flight-checks-agentic-ai/</guid>
      <pubDate>Thu, 07 May 2026 11:21:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pre-flight-checks-agentic-ai.m4a"
        type="audio/mp4"
        length="25484414"
      />
      <itunes:title>When Pre-Flight Checks Help (or Hurt) Agentic AI Plugins</itunes:title>
      <itunes:subtitle>How to decide when a pre-flight check is worth the latency cost — and how to write good ones.</itunes:subtitle>
      <itunes:summary><![CDATA[Building production-grade AI plugins means deciding when to add pre-flight checks — and when they just waste tokens. This episode explores the three signals that justify a pre-flight check, how to cache static checks safely, and why the best checks are diagnostic probes, not just gates. We also cover the tradeoffs between latency, cost, and reliability across different model contexts.]]></itunes:summary>
      <itunes:duration>1948</itunes:duration>
      <itunes:episode>2687</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pre-flight-checks-agentic-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pre-flight-checks-agentic-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Plugin Data Storage for AI Agents</title>
      <description><![CDATA[When building agent plugins that run across multiple operating systems, where should user data actually live? This episode dives into Daniel's practical question about separating plugin code from user secrets, preferences, and data files in a way that works on Linux, macOS, and Windows. We explore the XDG Base Directory specification, macOS Application Support conventions, and Windows AppData patterns — and why agents default to the wrong locations. Plus, we tackle secret management: how plugins can request credentials by name without knowing which secret backend the user employs, from dotenv files to Doppler to HashiCorp Vault. A deep look at the architectural patterns that make agent plugins portable and secure.]]></description>
      <link>https://myweirdprompts.com/episode/plugin-data-storage-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/plugin-data-storage-ai-agents/</guid>
      <pubDate>Thu, 07 May 2026 09:36:03 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/plugin-data-storage-ai-agents.m4a"
        type="audio/mp4"
        length="29070047"
      />
      <itunes:title>Plugin Data Storage for AI Agents</itunes:title>
      <itunes:subtitle>How to separate user data from plugin code across Linux, macOS, and Windows in agentic AI environments.</itunes:subtitle>
      <itunes:summary><![CDATA[When building agent plugins that run across multiple operating systems, where should user data actually live? This episode dives into Daniel's practical question about separating plugin code from user secrets, preferences, and data files in a way that works on Linux, macOS, and Windows. We explore the XDG Base Directory specification, macOS Application Support conventions, and Windows AppData patterns — and why agents default to the wrong locations. Plus, we tackle secret management: how plugins can request credentials by name without knowing which secret backend the user employs, from dotenv files to Doppler to HashiCorp Vault. A deep look at the architectural patterns that make agent plugins portable and secure.]]></itunes:summary>
      <itunes:duration>2239</itunes:duration>
      <itunes:episode>2685</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/plugin-data-storage-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/plugin-data-storage-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Agent Skills Collide: Context Windows &amp; Plugin Design</title>
      <description><![CDATA[When your Claude instance has dozens of plugins with overlapping skills — like a normalization skill in both a podcast plugin and a general audio plugin — how do you make sure the orchestrator picks the right one? This episode digs into a real engineering problem from listener Daniel, who's building a catalog system for agent skills. We explore whether expanding context windows will actually solve the disambiguation problem (spoiler: probably not), why skill descriptions are metadata that needs to survive regardless of token budgets, and how a two-tier disambiguation system using plugin-level descriptions can act as a pre-filter. Plus, why the catalog approach has durable value even as models evolve — because the real problem isn't context size, it's helping the orchestrator understand what lives where and when to use what.]]></description>
      <link>https://myweirdprompts.com/episode/agent-skills-plugin-disambiguation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-skills-plugin-disambiguation/</guid>
      <pubDate>Thu, 07 May 2026 09:32:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-skills-plugin-disambiguation.m4a"
        type="audio/mp4"
        length="35289466"
      />
      <itunes:title>When Agent Skills Collide: Context Windows &amp; Plugin Design</itunes:title>
      <itunes:subtitle>How to handle overlapping agent skills and whether context windows will ever make the problem go away.</itunes:subtitle>
      <itunes:summary><![CDATA[When your Claude instance has dozens of plugins with overlapping skills — like a normalization skill in both a podcast plugin and a general audio plugin — how do you make sure the orchestrator picks the right one? This episode digs into a real engineering problem from listener Daniel, who's building a catalog system for agent skills. We explore whether expanding context windows will actually solve the disambiguation problem (spoiler: probably not), why skill descriptions are metadata that needs to survive regardless of token budgets, and how a two-tier disambiguation system using plugin-level descriptions can act as a pre-filter. Plus, why the catalog approach has durable value even as models evolve — because the real problem isn't context size, it's helping the orchestrator understand what lives where and when to use what.]]></itunes:summary>
      <itunes:duration>2699</itunes:duration>
      <itunes:episode>2684</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-skills-plugin-disambiguation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-skills-plugin-disambiguation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>MCP vs Agent Skills: Context Wars</title>
      <description><![CDATA[A developer noticed his Claude agent's reasoning degrading from too many plugins and built a clever workaround — but a new model claiming a twelve million token context window threatens to make his fix obsolete. We dig into the tension between MCP (Model Context Protocol) and raw agent skills, exploring when each approach makes sense and how massive context windows change the calculus. Plus: the emerging problem of federated access control for agent teams. If you can't share root credentials with a junior dev in AWS, why would you in the agent world?]]></description>
      <link>https://myweirdprompts.com/episode/mcp-agent-skills-context/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-agent-skills-context/</guid>
      <pubDate>Thu, 07 May 2026 09:11:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-agent-skills-context.m4a"
        type="audio/mp4"
        length="27983625"
      />
      <itunes:title>MCP vs Agent Skills: Context Wars</itunes:title>
      <itunes:subtitle>When 12M token windows arrive, do MCP servers or agent skills win? Plus: federated access for agent teams.</itunes:subtitle>
      <itunes:summary><![CDATA[A developer noticed his Claude agent's reasoning degrading from too many plugins and built a clever workaround — but a new model claiming a twelve million token context window threatens to make his fix obsolete. We dig into the tension between MCP (Model Context Protocol) and raw agent skills, exploring when each approach makes sense and how massive context windows change the calculus. Plus: the emerging problem of federated access control for agent teams. If you can't share root credentials with a junior dev in AWS, why would you in the agent world?]]></itunes:summary>
      <itunes:duration>2162</itunes:duration>
      <itunes:episode>2683</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-agent-skills-context.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-agent-skills-context.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Live Retrieval vs. RAG: What an Agent Actually Does</title>
      <description><![CDATA[When you skip the traditional RAG database and let an AI agent fetch documents live, are you just creating a disposable vector store every time? A listener question from Daniel digs into this exact intuition. We break down what’s actually happening under the hood — from the transformer’s key-value cache to HNSW indexes — and explore the real engineering tradeoffs: maintenance burden vs. retrieval precision, latency vs. correctness guarantees, and why architectural regulations make a perfect test case. If you’ve ever wondered whether live retrieval is just lazy engineering or a smarter correctness play, this episode is for you.]]></description>
      <link>https://myweirdprompts.com/episode/live-retrieval-vs-rag/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/live-retrieval-vs-rag/</guid>
      <pubDate>Thu, 07 May 2026 08:33:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/live-retrieval-vs-rag.m4a"
        type="audio/mp4"
        length="30440674"
      />
      <itunes:title>Live Retrieval vs. RAG: What an Agent Actually Does</itunes:title>
      <itunes:subtitle>Does every AI conversation create a tiny vector store? We unpack the real tradeoffs between live document fetching and pre-indexed RAG.</itunes:subtitle>
      <itunes:summary><![CDATA[When you skip the traditional RAG database and let an AI agent fetch documents live, are you just creating a disposable vector store every time? A listener question from Daniel digs into this exact intuition. We break down what’s actually happening under the hood — from the transformer’s key-value cache to HNSW indexes — and explore the real engineering tradeoffs: maintenance burden vs. retrieval precision, latency vs. correctness guarantees, and why architectural regulations make a perfect test case. If you’ve ever wondered whether live retrieval is just lazy engineering or a smarter correctness play, this episode is for you.]]></itunes:summary>
      <itunes:duration>2343</itunes:duration>
      <itunes:episode>2682</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/live-retrieval-vs-rag.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/live-retrieval-vs-rag.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Memory Layers for AI Agents: SaaS vs Self-Hosted</title>
      <description><![CDATA[You've moved past the "what is mem0 and Zep and Letta" stage. Now you need to know what living with each option actually looks like at day 30 and day 180. This episode breaks down six memory layer products across two deployment modes: Zep Cloud, mem0's managed offering, and Letta Cloud on the SaaS side; Graphiti, self-hosted mem0, Cognee, and Letta self-hosted on the other. We cover what you get out of the box, what breaks, the real costs, and the lock-in risk when your agent's entire memory sits in someone else's database. Plus a framework for when SaaS wins, when self-hosting pays off, and the emerging hybrid pattern where curation logic is managed but storage stays in your VPC.]]></description>
      <link>https://myweirdprompts.com/episode/ai-memory-layers-saas-self-hosted/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-memory-layers-saas-self-hosted/</guid>
      <pubDate>Wed, 06 May 2026 19:09:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-memory-layers-saas-self-hosted.m4a"
        type="audio/mp4"
        length="17892566"
      />
      <itunes:title>Memory Layers for AI Agents: SaaS vs Self-Hosted</itunes:title>
      <itunes:subtitle>Zep, mem0, Letta, Graphiti, Cognee — which memory layer should you commit to for your AI agent?</itunes:subtitle>
      <itunes:summary><![CDATA[You've moved past the "what is mem0 and Zep and Letta" stage. Now you need to know what living with each option actually looks like at day 30 and day 180. This episode breaks down six memory layer products across two deployment modes: Zep Cloud, mem0's managed offering, and Letta Cloud on the SaaS side; Graphiti, self-hosted mem0, Cognee, and Letta self-hosted on the other. We cover what you get out of the box, what breaks, the real costs, and the lock-in risk when your agent's entire memory sits in someone else's database. Plus a framework for when SaaS wins, when self-hosting pays off, and the emerging hybrid pattern where curation logic is managed but storage stays in your VPC.]]></itunes:summary>
      <itunes:duration>1355</itunes:duration>
      <itunes:episode>2677</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-memory-layers-saas-self-hosted.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-memory-layers-saas-self-hosted.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Vector Database Schema Design for AI Memory Layers</title>
      <description><![CDATA[Most teams treat vector databases as flat blobs — pick an embedding model, dump everything in, and hope semantic search works. It doesn't. This episode unpacks how to deliberately shape your vector data architecture for serious AI memory layers. We cover when to use separate indexes versus namespaces, how to design per-document-type metadata schemas, why hybrid retrieval needs structured filtering before semantic search, and how a query router determines which fields to filter on. If your recall at top-K is a coin flip, the embedding model isn't the problem — your data architecture is.]]></description>
      <link>https://myweirdprompts.com/episode/vector-database-schema-design/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vector-database-schema-design/</guid>
      <pubDate>Wed, 06 May 2026 19:05:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vector-database-schema-design.m4a"
        type="audio/mp4"
        length="23100939"
      />
      <itunes:title>Vector Database Schema Design for AI Memory Layers</itunes:title>
      <itunes:subtitle>Stop dumping vectors blindly. Design metadata schemas and namespaces for retrieval that actually works at scale.</itunes:subtitle>
      <itunes:summary><![CDATA[Most teams treat vector databases as flat blobs — pick an embedding model, dump everything in, and hope semantic search works. It doesn't. This episode unpacks how to deliberately shape your vector data architecture for serious AI memory layers. We cover when to use separate indexes versus namespaces, how to design per-document-type metadata schemas, why hybrid retrieval needs structured filtering before semantic search, and how a query router determines which fields to filter on. If your recall at top-K is a coin flip, the embedding model isn't the problem — your data architecture is.]]></itunes:summary>
      <itunes:duration>1772</itunes:duration>
      <itunes:episode>2676</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vector-database-schema-design.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vector-database-schema-design.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Agent&apos;s Context Window Is Getting Eaten Before You Start</title>
      <description><![CDATA[When you install plugins in Claude Code, every skill and command gets eagerly loaded at session start, nibbling your context window before you type a single character. This episode explores an inverted architecture: a centralized catalogue server with a thin bridge plugin that fetches skills on demand. We dig into the eager vs. lazy trade-off, why the crossover point matters, and how the humble description field becomes the most important thing you write when the model only sees a menu, not the kitchen.]]></description>
      <link>https://myweirdprompts.com/episode/lazy-fetch-plugin-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/lazy-fetch-plugin-architecture/</guid>
      <pubDate>Wed, 06 May 2026 18:20:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/lazy-fetch-plugin-architecture.m4a"
        type="audio/mp4"
        length="18345265"
      />
      <itunes:title>Why Your Agent&apos;s Context Window Is Getting Eaten Before You Start</itunes:title>
      <itunes:subtitle>Stop shipping the whole toolbox to every session. A bridge plugin pattern that fetches skills on demand instead.</itunes:subtitle>
      <itunes:summary><![CDATA[When you install plugins in Claude Code, every skill and command gets eagerly loaded at session start, nibbling your context window before you type a single character. This episode explores an inverted architecture: a centralized catalogue server with a thin bridge plugin that fetches skills on demand. We dig into the eager vs. lazy trade-off, why the crossover point matters, and how the humble description field becomes the most important thing you write when the model only sees a menu, not the kitchen.]]></itunes:summary>
      <itunes:duration>1394</itunes:duration>
      <itunes:episode>2674</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/lazy-fetch-plugin-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/lazy-fetch-plugin-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Embedding Coupling Problem: Editing Vector Stores</title>
      <description><![CDATA[When you update text in a vector database, does the embedding update automatically? No—and that hidden coupling breaks assumptions about backups and edits. This episode unpacks the practical gotchas of Pinecone's architecture for agent memory stores.]]></description>
      <link>https://myweirdprompts.com/episode/pinecone-vector-database-backups/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pinecone-vector-database-backups/</guid>
      <pubDate>Wed, 06 May 2026 18:18:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pinecone-vector-database-backups.m4a"
        type="audio/mp4"
        length="18644713"
      />
      <itunes:title>The Embedding Coupling Problem: Editing Vector Stores</itunes:title>
      <itunes:subtitle>Can you edit or delete individual chunks in Pinecone? And can you actually back up a vector index? Yes—but with critical caveats.</itunes:subtitle>
      <itunes:summary><![CDATA[When you update text in a vector database, does the embedding update automatically? No—and that hidden coupling breaks assumptions about backups and edits. This episode unpacks the practical gotchas of Pinecone's architecture for agent memory stores.]]></itunes:summary>
      <itunes:duration>1414</itunes:duration>
      <itunes:episode>2673</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pinecone-vector-database-backups.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pinecone-vector-database-backups.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When a Flamethrower Is Overkill</title>
      <description><![CDATA[Daniel wants to scan messy Sharpie labels with his phone. Herman explains why a tiered OCR approach beats a vision language model for speed, reliability, and simplicity.]]></description>
      <link>https://myweirdprompts.com/episode/ocr-vs-vlm-label-scanning/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ocr-vs-vlm-label-scanning/</guid>
      <pubDate>Wed, 06 May 2026 15:31:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ocr-vs-vlm-label-scanning.m4a"
        type="audio/mp4"
        length="30203824"
      />
      <itunes:title>When a Flamethrower Is Overkill</itunes:title>
      <itunes:subtitle>Tesseract, EasyOCR, or a cloud vision model? How to build a fast, reliable label scanner for real-world conditions.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants to scan messy Sharpie labels with his phone. Herman explains why a tiered OCR approach beats a vision language model for speed, reliability, and simplicity.]]></itunes:summary>
      <itunes:duration>2326</itunes:duration>
      <itunes:episode>2668</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ocr-vs-vlm-label-scanning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ocr-vs-vlm-label-scanning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Catch an LLM&apos;s Bad Writing Habits</title>
      <description><![CDATA[Daniel asked a deceptively practical question: how do you systematically analyze a corpus of podcast transcripts to catch what a script-writing LLM overdoes — repeated words, stale jokes, dialogue patterns that need more variety? This episode walks through the full spectrum of techniques, from quick Python frequency counts with NLTK and spaCy to embedding-based clustering with sentence transformers and LLM-as-judge qualitative passes. Herman and Corn discuss when simple analysis is enough, when you need the heavy machinery, and — crucially — how to avoid optimizing for metrics that make content worse instead of better. They cover Goodhart's law in prompt engineering, the importance of multi-signal measurement, and a three-phase pipeline for closing the feedback loop between analysis and prompt improvement.]]></description>
      <link>https://myweirdprompts.com/episode/llm-writing-tics-corpus-analysis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-writing-tics-corpus-analysis/</guid>
      <pubDate>Tue, 05 May 2026 17:59:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-writing-tics-corpus-analysis.m4a"
        type="audio/mp4"
        length="22935691"
      />
      <itunes:title>How to Catch an LLM&apos;s Bad Writing Habits</itunes:title>
      <itunes:subtitle>A practical guide to analyzing podcast transcripts for repetitive language and dialogue patterns — from Python word counts to embedding clustering.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel asked a deceptively practical question: how do you systematically analyze a corpus of podcast transcripts to catch what a script-writing LLM overdoes — repeated words, stale jokes, dialogue patterns that need more variety? This episode walks through the full spectrum of techniques, from quick Python frequency counts with NLTK and spaCy to embedding-based clustering with sentence transformers and LLM-as-judge qualitative passes. Herman and Corn discuss when simple analysis is enough, when you need the heavy machinery, and — crucially — how to avoid optimizing for metrics that make content worse instead of better. They cover Goodhart's law in prompt engineering, the importance of multi-signal measurement, and a three-phase pipeline for closing the feedback loop between analysis and prompt improvement.]]></itunes:summary>
      <itunes:duration>1740</itunes:duration>
      <itunes:episode>2650</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-writing-tics-corpus-analysis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-writing-tics-corpus-analysis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Bridging Analog and Digital Note-Taking</title>
      <description><![CDATA[How Nano Banana's breakthrough in treating text as geometric shapes finally makes whiteboard-to-diagram conversion work, preserving spatial layout while fixing handwriting. We explore the pipeline, custom handwriting models, and what this means for bridging analog and digital note-taking.]]></description>
      <link>https://myweirdprompts.com/episode/whiteboard-clean-diagram-nano-banana/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/whiteboard-clean-diagram-nano-banana/</guid>
      <pubDate>Tue, 05 May 2026 13:05:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/whiteboard-clean-diagram-nano-banana.m4a"
        type="audio/mp4"
        length="26208044"
      />
      <itunes:title>Bridging Analog and Digital Note-Taking</itunes:title>
      <itunes:subtitle>How Nano Banana finally solves the text rendering problem, turning messy whiteboard photos into polished tech diagrams.</itunes:subtitle>
      <itunes:summary><![CDATA[How Nano Banana's breakthrough in treating text as geometric shapes finally makes whiteboard-to-diagram conversion work, preserving spatial layout while fixing handwriting. We explore the pipeline, custom handwriting models, and what this means for bridging analog and digital note-taking.]]></itunes:summary>
      <itunes:duration>2027</itunes:duration>
      <itunes:episode>2641</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/whiteboard-clean-diagram-nano-banana.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/whiteboard-clean-diagram-nano-banana.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Instructional Models Beat Conversational for Batch AI</title>
      <description><![CDATA[Batch inference isn't just cheaper tokens—it's a different paradigm. This episode explores why conversational models fail at batch processing and why instruction-tuned models are the better fit, plus real use cases like data annotation and synthetic data generation.]]></description>
      <link>https://myweirdprompts.com/episode/batch-inference-instructional-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/batch-inference-instructional-ai/</guid>
      <pubDate>Tue, 05 May 2026 11:30:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/batch-inference-instructional-ai.m4a"
        type="audio/mp4"
        length="29870080"
      />
      <itunes:title>Why Instructional Models Beat Conversational for Batch AI</itunes:title>
      <itunes:subtitle>Beyond cheaper tokens—how batch inference changes AI workflows and why instructional models beat conversational ones for automated jobs.</itunes:subtitle>
      <itunes:summary><![CDATA[Batch inference isn't just cheaper tokens—it's a different paradigm. This episode explores why conversational models fail at batch processing and why instruction-tuned models are the better fit, plus real use cases like data annotation and synthetic data generation.]]></itunes:summary>
      <itunes:duration>2293</itunes:duration>
      <itunes:episode>2640</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/batch-inference-instructional-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/batch-inference-instructional-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Layer That Makes Search Work</title>
      <description><![CDATA[Why does search often feel almost right but not quite? This episode unpacks the re-ranking step—the critical but invisible layer between broad retrieval and precise results—and why understanding it changes how you build search and RAG systems.]]></description>
      <link>https://myweirdprompts.com/episode/re-ranking-search-rag-pipelines/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/re-ranking-search-rag-pipelines/</guid>
      <pubDate>Tue, 05 May 2026 11:15:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/re-ranking-search-rag-pipelines.m4a"
        type="audio/mp4"
        length="26938413"
      />
      <itunes:title>The Hidden Layer That Makes Search Work</itunes:title>
      <itunes:subtitle>Why your search results miss the mark — and how cross-encoders fix it.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does search often feel almost right but not quite? This episode unpacks the re-ranking step—the critical but invisible layer between broad retrieval and precise results—and why understanding it changes how you build search and RAG systems.]]></itunes:summary>
      <itunes:duration>2060</itunes:duration>
      <itunes:episode>2639</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/re-ranking-search-rag-pipelines.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/re-ranking-search-rag-pipelines.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Build Disposable AI Agents at Runtime</title>
      <description><![CDATA[What if you could click a button and instantly get an AI assistant that knows everything about a single item in your home inventory—without pre-building hundreds of agents? This episode explores the engineering behind dynamically generated, disposable AI agents. We break down the architecture using the OpenAI Assistants API, LangChain, and simpler approaches, weighing tradeoffs between build complexity and runtime cost. The conversation covers retrieval optimization, context window management, and why good UI design can eliminate hard AI problems. Plus: why modern user manuals are terrible, how LLMs excel at extracting needles from legal-disclaimer haystacks, and the practical appeal of agents that exist for thirty seconds, answer one question, and vanish.]]></description>
      <link>https://myweirdprompts.com/episode/disposable-ai-agents-runtime/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/disposable-ai-agents-runtime/</guid>
      <pubDate>Tue, 05 May 2026 10:11:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/disposable-ai-agents-runtime.m4a"
        type="audio/mp4"
        length="28792781"
      />
      <itunes:title>How to Build Disposable AI Agents at Runtime</itunes:title>
      <itunes:subtitle>Create ephemeral AI agents that answer questions about specific items, then vanish. No persistent configuration needed.</itunes:subtitle>
      <itunes:summary><![CDATA[What if you could click a button and instantly get an AI assistant that knows everything about a single item in your home inventory—without pre-building hundreds of agents? This episode explores the engineering behind dynamically generated, disposable AI agents. We break down the architecture using the OpenAI Assistants API, LangChain, and simpler approaches, weighing tradeoffs between build complexity and runtime cost. The conversation covers retrieval optimization, context window management, and why good UI design can eliminate hard AI problems. Plus: why modern user manuals are terrible, how LLMs excel at extracting needles from legal-disclaimer haystacks, and the practical appeal of agents that exist for thirty seconds, answer one question, and vanish.]]></itunes:summary>
      <itunes:duration>2216</itunes:duration>
      <itunes:episode>2638</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/disposable-ai-agents-runtime.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/disposable-ai-agents-runtime.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Two-Stage Pipeline for Persistent User Memory</title>
      <description><![CDATA[How do you mine raw, messy prompts for durable personal facts and keep that memory consistent as preferences shift and contradictions emerge? This episode unpacks the extraction and maintenance architecture behind agentic systems that actually remember users.]]></description>
      <link>https://myweirdprompts.com/episode/latent-value-prompt-extraction/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/latent-value-prompt-extraction/</guid>
      <pubDate>Mon, 04 May 2026 15:36:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/latent-value-prompt-extraction.m4a"
        type="audio/mp4"
        length="32407443"
      />
      <itunes:title>The Two-Stage Pipeline for Persistent User Memory</itunes:title>
      <itunes:subtitle>How to extract durable personal context from raw prompts and build a self-healing memory layer for AI systems.</itunes:subtitle>
      <itunes:summary><![CDATA[How do you mine raw, messy prompts for durable personal facts and keep that memory consistent as preferences shift and contradictions emerge? This episode unpacks the extraction and maintenance architecture behind agentic systems that actually remember users.]]></itunes:summary>
      <itunes:duration>2492</itunes:duration>
      <itunes:episode>2634</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/latent-value-prompt-extraction.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/latent-value-prompt-extraction.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Text Normalization&apos;s Hidden Complexity</title>
      <description><![CDATA[When a TTS engine reads 'WHAT' as 'W-H-A-T', the problem isn't acronyms—it's text normalization. This episode explores the engineering challenge of disambiguating raw text for speech synthesis, from rule-based regex to BERT sidecars and markup schemas.]]></description>
      <link>https://myweirdprompts.com/episode/tts-acronym-handling-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tts-acronym-handling-pipeline/</guid>
      <pubDate>Sun, 03 May 2026 13:17:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tts-acronym-handling-pipeline.m4a"
        type="audio/mp4"
        length="29517010"
      />
      <itunes:title>Text Normalization&apos;s Hidden Complexity</itunes:title>
      <itunes:subtitle>How to handle acronyms in text-to-speech pipelines using BERT models, lexicons, and layered preprocessing.</itunes:subtitle>
      <itunes:summary><![CDATA[When a TTS engine reads 'WHAT' as 'W-H-A-T', the problem isn't acronyms—it's text normalization. This episode explores the engineering challenge of disambiguating raw text for speech synthesis, from rule-based regex to BERT sidecars and markup schemas.]]></itunes:summary>
      <itunes:duration>2264</itunes:duration>
      <itunes:episode>2618</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tts-acronym-handling-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tts-acronym-handling-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Defining the Agentic Workspace</title>
      <description><![CDATA[Daniel's refactoring sprint reveals a conceptual leap: treating your computer as a place where agents act, not where you do things. The hosts explore how composable CLI tools and a workspace-first mindset can transform creative production.]]></description>
      <link>https://myweirdprompts.com/episode/agent-skills-creative-workflows/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-skills-creative-workflows/</guid>
      <pubDate>Sat, 02 May 2026 13:44:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-skills-creative-workflows.m4a"
        type="audio/mp4"
        length="33610775"
      />
      <itunes:title>Defining the Agentic Workspace</itunes:title>
      <itunes:subtitle>How composable AI agent skills turn tedious media tasks into one-instruction operations for creatives.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel's refactoring sprint reveals a conceptual leap: treating your computer as a place where agents act, not where you do things. The hosts explore how composable CLI tools and a workspace-first mindset can transform creative production.]]></itunes:summary>
      <itunes:duration>2593</itunes:duration>
      <itunes:episode>2603</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-skills-creative-workflows.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-skills-creative-workflows.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Circadian Lighting Gradients in Home Assistant</title>
      <description><![CDATA[Daniel and Hannah wanted circadian lighting that shifts color temperature and brightness gradually throughout the day — but their rigid red-light-only nighttime mode was too inflexible. This episode explores how to build a smooth gradient using Home Assistant's Adaptive Lighting integration, why melanopic lux matters more than simple "warm light," and how to handle emergency overrides like red alerts without breaking the system. We cover profile design, latitude-dependent sunset timing, and the cleanest automation architecture for priority overrides.]]></description>
      <link>https://myweirdprompts.com/episode/circadian-lighting-home-assistant/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/circadian-lighting-home-assistant/</guid>
      <pubDate>Sat, 02 May 2026 12:58:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/circadian-lighting-home-assistant.m4a"
        type="audio/mp4"
        length="25844788"
      />
      <itunes:title>Circadian Lighting Gradients in Home Assistant</itunes:title>
      <itunes:subtitle>How to build a smooth, override-friendly circadian lighting system using Adaptive Lighting in Home Assistant.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel and Hannah wanted circadian lighting that shifts color temperature and brightness gradually throughout the day — but their rigid red-light-only nighttime mode was too inflexible. This episode explores how to build a smooth gradient using Home Assistant's Adaptive Lighting integration, why melanopic lux matters more than simple "warm light," and how to handle emergency overrides like red alerts without breaking the system. We cover profile design, latitude-dependent sunset timing, and the cleanest automation architecture for priority overrides.]]></itunes:summary>
      <itunes:duration>1983</itunes:duration>
      <itunes:episode>2600</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/circadian-lighting-home-assistant.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/circadian-lighting-home-assistant.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Voice Control for Renters: $25 Per Room, No Wall Damage</title>
      <description><![CDATA[Daniel wants voice control throughout his 60-square-meter Jerusalem rental — no wall damage, no cloud dependency, and a budget that won't break the bank. We break down how ESP32-S3 satellites running MicroWakeWord and ESPHome can deliver per-room voice control for $20-25 each, with centralized processing via Home Assistant. No screens, no mounting, just small boxes on shelves that handle wake word detection locally and stream audio to a server for speech-to-text and intent processing. Perfect for parents with full hands and landlords with strict rules.]]></description>
      <link>https://myweirdprompts.com/episode/voice-control-renters-budget/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-control-renters-budget/</guid>
      <pubDate>Sat, 02 May 2026 12:23:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-control-renters-budget.m4a"
        type="audio/mp4"
        length="25958682"
      />
      <itunes:title>Voice Control for Renters: $25 Per Room, No Wall Damage</itunes:title>
      <itunes:subtitle>Distributed voice control on a budget with wake words, centralized processing, and zero wall damage — perfect for rentals.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel wants voice control throughout his 60-square-meter Jerusalem rental — no wall damage, no cloud dependency, and a budget that won't break the bank. We break down how ESP32-S3 satellites running MicroWakeWord and ESPHome can deliver per-room voice control for $20-25 each, with centralized processing via Home Assistant. No screens, no mounting, just small boxes on shelves that handle wake word detection locally and stream audio to a server for speech-to-text and intent processing. Perfect for parents with full hands and landlords with strict rules.]]></itunes:summary>
      <itunes:duration>1990</itunes:duration>
      <itunes:episode>2597</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-control-renters-budget.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-control-renters-budget.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hierarchy of Immutable Code</title>
      <description><![CDATA[What really separates firmware from software? This episode explores the layered architecture of bootloaders, from the physically unchangeable mask ROM in an ESP32 to the one-time programmable e-fuses OnePlus used to prevent bootloader unlocking. We trace the hierarchy of mutability across devices — from microcontrollers to modern UEFI systems — and examine how hardware-enforced privilege levels make some code permanently untouchable. Along the way, we discuss anti-rollback protection, firmware persistence mechanisms like Computrace, and why the reset button on a dev board is a hardware escape hatch that software can never override.]]></description>
      <link>https://myweirdprompts.com/episode/firmware-bootloader-hierarchy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/firmware-bootloader-hierarchy/</guid>
      <pubDate>Sat, 02 May 2026 10:27:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/firmware-bootloader-hierarchy.m4a"
        type="audio/mp4"
        length="24138613"
      />
      <itunes:title>The Hierarchy of Immutable Code</itunes:title>
      <itunes:subtitle>From mask ROM to e-fuses: how hardware enforces a hierarchy of mutability in every computing device.</itunes:subtitle>
      <itunes:summary><![CDATA[What really separates firmware from software? This episode explores the layered architecture of bootloaders, from the physically unchangeable mask ROM in an ESP32 to the one-time programmable e-fuses OnePlus used to prevent bootloader unlocking. We trace the hierarchy of mutability across devices — from microcontrollers to modern UEFI systems — and examine how hardware-enforced privilege levels make some code permanently untouchable. Along the way, we discuss anti-rollback protection, firmware persistence mechanisms like Computrace, and why the reset button on a dev board is a hardware escape hatch that software can never override.]]></itunes:summary>
      <itunes:duration>1859</itunes:duration>
      <itunes:episode>2594</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/firmware-bootloader-hierarchy.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/firmware-bootloader-hierarchy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Decoupling Script from Voice</title>
      <description><![CDATA[What if listeners could choose any voice for a podcast host? We explore the technical feasibility of dynamic voice replacement, from voice cloning embeddings to on-device rendering, and what it means for the future of personalized audio.]]></description>
      <link>https://myweirdprompts.com/episode/personalized-podcast-voices/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/personalized-podcast-voices/</guid>
      <pubDate>Sat, 02 May 2026 09:47:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/personalized-podcast-voices.m4a"
        type="audio/mp4"
        length="24456252"
      />
      <itunes:title>Decoupling Script from Voice</itunes:title>
      <itunes:subtitle>How dynamic voice replacement could let listeners choose who narrates each host&apos;s lines.</itunes:subtitle>
      <itunes:summary><![CDATA[What if listeners could choose any voice for a podcast host? We explore the technical feasibility of dynamic voice replacement, from voice cloning embeddings to on-device rendering, and what it means for the future of personalized audio.]]></itunes:summary>
      <itunes:duration>1870</itunes:duration>
      <itunes:episode>2591</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/personalized-podcast-voices.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/personalized-podcast-voices.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Uncanny Valley of Clean Speech</title>
      <description><![CDATA[When removing 'um's from audio makes you sound less human, and keeping them makes you sound less polished. This episode explores the sweet spot between polish and naturalness in speech editing pipelines.]]></description>
      <link>https://myweirdprompts.com/episode/disfluency-detection-speech-cleaning/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/disfluency-detection-speech-cleaning/</guid>
      <pubDate>Sat, 02 May 2026 09:24:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/disfluency-detection-speech-cleaning.m4a"
        type="audio/mp4"
        length="22059328"
      />
      <itunes:title>The Uncanny Valley of Clean Speech</itunes:title>
      <itunes:subtitle>How transformer models distinguish &quot;um&quot; from meaningful speech — and why removing too much makes you sound like a robot.</itunes:subtitle>
      <itunes:summary><![CDATA[When removing 'um's from audio makes you sound less human, and keeping them makes you sound less polished. This episode explores the sweet spot between polish and naturalness in speech editing pipelines.]]></itunes:summary>
      <itunes:duration>1699</itunes:duration>
      <itunes:episode>2590</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/disfluency-detection-speech-cleaning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/disfluency-detection-speech-cleaning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Pseudo-Personalized Emails: The New Spam Uncanny Valley</title>
      <description><![CDATA[Daniel sent in a frustration many technical folks share: those emails that pretend to be personal outreach but are clearly automated scraping spam, with no unsubscribe link and just enough scraped detail to feel almost real. We break down why this "pseudo-personalization" is technically distinct from traditional spam, the legal gray zones it exploits, and practical filtering approaches — from domain age checks in n8n workflows to LLM-based classification with soft-fail safety nets. If you're tired of the uncanny valley of fake personal outreach, this episode gives you concrete strategies to clean up your inbox.]]></description>
      <link>https://myweirdprompts.com/episode/pseudo-personalized-email-filtering/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pseudo-personalized-email-filtering/</guid>
      <pubDate>Fri, 01 May 2026 12:32:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pseudo-personalized-email-filtering.m4a"
        type="audio/mp4"
        length="26328177"
      />
      <itunes:title>Pseudo-Personalized Emails: The New Spam Uncanny Valley</itunes:title>
      <itunes:subtitle>How to detect and filter AI-generated outreach emails that fake personal connection without nuking legitimate messages.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel sent in a frustration many technical folks share: those emails that pretend to be personal outreach but are clearly automated scraping spam, with no unsubscribe link and just enough scraped detail to feel almost real. We break down why this "pseudo-personalization" is technically distinct from traditional spam, the legal gray zones it exploits, and practical filtering approaches — from domain age checks in n8n workflows to LLM-based classification with soft-fail safety nets. If you're tired of the uncanny valley of fake personal outreach, this episode gives you concrete strategies to clean up your inbox.]]></itunes:summary>
      <itunes:duration>2022</itunes:duration>
      <itunes:episode>2586</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pseudo-personalized-email-filtering.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pseudo-personalized-email-filtering.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Pristine Key Namespace Nobody Uses</title>
      <description><![CDATA[F13 through F24 are recognized by every OS but shipped on zero consumer keyboards. We explore how to claim this untouched key range for macros, layers, and voice dictation workflows—and why it's the cleanest hack for power users.]]></description>
      <link>https://myweirdprompts.com/episode/f13-f24-macro-keys-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/f13-f24-macro-keys-guide/</guid>
      <pubDate>Fri, 01 May 2026 12:30:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/f13-f24-macro-keys-guide.m4a"
        type="audio/mp4"
        length="27803899"
      />
      <itunes:title>The Pristine Key Namespace Nobody Uses</itunes:title>
      <itunes:subtitle>How unused keyboard keys, custom firmware, and layered macros can transform your workflow.</itunes:subtitle>
      <itunes:summary><![CDATA[F13 through F24 are recognized by every OS but shipped on zero consumer keyboards. We explore how to claim this untouched key range for macros, layers, and voice dictation workflows—and why it's the cleanest hack for power users.]]></itunes:summary>
      <itunes:duration>2132</itunes:duration>
      <itunes:episode>2585</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/f13-f24-macro-keys-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/f13-f24-macro-keys-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Your Browser Does to Mic Audio Before It Reaches Your Server</title>
      <description><![CDATA[Most developers copy-paste the getUserMedia snippet from MDN, wire up a MediaRecorder, and never think about it again. But what's actually happening under the hood varies wildly across browsers. This episode unpacks the hidden audio pipeline in Chrome, Firefox, and Safari — from default sample rates that drop to 8 kHz on mobile, to Opus codec quirks where higher bitrates can actually hurt transcription accuracy. We explore the constraints API (which is a polite request, not a command), the destructive effects of echo cancellation and noise suppression on speech-to-text, and the practical tools like RecordRTC and Web Audio API for taking back control. If you're building a browser-based recording app and wondering why transcription quality varies between users, this is the episode for you.]]></description>
      <link>https://myweirdprompts.com/episode/browser-mic-audio-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/browser-mic-audio-pipeline/</guid>
      <pubDate>Fri, 01 May 2026 11:55:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/browser-mic-audio-pipeline.m4a"
        type="audio/mp4"
        length="24535653"
      />
      <itunes:title>What Your Browser Does to Mic Audio Before It Reaches Your Server</itunes:title>
      <itunes:subtitle>getUserMedia returns audio, but not raw audio. Here&apos;s what browsers actually do to your mic feed before it hits your server.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers copy-paste the getUserMedia snippet from MDN, wire up a MediaRecorder, and never think about it again. But what's actually happening under the hood varies wildly across browsers. This episode unpacks the hidden audio pipeline in Chrome, Firefox, and Safari — from default sample rates that drop to 8 kHz on mobile, to Opus codec quirks where higher bitrates can actually hurt transcription accuracy. We explore the constraints API (which is a polite request, not a command), the destructive effects of echo cancellation and noise suppression on speech-to-text, and the practical tools like RecordRTC and Web Audio API for taking back control. If you're building a browser-based recording app and wondering why transcription quality varies between users, this is the episode for you.]]></itunes:summary>
      <itunes:duration>1873</itunes:duration>
      <itunes:episode>2582</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/browser-mic-audio-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/browser-mic-audio-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Deliberately Slow Deployment Pipelines</title>
      <description><![CDATA[When your CI/CD pipeline is optimized purely for speed, you risk turning it into a firehose pointed at your users. This episode explores the neglected middle of software delivery: deliberately slow deployment pipelines designed for stability and quality control. We cover practical tooling — GitLab compliance pipelines, GitHub deployment protection rules, Cloudflare staging mode, and Jenkins input steps — and examine how AI-generated code and automated content are making these intentional gates more critical than ever. Whether you're shipping medical device software, financial systems, or AI-generated podcast episodes, learn how to build pipelines that filter before they release.]]></description>
      <link>https://myweirdprompts.com/episode/slow-deployment-pipeline-design/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/slow-deployment-pipeline-design/</guid>
      <pubDate>Fri, 01 May 2026 11:21:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/slow-deployment-pipeline-design.m4a"
        type="audio/mp4"
        length="23362766"
      />
      <itunes:title>Building Deliberately Slow Deployment Pipelines</itunes:title>
      <itunes:subtitle>How to build CI/CD pipelines designed as filters, not firehoses — with manual gates, staging environments, and quality checks.</itunes:subtitle>
      <itunes:summary><![CDATA[When your CI/CD pipeline is optimized purely for speed, you risk turning it into a firehose pointed at your users. This episode explores the neglected middle of software delivery: deliberately slow deployment pipelines designed for stability and quality control. We cover practical tooling — GitLab compliance pipelines, GitHub deployment protection rules, Cloudflare staging mode, and Jenkins input steps — and examine how AI-generated code and automated content are making these intentional gates more critical than ever. Whether you're shipping medical device software, financial systems, or AI-generated podcast episodes, learn how to build pipelines that filter before they release.]]></itunes:summary>
      <itunes:duration>1784</itunes:duration>
      <itunes:episode>2578</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/slow-deployment-pipeline-design.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/slow-deployment-pipeline-design.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fixing Hidden UI Bugs on Real Devices</title>
      <description><![CDATA[A developer’s dropdown menu works perfectly on a Pixel but gets obscured on a OnePlus. What automated tooling can catch these maddening layout and interaction bugs before users quietly quit your app? This episode explores visual regression testing (Percy, Applitools), end-to-end frameworks (Playwright, Cypress), real device clouds (BrowserStack), and the critical distinction between elements visible in the DOM versus actually visible to a user. We also break down how testing strategies shift for React Native, Flutter, and PWA codebases — and why emulation alone isn’t enough.]]></description>
      <link>https://myweirdprompts.com/episode/hidden-ui-bugs-testing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hidden-ui-bugs-testing/</guid>
      <pubDate>Fri, 01 May 2026 11:12:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hidden-ui-bugs-testing.m4a"
        type="audio/mp4"
        length="21349415"
      />
      <itunes:title>Fixing Hidden UI Bugs on Real Devices</itunes:title>
      <itunes:subtitle>Tools and strategies to catch layout failures across devices before users abandon your app.</itunes:subtitle>
      <itunes:summary><![CDATA[A developer’s dropdown menu works perfectly on a Pixel but gets obscured on a OnePlus. What automated tooling can catch these maddening layout and interaction bugs before users quietly quit your app? This episode explores visual regression testing (Percy, Applitools), end-to-end frameworks (Playwright, Cypress), real device clouds (BrowserStack), and the critical distinction between elements visible in the DOM versus actually visible to a user. We also break down how testing strategies shift for React Native, Flutter, and PWA codebases — and why emulation alone isn’t enough.]]></itunes:summary>
      <itunes:duration>1611</itunes:duration>
      <itunes:episode>2577</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hidden-ui-bugs-testing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hidden-ui-bugs-testing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond Pixels: Controlling Apps Without Vision</title>
      <description><![CDATA[When MCP agents move from the cloud to local machines, they hit a wall: proprietary software with no CLI. Most developers reach for vision-based automation—screenshots, OCR, click simulation—but it’s slow and brittle. This episode explores two far better alternatives: accessibility APIs (UI Automation on Windows, Accessibility API on macOS) and interprocess communication hooks like COM and AppleScript. You’ll learn how to tap into an application’s internal widget tree, invoke actions directly, and achieve microsecond latency instead of hundreds of milliseconds. Perfect for anyone building local MCP servers that need to control Photoshop, Excel, Final Cut Pro, or any proprietary app.]]></description>
      <link>https://myweirdprompts.com/episode/controlling-apps-without-vision/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/controlling-apps-without-vision/</guid>
      <pubDate>Fri, 01 May 2026 09:11:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/controlling-apps-without-vision.m4a"
        type="audio/mp4"
        length="43376850"
      />
      <itunes:title>Beyond Pixels: Controlling Apps Without Vision</itunes:title>
      <itunes:subtitle>How MCP agents can use accessibility APIs and COM to control Windows and macOS apps at the protocol level.</itunes:subtitle>
      <itunes:summary><![CDATA[When MCP agents move from the cloud to local machines, they hit a wall: proprietary software with no CLI. Most developers reach for vision-based automation—screenshots, OCR, click simulation—but it’s slow and brittle. This episode explores two far better alternatives: accessibility APIs (UI Automation on Windows, Accessibility API on macOS) and interprocess communication hooks like COM and AppleScript. You’ll learn how to tap into an application’s internal widget tree, invoke actions directly, and achieve microsecond latency instead of hundreds of milliseconds. Perfect for anyone building local MCP servers that need to control Photoshop, Excel, Final Cut Pro, or any proprietary app.]]></itunes:summary>
      <itunes:duration>3359</itunes:duration>
      <itunes:episode>2567</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/controlling-apps-without-vision.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/controlling-apps-without-vision.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Audio Fingerprinting Actually Works</title>
      <description><![CDATA[Most people know audio fingerprinting as the magic behind Shazam and YouTube Content ID, but the actual mechanics are surprisingly elegant. This episode breaks down the entire pipeline step by step: how a short-time Fourier transform turns audio into a spectrogram, how peak picking filters out noise and compression artifacts, and how constellation maps and hash pairs enable near-instant matching against millions of songs. We also explore a concrete meta-example: how the My Weird Prompts production pipeline uses the same technique to locate fixed audio segments in variable-length TTS output — without relying on timestamps at all.]]></description>
      <link>https://myweirdprompts.com/episode/audio-fingerprinting-mechanics-shazam/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-fingerprinting-mechanics-shazam/</guid>
      <pubDate>Fri, 01 May 2026 08:45:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-fingerprinting-mechanics-shazam.m4a"
        type="audio/mp4"
        length="20525969"
      />
      <itunes:title>How Audio Fingerprinting Actually Works</itunes:title>
      <itunes:subtitle>Spectrogram peaks, constellation maps, and hash matching — the elegant mechanics behind identifying any song in seconds.</itunes:subtitle>
      <itunes:summary><![CDATA[Most people know audio fingerprinting as the magic behind Shazam and YouTube Content ID, but the actual mechanics are surprisingly elegant. This episode breaks down the entire pipeline step by step: how a short-time Fourier transform turns audio into a spectrogram, how peak picking filters out noise and compression artifacts, and how constellation maps and hash pairs enable near-instant matching against millions of songs. We also explore a concrete meta-example: how the My Weird Prompts production pipeline uses the same technique to locate fixed audio segments in variable-length TTS output — without relying on timestamps at all.]]></itunes:summary>
      <itunes:duration>1568</itunes:duration>
      <itunes:episode>2563</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-fingerprinting-mechanics-shazam.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-fingerprinting-mechanics-shazam.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Smartest Path to Python for AI</title>
      <description><![CDATA[The advice "just build something" is the most frustrating thing you can hear as a beginner. This episode cuts through the noise to map out a clear, three-phase path for learning Python specifically for AI and machine learning. We break down the best resources for each stage: from syntax fundamentals (Coursera’s Dr. Chuck vs. Automate the Boring Stuff), to computational thinking (MIT OCW), to interactive practice (Codecademy, Boot.dev, Exercism), and finally, applied Python for real-world development (Real Python). If you want to skip the frustration of jumping straight into PyTorch without knowing how a for loop works, this roadmap is for you.]]></description>
      <link>https://myweirdprompts.com/episode/best-python-path-for-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/best-python-path-for-ai/</guid>
      <pubDate>Fri, 01 May 2026 08:28:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/best-python-path-for-ai.m4a"
        type="audio/mp4"
        length="22751604"
      />
      <itunes:title>The Smartest Path to Python for AI</itunes:title>
      <itunes:subtitle>A practical guide to the best courses and platforms for learning Python, specifically for machine learning.</itunes:subtitle>
      <itunes:summary><![CDATA[The advice "just build something" is the most frustrating thing you can hear as a beginner. This episode cuts through the noise to map out a clear, three-phase path for learning Python specifically for AI and machine learning. We break down the best resources for each stage: from syntax fundamentals (Coursera’s Dr. Chuck vs. Automate the Boring Stuff), to computational thinking (MIT OCW), to interactive practice (Codecademy, Boot.dev, Exercism), and finally, applied Python for real-world development (Real Python). If you want to skip the frustration of jumping straight into PyTorch without knowing how a for loop works, this roadmap is for you.]]></itunes:summary>
      <itunes:duration>1743</itunes:duration>
      <itunes:episode>2559</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/best-python-path-for-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/best-python-path-for-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Progressive Disclosure Saves MCP from Token Bloat</title>
      <description><![CDATA[Dumping every tool schema into the context window might seem simple, but it burns tokens and tanks model accuracy. This episode explores progressive disclosure — lazy-loading, namespacing, and on-demand reveal — and why it's become essential for scaling the Model Context Protocol. We break down three concrete implementations: paddo's mcp-code-wrapper (speculative execution with just-in-time schema discovery), paralleldrive's jiron (semantic routing with top-k tool group selection), and colinhale1's progressive-reveal-mcp (non-executable capability descriptors with a meta-tool for expansion). Each takes a different approach to the same core tension: how much should the model know about what it doesn't know? We also cover the accuracy data — tool selection dropping from 94% to the low 70s with 40+ tools — and whether agent skills are the natural next step.]]></description>
      <link>https://myweirdprompts.com/episode/progressive-disclosure-mcp-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/progressive-disclosure-mcp-tools/</guid>
      <pubDate>Thu, 30 Apr 2026 21:00:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/progressive-disclosure-mcp-tools.m4a"
        type="audio/mp4"
        length="20358069"
      />
      <itunes:title>How Progressive Disclosure Saves MCP from Token Bloat</itunes:title>
      <itunes:subtitle>Why dumping all tool schemas into context breaks accuracy — and three implementations that fix it.</itunes:subtitle>
      <itunes:summary><![CDATA[Dumping every tool schema into the context window might seem simple, but it burns tokens and tanks model accuracy. This episode explores progressive disclosure — lazy-loading, namespacing, and on-demand reveal — and why it's become essential for scaling the Model Context Protocol. We break down three concrete implementations: paddo's mcp-code-wrapper (speculative execution with just-in-time schema discovery), paralleldrive's jiron (semantic routing with top-k tool group selection), and colinhale1's progressive-reveal-mcp (non-executable capability descriptors with a meta-tool for expansion). Each takes a different approach to the same core tension: how much should the model know about what it doesn't know? We also cover the accuracy data — tool selection dropping from 94% to the low 70s with 40+ tools — and whether agent skills are the natural next step.]]></itunes:summary>
      <itunes:duration>1552</itunes:duration>
      <itunes:episode>2551</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/progressive-disclosure-mcp-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/progressive-disclosure-mcp-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Idempotent Pipelines: Checkpoints, Manifests &amp; Safe Re-Runs</title>
      <description><![CDATA[What does it actually mean to build idempotent data pipelines and deployment scripts? This episode unpacks the practical engineering definition — not mathematical purity, but making re-runs safe and resumable. We cover checkpointing pitfalls (flag files that lie), manifest files with content hashes, transactional writes using atomic renames, and deterministic state checks that reconcile your memory with ground truth. Plus: why you should never trust an API's idempotency claims, and how to avoid a $40,000 billing disaster from a retry loop.]]></description>
      <link>https://myweirdprompts.com/episode/idempotent-pipelines-checkpoints-manifests/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/idempotent-pipelines-checkpoints-manifests/</guid>
      <pubDate>Thu, 30 Apr 2026 19:00:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/idempotent-pipelines-checkpoints-manifests.m4a"
        type="audio/mp4"
        length="21201898"
      />
      <itunes:title>Idempotent Pipelines: Checkpoints, Manifests &amp; Safe Re-Runs</itunes:title>
      <itunes:subtitle>How to design scripts and pipelines so re-running them is safe, even after a crash mid-execution.</itunes:subtitle>
      <itunes:summary><![CDATA[What does it actually mean to build idempotent data pipelines and deployment scripts? This episode unpacks the practical engineering definition — not mathematical purity, but making re-runs safe and resumable. We cover checkpointing pitfalls (flag files that lie), manifest files with content hashes, transactional writes using atomic renames, and deterministic state checks that reconcile your memory with ground truth. Plus: why you should never trust an API's idempotency claims, and how to avoid a $40,000 billing disaster from a retry loop.]]></itunes:summary>
      <itunes:duration>1625</itunes:duration>
      <itunes:episode>2550</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/idempotent-pipelines-checkpoints-manifests.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/idempotent-pipelines-checkpoints-manifests.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Static vs Server-Side: What Actually Happens When You Deploy</title>
      <description><![CDATA[A developer noticed something strange: he pushed a change, reloaded his production page, and content appeared instantly from a database — on what he thought was a static serverless site. This episode unpacks the two fundamentally different methods for getting data from backend to frontend in serverless environments: static site generation (SSG) and server-side rendering (SSR), plus the hybrid approach called incremental static regeneration (ISR) that Vercel popularized. We explore why one developer actually prefers the "slower" static method, the security implications of each approach, how Neon's serverless Postgres solves the connection pooling problem, and why the choice between these architectures affects everything from cost predictability to vendor lock-in. The episode covers the trade-offs between build-time rendering and on-demand rendering, and why understanding this distinction is the key to making informed architectural decisions.]]></description>
      <link>https://myweirdprompts.com/episode/static-server-side-rendering-comparison/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/static-server-side-rendering-comparison/</guid>
      <pubDate>Thu, 30 Apr 2026 08:44:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/static-server-side-rendering-comparison.m4a"
        type="audio/mp4"
        length="31942069"
      />
      <itunes:title>Static vs Server-Side: What Actually Happens When You Deploy</itunes:title>
      <itunes:subtitle>The moment you see content appear instantly on production and realize it wasn&apos;t pre-built — that&apos;s when architecture gets interesting.</itunes:subtitle>
      <itunes:summary><![CDATA[A developer noticed something strange: he pushed a change, reloaded his production page, and content appeared instantly from a database — on what he thought was a static serverless site. This episode unpacks the two fundamentally different methods for getting data from backend to frontend in serverless environments: static site generation (SSG) and server-side rendering (SSR), plus the hybrid approach called incremental static regeneration (ISR) that Vercel popularized. We explore why one developer actually prefers the "slower" static method, the security implications of each approach, how Neon's serverless Postgres solves the connection pooling problem, and why the choice between these architectures affects everything from cost predictability to vendor lock-in. The episode covers the trade-offs between build-time rendering and on-demand rendering, and why understanding this distinction is the key to making informed architectural decisions.]]></itunes:summary>
      <itunes:duration>2487</itunes:duration>
      <itunes:episode>2548</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/static-server-side-rendering-comparison.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/static-server-side-rendering-comparison.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Self-Hosted Podcast Analytics &amp; Caching Fixes</title>
      <description><![CDATA[Running your own podcast infrastructure means wrestling with analytics that don't lie and caching that does. This episode tackles three real-world questions from a listener named Daniel: how to measure listenership without invasive tracking tools, why episodes can lag 4-5 minutes behind on Spotify even when they're live on your site, and how to control Cloudflare's caching to fix that delay. We break down what R2's built-in analytics actually tell you, why heavy tracking tools like Chartable are both privacy-hostile and technically fragile, and practical strategies for getting verified numbers that sponsors will trust. Plus: the specific caching headers and cache-busting techniques that solve the "published but not appearing" problem.]]></description>
      <link>https://myweirdprompts.com/episode/self-hosted-podcast-analytics-caching/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/self-hosted-podcast-analytics-caching/</guid>
      <pubDate>Thu, 30 Apr 2026 08:31:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/self-hosted-podcast-analytics-caching.m4a"
        type="audio/mp4"
        length="34149408"
      />
      <itunes:title>Self-Hosted Podcast Analytics &amp; Caching Fixes</itunes:title>
      <itunes:subtitle>How to track listeners, handle caching delays, and get sponsor-ready numbers when self-hosting on Cloudflare R2 or S3.</itunes:subtitle>
      <itunes:summary><![CDATA[Running your own podcast infrastructure means wrestling with analytics that don't lie and caching that does. This episode tackles three real-world questions from a listener named Daniel: how to measure listenership without invasive tracking tools, why episodes can lag 4-5 minutes behind on Spotify even when they're live on your site, and how to control Cloudflare's caching to fix that delay. We break down what R2's built-in analytics actually tell you, why heavy tracking tools like Chartable are both privacy-hostile and technically fragile, and practical strategies for getting verified numbers that sponsors will trust. Plus: the specific caching headers and cache-busting techniques that solve the "published but not appearing" problem.]]></itunes:summary>
      <itunes:duration>2668</itunes:duration>
      <itunes:episode>2547</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/self-hosted-podcast-analytics-caching.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/self-hosted-podcast-analytics-caching.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Invisible Engineering Behind a Single Click</title>
      <description><![CDATA[What actually happens when you click to delete or move an object in a photo editor? This episode unpacks the segmentation models, inpainting, and fallback logic that turn a click into a mask, and why the wrapper matters more than the model.]]></description>
      <link>https://myweirdprompts.com/episode/ai-editing-segmentation-inpainting/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-editing-segmentation-inpainting/</guid>
      <pubDate>Thu, 30 Apr 2026 08:16:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-editing-segmentation-inpainting.m4a"
        type="audio/mp4"
        length="30334336"
      />
      <itunes:title>The Invisible Engineering Behind a Single Click</itunes:title>
      <itunes:subtitle>The technical stack behind click-to-edit features in tools like Canva and Google Photos — from segmentation to inpainting.</itunes:subtitle>
      <itunes:summary><![CDATA[What actually happens when you click to delete or move an object in a photo editor? This episode unpacks the segmentation models, inpainting, and fallback logic that turn a click into a mask, and why the wrapper matters more than the model.]]></itunes:summary>
      <itunes:duration>2368</itunes:duration>
      <itunes:episode>2546</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-editing-segmentation-inpainting.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-editing-segmentation-inpainting.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Visual AI Pipelines: Beyond Python Glue Code</title>
      <description><![CDATA[You've prototyped a generative AI workflow in Google's AI Studio, tuned the temperature, locked in the system instruction — now what? This episode explores the growing ecosystem of visual programming tools that sit between raw Python scripts and full node-based environments. We survey the landscape: ComfyUI's extensible node graph, Fal's hosted workflow builder, Dify's LLM-focused pipelines, Flowise for chatbots, and the fragmentation problem that still drives many creators back to Python. For anyone doing creative AI work who finds code editors break their flow, this is a guide to the tools that let you see your pipeline instead of reading it.]]></description>
      <link>https://myweirdprompts.com/episode/visual-ai-pipeline-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/visual-ai-pipeline-tools/</guid>
      <pubDate>Thu, 30 Apr 2026 08:16:03 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/visual-ai-pipeline-tools.m4a"
        type="audio/mp4"
        length="30927898"
      />
      <itunes:title>Visual AI Pipelines: Beyond Python Glue Code</itunes:title>
      <itunes:subtitle>From ComfyUI to Dify — a tour of visual tools for building modular AI workflows without writing glue code.</itunes:subtitle>
      <itunes:summary><![CDATA[You've prototyped a generative AI workflow in Google's AI Studio, tuned the temperature, locked in the system instruction — now what? This episode explores the growing ecosystem of visual programming tools that sit between raw Python scripts and full node-based environments. We survey the landscape: ComfyUI's extensible node graph, Fal's hosted workflow builder, Dify's LLM-focused pipelines, Flowise for chatbots, and the fragmentation problem that still drives many creators back to Python. For anyone doing creative AI work who finds code editors break their flow, this is a guide to the tools that let you see your pipeline instead of reading it.]]></itunes:summary>
      <itunes:duration>2414</itunes:duration>
      <itunes:episode>2545</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/visual-ai-pipeline-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/visual-ai-pipeline-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Make AI Architectural Renders Photoreal Without Breaking Geometry</title>
      <description><![CDATA[When Daniel and Hannah ran a precise Revit render through an image-to-image model, the result was technically impressive but weirdly fake — uncanny valley for buildings. This episode breaks down why diffusion models produce that glossy "video game" look, why temperature isn't the knob you think it is, and how to build a multi-stage pipeline using ControlNets, depth maps, and photographic process prompts to achieve photorealism without distorting geometry. We explore the three layers of the problem (training data bias, regression to the mean, noise profile mismatch), the practical workflow using ComfyUI, and why architecture clients have lower tolerance for AI weirdness than concept artists.]]></description>
      <link>https://myweirdprompts.com/episode/ai-architectural-renders-photorealism/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-architectural-renders-photorealism/</guid>
      <pubDate>Thu, 30 Apr 2026 08:02:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-architectural-renders-photorealism.m4a"
        type="audio/mp4"
        length="24456212"
      />
      <itunes:title>How to Make AI Architectural Renders Photoreal Without Breaking Geometry</itunes:title>
      <itunes:subtitle>Fixing the uncanny valley in AI-enhanced architectural renders — without breaking the geometry.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel and Hannah ran a precise Revit render through an image-to-image model, the result was technically impressive but weirdly fake — uncanny valley for buildings. This episode breaks down why diffusion models produce that glossy "video game" look, why temperature isn't the knob you think it is, and how to build a multi-stage pipeline using ControlNets, depth maps, and photographic process prompts to achieve photorealism without distorting geometry. We explore the three layers of the problem (training data bias, regression to the mean, noise profile mismatch), the practical workflow using ComfyUI, and why architecture clients have lower tolerance for AI weirdness than concept artists.]]></itunes:summary>
      <itunes:duration>1908</itunes:duration>
      <itunes:episode>2544</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-architectural-renders-photorealism.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-architectural-renders-photorealism.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Base64 Adds 33% Overhead (And Why You Still Need It)</title>
      <description><![CDATA[Base64 isn't compression—it's safety. This episode explains why audio pipelines rely on a text-safe encoding that inflates file size, and when to choose streaming over batch processing for voice APIs.]]></description>
      <link>https://myweirdprompts.com/episode/base64-audio-api-limits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/base64-audio-api-limits/</guid>
      <pubDate>Thu, 30 Apr 2026 07:46:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/base64-audio-api-limits.m4a"
        type="audio/mp4"
        length="29343284"
      />
      <itunes:title>Why Base64 Adds 33% Overhead (And Why You Still Need It)</itunes:title>
      <itunes:subtitle>Base64 isn’t compression — it’s a safe transport encoding. Here’s how it works with audio APIs and where its limits are.</itunes:subtitle>
      <itunes:summary><![CDATA[Base64 isn't compression—it's safety. This episode explains why audio pipelines rely on a text-safe encoding that inflates file size, and when to choose streaming over batch processing for voice APIs.]]></itunes:summary>
      <itunes:duration>2237</itunes:duration>
      <itunes:episode>2543</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/base64-audio-api-limits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/base64-audio-api-limits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Agent-to-Agent Scheduling: Building the Calendly for AI</title>
      <description><![CDATA[What if your email signature contained a link designed for AI agents, not humans? One listener proposed exactly that: a "junction" where two agents can negotiate schedules, check availability, and book meetings in a credentialed environment. This episode explores what already exists for agent-to-agent handoffs — including Google's Agent-to-Agent Protocol (A2A) and Anthropic's Remote MCP — and walks through the three hard problems of authentication, capability discovery, and negotiation that any such system must solve.]]></description>
      <link>https://myweirdprompts.com/episode/agent-to-agent-scheduling/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-to-agent-scheduling/</guid>
      <pubDate>Wed, 29 Apr 2026 23:19:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-to-agent-scheduling.m4a"
        type="audio/mp4"
        length="29675768"
      />
      <itunes:title>Agent-to-Agent Scheduling: Building the Calendly for AI</itunes:title>
      <itunes:subtitle>How Google&apos;s A2A protocol and Anthropic&apos;s Remote MCP could power a new kind of agent handoff for scheduling meetings.</itunes:subtitle>
      <itunes:summary><![CDATA[What if your email signature contained a link designed for AI agents, not humans? One listener proposed exactly that: a "junction" where two agents can negotiate schedules, check availability, and book meetings in a credentialed environment. This episode explores what already exists for agent-to-agent handoffs — including Google's Agent-to-Agent Protocol (A2A) and Anthropic's Remote MCP — and walks through the three hard problems of authentication, capability discovery, and negotiation that any such system must solve.]]></itunes:summary>
      <itunes:duration>2299</itunes:duration>
      <itunes:episode>2541</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-to-agent-scheduling.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-to-agent-scheduling.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Does Your AI Framework Change the Output?</title>
      <description><![CDATA[When you build an AI agent pipeline, does the framework you choose actually change what comes out the other end? This episode explores a real-world case: a multi-agent security report generator for Israel, built with LangGraph. We compare it against two alternatives — Deep Agents and Pydantic — using identical models, tools, and prompts. The surprising finding: the harness itself shapes the output in fundamental ways, from stopping conditions to output structure to how domain expertise gets encoded. We discuss why LangGraph's graph-based approach lets you embed real judgment, why Deep Agents structurally amplifies noise, and why Pydantic constrains what can be said rather than how you get there. For anyone building agentic systems, this is a deep dive into why the plumbing matters as much as the model.]]></description>
      <link>https://myweirdprompts.com/episode/ai-framework-output-differences/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-framework-output-differences/</guid>
      <pubDate>Wed, 29 Apr 2026 21:47:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-framework-output-differences.m4a"
        type="audio/mp4"
        length="23283275"
      />
      <itunes:title>Does Your AI Framework Change the Output?</itunes:title>
      <itunes:subtitle>Same model, same prompts, different harness. Does the plumbing change the water?</itunes:subtitle>
      <itunes:summary><![CDATA[When you build an AI agent pipeline, does the framework you choose actually change what comes out the other end? This episode explores a real-world case: a multi-agent security report generator for Israel, built with LangGraph. We compare it against two alternatives — Deep Agents and Pydantic — using identical models, tools, and prompts. The surprising finding: the harness itself shapes the output in fundamental ways, from stopping conditions to output structure to how domain expertise gets encoded. We discuss why LangGraph's graph-based approach lets you embed real judgment, why Deep Agents structurally amplifies noise, and why Pydantic constrains what can be said rather than how you get there. For anyone building agentic systems, this is a deep dive into why the plumbing matters as much as the model.]]></itunes:summary>
      <itunes:duration>1804</itunes:duration>
      <itunes:episode>2540</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-framework-output-differences.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-framework-output-differences.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Self-Hosted Zapier Alternatives in 2026</title>
      <description><![CDATA[Thinking about ditching Zapier for a self-hosted alternative? This episode breaks down the three distinct philosophies of personal automation tools in 2026: visual workflow builders like n8n, agent-based systems like Huginn, and code-first schedulers like Dagu. We explore the tradeoffs between them — from n8n’s polished drag-and-drop interface with 400+ integrations to Huginn’s event-bus architecture for complex monitoring patterns, and Dagu’s minimalist YAML-driven approach for scripters. Plus: why self-hosting isn’t just about cost savings, but about keeping your sensitive data — bank alerts, medical reminders, private calendar entries — off third-party servers. If you’re running a Raspberry Pi or a small VPS, you’ll want to hear which tool fits your skill level and automation style.]]></description>
      <link>https://myweirdprompts.com/episode/self-hosted-zapier-alternatives/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/self-hosted-zapier-alternatives/</guid>
      <pubDate>Wed, 29 Apr 2026 21:07:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/self-hosted-zapier-alternatives.m4a"
        type="audio/mp4"
        length="18909299"
      />
      <itunes:title>Self-Hosted Zapier Alternatives in 2026</itunes:title>
      <itunes:subtitle>n8n, Huginn, and Dagu compared for personal automation on your own hardware.</itunes:subtitle>
      <itunes:summary><![CDATA[Thinking about ditching Zapier for a self-hosted alternative? This episode breaks down the three distinct philosophies of personal automation tools in 2026: visual workflow builders like n8n, agent-based systems like Huginn, and code-first schedulers like Dagu. We explore the tradeoffs between them — from n8n’s polished drag-and-drop interface with 400+ integrations to Huginn’s event-bus architecture for complex monitoring patterns, and Dagu’s minimalist YAML-driven approach for scripters. Plus: why self-hosting isn’t just about cost savings, but about keeping your sensitive data — bank alerts, medical reminders, private calendar entries — off third-party servers. If you’re running a Raspberry Pi or a small VPS, you’ll want to hear which tool fits your skill level and automation style.]]></itunes:summary>
      <itunes:duration>1460</itunes:duration>
      <itunes:episode>2536</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/self-hosted-zapier-alternatives.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/self-hosted-zapier-alternatives.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside LangChain&apos;s Deep Agents: What&apos;s Actually in the Box</title>
      <description><![CDATA[We explore the LangChain deep agents repository — an open-source agent harness that ships with a full terminal-based coding CLI, sub-agents with isolated context windows, async delegation patterns, and a systematic evaluation framework. Unlike most "batteries included" frameworks, this one delivers planning tools, filesystem operations, shell access with structural security boundaries, portable skills, GitHub Actions integration, and multi-provider LLM support. The architecture is opinionated, production-ready, and built on LangGraph with streaming, persistence, and checkpointing.]]></description>
      <link>https://myweirdprompts.com/episode/langchain-deep-agents-analysis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/langchain-deep-agents-analysis/</guid>
      <pubDate>Wed, 29 Apr 2026 21:07:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/langchain-deep-agents-analysis.m4a"
        type="audio/mp4"
        length="24394296"
      />
      <itunes:title>Inside LangChain&apos;s Deep Agents: What&apos;s Actually in the Box</itunes:title>
      <itunes:subtitle>A deep dive into the batteries-included agent harness with terminal CLI, sub-agents, and production-ready evaluation.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the LangChain deep agents repository — an open-source agent harness that ships with a full terminal-based coding CLI, sub-agents with isolated context windows, async delegation patterns, and a systematic evaluation framework. Unlike most "batteries included" frameworks, this one delivers planning tools, filesystem operations, shell access with structural security boundaries, portable skills, GitHub Actions integration, and multi-provider LLM support. The architecture is opinionated, production-ready, and built on LangGraph with streaming, persistence, and checkpointing.]]></itunes:summary>
      <itunes:duration>1885</itunes:duration>
      <itunes:episode>2535</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/langchain-deep-agents-analysis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/langchain-deep-agents-analysis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can AI Generate Diagrams Without Typo Disasters?</title>
      <description><![CDATA[Technical diagramming sits in an awkward gap between reliable-but-ugly tools like Mermaid and visually stunning-but-unreliable text-to-image models. This episode explores why diffusion models struggle with character-level accuracy, how models like NanoBanana 2 are improving text rendering, and what hybrid approaches — from structured canvas generation to specialized tools like Diagramly — are emerging to solve the problem. We also cover practical prompting techniques for getting cleaner labels out of existing models, and why decoupling text from visual generation may be the real path forward for production-ready diagramming.]]></description>
      <link>https://myweirdprompts.com/episode/ai-diagram-text-reliability/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-diagram-text-reliability/</guid>
      <pubDate>Wed, 29 Apr 2026 14:13:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-diagram-text-reliability.m4a"
        type="audio/mp4"
        length="26330668"
      />
      <itunes:title>Can AI Generate Diagrams Without Typo Disasters?</itunes:title>
      <itunes:subtitle>Why AI diagram tools still mangle text labels — and what to do about it today.</itunes:subtitle>
      <itunes:summary><![CDATA[Technical diagramming sits in an awkward gap between reliable-but-ugly tools like Mermaid and visually stunning-but-unreliable text-to-image models. This episode explores why diffusion models struggle with character-level accuracy, how models like NanoBanana 2 are improving text rendering, and what hybrid approaches — from structured canvas generation to specialized tools like Diagramly — are emerging to solve the problem. We also cover practical prompting techniques for getting cleaner labels out of existing models, and why decoupling text from visual generation may be the real path forward for production-ready diagramming.]]></itunes:summary>
      <itunes:duration>2047</itunes:duration>
      <itunes:episode>2534</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-diagram-text-reliability.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-diagram-text-reliability.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Unsloth Makes LLM Fine-Tuning 2x Faster</title>
      <description><![CDATA[Unsloth has become the go-to library for fine-tuning large language models, promising dramatically faster training and lower memory usage without sacrificing output quality. This episode breaks down the technical innovations behind it—custom Triton kernels, optimized attention mechanisms, and smarter recomputation strategies—and explains why it's not just hype. We cover how Unsloth integrates with QLoRA to enable fine-tuning on a single consumer GPU, the key use cases from instruction tuning to domain adaptation, and why Hugging Face hasn't simply absorbed these optimizations. Whether you're a hobbyist or running production workloads, understanding Unsloth's approach reveals broader truths about where the bottlenecks really are in modern AI training.]]></description>
      <link>https://myweirdprompts.com/episode/unsloth-llm-fine-tuning-optimization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unsloth-llm-fine-tuning-optimization/</guid>
      <pubDate>Wed, 29 Apr 2026 02:49:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unsloth-llm-fine-tuning-optimization.m4a"
        type="audio/mp4"
        length="23807649"
      />
      <itunes:title>How Unsloth Makes LLM Fine-Tuning 2x Faster</itunes:title>
      <itunes:subtitle>Unsloth cuts memory usage by 50-70% and speeds up training 2.2x for models like Llama 3 and Mistral.</itunes:subtitle>
      <itunes:summary><![CDATA[Unsloth has become the go-to library for fine-tuning large language models, promising dramatically faster training and lower memory usage without sacrificing output quality. This episode breaks down the technical innovations behind it—custom Triton kernels, optimized attention mechanisms, and smarter recomputation strategies—and explains why it's not just hype. We cover how Unsloth integrates with QLoRA to enable fine-tuning on a single consumer GPU, the key use cases from instruction tuning to domain adaptation, and why Hugging Face hasn't simply absorbed these optimizations. Whether you're a hobbyist or running production workloads, understanding Unsloth's approach reveals broader truths about where the bottlenecks really are in modern AI training.]]></itunes:summary>
      <itunes:duration>1840</itunes:duration>
      <itunes:episode>2517</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unsloth-llm-fine-tuning-optimization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unsloth-llm-fine-tuning-optimization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>WebSockets vs SSE: Choosing the Right Real-Time Connection</title>
      <description><![CDATA[WebSockets and Server-Sent Events both enable real-time communication, but they serve fundamentally different purposes. This episode breaks down the core distinction: WebSockets are full-duplex (two-way), while SSE is half-duplex (server-to-client only). We explore the handshake differences, protocol overhead, browser support, and practical deployment considerations. Learn why SSE is often the simpler choice for notification feeds and dashboards, when WebSockets are mandatory for multiplayer games and collaborative editors, and how HTTP chunked transfer encoding fits into the picture. We also cover authentication limitations, reconnection logic, scaling considerations, and the emerging WebTransport alternative.]]></description>
      <link>https://myweirdprompts.com/episode/websockets-vs-sse-realtime/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/websockets-vs-sse-realtime/</guid>
      <pubDate>Wed, 29 Apr 2026 02:29:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/websockets-vs-sse-realtime.m4a"
        type="audio/mp4"
        length="27764690"
      />
      <itunes:title>WebSockets vs SSE: Choosing the Right Real-Time Connection</itunes:title>
      <itunes:subtitle>WebSockets vs Server-Sent Events: when to use full-duplex vs one-way streaming, and why most developers pick wrong.</itunes:subtitle>
      <itunes:summary><![CDATA[WebSockets and Server-Sent Events both enable real-time communication, but they serve fundamentally different purposes. This episode breaks down the core distinction: WebSockets are full-duplex (two-way), while SSE is half-duplex (server-to-client only). We explore the handshake differences, protocol overhead, browser support, and practical deployment considerations. Learn why SSE is often the simpler choice for notification feeds and dashboards, when WebSockets are mandatory for multiplayer games and collaborative editors, and how HTTP chunked transfer encoding fits into the picture. We also cover authentication limitations, reconnection logic, scaling considerations, and the emerging WebTransport alternative.]]></itunes:summary>
      <itunes:duration>2151</itunes:duration>
      <itunes:episode>2514</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/websockets-vs-sse-realtime.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/websockets-vs-sse-realtime.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Measuring AI API Latency Through the Black Box</title>
      <description><![CDATA[Ever felt your AI tool was sluggish, only to see a green status page? This episode dives into practical ways to measure what's happening under the hood. From Claude Code's built-in OpenTelemetry support to mitmproxy's HTTPS inspection, we explore how to capture real timing data, token counts, and rate-limit headers. You'll learn to distinguish between queuing delays, compute contention, and throttling — and finally have evidence instead of suspicion.]]></description>
      <link>https://myweirdprompts.com/episode/measuring-ai-api-latency/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/measuring-ai-api-latency/</guid>
      <pubDate>Wed, 29 Apr 2026 00:24:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/measuring-ai-api-latency.m4a"
        type="audio/mp4"
        length="20401725"
      />
      <itunes:title>Measuring AI API Latency Through the Black Box</itunes:title>
      <itunes:subtitle>How to benchmark token throughput and debug slowdowns in closed CLI tools like Claude Code using OpenTelemetry and mitmproxy.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever felt your AI tool was sluggish, only to see a green status page? This episode dives into practical ways to measure what's happening under the hood. From Claude Code's built-in OpenTelemetry support to mitmproxy's HTTPS inspection, we explore how to capture real timing data, token counts, and rate-limit headers. You'll learn to distinguish between queuing delays, compute contention, and throttling — and finally have evidence instead of suspicion.]]></itunes:summary>
      <itunes:duration>1576</itunes:duration>
      <itunes:episode>2511</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/measuring-ai-api-latency.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/measuring-ai-api-latency.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why CORS Doesn&apos;t Protect Your Server</title>
      <description><![CDATA[Most developers think CORS protects their API. It doesn't. This episode reframes CORS as a relaxation of the Same-Origin Policy that protects users, not servers, and explains why the threat model changes everything about how you configure it.]]></description>
      <link>https://myweirdprompts.com/episode/cors-cross-origin-resource-sharing-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cors-cross-origin-resource-sharing-guide/</guid>
      <pubDate>Tue, 28 Apr 2026 10:04:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cors-cross-origin-resource-sharing-guide.m4a"
        type="audio/mp4"
        length="21738689"
      />
      <itunes:title>Why CORS Doesn&apos;t Protect Your Server</itunes:title>
      <itunes:subtitle>Why browsers block cross-origin requests, how CORS actually works, and the common pitfalls that trip up developers.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers think CORS protects their API. It doesn't. This episode reframes CORS as a relaxation of the Same-Origin Policy that protects users, not servers, and explains why the threat model changes everything about how you configure it.]]></itunes:summary>
      <itunes:duration>1688</itunes:duration>
      <itunes:episode>2508</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cors-cross-origin-resource-sharing-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cors-cross-origin-resource-sharing-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Squashing Database Migrations Without Breaking Production</title>
      <description><![CDATA[If you've ever opened a migrations folder and found yourself on an archaeological dig through years of schema decisions, this episode is for you. We break down the safe, tool-agnostic technique for squashing old database migrations into a single baseline file — a pattern GitLab has been running in production since 2023. We cover the five-step mechanical process (fresh database, schema dump, baseline file, history table update, file deletion), why production never touches the squashed file, and the real performance gains you can expect (up to 64% speedup on projects with 35+ migrations). We also explore the underexplored problem of generating human-readable schema changelogs at version boundaries — and why the industry's linear migration model is still the right default for recent history where data transformations matter.]]></description>
      <link>https://myweirdprompts.com/episode/database-migration-squashing-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/database-migration-squashing-guide/</guid>
      <pubDate>Tue, 28 Apr 2026 08:27:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/database-migration-squashing-guide.m4a"
        type="audio/mp4"
        length="23129341"
      />
      <itunes:title>Squashing Database Migrations Without Breaking Production</itunes:title>
      <itunes:subtitle>How to safely squash old migrations, cut deploy times, and generate schema documentation at version boundaries.</itunes:subtitle>
      <itunes:summary><![CDATA[If you've ever opened a migrations folder and found yourself on an archaeological dig through years of schema decisions, this episode is for you. We break down the safe, tool-agnostic technique for squashing old database migrations into a single baseline file — a pattern GitLab has been running in production since 2023. We cover the five-step mechanical process (fresh database, schema dump, baseline file, history table update, file deletion), why production never touches the squashed file, and the real performance gains you can expect (up to 64% speedup on projects with 35+ migrations). We also explore the underexplored problem of generating human-readable schema changelogs at version boundaries — and why the industry's linear migration model is still the right default for recent history where data transformations matter.]]></itunes:summary>
      <itunes:duration>1797</itunes:duration>
      <itunes:episode>2506</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/database-migration-squashing-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/database-migration-squashing-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Self-Hosted Search Actually Works for AI Agents</title>
      <description><![CDATA[When an AI agent sends a search query instead of a human, does the retrieval itself change? This episode unpacks SearXNG, the self-hosted metasearch engine that forwards queries to over 70 upstream providers, anonymizes them, and aggregates results without building its own index. We explore the technical pipeline — adapter modules, weighted position sum scoring, and the JSON API — then dive into how AI agents consume search results differently. With zero-click searches now at 60% for AI queries and agent traffic up 7,851% year over year, the question isn't whether search is changing — it's whether the tools we use to search need to change too.]]></description>
      <link>https://myweirdprompts.com/episode/self-hosted-search-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/self-hosted-search-ai-agents/</guid>
      <pubDate>Tue, 28 Apr 2026 07:42:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/self-hosted-search-ai-agents.m4a"
        type="audio/mp4"
        length="21123404"
      />
      <itunes:title>How Self-Hosted Search Actually Works for AI Agents</itunes:title>
      <itunes:subtitle>SearXNG isn&apos;t a crawler — it&apos;s a metasearch router. Here&apos;s how it works and why AI agents change everything.</itunes:subtitle>
      <itunes:summary><![CDATA[When an AI agent sends a search query instead of a human, does the retrieval itself change? This episode unpacks SearXNG, the self-hosted metasearch engine that forwards queries to over 70 upstream providers, anonymizes them, and aggregates results without building its own index. We explore the technical pipeline — adapter modules, weighted position sum scoring, and the JSON API — then dive into how AI agents consume search results differently. With zero-click searches now at 60% for AI queries and agent traffic up 7,851% year over year, the question isn't whether search is changing — it's whether the tools we use to search need to change too.]]></itunes:summary>
      <itunes:duration>1641</itunes:duration>
      <itunes:episode>2505</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/self-hosted-search-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/self-hosted-search-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Gap Between Your Mental Model and the Wire</title>
      <description><![CDATA[Most developers operate on a fuzzy mental model of what happens when they fire off an API call. This episode gets concrete about the cascade from DNS to response, the headers that negotiate everything, and why understanding the wire format matters even when tools abstract it away.]]></description>
      <link>https://myweirdprompts.com/episode/api-request-anatomy-dns-response/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/api-request-anatomy-dns-response/</guid>
      <pubDate>Tue, 28 Apr 2026 00:59:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/api-request-anatomy-dns-response.m4a"
        type="audio/mp4"
        length="21347676"
      />
      <itunes:title>The Gap Between Your Mental Model and the Wire</itunes:title>
      <itunes:subtitle>What really happens when you press Enter on a URL? From DNS to TLS to headers, we break down the full lifecycle.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers operate on a fuzzy mental model of what happens when they fire off an API call. This episode gets concrete about the cascade from DNS to response, the headers that negotiate everything, and why understanding the wire format matters even when tools abstract it away.]]></itunes:summary>
      <itunes:duration>1655</itunes:duration>
      <itunes:episode>2503</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/api-request-anatomy-dns-response.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/api-request-anatomy-dns-response.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Describing a Neighborhood: Databases Without Screens</title>
      <description><![CDATA[How do you teach relational database design using only your voice? This episode explores why SQL concepts like foreign keys and constraints are fundamentally about relationships you can describe in plain English — no diagrams needed.]]></description>
      <link>https://myweirdprompts.com/episode/postgresql-movie-theater-database/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/postgresql-movie-theater-database/</guid>
      <pubDate>Tue, 28 Apr 2026 00:42:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/postgresql-movie-theater-database.m4a"
        type="audio/mp4"
        length="29629413"
      />
      <itunes:title>Describing a Neighborhood: Databases Without Screens</itunes:title>
      <itunes:subtitle>Can you design a relational database using only your voice? We coach a beginner through PostgreSQL from scratch.</itunes:subtitle>
      <itunes:summary><![CDATA[How do you teach relational database design using only your voice? This episode explores why SQL concepts like foreign keys and constraints are fundamentally about relationships you can describe in plain English — no diagrams needed.]]></itunes:summary>
      <itunes:duration>2307</itunes:duration>
      <itunes:episode>2501</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/postgresql-movie-theater-database.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/postgresql-movie-theater-database.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Coding by Voice: Teaching a Beginner TypeScript</title>
      <description><![CDATA[Can you teach someone to program using only spoken instructions? This episode puts that question to the test as two hosts coach a complete beginner through building a real TypeScript program, explaining every step and mistake along the way.]]></description>
      <link>https://myweirdprompts.com/episode/typescript-tip-calculator-beginners/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/typescript-tip-calculator-beginners/</guid>
      <pubDate>Tue, 28 Apr 2026 00:26:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/typescript-tip-calculator-beginners.m4a"
        type="audio/mp4"
        length="28512715"
      />
      <itunes:title>Coding by Voice: Teaching a Beginner TypeScript</itunes:title>
      <itunes:subtitle>Learn to code by building a real TypeScript tip calculator — no experience needed, just your ears and keyboard.</itunes:subtitle>
      <itunes:summary><![CDATA[Can you teach someone to program using only spoken instructions? This episode puts that question to the test as two hosts coach a complete beginner through building a real TypeScript program, explaining every step and mistake along the way.]]></itunes:summary>
      <itunes:duration>2219</itunes:duration>
      <itunes:episode>2499</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/typescript-tip-calculator-beginners.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/typescript-tip-calculator-beginners.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Build Your First Python Program in 7 Lines</title>
      <description><![CDATA[In this episode, we take on a challenge: teach someone who has never written a single line of code to build a working Python program from scratch using only our voices. We choose the guess-the-number game because it teaches importing modules, variables, user input, type conversion, conditionals, loops, and f-strings in just seven lines. We walk through every character, punctuation mark, and indent across five stages—from "Hello, World!" to a fully interactive game—and cover essential setup like installing Python, choosing a text editor, and running your program from the terminal. No prior experience required.]]></description>
      <link>https://myweirdprompts.com/episode/guess-the-number-python-beginner/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/guess-the-number-python-beginner/</guid>
      <pubDate>Tue, 28 Apr 2026 00:20:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/guess-the-number-python-beginner.m4a"
        type="audio/mp4"
        length="23877348"
      />
      <itunes:title>Build Your First Python Program in 7 Lines</itunes:title>
      <itunes:subtitle>We coach a complete beginner through building a working Python game using only voice—no screenshare, no diagrams.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we take on a challenge: teach someone who has never written a single line of code to build a working Python program from scratch using only our voices. We choose the guess-the-number game because it teaches importing modules, variables, user input, type conversion, conditionals, loops, and f-strings in just seven lines. We walk through every character, punctuation mark, and indent across five stages—from "Hello, World!" to a fully interactive game—and cover essential setup like installing Python, choosing a text editor, and running your program from the terminal. No prior experience required.]]></itunes:summary>
      <itunes:duration>1855</itunes:duration>
      <itunes:episode>2498</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/guess-the-number-python-beginner.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/guess-the-number-python-beginner.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Tracing One Python Print Through 6 Abstraction Layers</title>
      <description><![CDATA[Most developers have only a fuzzy sense of what happens between their fingertips and the silicon. This episode traces a single Python `print("Hello")` statement through every layer of abstraction: CPython bytecode compilation, the virtual machine loop, glibc buffering, the system call boundary, the Linux kernel's VFS and terminal driver, and finally the hardware itself. We contrast this with C and Rust's approaches, examine why Python generates 562 system calls vs C's 34, and explore what "high-level" and "low-level" actually mean concretely — not as textbook definitions, but as countable layers with real costs. Understanding where your abstractions live and what they cost is no longer academic; it's a practical engineering decision for cold starts, edge computing, and systems programming.]]></description>
      <link>https://myweirdprompts.com/episode/python-abstraction-stack-trace/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/python-abstraction-stack-trace/</guid>
      <pubDate>Tue, 28 Apr 2026 00:08:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/python-abstraction-stack-trace.m4a"
        type="audio/mp4"
        length="24573831"
      />
      <itunes:title>Tracing One Python Print Through 6 Abstraction Layers</itunes:title>
      <itunes:subtitle>What actually happens when you print &quot;Hello&quot; in Python? Six layers, 562 system calls, and a hardware-enforced kernel boundary.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers have only a fuzzy sense of what happens between their fingertips and the silicon. This episode traces a single Python `print("Hello")` statement through every layer of abstraction: CPython bytecode compilation, the virtual machine loop, glibc buffering, the system call boundary, the Linux kernel's VFS and terminal driver, and finally the hardware itself. We contrast this with C and Rust's approaches, examine why Python generates 562 system calls vs C's 34, and explore what "high-level" and "low-level" actually mean concretely — not as textbook definitions, but as countable layers with real costs. Understanding where your abstractions live and what they cost is no longer academic; it's a practical engineering decision for cold starts, edge computing, and systems programming.]]></itunes:summary>
      <itunes:duration>1914</itunes:duration>
      <itunes:episode>2497</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/python-abstraction-stack-trace.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/python-abstraction-stack-trace.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Are Hidden API Endpoints Leaks or Just Plumbing?</title>
      <description><![CDATA[When you open Chrome DevTools and watch the XHR requests fly by, you'll often find dozens of unauthenticated JSON endpoints sitting behind polished frontends. Are these data leaks, intentional public APIs, or just the natural plumbing of modern single-page apps? This episode explores what happens when LLM agents like Claude systematically discover and document undocumented APIs — and why the old assumption that "if it's not documented, it's private" no longer holds. We examine the spectrum from benign public data endpoints to genuine Broken Object Level Authorization vulnerabilities, the novel attack surface created by agent-driven DevTools access, and why every developer should adopt a "public by default" mindset for frontend-consumed APIs.]]></description>
      <link>https://myweirdprompts.com/episode/hidden-api-endpoints-leaks-plumbing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hidden-api-endpoints-leaks-plumbing/</guid>
      <pubDate>Mon, 27 Apr 2026 23:30:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hidden-api-endpoints-leaks-plumbing.m4a"
        type="audio/mp4"
        length="19907070"
      />
      <itunes:title>Are Hidden API Endpoints Leaks or Just Plumbing?</itunes:title>
      <itunes:subtitle>When LLM agents discover unauthenticated JSON endpoints in browser DevTools, is it a security breach or just reading the page?</itunes:subtitle>
      <itunes:summary><![CDATA[When you open Chrome DevTools and watch the XHR requests fly by, you'll often find dozens of unauthenticated JSON endpoints sitting behind polished frontends. Are these data leaks, intentional public APIs, or just the natural plumbing of modern single-page apps? This episode explores what happens when LLM agents like Claude systematically discover and document undocumented APIs — and why the old assumption that "if it's not documented, it's private" no longer holds. We examine the spectrum from benign public data endpoints to genuine Broken Object Level Authorization vulnerabilities, the novel attack surface created by agent-driven DevTools access, and why every developer should adopt a "public by default" mindset for frontend-consumed APIs.]]></itunes:summary>
      <itunes:duration>1526</itunes:duration>
      <itunes:episode>2496</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hidden-api-endpoints-leaks-plumbing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hidden-api-endpoints-leaks-plumbing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Bake Personality Into an LLM in 15 Minutes</title>
      <description><![CDATA[Want an LLM that doesn't default to cheerful, hedged over-explaining? This episode unpacks the state-of-the-art recipe for baking real personality into model weights — not just system prompts. We break down the Grumpy Italian Chef case study (a 1.2B model trained in 15 minutes on a consumer GPU), explain the SFT + DPO pipeline, and explore how much data you actually need for style transfer vs. robust persona alignment. Plus: tooling options (Unsloth, LlamaFactory, Axolotl), the beta personality dial, and the philosophical question of whether different alignment is misalignment.]]></description>
      <link>https://myweirdprompts.com/episode/llm-personality-fine-tuning-sft-dpo/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-personality-fine-tuning-sft-dpo/</guid>
      <pubDate>Mon, 27 Apr 2026 22:48:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-personality-fine-tuning-sft-dpo.m4a"
        type="audio/mp4"
        length="20092492"
      />
      <itunes:title>How to Bake Personality Into an LLM in 15 Minutes</itunes:title>
      <itunes:subtitle>Fine-tune a model&apos;s personality with ~300 examples and a consumer GPU. SFT + DPO explained.</itunes:subtitle>
      <itunes:summary><![CDATA[Want an LLM that doesn't default to cheerful, hedged over-explaining? This episode unpacks the state-of-the-art recipe for baking real personality into model weights — not just system prompts. We break down the Grumpy Italian Chef case study (a 1.2B model trained in 15 minutes on a consumer GPU), explain the SFT + DPO pipeline, and explore how much data you actually need for style transfer vs. robust persona alignment. Plus: tooling options (Unsloth, LlamaFactory, Axolotl), the beta personality dial, and the philosophical question of whether different alignment is misalignment.]]></itunes:summary>
      <itunes:duration>1541</itunes:duration>
      <itunes:episode>2495</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-personality-fine-tuning-sft-dpo.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-personality-fine-tuning-sft-dpo.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Active Prompt Engineering: Daniel&apos;s Diff-Based Loop</title>
      <description><![CDATA[This episode explores a listener's innovative approach to building a structured dataset from voice-dictated AI prompts. Daniel's method iterates between hand-annotating gold rows, running Claude Sonnet 4.6 with few-shot exemplars, and diffing outputs to find which rows changed most between iterations. We trace this to prior art in Active Prompt Engineering (APE), discuss why inter-iteration prediction change is a clever computational hack, and examine its blind spots — particularly rows that are consistently wrong across iterations. The conversation covers convergence criteria, the economics of active learning with cheap inference, and the critical distinction between converging a prompt versus converging an exemplar selection strategy. We also address few-shot leakage, held-out evaluation, and whether publishing a prompt as an artifact is meaningful without rigorous evaluation.]]></description>
      <link>https://myweirdprompts.com/episode/active-prompt-engineering-diff-loop/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/active-prompt-engineering-diff-loop/</guid>
      <pubDate>Mon, 27 Apr 2026 21:25:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/active-prompt-engineering-diff-loop.m4a"
        type="audio/mp4"
        length="20290245"
      />
      <itunes:title>Active Prompt Engineering: Daniel&apos;s Diff-Based Loop</itunes:title>
      <itunes:subtitle>A deep dive into iterative prompt refinement using inter-iteration prediction change as an uncertainty signal.</itunes:subtitle>
      <itunes:summary><![CDATA[This episode explores a listener's innovative approach to building a structured dataset from voice-dictated AI prompts. Daniel's method iterates between hand-annotating gold rows, running Claude Sonnet 4.6 with few-shot exemplars, and diffing outputs to find which rows changed most between iterations. We trace this to prior art in Active Prompt Engineering (APE), discuss why inter-iteration prediction change is a clever computational hack, and examine its blind spots — particularly rows that are consistently wrong across iterations. The conversation covers convergence criteria, the economics of active learning with cheap inference, and the critical distinction between converging a prompt versus converging an exemplar selection strategy. We also address few-shot leakage, held-out evaluation, and whether publishing a prompt as an artifact is meaningful without rigorous evaluation.]]></itunes:summary>
      <itunes:duration>1576</itunes:duration>
      <itunes:episode>2494</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/active-prompt-engineering-diff-loop.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/active-prompt-engineering-diff-loop.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Are You Writing for Humans or AI Agents?</title>
      <description><![CDATA[When you put structured data on GitHub, who's your real audience — future humans or AI agents? This episode explores Daniel's clever workflow of using public repositories as agent-accessible context, and the deeper question it raises about parallel documentation standards. We break down the emerging landscape of llms.txt, agenticweb.md, and AGENTS.md files, the surprising truth about whether any AI actually reads them, and why JSON (specifically NDJSON) is becoming the default format for agent consumption. Plus: the trust problem with agent-targeted content, the convergence thesis, and practical advice for anyone publishing information in an era where both humans and machines need to understand it.]]></description>
      <link>https://myweirdprompts.com/episode/writing-for-humans-or-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/writing-for-humans-or-ai-agents/</guid>
      <pubDate>Mon, 27 Apr 2026 18:34:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/writing-for-humans-or-ai-agents.m4a"
        type="audio/mp4"
        length="29770114"
      />
      <itunes:title>Are You Writing for Humans or AI Agents?</itunes:title>
      <itunes:subtitle>How GitHub repos, JSON formats, and competing standards are reshaping who (and what) you&apos;re publishing for.</itunes:subtitle>
      <itunes:summary><![CDATA[When you put structured data on GitHub, who's your real audience — future humans or AI agents? This episode explores Daniel's clever workflow of using public repositories as agent-accessible context, and the deeper question it raises about parallel documentation standards. We break down the emerging landscape of llms.txt, agenticweb.md, and AGENTS.md files, the surprising truth about whether any AI actually reads them, and why JSON (specifically NDJSON) is becoming the default format for agent consumption. Plus: the trust problem with agent-targeted content, the convergence thesis, and practical advice for anyone publishing information in an era where both humans and machines need to understand it.]]></itunes:summary>
      <itunes:duration>2308</itunes:duration>
      <itunes:episode>2493</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/writing-for-humans-or-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/writing-for-humans-or-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Agents Collapse Stack Evaluation from Weeks to Seconds</title>
      <description><![CDATA[How agentic code tools like Claude Code transform library selection from a high-stakes quarterly ritual into a fast, iterative loop—and why documenting decisions for both humans and future agents is the new challenge.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-stack-selection-github/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-stack-selection-github/</guid>
      <pubDate>Mon, 27 Apr 2026 13:30:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-stack-selection-github.m4a"
        type="audio/mp4"
        length="21700233"
      />
      <itunes:title>When AI Agents Collapse Stack Evaluation from Weeks to Seconds</itunes:title>
      <itunes:subtitle>How Claude Code and agentic AI are turning GitHub into a discovery layer and collapsing library evaluation from weeks to seconds.</itunes:subtitle>
      <itunes:summary><![CDATA[How agentic code tools like Claude Code transform library selection from a high-stakes quarterly ritual into a fast, iterative loop—and why documenting decisions for both humans and future agents is the new challenge.]]></itunes:summary>
      <itunes:duration>1663</itunes:duration>
      <itunes:episode>2492</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-stack-selection-github.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-stack-selection-github.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Hybrid Pipelines for Entity Resolution</title>
      <description><![CDATA[When building a daily intelligence briefing, should you rely on a dedicated NER model or a general-purpose LLM? This episode explores a hybrid approach that combines deterministic gazetteers with lightweight language models, and why the answer depends on controllability, latency, and the messy reality of Persian and Arabic romanization.]]></description>
      <link>https://myweirdprompts.com/episode/iran-israel-entity-mapping/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/iran-israel-entity-mapping/</guid>
      <pubDate>Mon, 27 Apr 2026 11:45:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/iran-israel-entity-mapping.m4a"
        type="audio/mp4"
        length="19298476"
      />
      <itunes:title>Hybrid Pipelines for Entity Resolution</itunes:title>
      <itunes:subtitle>Classic NLP pipelines vs. lightweight LLMs for handling Hezbollah’s half-dozen spellings.</itunes:subtitle>
      <itunes:summary><![CDATA[When building a daily intelligence briefing, should you rely on a dedicated NER model or a general-purpose LLM? This episode explores a hybrid approach that combines deterministic gazetteers with lightweight language models, and why the answer depends on controllability, latency, and the messy reality of Persian and Arabic romanization.]]></itunes:summary>
      <itunes:duration>1471</itunes:duration>
      <itunes:episode>2488</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/iran-israel-entity-mapping.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/iran-israel-entity-mapping.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Noise Reduction Can Ruin Transcription Accuracy</title>
      <description><![CDATA[Most developers assume cleaner audio means better transcription — but the research shows the opposite. This episode explores the noise reduction paradox: why modern ASR models actually perform worse on denoised audio, and how to build a pipeline that serves both transcription accuracy and podcast-quality output. We break down the algorithm landscape from heavyweight machine learning to ultra-lightweight DSP hybrids, explain why babble noise and Irish accents create special challenges, and lay out a two-path architecture that optimizes for each use case separately. If you're building a voice app and wondering whether to clean audio before or after transcription, this episode will save you weeks of trial and error.]]></description>
      <link>https://myweirdprompts.com/episode/noise-reduction-transcription-paradox/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/noise-reduction-transcription-paradox/</guid>
      <pubDate>Mon, 27 Apr 2026 11:33:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/noise-reduction-transcription-paradox.m4a"
        type="audio/mp4"
        length="21199137"
      />
      <itunes:title>Why Noise Reduction Can Ruin Transcription Accuracy</itunes:title>
      <itunes:subtitle>Cleaning audio before transcription can increase errors by up to 46%. Here&apos;s the right approach for your voice app.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers assume cleaner audio means better transcription — but the research shows the opposite. This episode explores the noise reduction paradox: why modern ASR models actually perform worse on denoised audio, and how to build a pipeline that serves both transcription accuracy and podcast-quality output. We break down the algorithm landscape from heavyweight machine learning to ultra-lightweight DSP hybrids, explain why babble noise and Irish accents create special challenges, and lay out a two-path architecture that optimizes for each use case separately. If you're building a voice app and wondering whether to clean audio before or after transcription, this episode will save you weeks of trial and error.]]></itunes:summary>
      <itunes:duration>1647</itunes:duration>
      <itunes:episode>2486</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/noise-reduction-transcription-paradox.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/noise-reduction-transcription-paradox.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Substitution Anonymization: Privacy Without Utility Loss</title>
      <description><![CDATA[When stripping PII from training data, redaction destroys utility—but substitution preserves it. We explore how small local models can generate synthetic voice notes and calendar entries with near-perfect privacy recall and minimal accuracy loss, and why this matters for anyone building classifiers on sensitive data.]]></description>
      <link>https://myweirdprompts.com/episode/synthetic-data-generation-pii/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/synthetic-data-generation-pii/</guid>
      <pubDate>Mon, 27 Apr 2026 11:13:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/synthetic-data-generation-pii.m4a"
        type="audio/mp4"
        length="19096087"
      />
      <itunes:title>Substitution Anonymization: Privacy Without Utility Loss</itunes:title>
      <itunes:subtitle>How to generate realistic synthetic voice notes and calendar data with zero PII exposure risk.</itunes:subtitle>
      <itunes:summary><![CDATA[When stripping PII from training data, redaction destroys utility—but substitution preserves it. We explore how small local models can generate synthetic voice notes and calendar entries with near-perfect privacy recall and minimal accuracy loss, and why this matters for anyone building classifiers on sensitive data.]]></itunes:summary>
      <itunes:duration>1481</itunes:duration>
      <itunes:episode>2483</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/synthetic-data-generation-pii.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/synthetic-data-generation-pii.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Chatbots Leak Your PDFs via Public S3 Buckets</title>
      <description><![CDATA[A user uploaded a sensitive PDF to a major AI chatbot, received a link back, and discovered that link pointed to a publicly accessible S3 bucket with no authentication. The vendor's response: "Don't worry, the URL is long and random and expires automatically." This episode examines the real-world case Daniel submitted, exploring whether security by obscurity is ever legitimate, how bug bounty programs handle these findings, and why the rise of quantum computing completely changes the risk calculus. We break down the distinction between security with obscurity versus security by obscurity, the AWS guidance explicitly warning against this practice, and why AI chatbots face unique trust issues when users upload legal documents, medical records, and trade secrets.]]></description>
      <link>https://myweirdprompts.com/episode/ai-chatbot-s3-bucket-leak/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-chatbot-s3-bucket-leak/</guid>
      <pubDate>Mon, 27 Apr 2026 11:05:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-chatbot-s3-bucket-leak.m4a"
        type="audio/mp4"
        length="20438926"
      />
      <itunes:title>When AI Chatbots Leak Your PDFs via Public S3 Buckets</itunes:title>
      <itunes:subtitle>A user uploaded a sensitive PDF to an AI chatbot. The chatbot stored it in a public S3 bucket with zero authentication.</itunes:subtitle>
      <itunes:summary><![CDATA[A user uploaded a sensitive PDF to a major AI chatbot, received a link back, and discovered that link pointed to a publicly accessible S3 bucket with no authentication. The vendor's response: "Don't worry, the URL is long and random and expires automatically." This episode examines the real-world case Daniel submitted, exploring whether security by obscurity is ever legitimate, how bug bounty programs handle these findings, and why the rise of quantum computing completely changes the risk calculus. We break down the distinction between security with obscurity versus security by obscurity, the AWS guidance explicitly warning against this practice, and why AI chatbots face unique trust issues when users upload legal documents, medical records, and trade secrets.]]></itunes:summary>
      <itunes:duration>1571</itunes:duration>
      <itunes:episode>2482</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-chatbot-s3-bucket-leak.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-chatbot-s3-bucket-leak.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>MCP File Handling: Why Your Base64 Upload Breaks at 4MB</title>
      <description><![CDATA[The Model Context Protocol has a dirty secret: there's no standard way to pass files between servers and clients. Base64 encoding hits hard 4MB limits, burns through token budgets, and fails silently in production. Presigned URL patterns require manual domain whitelisting that non-technical users can't manage. And while some builders run MinIO S3 buckets on their MCP servers, every implementation reinvents the wheel differently. This episode unpacks SEP 2356 — the draft proposal for a new `mcpFile` JSON Schema keyword — and explores why the protocol's file handling schizophrenia (local-first vs remote-first) means centralized gateways need their own storage architecture. For anyone building MCP toolkits across desktop, workstation, and mobile, this is the problem nobody's solved yet.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-file-handling-upload-issues/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-file-handling-upload-issues/</guid>
      <pubDate>Mon, 27 Apr 2026 10:35:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-file-handling-upload-issues.m4a"
        type="audio/mp4"
        length="18882028"
      />
      <itunes:title>MCP File Handling: Why Your Base64 Upload Breaks at 4MB</itunes:title>
      <itunes:subtitle>MCP has no standard file input. Base64 breaks at 4MB, presigned URLs need whitelisting, and MinIO workarounds aren&apos;t standardized.</itunes:subtitle>
      <itunes:summary><![CDATA[The Model Context Protocol has a dirty secret: there's no standard way to pass files between servers and clients. Base64 encoding hits hard 4MB limits, burns through token budgets, and fails silently in production. Presigned URL patterns require manual domain whitelisting that non-technical users can't manage. And while some builders run MinIO S3 buckets on their MCP servers, every implementation reinvents the wheel differently. This episode unpacks SEP 2356 — the draft proposal for a new `mcpFile` JSON Schema keyword — and explores why the protocol's file handling schizophrenia (local-first vs remote-first) means centralized gateways need their own storage architecture. For anyone building MCP toolkits across desktop, workstation, and mobile, this is the problem nobody's solved yet.]]></itunes:summary>
      <itunes:duration>1461</itunes:duration>
      <itunes:episode>2478</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-file-handling-upload-issues.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-file-handling-upload-issues.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Stop Polling: Push-to-Deploy for Solo Devs</title>
      <description><![CDATA[Most developers over-engineer deployment. Jenkins, GitLab CI, Kubernetes — all massive overkill for a solo dev or team of three. This episode breaks down the push-to-deploy model: how a simple GitHub Actions workflow with deploy keys replaces polling cron jobs, eliminates the need for a dedicated CI server, and handles production deployments in about four lines of YAML. We trace the path from minimal setup to the point where you'd actually need something more elaborate, and explore why understanding event-driven deployment is a superpower for small teams burning cash on infrastructure they don't need.]]></description>
      <link>https://myweirdprompts.com/episode/push-to-deploy-solo-devs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/push-to-deploy-solo-devs/</guid>
      <pubDate>Mon, 27 Apr 2026 08:21:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/push-to-deploy-solo-devs.m4a"
        type="audio/mp4"
        length="20338294"
      />
      <itunes:title>Stop Polling: Push-to-Deploy for Solo Devs</itunes:title>
      <itunes:subtitle>Why your cron job is obsolete. Push-to-deploy with GitHub Actions and deploy keys — the simplest setup that actually works.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers over-engineer deployment. Jenkins, GitLab CI, Kubernetes — all massive overkill for a solo dev or team of three. This episode breaks down the push-to-deploy model: how a simple GitHub Actions workflow with deploy keys replaces polling cron jobs, eliminates the need for a dedicated CI server, and handles production deployments in about four lines of YAML. We trace the path from minimal setup to the point where you'd actually need something more elaborate, and explore why understanding event-driven deployment is a superpower for small teams burning cash on infrastructure they don't need.]]></itunes:summary>
      <itunes:duration>1578</itunes:duration>
      <itunes:episode>2477</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/push-to-deploy-solo-devs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/push-to-deploy-solo-devs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Database Backups Without the Bloat</title>
      <description><![CDATA[Most database backup tools have been free and open-source for decades — but the expertise to use them wasn’t. This episode breaks down the actual CLI tools for Postgres, SQLite, and SQL Server backups: pg_dump vs pg_basebackup, the role of WAL archiving, native incremental backups in Postgres 17, and why AI coding agents are making DBA-level backup strategies accessible to anyone. We cover Docker-hosted databases, retention policies, and when to reach for pgBackRest, WAL-G, or just a simple sqlite3 command. If you’ve been wondering whether you really need a commercial backup tool for your CRM or home inventory system, the answer might surprise you.]]></description>
      <link>https://myweirdprompts.com/episode/database-backups-cli-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/database-backups-cli-tools/</guid>
      <pubDate>Mon, 27 Apr 2026 08:11:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/database-backups-cli-tools.m4a"
        type="audio/mp4"
        length="18603069"
      />
      <itunes:title>Database Backups Without the Bloat</itunes:title>
      <itunes:subtitle>pg_dump, WAL archiving, and the free tools that beat expensive commercial backup software.</itunes:subtitle>
      <itunes:summary><![CDATA[Most database backup tools have been free and open-source for decades — but the expertise to use them wasn’t. This episode breaks down the actual CLI tools for Postgres, SQLite, and SQL Server backups: pg_dump vs pg_basebackup, the role of WAL archiving, native incremental backups in Postgres 17, and why AI coding agents are making DBA-level backup strategies accessible to anyone. We cover Docker-hosted databases, retention policies, and when to reach for pgBackRest, WAL-G, or just a simple sqlite3 command. If you’ve been wondering whether you really need a commercial backup tool for your CRM or home inventory system, the answer might surprise you.]]></itunes:summary>
      <itunes:duration>1431</itunes:duration>
      <itunes:episode>2476</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/database-backups-cli-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/database-backups-cli-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Docker Volumes: Why They Can&apos;t Move and What To Do</title>
      <description><![CDATA[Docker solved "works on my machine" for applications — but when you need to move your actual data between hosts, you're suddenly on your own. This episode unpacks why Docker volumes aren't portable by design, the painful workarounds everyone eventually discovers (tar over SSH, rsync, bind mounts), and the real tradeoffs between cloud storage drivers, managed databases, and manual scripts. We cover the full spectrum from single-server setups to Kubernetes, plus practical backup strategies using restic, Borg, and the all-important rule: if it's a database, use the dump tool. Whether you're a solo developer migrating a home server or a small team managing multiple Docker hosts, this episode gives you the honest, unvarnished picture of Docker's biggest blind spot.]]></description>
      <link>https://myweirdprompts.com/episode/docker-volume-migration-portability/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/docker-volume-migration-portability/</guid>
      <pubDate>Mon, 27 Apr 2026 08:02:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/docker-volume-migration-portability.m4a"
        type="audio/mp4"
        length="18874582"
      />
      <itunes:title>Docker Volumes: Why They Can&apos;t Move and What To Do</itunes:title>
      <itunes:subtitle>Docker made apps portable but left your data stuck. Here&apos;s how to actually move volumes between hosts.</itunes:subtitle>
      <itunes:summary><![CDATA[Docker solved "works on my machine" for applications — but when you need to move your actual data between hosts, you're suddenly on your own. This episode unpacks why Docker volumes aren't portable by design, the painful workarounds everyone eventually discovers (tar over SSH, rsync, bind mounts), and the real tradeoffs between cloud storage drivers, managed databases, and manual scripts. We cover the full spectrum from single-server setups to Kubernetes, plus practical backup strategies using restic, Borg, and the all-important rule: if it's a database, use the dump tool. Whether you're a solo developer migrating a home server or a small team managing multiple Docker hosts, this episode gives you the honest, unvarnished picture of Docker's biggest blind spot.]]></itunes:summary>
      <itunes:duration>1442</itunes:duration>
      <itunes:episode>2475</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/docker-volume-migration-portability.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/docker-volume-migration-portability.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Private Container Registries: Docker Hub vs GHCR vs Self-Hosting</title>
      <description><![CDATA[If you're building containers on an x86 machine and deploying to Raspberry Pis, you're facing the multi-architecture problem — and the question of where to store your private images. This episode breaks down three options: Docker Hub's restrictive free tier, GitHub Container Registry's misleading "unlimited" repos, and the operational realities of self-hosting with CNCF Distribution, Harbor, or Nexus. We cover Docker Buildx for cross-platform builds, QEMU emulation slowdowns, GHCR's security leak history, and whether paying $5/month for Docker Hub Pro beats maintaining your own registry.]]></description>
      <link>https://myweirdprompts.com/episode/private-container-registries-comparison/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/private-container-registries-comparison/</guid>
      <pubDate>Mon, 27 Apr 2026 07:58:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/private-container-registries-comparison.m4a"
        type="audio/mp4"
        length="19790801"
      />
      <itunes:title>Private Container Registries: Docker Hub vs GHCR vs Self-Hosting</itunes:title>
      <itunes:subtitle>Comparing Docker Hub, GitHub Container Registry, and self-hosted options for private container storage.</itunes:subtitle>
      <itunes:summary><![CDATA[If you're building containers on an x86 machine and deploying to Raspberry Pis, you're facing the multi-architecture problem — and the question of where to store your private images. This episode breaks down three options: Docker Hub's restrictive free tier, GitHub Container Registry's misleading "unlimited" repos, and the operational realities of self-hosting with CNCF Distribution, Harbor, or Nexus. We cover Docker Buildx for cross-platform builds, QEMU emulation slowdowns, GHCR's security leak history, and whether paying $5/month for Docker Hub Pro beats maintaining your own registry.]]></itunes:summary>
      <itunes:duration>1528</itunes:duration>
      <itunes:episode>2474</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/private-container-registries-comparison.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/private-container-registries-comparison.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>GitHub Actions Beyond CI/CD: What You&apos;re Missing</title>
      <description><![CDATA[Most developers treat GitHub Actions as a simple CI/CD tool — push code, run tests, done. But under the hood, it's a flexible orchestration platform that can run scheduled cron jobs, deploy directly to a VPS via self-hosted runners, automate NPM package publishing with supply chain provenance, and even build self-healing repositories that auto-fix failing tests. This episode explores the full range of what Actions can do, from the practical (database backups on a schedule) to the experimental (incident response via repository dispatch webhooks). If you've been sleeping on GitHub Actions, this is your wake-up call.]]></description>
      <link>https://myweirdprompts.com/episode/github-actions-beyond-ci-cd/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/github-actions-beyond-ci-cd/</guid>
      <pubDate>Mon, 27 Apr 2026 07:54:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/github-actions-beyond-ci-cd.m4a"
        type="audio/mp4"
        length="19555754"
      />
      <itunes:title>GitHub Actions Beyond CI/CD: What You&apos;re Missing</itunes:title>
      <itunes:subtitle>Cron jobs, self-hosted runners, NPM publishing, and self-healing repos — GitHub Actions does way more than run tests.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers treat GitHub Actions as a simple CI/CD tool — push code, run tests, done. But under the hood, it's a flexible orchestration platform that can run scheduled cron jobs, deploy directly to a VPS via self-hosted runners, automate NPM package publishing with supply chain provenance, and even build self-healing repositories that auto-fix failing tests. This episode explores the full range of what Actions can do, from the practical (database backups on a schedule) to the experimental (incident response via repository dispatch webhooks). If you've been sleeping on GitHub Actions, this is your wake-up call.]]></itunes:summary>
      <itunes:duration>1505</itunes:duration>
      <itunes:episode>2473</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/github-actions-beyond-ci-cd.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/github-actions-beyond-ci-cd.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Guardrails Break: The Hidden Costs of AI Gateway Filtering</title>
      <description><![CDATA[AI gateways promise uniform guardrails, but aggressive filtering can block legitimate workflows and drive users to shadow AI. This episode explores the real tradeoffs between precision and recall, latency spikes, and why teams end up stacking gateways on gateways.]]></description>
      <link>https://myweirdprompts.com/episode/ai-gateway-guardrails-tradeoffs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-gateway-guardrails-tradeoffs/</guid>
      <pubDate>Mon, 27 Apr 2026 07:43:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-gateway-guardrails-tradeoffs.m4a"
        type="audio/mp4"
        length="19232543"
      />
      <itunes:title>When Guardrails Break: The Hidden Costs of AI Gateway Filtering</itunes:title>
      <itunes:subtitle>PII detection at the gateway layer can block legitimate invoices. Here&apos;s how guardrails actually work and where they fail.</itunes:subtitle>
      <itunes:summary><![CDATA[AI gateways promise uniform guardrails, but aggressive filtering can block legitimate workflows and drive users to shadow AI. This episode explores the real tradeoffs between precision and recall, latency spikes, and why teams end up stacking gateways on gateways.]]></itunes:summary>
      <itunes:duration>1476</itunes:duration>
      <itunes:episode>2472</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-gateway-guardrails-tradeoffs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-gateway-guardrails-tradeoffs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where Intelligence Should Live in Your Pipeline</title>
      <description><![CDATA[When does a dedicated prompt enhancer improve image generation, and when does it introduce noise? We explore the tradeoffs using Baidu's ERNIE-Image, ComfyUI workflows, and research showing small fine-tuned models can beat large ones by 30% in specialized domains.]]></description>
      <link>https://myweirdprompts.com/episode/prompt-enhancement-small-models/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/prompt-enhancement-small-models/</guid>
      <pubDate>Mon, 27 Apr 2026 07:37:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/prompt-enhancement-small-models.m4a"
        type="audio/mp4"
        length="25984905"
      />
      <itunes:title>Where Intelligence Should Live in Your Pipeline</itunes:title>
      <itunes:subtitle>When should you fine-tune a tiny model for prompt enhancement instead of prompting a large one? The answer depends on latency, precision, and domain.</itunes:subtitle>
      <itunes:summary><![CDATA[When does a dedicated prompt enhancer improve image generation, and when does it introduce noise? We explore the tradeoffs using Baidu's ERNIE-Image, ComfyUI workflows, and research showing small fine-tuned models can beat large ones by 30% in specialized domains.]]></itunes:summary>
      <itunes:duration>2004</itunes:duration>
      <itunes:episode>2470</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/prompt-enhancement-small-models.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/prompt-enhancement-small-models.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Embedding Model Deprecation: RAG&apos;s Silent Killer</title>
      <description><![CDATA[When OpenAI retires an embedding model like ada-002, your RAG pipeline doesn’t crash — it just gets subtly worse until users lose trust. This episode unpacks the $40,000 re-embedding nightmare one company faced, and explores three strategies to avoid it: event-driven re-embedding with PostgreSQL triggers, sidestepping embeddings entirely via the Model Context Protocol (MCP) for structured data, and client-side embedding caching with TTLs for gradual, non-breaking migrations. We also cover the VICE scoring model for choosing between vector search and traditional search, why top coding tools have abandoned vector RAG for AST-based retrieval, and the hybrid patterns that combine BM25, vector similarity, and cross-encoders.]]></description>
      <link>https://myweirdprompts.com/episode/embedding-deprecation-rag-fixes/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/embedding-deprecation-rag-fixes/</guid>
      <pubDate>Sun, 26 Apr 2026 22:17:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/embedding-deprecation-rag-fixes.m4a"
        type="audio/mp4"
        length="20361441"
      />
      <itunes:title>Embedding Model Deprecation: RAG&apos;s Silent Killer</itunes:title>
      <itunes:subtitle>When OpenAI retires an embedding model, your RAG pipeline breaks silently. Here’s how to fix it.</itunes:subtitle>
      <itunes:summary><![CDATA[When OpenAI retires an embedding model like ada-002, your RAG pipeline doesn’t crash — it just gets subtly worse until users lose trust. This episode unpacks the $40,000 re-embedding nightmare one company faced, and explores three strategies to avoid it: event-driven re-embedding with PostgreSQL triggers, sidestepping embeddings entirely via the Model Context Protocol (MCP) for structured data, and client-side embedding caching with TTLs for gradual, non-breaking migrations. We also cover the VICE scoring model for choosing between vector search and traditional search, why top coding tools have abandoned vector RAG for AST-based retrieval, and the hybrid patterns that combine BM25, vector similarity, and cross-encoders.]]></itunes:summary>
      <itunes:duration>1566</itunes:duration>
      <itunes:episode>2469</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/embedding-deprecation-rag-fixes.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/embedding-deprecation-rag-fixes.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Tokens Meet GPU Seconds</title>
      <description><![CDATA[Why can't you just add up your AI spending across providers? Because some bill by tokens, others by GPU seconds — and there's no exchange rate. This episode explores the structural mess behind unified cost tracking and what to do about it.]]></description>
      <link>https://myweirdprompts.com/episode/ai-api-cost-tracking/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-api-cost-tracking/</guid>
      <pubDate>Sun, 26 Apr 2026 18:08:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-api-cost-tracking.m4a"
        type="audio/mp4"
        length="21340765"
      />
      <itunes:title>When Tokens Meet GPU Seconds</itunes:title>
      <itunes:subtitle>How to track AI spend across Open Router, Replicate, and more — without a unified dashboard.</itunes:subtitle>
      <itunes:summary><![CDATA[Why can't you just add up your AI spending across providers? Because some bill by tokens, others by GPU seconds — and there's no exchange rate. This episode explores the structural mess behind unified cost tracking and what to do about it.]]></itunes:summary>
      <itunes:duration>1651</itunes:duration>
      <itunes:episode>2468</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-api-cost-tracking.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-api-cost-tracking.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Time Tax on API Access</title>
      <description><![CDATA[Why can't you buy your way to enterprise API throughput? This episode reveals how OpenAI and Anthropic's tiered billing systems use time-gated advancement as a deliberate anti-fraud mechanism that also creates vendor lock-in, and what it means for startups scaling on these platforms.]]></description>
      <link>https://myweirdprompts.com/episode/openai-anthropic-tiered-billing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/openai-anthropic-tiered-billing/</guid>
      <pubDate>Sun, 26 Apr 2026 17:57:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/openai-anthropic-tiered-billing.m4a"
        type="audio/mp4"
        length="17608950"
      />
      <itunes:title>The Time Tax on API Access</itunes:title>
      <itunes:subtitle>How OpenAI and Anthropic structure API tiers, rate limits, and why your billing history matters more than you think.</itunes:subtitle>
      <itunes:summary><![CDATA[Why can't you buy your way to enterprise API throughput? This episode reveals how OpenAI and Anthropic's tiered billing systems use time-gated advancement as a deliberate anti-fraud mechanism that also creates vendor lock-in, and what it means for startups scaling on these platforms.]]></itunes:summary>
      <itunes:duration>1373</itunes:duration>
      <itunes:episode>2467</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/openai-anthropic-tiered-billing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/openai-anthropic-tiered-billing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Trap of Embedding Model Lock-In</title>
      <description><![CDATA[Most teams building RAG pipelines focus on getting retrieval working, not on what happens when their embedding model gets deprecated. But vendor deprecation is not a question of if — it's when. This episode explores why stored vectors are locked to a specific model's geometric space, the real costs of re-embedding at scale, and why most production RAG systems are quietly serving degraded results. We cover blue-green migration patterns, the critical need to store original source text alongside embeddings, and why self-hosting open-source models might be the only way to avoid vendor lifecycle risk. If you're building a RAG system in production, this is the episode that will make you rethink your architecture.]]></description>
      <link>https://myweirdprompts.com/episode/embedding-model-lock-in-rag/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/embedding-model-lock-in-rag/</guid>
      <pubDate>Sun, 26 Apr 2026 17:47:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/embedding-model-lock-in-rag.m4a"
        type="audio/mp4"
        length="21247402"
      />
      <itunes:title>The Hidden Trap of Embedding Model Lock-In</itunes:title>
      <itunes:subtitle>What happens when your vector database works great — until your embedding model gets deprecated and your vectors become useless.</itunes:subtitle>
      <itunes:summary><![CDATA[Most teams building RAG pipelines focus on getting retrieval working, not on what happens when their embedding model gets deprecated. But vendor deprecation is not a question of if — it's when. This episode explores why stored vectors are locked to a specific model's geometric space, the real costs of re-embedding at scale, and why most production RAG systems are quietly serving degraded results. We cover blue-green migration patterns, the critical need to store original source text alongside embeddings, and why self-hosting open-source models might be the only way to avoid vendor lifecycle risk. If you're building a RAG system in production, this is the episode that will make you rethink your architecture.]]></itunes:summary>
      <itunes:duration>1645</itunes:duration>
      <itunes:episode>2466</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/embedding-model-lock-in-rag.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/embedding-model-lock-in-rag.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>JSON-L vs Parquet: When Each Format Wins</title>
      <description><![CDATA[JSON-L and Parquet are the workhorses of modern data pipelines, but they solve fundamentally different problems. This episode explores exactly how far JSON-L can scale before memory becomes an issue, why Parquet achieves 5x compression over CSV, and the surprising trade-offs between streaming simplicity and columnar performance. We dig into Hugging Face's automatic Parquet conversion, the small-files problem nobody talks about, and when the file-as-database pattern actually beats MongoDB and SQLite. If you've ever wondered whether to reach for JSON-L or Parquet, this episode gives you the concrete heuristics to decide.]]></description>
      <link>https://myweirdprompts.com/episode/jsonl-parquet-data-formats/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/jsonl-parquet-data-formats/</guid>
      <pubDate>Sun, 26 Apr 2026 17:44:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/jsonl-parquet-data-formats.m4a"
        type="audio/mp4"
        length="22199406"
      />
      <itunes:title>JSON-L vs Parquet: When Each Format Wins</itunes:title>
      <itunes:subtitle>How far can JSON-L scale before it breaks? And why does Parquet dominate for millions of rows?</itunes:subtitle>
      <itunes:summary><![CDATA[JSON-L and Parquet are the workhorses of modern data pipelines, but they solve fundamentally different problems. This episode explores exactly how far JSON-L can scale before memory becomes an issue, why Parquet achieves 5x compression over CSV, and the surprising trade-offs between streaming simplicity and columnar performance. We dig into Hugging Face's automatic Parquet conversion, the small-files problem nobody talks about, and when the file-as-database pattern actually beats MongoDB and SQLite. If you've ever wondered whether to reach for JSON-L or Parquet, this episode gives you the concrete heuristics to decide.]]></itunes:summary>
      <itunes:duration>1727</itunes:duration>
      <itunes:episode>2465</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/jsonl-parquet-data-formats.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/jsonl-parquet-data-formats.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Batch APIs: The 50% Discount You&apos;re Probably Misusing</title>
      <description><![CDATA[Batch APIs from OpenAI, Anthropic, and Google promise 50% discounts on inference — but most developers misunderstand what's actually happening under the hood. This episode breaks down the real economics: off-peak GPU utilization, inference engine batching, and yield management for AI clusters. We cover the latency tradeoffs that make batch APIs useless for conversational UIs but essential for classification, extraction, and synthetic data pipelines. Plus: provider-by-provider comparison, the breakeven point where batch savings justify engineering overhead, and practical gotchas that don't show up in documentation.]]></description>
      <link>https://myweirdprompts.com/episode/batch-apis-discount-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/batch-apis-discount-explained/</guid>
      <pubDate>Sun, 26 Apr 2026 16:49:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/batch-apis-discount-explained.m4a"
        type="audio/mp4"
        length="19733177"
      />
      <itunes:title>Batch APIs: The 50% Discount You&apos;re Probably Misusing</itunes:title>
      <itunes:subtitle>Batch inference APIs offer 50% off — but only for the right workloads. Here&apos;s when they actually make sense.</itunes:subtitle>
      <itunes:summary><![CDATA[Batch APIs from OpenAI, Anthropic, and Google promise 50% discounts on inference — but most developers misunderstand what's actually happening under the hood. This episode breaks down the real economics: off-peak GPU utilization, inference engine batching, and yield management for AI clusters. We cover the latency tradeoffs that make batch APIs useless for conversational UIs but essential for classification, extraction, and synthetic data pipelines. Plus: provider-by-provider comparison, the breakeven point where batch savings justify engineering overhead, and practical gotchas that don't show up in documentation.]]></itunes:summary>
      <itunes:duration>1515</itunes:duration>
      <itunes:episode>2464</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/batch-apis-discount-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/batch-apis-discount-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Tmux vs Modern Terminals: What Multiplexing Actually Gets You</title>
      <description><![CDATA[What does multiplexing actually mean — from frequency-division in radio to terminal multiplexers like tmux, screen, and Zellij? This episode breaks down the original engineering concept, then examines whether tmux still makes sense in 2024 when modern terminals like WezTerm and Ghostty offer built-in multiplexing over SSH. We cover the five concrete benefits of tmux (session persistence, single SSH connections, scripted layouts, shared sessions, consistent scrollback) and the genuine downsides (learning curve, copy-paste friction, scrollback quirks, performance overhead). Plus: how WezTerm's multiplexing architecture uses SSH's native multi-channel support to give you one-connection-multiple-shells without any server-side software.]]></description>
      <link>https://myweirdprompts.com/episode/tmux-multiplexing-modern-terminals/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tmux-multiplexing-modern-terminals/</guid>
      <pubDate>Sun, 26 Apr 2026 15:04:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tmux-multiplexing-modern-terminals.m4a"
        type="audio/mp4"
        length="21426951"
      />
      <itunes:title>Tmux vs Modern Terminals: What Multiplexing Actually Gets You</itunes:title>
      <itunes:subtitle>What multiplexing actually means, why tmux still matters, and how WezTerm and Ghostty changed the calculus.</itunes:subtitle>
      <itunes:summary><![CDATA[What does multiplexing actually mean — from frequency-division in radio to terminal multiplexers like tmux, screen, and Zellij? This episode breaks down the original engineering concept, then examines whether tmux still makes sense in 2024 when modern terminals like WezTerm and Ghostty offer built-in multiplexing over SSH. We cover the five concrete benefits of tmux (session persistence, single SSH connections, scripted layouts, shared sessions, consistent scrollback) and the genuine downsides (learning curve, copy-paste friction, scrollback quirks, performance overhead). Plus: how WezTerm's multiplexing architecture uses SSH's native multi-channel support to give you one-connection-multiple-shells without any server-side software.]]></itunes:summary>
      <itunes:duration>1660</itunes:duration>
      <itunes:episode>2463</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tmux-multiplexing-modern-terminals.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tmux-multiplexing-modern-terminals.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Pick Two: Server-Resident, Mobile-Native, Agentic CLI in 2026</title>
      <description><![CDATA[A listener asks how to run agentic CLI tools like Claude Code on a server and access them from a phone — not just VNC into a desktop. We separate the sysadmin case (Tailscale + SSH + tmux + mosh) from the dev work case (Codespaces, Coder, Cloudflare Access + code-server), then survey the landscape: Anthropic's Remote Control, Sculptor from Imbue, third-party mobile wrappers, and Managed Agents. The honest answer: in 2026, you can pick two of three — server-resident, mobile-native, agentic. Here's how to choose your pair.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-cli-remote-mobile-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-cli-remote-mobile-2026/</guid>
      <pubDate>Sun, 26 Apr 2026 15:02:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-cli-remote-mobile-2026.m4a"
        type="audio/mp4"
        length="12338243"
      />
      <itunes:title>Pick Two: Server-Resident, Mobile-Native, Agentic CLI in 2026</itunes:title>
      <itunes:subtitle>How to run Claude Code on a server and use it from your phone — the honest tradeoffs in 2026.</itunes:subtitle>
      <itunes:summary><![CDATA[A listener asks how to run agentic CLI tools like Claude Code on a server and access them from a phone — not just VNC into a desktop. We separate the sysadmin case (Tailscale + SSH + tmux + mosh) from the dev work case (Codespaces, Coder, Cloudflare Access + code-server), then survey the landscape: Anthropic's Remote Control, Sculptor from Imbue, third-party mobile wrappers, and Managed Agents. The honest answer: in 2026, you can pick two of three — server-resident, mobile-native, agentic. Here's how to choose your pair.]]></itunes:summary>
      <itunes:duration>934</itunes:duration>
      <itunes:episode>2462</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-cli-remote-mobile-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-cli-remote-mobile-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Claude Code&apos;s Conversation Compaction Actually Works</title>
      <description><![CDATA[A deep technical breakdown of Claude Code's conversation compaction system — the three-tier architecture, the separate model call for summarization, the nine-section structured prompt, and the in-memory swap mechanics. We cover the trigger conditions, what survives compaction versus what gets lost, the reconstruction phase that re-reads files, and the critical asymmetry: compaction preserves what to do next but systematically drops why we did what we did. Includes token savings benchmarks, power user strategies like CLAUDE.md as persistent storage, and the philosophical question of whether a post-compaction agent is the same agent or a new instance reading a briefing.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-conversation-compaction/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-conversation-compaction/</guid>
      <pubDate>Sun, 26 Apr 2026 15:00:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-conversation-compaction.m4a"
        type="audio/mp4"
        length="20159993"
      />
      <itunes:title>How Claude Code&apos;s Conversation Compaction Actually Works</itunes:title>
      <itunes:subtitle>The three-tier system, what survives, what dies, and why you shouldn&apos;t rely on auto-compact.</itunes:subtitle>
      <itunes:summary><![CDATA[A deep technical breakdown of Claude Code's conversation compaction system — the three-tier architecture, the separate model call for summarization, the nine-section structured prompt, and the in-memory swap mechanics. We cover the trigger conditions, what survives compaction versus what gets lost, the reconstruction phase that re-reads files, and the critical asymmetry: compaction preserves what to do next but systematically drops why we did what we did. Includes token savings benchmarks, power user strategies like CLAUDE.md as persistent storage, and the philosophical question of whether a post-compaction agent is the same agent or a new instance reading a briefing.]]></itunes:summary>
      <itunes:duration>1559</itunes:duration>
      <itunes:episode>2461</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-conversation-compaction.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-conversation-compaction.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Shopping in a Fragmented Market</title>
      <description><![CDATA[What does it take to automate shopping when every store has its own checkout, shipping varies by neighborhood, and sites break without warning? This episode explores the architecture and tradeoffs of building an AI agent for Israel's chaotic e-commerce landscape.]]></description>
      <link>https://myweirdprompts.com/episode/personal-shopping-agent-israel/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/personal-shopping-agent-israel/</guid>
      <pubDate>Sun, 26 Apr 2026 13:50:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/personal-shopping-agent-israel.m4a"
        type="audio/mp4"
        length="23758645"
      />
      <itunes:title>Shopping in a Fragmented Market</itunes:title>
      <itunes:subtitle>The real challenges of building an AI agent that navigates Hebrew e-commerce, geographic shipping quirks, and whitelist curation.</itunes:subtitle>
      <itunes:summary><![CDATA[What does it take to automate shopping when every store has its own checkout, shipping varies by neighborhood, and sites break without warning? This episode explores the architecture and tradeoffs of building an AI agent for Israel's chaotic e-commerce landscape.]]></itunes:summary>
      <itunes:duration>1836</itunes:duration>
      <itunes:episode>2460</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/personal-shopping-agent-israel.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/personal-shopping-agent-israel.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Drizzle vs Prisma: Which ORM Wins for AI-Native Backends?</title>
      <description><![CDATA[When building an AI-native backend with MCP server integration, your ORM choice matters more than ever. This episode compares Drizzle and Prisma across key dimensions: AI-friendliness for code generation, migration workflows, schema design philosophy, and guardrails for catching AI-generated errors before they hit production. We explore why Drizzle currently offers the cleanest path for MCP-compatible backends, why Prisma Next is betting on "agent-centric development" with machine-readable error codes and compile-time guardrails, and why neither ORM supports rollbacks by design. Plus, insights from a recent survey of migration practices across 40+ major open-source projects.]]></description>
      <link>https://myweirdprompts.com/episode/drizzle-prisma-orm-ai-native/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/drizzle-prisma-orm-ai-native/</guid>
      <pubDate>Sun, 26 Apr 2026 13:40:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/drizzle-prisma-orm-ai-native.m4a"
        type="audio/mp4"
        length="25497759"
      />
      <itunes:title>Drizzle vs Prisma: Which ORM Wins for AI-Native Backends?</itunes:title>
      <itunes:subtitle>Comparing Drizzle and Prisma for AI-native backends, MCP servers, and the future of agent-centric development.</itunes:subtitle>
      <itunes:summary><![CDATA[When building an AI-native backend with MCP server integration, your ORM choice matters more than ever. This episode compares Drizzle and Prisma across key dimensions: AI-friendliness for code generation, migration workflows, schema design philosophy, and guardrails for catching AI-generated errors before they hit production. We explore why Drizzle currently offers the cleanest path for MCP-compatible backends, why Prisma Next is betting on "agent-centric development" with machine-readable error codes and compile-time guardrails, and why neither ORM supports rollbacks by design. Plus, insights from a recent survey of migration practices across 40+ major open-source projects.]]></itunes:summary>
      <itunes:duration>1995</itunes:duration>
      <itunes:episode>2459</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/drizzle-prisma-orm-ai-native.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/drizzle-prisma-orm-ai-native.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Choosing Between AI Cloud Providers</title>
      <description><![CDATA[The cost of renting GPUs from hyperscalers like AWS can be three to six times higher than newer AI cloud providers. But price isn't everything. This episode breaks down the structural reasons for the price gap, the hidden traps like data egress fees and compliance ceilings, and provides a practical decision framework for choosing between Modal, RunPod, Nebius, and Baseten. We explore when developer experience trumps raw cost, the importance of InfiniBand for training, and how to avoid lock-in.]]></description>
      <link>https://myweirdprompts.com/episode/ai-cloud-providers-comparison/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-cloud-providers-comparison/</guid>
      <pubDate>Sun, 26 Apr 2026 11:36:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-cloud-providers-comparison.m4a"
        type="audio/mp4"
        length="22238052"
      />
      <itunes:title>Choosing Between AI Cloud Providers</itunes:title>
      <itunes:subtitle>A practical guide to choosing between Modal, RunPod, Nebius, and Baseten for AI workloads.</itunes:subtitle>
      <itunes:summary><![CDATA[The cost of renting GPUs from hyperscalers like AWS can be three to six times higher than newer AI cloud providers. But price isn't everything. This episode breaks down the structural reasons for the price gap, the hidden traps like data egress fees and compliance ceilings, and provides a practical decision framework for choosing between Modal, RunPod, Nebius, and Baseten. We explore when developer experience trumps raw cost, the importance of InfiniBand for training, and how to avoid lock-in.]]></itunes:summary>
      <itunes:duration>1722</itunes:duration>
      <itunes:episode>2456</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-cloud-providers-comparison.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-cloud-providers-comparison.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Custom IDs: UUIDs vs Human-Readable Keys</title>
      <description><![CDATA[UUIDs are secure but unreadable. Auto-incrementing integers are simple but leak information. This episode explores the real-world trade-offs in database ID design, from Stripe's elegant prefixed IDs to the emerging TypeID standard that combines type safety with time-sortable UUIDv7. We cover hybrid schema patterns, why surrogate keys matter for resilience, and practical invoice numbering strategies for small businesses. If you've ever inherited a system with bad ID choices, this one's for you.]]></description>
      <link>https://myweirdprompts.com/episode/custom-ids-uuids-human-readable/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/custom-ids-uuids-human-readable/</guid>
      <pubDate>Sun, 26 Apr 2026 10:21:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/custom-ids-uuids-human-readable.m4a"
        type="audio/mp4"
        length="21447061"
      />
      <itunes:title>Custom IDs: UUIDs vs Human-Readable Keys</itunes:title>
      <itunes:subtitle>How to design database IDs that balance security, human readability, and performance — with lessons from Stripe and TypeID.</itunes:subtitle>
      <itunes:summary><![CDATA[UUIDs are secure but unreadable. Auto-incrementing integers are simple but leak information. This episode explores the real-world trade-offs in database ID design, from Stripe's elegant prefixed IDs to the emerging TypeID standard that combines type safety with time-sortable UUIDv7. We cover hybrid schema patterns, why surrogate keys matter for resilience, and practical invoice numbering strategies for small businesses. If you've ever inherited a system with bad ID choices, this one's for you.]]></itunes:summary>
      <itunes:duration>1639</itunes:duration>
      <itunes:episode>2444</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/custom-ids-uuids-human-readable.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/custom-ids-uuids-human-readable.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Podcast RSS Feeds Can Speak Every Language</title>
      <description><![CDATA[Over 460 million people listen to podcasts monthly, but most shows are locked in a single language. This episode explores how the Podcasting 2.0 namespace, transcript tags with timing data, and the proposed "alternative enclosure" tag could let creators publish once and let apps handle localization. We break down the actual XML plumbing, the cost per language (roughly $1 for translation + $15 for TTS generation), and why the shift from server-side to client-side localization changes everything. Plus: how voice cloning tools like ElevenLabs now support 29 languages at 95%+ intelligibility, and why the "podcast:voice" tag could let creators specify custom voice profiles for each language.]]></description>
      <link>https://myweirdprompts.com/episode/multilingual-podcast-rss-feeds/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multilingual-podcast-rss-feeds/</guid>
      <pubDate>Sun, 26 Apr 2026 10:17:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multilingual-podcast-rss-feeds.m4a"
        type="audio/mp4"
        length="21409696"
      />
      <itunes:title>How Podcast RSS Feeds Can Speak Every Language</itunes:title>
      <itunes:subtitle>One RSS feed, a transcript tag, and TTS voice cloning — the emerging standard for letting any podcast speak any language.</itunes:subtitle>
      <itunes:summary><![CDATA[Over 460 million people listen to podcasts monthly, but most shows are locked in a single language. This episode explores how the Podcasting 2.0 namespace, transcript tags with timing data, and the proposed "alternative enclosure" tag could let creators publish once and let apps handle localization. We break down the actual XML plumbing, the cost per language (roughly $1 for translation + $15 for TTS generation), and why the shift from server-side to client-side localization changes everything. Plus: how voice cloning tools like ElevenLabs now support 29 languages at 95%+ intelligibility, and why the "podcast:voice" tag could let creators specify custom voice profiles for each language.]]></itunes:summary>
      <itunes:duration>1659</itunes:duration>
      <itunes:episode>2443</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multilingual-podcast-rss-feeds.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multilingual-podcast-rss-feeds.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When One Sentence Beats Four Clicks</title>
      <description><![CDATA[What happens to system quality when updating a database takes a sentence instead of a dashboard? This episode explores how agent-first design lowers the friction of maintenance—and why the real bottleneck isn't the AI, but identity and authorization.]]></description>
      <link>https://myweirdprompts.com/episode/agent-first-backends-no-dashboard/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-first-backends-no-dashboard/</guid>
      <pubDate>Sun, 26 Apr 2026 09:58:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-first-backends-no-dashboard.m4a"
        type="audio/mp4"
        length="23927041"
      />
      <itunes:title>When One Sentence Beats Four Clicks</itunes:title>
      <itunes:subtitle>What happens when you ditch the admin panel and let AI agents manage your systems directly?</itunes:subtitle>
      <itunes:summary><![CDATA[What happens to system quality when updating a database takes a sentence instead of a dashboard? This episode explores how agent-first design lowers the friction of maintenance—and why the real bottleneck isn't the AI, but identity and authorization.]]></itunes:summary>
      <itunes:duration>1846</itunes:duration>
      <itunes:episode>2441</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-first-backends-no-dashboard.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-first-backends-no-dashboard.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Build Your Own CRM With AI Agents</title>
      <description><![CDATA[Most CRMs feel like they're shouting at you — drip cadences, pipeline views, and features designed for sales managers, not solo operators. If you're a solo professional who wants to track interesting companies, do deep research, and manage relationships without the overhead, the off-the-shelf options are expensive, cognitively draining, and philosophically misaligned with how you actually work.

This episode explores why now is the perfect time to build your own micro-CRM using AI agents and lightweight databases. We break down three paths: off-the-shelf CRMs (expensive and misaligned), no-code platforms like Airtable or ToolJet (flexible but limited), and a full DIY stack using Supabase, Claude API, and MCP servers (full control, under $30/month). For solo operators who already use AI tools for client work, the build path isn't just feasible — it's a strategic advantage that demonstrates the value you sell to others.]]></description>
      <link>https://myweirdprompts.com/episode/build-your-own-crm-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/build-your-own-crm-ai/</guid>
      <pubDate>Sun, 26 Apr 2026 09:58:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/build-your-own-crm-ai.m4a"
        type="audio/mp4"
        length="27907730"
      />
      <itunes:title>Build Your Own CRM With AI Agents</itunes:title>
      <itunes:subtitle>Off-the-shelf CRMs are built for sales teams, not solo operators. Here&apos;s why building your own with AI might be smarter.</itunes:subtitle>
      <itunes:summary><![CDATA[Most CRMs feel like they're shouting at you — drip cadences, pipeline views, and features designed for sales managers, not solo operators. If you're a solo professional who wants to track interesting companies, do deep research, and manage relationships without the overhead, the off-the-shelf options are expensive, cognitively draining, and philosophically misaligned with how you actually work.

This episode explores why now is the perfect time to build your own micro-CRM using AI agents and lightweight databases. We break down three paths: off-the-shelf CRMs (expensive and misaligned), no-code platforms like Airtable or ToolJet (flexible but limited), and a full DIY stack using Supabase, Claude API, and MCP servers (full control, under $30/month). For solo operators who already use AI tools for client work, the build path isn't just feasible — it's a strategic advantage that demonstrates the value you sell to others.]]></itunes:summary>
      <itunes:duration>2175</itunes:duration>
      <itunes:episode>2440</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/build-your-own-crm-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/build-your-own-crm-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Collapses the Framework Decision</title>
      <description><![CDATA[For small teams without coding expertise, choosing a front-end framework used to be paralyzing. Now AI-driven tools like Lovable and Bolt are making that decision irrelevant — but only if you know when to use them and when to stick with traditional stacks.]]></description>
      <link>https://myweirdprompts.com/episode/airtable-trap-front-end-choices/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/airtable-trap-front-end-choices/</guid>
      <pubDate>Sun, 26 Apr 2026 09:55:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/airtable-trap-front-end-choices.m4a"
        type="audio/mp4"
        length="23146184"
      />
      <itunes:title>AI Collapses the Framework Decision</itunes:title>
      <itunes:subtitle>Why Airtable fails for multi-user tools, and how AI builders are changing the framework decision for small businesses.</itunes:subtitle>
      <itunes:summary><![CDATA[For small teams without coding expertise, choosing a front-end framework used to be paralyzing. Now AI-driven tools like Lovable and Bolt are making that decision irrelevant — but only if you know when to use them and when to stick with traditional stacks.]]></itunes:summary>
      <itunes:duration>1776</itunes:duration>
      <itunes:episode>2439</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/airtable-trap-front-end-choices.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/airtable-trap-front-end-choices.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Folder Illusion: How Object Storage Fakes Hierarchy</title>
      <description><![CDATA[Object storage looks like it has folders, but that's a complete fiction. This episode unpacks how flat namespaces create the illusion of directories, why renaming a folder means rewriting every object, and what that means for how we actually use cloud storage.]]></description>
      <link>https://myweirdprompts.com/episode/object-storage-blobs-hierarchy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/object-storage-blobs-hierarchy/</guid>
      <pubDate>Sun, 26 Apr 2026 09:35:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/object-storage-blobs-hierarchy.m4a"
        type="audio/mp4"
        length="20128142"
      />
      <itunes:title>The Folder Illusion: How Object Storage Fakes Hierarchy</itunes:title>
      <itunes:subtitle>Blobs, flat namespaces, and why those &quot;folders&quot; in cloud storage are complete illusions.</itunes:subtitle>
      <itunes:summary><![CDATA[Object storage looks like it has folders, but that's a complete fiction. This episode unpacks how flat namespaces create the illusion of directories, why renaming a folder means rewriting every object, and what that means for how we actually use cloud storage.]]></itunes:summary>
      <itunes:duration>1554</itunes:duration>
      <itunes:episode>2438</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/object-storage-blobs-hierarchy.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/object-storage-blobs-hierarchy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Difficulty of Data Modeling</title>
      <description><![CDATA[CRUD is easy, but designing the data model is where you paint yourself into a corner. This episode explores the messy landscape of pre-built schema templates for small business apps—and why even a good template only saves you from the blank page.]]></description>
      <link>https://myweirdprompts.com/episode/pre-built-crm-schema-templates/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pre-built-crm-schema-templates/</guid>
      <pubDate>Sun, 26 Apr 2026 09:20:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pre-built-crm-schema-templates.m4a"
        type="audio/mp4"
        length="19173826"
      />
      <itunes:title>The Hidden Difficulty of Data Modeling</itunes:title>
      <itunes:subtitle>Stop designing database schemas from scratch. Here&apos;s where to find ready-made templates for common business apps.</itunes:subtitle>
      <itunes:summary><![CDATA[CRUD is easy, but designing the data model is where you paint yourself into a corner. This episode explores the messy landscape of pre-built schema templates for small business apps—and why even a good template only saves you from the blank page.]]></itunes:summary>
      <itunes:duration>1486</itunes:duration>
      <itunes:episode>2435</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pre-built-crm-schema-templates.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pre-built-crm-schema-templates.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Spreadsheets to Databases: The Mental Shift</title>
      <description><![CDATA[Most teams migrate from spreadsheets to databases but keep thinking in grids. This episode breaks down the single mental shift that separates a proper relational model from a glorified spreadsheet: moving from embedding data to referencing it. We cover how to identify your core business nouns, model one-to-many and many-to-many relationships with foreign keys and junction tables, and why the real work happens on paper before you touch any software. If you've ever felt your spreadsheet is a "cry for help," this is your practical primer on database thinking.]]></description>
      <link>https://myweirdprompts.com/episode/spreadsheets-to-databases-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/spreadsheets-to-databases-guide/</guid>
      <pubDate>Sun, 26 Apr 2026 09:09:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/spreadsheets-to-databases-guide.m4a"
        type="audio/mp4"
        length="17907935"
      />
      <itunes:title>From Spreadsheets to Databases: The Mental Shift</itunes:title>
      <itunes:subtitle>Stop treating databases like bigger spreadsheets. Learn the one conceptual shift that actually matters.</itunes:subtitle>
      <itunes:summary><![CDATA[Most teams migrate from spreadsheets to databases but keep thinking in grids. This episode breaks down the single mental shift that separates a proper relational model from a glorified spreadsheet: moving from embedding data to referencing it. We cover how to identify your core business nouns, model one-to-many and many-to-many relationships with foreign keys and junction tables, and why the real work happens on paper before you touch any software. If you've ever felt your spreadsheet is a "cry for help," this is your practical primer on database thinking.]]></itunes:summary>
      <itunes:duration>1377</itunes:duration>
      <itunes:episode>2434</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/spreadsheets-to-databases-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/spreadsheets-to-databases-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Cost of Flexibility in Chip Design</title>
      <description><![CDATA[Why do hyperscalers bet on custom silicon despite massive upfront costs? This episode explores the brutal trade-off between flexibility and efficiency—where a GPU's versatility costs 3,000x more energy than a single-purpose ASIC.]]></description>
      <link>https://myweirdprompts.com/episode/custom-silicon-design-flow/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/custom-silicon-design-flow/</guid>
      <pubDate>Sat, 25 Apr 2026 23:16:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/custom-silicon-design-flow.m4a"
        type="audio/mp4"
        length="21088802"
      />
      <itunes:title>The Hidden Cost of Flexibility in Chip Design</itunes:title>
      <itunes:subtitle>The economics and engineering of ASICs vs. CPUs and GPUs, from transistor placement to hyperscaler strategy.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do hyperscalers bet on custom silicon despite massive upfront costs? This episode explores the brutal trade-off between flexibility and efficiency—where a GPU's versatility costs 3,000x more energy than a single-purpose ASIC.]]></itunes:summary>
      <itunes:duration>1611</itunes:duration>
      <itunes:episode>2432</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/custom-silicon-design-flow.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/custom-silicon-design-flow.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can One Button Solve Your Streaming Frustrations?</title>
      <description><![CDATA[Tired of searching across five apps only to find that perfect movie is geo-blocked? We break down the streaming recommendation landscape — from JustWatch's spotty availability data to Trakt's beautiful watch history tracking with zero access info. Plus, we explore whether MCP (Model Context Protocol) could finally bridge the gap between "what you'd love to watch" and "what you can actually watch right now." If you've ever wished for a single button that just works, this episode is for you.]]></description>
      <link>https://myweirdprompts.com/episode/streaming-recommendation-tools-review/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/streaming-recommendation-tools-review/</guid>
      <pubDate>Sat, 25 Apr 2026 20:06:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/streaming-recommendation-tools-review.m4a"
        type="audio/mp4"
        length="21606097"
      />
      <itunes:title>Can One Button Solve Your Streaming Frustrations?</itunes:title>
      <itunes:subtitle>A deep dive into JustWatch, Trakt, Letterboxd, and why your ideal streaming app doesn&apos;t exist yet.</itunes:subtitle>
      <itunes:summary><![CDATA[Tired of searching across five apps only to find that perfect movie is geo-blocked? We break down the streaming recommendation landscape — from JustWatch's spotty availability data to Trakt's beautiful watch history tracking with zero access info. Plus, we explore whether MCP (Model Context Protocol) could finally bridge the gap between "what you'd love to watch" and "what you can actually watch right now." If you've ever wished for a single button that just works, this episode is for you.]]></itunes:summary>
      <itunes:duration>1668</itunes:duration>
      <itunes:episode>2425</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/streaming-recommendation-tools-review.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/streaming-recommendation-tools-review.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Tool-Calling Benchmarks Miss About Production Failures</title>
      <description><![CDATA[Most tool-calling evaluations hide more than they reveal. This episode breaks down three fundamentally different benchmarks — the Berkeley Function Calling Leaderboard, tau-bench from Sierra Research, and Nexus from Nexusflow — and explains what each one actually measures versus what it misses. BFCL's AST evaluation catches structural errors but is blind to semantic wrongness. Tau-bench grades on final database state instead of tool-call sequences, revealing reliability gaps that single-shot scores hide. Nexus exposes how models collapse on long-tail, specialized APIs they've never seen. Then we go deeper into the production failure modes no benchmark tests: hallucinated tool names, parallel call ordering errors, schema drift across model versions, and sycophantic confirmation of wrong arguments. If you're building agents that call tools in production, this episode explains why leaderboard numbers are dangerously incomplete.]]></description>
      <link>https://myweirdprompts.com/episode/tool-calling-benchmark-production-failures/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tool-calling-benchmark-production-failures/</guid>
      <pubDate>Sat, 25 Apr 2026 14:14:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tool-calling-benchmark-production-failures.m4a"
        type="audio/mp4"
        length="21624540"
      />
      <itunes:title>What Tool-Calling Benchmarks Miss About Production Failures</itunes:title>
      <itunes:subtitle>BFCL, tau-bench, and Nexus each reveal different failure modes. None of them test what actually kills production agents.</itunes:subtitle>
      <itunes:summary><![CDATA[Most tool-calling evaluations hide more than they reveal. This episode breaks down three fundamentally different benchmarks — the Berkeley Function Calling Leaderboard, tau-bench from Sierra Research, and Nexus from Nexusflow — and explains what each one actually measures versus what it misses. BFCL's AST evaluation catches structural errors but is blind to semantic wrongness. Tau-bench grades on final database state instead of tool-call sequences, revealing reliability gaps that single-shot scores hide. Nexus exposes how models collapse on long-tail, specialized APIs they've never seen. Then we go deeper into the production failure modes no benchmark tests: hallucinated tool names, parallel call ordering errors, schema drift across model versions, and sycophantic confirmation of wrong arguments. If you're building agents that call tools in production, this episode explains why leaderboard numbers are dangerously incomplete.]]></itunes:summary>
      <itunes:duration>1679</itunes:duration>
      <itunes:episode>2404</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tool-calling-benchmark-production-failures.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tool-calling-benchmark-production-failures.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Choosing Your LLM Eval Framework</title>
      <description><![CDATA[An opinionated architectural shootout of four major LLM evaluation harnesses — Inspect, Promptfoo, DeepEval, and Braintrust — stress-tested on multi-turn conversations, tool-using agents, async execution, and CI integration. No hedging: we pick winners for research labs, startups, and enterprise teams.]]></description>
      <link>https://myweirdprompts.com/episode/llm-evaluation-frameworks-compared/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-evaluation-frameworks-compared/</guid>
      <pubDate>Sat, 25 Apr 2026 14:14:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-evaluation-frameworks-compared.m4a"
        type="audio/mp4"
        length="21006661"
      />
      <itunes:title>Choosing Your LLM Eval Framework</itunes:title>
      <itunes:subtitle>An architectural shootout of four major LLM evaluation harnesses — where each shines and where each breaks down.</itunes:subtitle>
      <itunes:summary><![CDATA[An opinionated architectural shootout of four major LLM evaluation harnesses — Inspect, Promptfoo, DeepEval, and Braintrust — stress-tested on multi-turn conversations, tool-using agents, async execution, and CI integration. No hedging: we pick winners for research labs, startups, and enterprise teams.]]></itunes:summary>
      <itunes:duration>1646</itunes:duration>
      <itunes:episode>2403</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-evaluation-frameworks-compared.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-evaluation-frameworks-compared.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Geospatial Gold Rush: Who&apos;s Hiring Satellite Sleuths?</title>
      <description><![CDATA[Satellite imagery isn't just for spies anymore. Industries from agriculture to insurance are scrambling to hire analysts who can turn pixels into profits—using tools like QGIS, Planet Labs data, and Python’s geospatial stack. This episode maps out the hottest job markets (spoiler: John Deere paid $300M for this tech), the surprising crossover between war zones and soybean futures, and the exact skills that command six-figure salaries. Learn how Ukraine rewrote the rules and why free Sentinel data unlocked a $134B industry.]]></description>
      <link>https://myweirdprompts.com/episode/geospatial-jobs-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/geospatial-jobs-tools/</guid>
      <pubDate>Fri, 24 Apr 2026 13:51:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/geospatial-jobs-tools.m4a"
        type="audio/mp4"
        length="18119118"
      />
      <itunes:title>Geospatial Gold Rush: Who&apos;s Hiring Satellite Sleuths?</itunes:title>
      <itunes:subtitle>From crop health to cargo routes, discover which industries are paying top dollar for geospatial analysis skills—and the tools they use daily.</itunes:subtitle>
      <itunes:summary><![CDATA[Satellite imagery isn't just for spies anymore. Industries from agriculture to insurance are scrambling to hire analysts who can turn pixels into profits—using tools like QGIS, Planet Labs data, and Python’s geospatial stack. This episode maps out the hottest job markets (spoiler: John Deere paid $300M for this tech), the surprising crossover between war zones and soybean futures, and the exact skills that command six-figure salaries. Learn how Ukraine rewrote the rules and why free Sentinel data unlocked a $134B industry.]]></itunes:summary>
      <itunes:duration>1393</itunes:duration>
      <itunes:episode>2402</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/geospatial-jobs-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/geospatial-jobs-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Designing Data Models That Mirror Your Work</title>
      <description><![CDATA[Why off-the-shelf software fails niche small businesses and how building custom tools around your actual workflow—using Airtable, Firebase, or AI agents—can solve the mismatch without coding from scratch.]]></description>
      <link>https://myweirdprompts.com/episode/small-business-custom-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/small-business-custom-tools/</guid>
      <pubDate>Fri, 24 Apr 2026 13:45:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/small-business-custom-tools.m4a"
        type="audio/mp4"
        length="17293885"
      />
      <itunes:title>Designing Data Models That Mirror Your Work</itunes:title>
      <itunes:subtitle>Why 60% of small businesses hate off-the-shelf SaaS—and how to build tools that actually fit your workflow.</itunes:subtitle>
      <itunes:summary><![CDATA[Why off-the-shelf software fails niche small businesses and how building custom tools around your actual workflow—using Airtable, Firebase, or AI agents—can solve the mismatch without coding from scratch.]]></itunes:summary>
      <itunes:duration>1325</itunes:duration>
      <itunes:episode>2401</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/small-business-custom-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/small-business-custom-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Claude Code’s Hidden Context Tax</title>
      <description><![CDATA[Bigger context windows don’t mean unlimited working memory—Claude’s hidden overhead can quietly degrade performance. This episode breaks down the hierarchy of context costs, from subagents (400-800 tokens each) to lazy-loaded MCP tools (near-free). Learn why eager vs. lazy loading matters more than raw size, how plugin sprawl inflates your startup tax, and practical optimizations to reclaim 25-30% of your effective context. The difference isn’t just speed—it’s the gap between a focused assistant and one that forgets mid-task.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-context-bloat/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-context-bloat/</guid>
      <pubDate>Fri, 24 Apr 2026 13:43:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-context-bloat.m4a"
        type="audio/mp4"
        length="18166586"
      />
      <itunes:title>Claude Code’s Hidden Context Tax</itunes:title>
      <itunes:subtitle>How Claude’s eager-loaded primitives silently consume context—and how to optimize your setup for sharper performance.</itunes:subtitle>
      <itunes:summary><![CDATA[Bigger context windows don’t mean unlimited working memory—Claude’s hidden overhead can quietly degrade performance. This episode breaks down the hierarchy of context costs, from subagents (400-800 tokens each) to lazy-loaded MCP tools (near-free). Learn why eager vs. lazy loading matters more than raw size, how plugin sprawl inflates your startup tax, and practical optimizations to reclaim 25-30% of your effective context. The difference isn’t just speed—it’s the gap between a focused assistant and one that forgets mid-task.]]></itunes:summary>
      <itunes:duration>1383</itunes:duration>
      <itunes:episode>2400</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-context-bloat.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-context-bloat.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Data Becomes the Decision Framework</title>
      <description><![CDATA[How do emergency responders turn a flood of live feeds into a prioritized action list? This episode explores the technology and philosophy behind dashboards that don't just display data—they become the decision itself.]]></description>
      <link>https://myweirdprompts.com/episode/crisis-dashboards-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/crisis-dashboards-tools/</guid>
      <pubDate>Fri, 24 Apr 2026 12:49:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/crisis-dashboards-tools.m4a"
        type="audio/mp4"
        length="19196040"
      />
      <itunes:title>When Data Becomes the Decision Framework</itunes:title>
      <itunes:subtitle>Discover how situational awareness dashboards transform chaos into actionable insights during emergencies like earthquakes and hurricanes.</itunes:subtitle>
      <itunes:summary><![CDATA[How do emergency responders turn a flood of live feeds into a prioritized action list? This episode explores the technology and philosophy behind dashboards that don't just display data—they become the decision itself.]]></itunes:summary>
      <itunes:duration>1470</itunes:duration>
      <itunes:episode>2397</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/crisis-dashboards-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/crisis-dashboards-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Engineering a News Pipeline for the Edges</title>
      <description><![CDATA[How do you build an automated news feed that reliably surfaces underreported developments—like drug price spikes or infrastructure damage—alongside major headlines? This episode explores the technical challenge of balancing consensus news with novel, credible data points in fast-moving conflicts.]]></description>
      <link>https://myweirdprompts.com/episode/israel-iran-news-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/israel-iran-news-pipeline/</guid>
      <pubDate>Fri, 24 Apr 2026 12:34:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/israel-iran-news-pipeline.m4a"
        type="audio/mp4"
        length="19230613"
      />
      <itunes:title>Engineering a News Pipeline for the Edges</itunes:title>
      <itunes:subtitle>Building a news pipeline that goes beyond headlines to reveal underreported developments in Israel-Iran coverage—without amplifying noise.</itunes:subtitle>
      <itunes:summary><![CDATA[How do you build an automated news feed that reliably surfaces underreported developments—like drug price spikes or infrastructure damage—alongside major headlines? This episode explores the technical challenge of balancing consensus news with novel, credible data points in fast-moving conflicts.]]></itunes:summary>
      <itunes:duration>1478</itunes:duration>
      <itunes:episode>2395</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/israel-iran-news-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/israel-iran-news-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Anti-Bot Defenses Break Accessibility</title>
      <description><![CDATA[Government and banking sites block foreign IPs and fingerprint headless browsers to stop fraud—but what happens when those defenses lock out legitimate users and AI agents? This episode explores the collateral damage of the automation arms race and whether standards like WebMCP can restore balance.]]></description>
      <link>https://myweirdprompts.com/episode/browser-automation-israel-georestrictions/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/browser-automation-israel-georestrictions/</guid>
      <pubDate>Thu, 23 Apr 2026 18:18:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/browser-automation-israel-georestrictions.m4a"
        type="audio/mp4"
        length="19806120"
      />
      <itunes:title>When Anti-Bot Defenses Break Accessibility</itunes:title>
      <itunes:subtitle>How browser automation hits a wall with Israel&apos;s strict geo-restrictions and anti-bot measures—and what practical workarounds exist.</itunes:subtitle>
      <itunes:summary><![CDATA[Government and banking sites block foreign IPs and fingerprint headless browsers to stop fraud—but what happens when those defenses lock out legitimate users and AI agents? This episode explores the collateral damage of the automation arms race and whether standards like WebMCP can restore balance.]]></itunes:summary>
      <itunes:duration>1532</itunes:duration>
      <itunes:episode>2391</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/browser-automation-israel-georestrictions.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/browser-automation-israel-georestrictions.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Low-Grade Digital Arms Race</title>
      <description><![CDATA[Browser automation is reshaping the web, but it's also triggering a quiet war between users and anti-bot defenses. This episode explores the practical tools, the ethical gray zones, and whether a sustainable compromise between accessibility and security is possible.]]></description>
      <link>https://myweirdprompts.com/episode/browser-automation-web-interaction/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/browser-automation-web-interaction/</guid>
      <pubDate>Thu, 23 Apr 2026 16:45:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/browser-automation-web-interaction.m4a"
        type="audio/mp4"
        length="18939111"
      />
      <itunes:title>The Low-Grade Digital Arms Race</itunes:title>
      <itunes:subtitle>Discover how browser automation is reshaping web interaction, from job applications to navigating geo-restrictions and anti-bot measures.</itunes:subtitle>
      <itunes:summary><![CDATA[Browser automation is reshaping the web, but it's also triggering a quiet war between users and anti-bot defenses. This episode explores the practical tools, the ethical gray zones, and whether a sustainable compromise between accessibility and security is possible.]]></itunes:summary>
      <itunes:duration>1445</itunes:duration>
      <itunes:episode>2390</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/browser-automation-web-interaction.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/browser-automation-web-interaction.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Tool Picker to Problem Solver</title>
      <description><![CDATA[What happens when you no longer need to choose which AI model to use? This episode explores OpenRouter's intelligent routing as a case study in the shift from manual tool selection to capability-based problem solving, and what that means for how we interact with AI.]]></description>
      <link>https://myweirdprompts.com/episode/openrouter-model-selection/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/openrouter-model-selection/</guid>
      <pubDate>Thu, 23 Apr 2026 07:08:03 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/openrouter-model-selection.m4a"
        type="audio/mp4"
        length="19043013"
      />
      <itunes:title>From Tool Picker to Problem Solver</itunes:title>
      <itunes:subtitle>Discover how OpenRouter intelligently routes your prompts to the most optimized AI model, reshaping how we interact with AI tools.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when you no longer need to choose which AI model to use? This episode explores OpenRouter's intelligent routing as a case study in the shift from manual tool selection to capability-based problem solving, and what that means for how we interact with AI.]]></itunes:summary>
      <itunes:duration>1457</itunes:duration>
      <itunes:episode>2388</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/openrouter-model-selection.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/openrouter-model-selection.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Multi-Stage Pipeline Behind Netflix&apos;s Recommendations</title>
      <description><![CDATA[How do recommendation engines serve billions of personalized suggestions daily? This episode dissects the cascade of candidate generation, ranking, and reranking, and explores where modern AI like embeddings and LLMs fits into a stack built on matrix factorization and gradient-boosted trees.]]></description>
      <link>https://myweirdprompts.com/episode/recommendation-engines-ai-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/recommendation-engines-ai-pipeline/</guid>
      <pubDate>Tue, 21 Apr 2026 23:07:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/recommendation-engines-ai-pipeline.m4a"
        type="audio/mp4"
        length="19004119"
      />
      <itunes:title>The Multi-Stage Pipeline Behind Netflix&apos;s Recommendations</itunes:title>
      <itunes:subtitle>Unpacking the multi-stage AI pipeline behind Netflix, Spotify, and Amazon’s &quot;you might also like&quot; suggestions—from candidate generation to real-tim...</itunes:subtitle>
      <itunes:summary><![CDATA[How do recommendation engines serve billions of personalized suggestions daily? This episode dissects the cascade of candidate generation, ranking, and reranking, and explores where modern AI like embeddings and LLMs fits into a stack built on matrix factorization and gradient-boosted trees.]]></itunes:summary>
      <itunes:duration>1433</itunes:duration>
      <itunes:episode>2368</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/recommendation-engines-ai-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/recommendation-engines-ai-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why LLMs Forget the Middle of Long Conversations</title>
      <description><![CDATA[Ever noticed how large language models seem to lose track of things in the middle of long conversations? This episode dives into the science behind this phenomenon, exploring transformer attention mechanisms, positional encodings, and attention dilution. We also discuss practical engineering solutions, like Claude Code’s periodic reminders, and unpack research findings from Stanford’s "Lost in the Middle" paper. Whether you’re a developer or just curious about AI, this episode sheds light on a challenge every LLM user encounters.]]></description>
      <link>https://myweirdprompts.com/episode/llm-context-middle-problem/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-context-middle-problem/</guid>
      <pubDate>Tue, 21 Apr 2026 17:43:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-context-middle-problem.m4a"
        type="audio/mp4"
        length="29399060"
      />
      <itunes:title>Why LLMs Forget the Middle of Long Conversations</itunes:title>
      <itunes:subtitle>Why do large language models struggle with the middle of long conversations? Explore the science behind attention dilution and practical fixes.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever noticed how large language models seem to lose track of things in the middle of long conversations? This episode dives into the science behind this phenomenon, exploring transformer attention mechanisms, positional encodings, and attention dilution. We also discuss practical engineering solutions, like Claude Code’s periodic reminders, and unpack research findings from Stanford’s "Lost in the Middle" paper. Whether you’re a developer or just curious about AI, this episode sheds light on a challenge every LLM user encounters.]]></itunes:summary>
      <itunes:duration>2291</itunes:duration>
      <itunes:episode>2366</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-context-middle-problem.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-context-middle-problem.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Custom Home Alarm Panel with ESP32</title>
      <description><![CDATA[Ever wished for a physical button to arm your home alarm system? This episode dives into building a custom alarm panel from scratch, designed to integrate seamlessly with Home Assistant. Learn why tactile buttons beat phone apps in moments of stress, how Zigbee sensors create a robust home alarm network, and why ESP32 is the perfect microcontroller for this project. We’ll walk through component choices, from Omron switches to diffused LEDs, and explore the benefits of local control over cloud-dependent solutions. Whether you’re a beginner or a seasoned maker, this project offers a satisfying blend of simplicity and functionality.]]></description>
      <link>https://myweirdprompts.com/episode/custom-alarm-panel-esp32/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/custom-alarm-panel-esp32/</guid>
      <pubDate>Tue, 21 Apr 2026 13:48:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/custom-alarm-panel-esp32.m4a"
        type="audio/mp4"
        length="22956362"
      />
      <itunes:title>Building a Custom Home Alarm Panel with ESP32</itunes:title>
      <itunes:subtitle>Discover how to build a tactile, local-control alarm panel for Home Assistant using ESP32, Omron buttons, and Zigbee sensors.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wished for a physical button to arm your home alarm system? This episode dives into building a custom alarm panel from scratch, designed to integrate seamlessly with Home Assistant. Learn why tactile buttons beat phone apps in moments of stress, how Zigbee sensors create a robust home alarm network, and why ESP32 is the perfect microcontroller for this project. We’ll walk through component choices, from Omron switches to diffused LEDs, and explore the benefits of local control over cloud-dependent solutions. Whether you’re a beginner or a seasoned maker, this project offers a satisfying blend of simplicity and functionality.]]></itunes:summary>
      <itunes:duration>1790</itunes:duration>
      <itunes:episode>2365</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/custom-alarm-panel-esp32.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/custom-alarm-panel-esp32.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The $1000 Military Clock You Can Build for $30</title>
      <description><![CDATA[Why does Zulu time matter for military operations? And how can you build a dual-timezone clock for a fraction of the commercial cost? This episode walks through an ESP32 project that syncs to NTP and displays local and UTC time side by side.]]></description>
      <link>https://myweirdprompts.com/episode/dual-timezone-clock-esp32/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dual-timezone-clock-esp32/</guid>
      <pubDate>Tue, 21 Apr 2026 13:33:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dual-timezone-clock-esp32.m4a"
        type="audio/mp4"
        length="31463600"
      />
      <itunes:title>The $1000 Military Clock You Can Build for $30</itunes:title>
      <itunes:subtitle>Learn how to build a precise dual-timezone clock using an ESP32 microcontroller, LCD displays, and USB-C power.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does Zulu time matter for military operations? And how can you build a dual-timezone clock for a fraction of the commercial cost? This episode walks through an ESP32 project that syncs to NTP and displays local and UTC time side by side.]]></itunes:summary>
      <itunes:duration>2461</itunes:duration>
      <itunes:episode>2364</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dual-timezone-clock-esp32.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dual-timezone-clock-esp32.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Chasm Between Breadboard and Pacemaker</title>
      <description><![CDATA[What separates a hobbyist microcontroller from the silicon inside a pacemaker? This episode maps the four categories of embedded systems and reveals how regulatory, reliability, and environmental constraints create a chasm that 'small computer' doesn't capture.]]></description>
      <link>https://myweirdprompts.com/episode/embedded-systems-breadboards-pacemakers/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/embedded-systems-breadboards-pacemakers/</guid>
      <pubDate>Tue, 21 Apr 2026 13:25:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/embedded-systems-breadboards-pacemakers.m4a"
        type="audio/mp4"
        length="25711374"
      />
      <itunes:title>The Chasm Between Breadboard and Pacemaker</itunes:title>
      <itunes:subtitle>How do tiny computers power everything from hobbyist projects to life-saving medical implants? The engineering constraints are worlds apart.</itunes:subtitle>
      <itunes:summary><![CDATA[What separates a hobbyist microcontroller from the silicon inside a pacemaker? This episode maps the four categories of embedded systems and reveals how regulatory, reliability, and environmental constraints create a chasm that 'small computer' doesn't capture.]]></itunes:summary>
      <itunes:duration>1979</itunes:duration>
      <itunes:episode>2363</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/embedded-systems-breadboards-pacemakers.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/embedded-systems-breadboards-pacemakers.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Cognitive Load of Logs</title>
      <description><![CDATA[Linux gives you depth and control, but that depth creates instability if you're not watching. This episode explores why manual log reading is like spotting a wrong note in a symphony—and how agentic AI tools might finally resolve the tradeoff between customization and vigilance.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-linux-logs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-linux-logs/</guid>
      <pubDate>Tue, 21 Apr 2026 12:48:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-linux-logs.m4a"
        type="audio/mp4"
        length="25591489"
      />
      <itunes:title>The Cognitive Load of Logs</itunes:title>
      <itunes:subtitle>How Claude Code transforms Linux system administration by automating log analysis and proactive maintenance.</itunes:subtitle>
      <itunes:summary><![CDATA[Linux gives you depth and control, but that depth creates instability if you're not watching. This episode explores why manual log reading is like spotting a wrong note in a symphony—and how agentic AI tools might finally resolve the tradeoff between customization and vigilance.]]></itunes:summary>
      <itunes:duration>1958</itunes:duration>
      <itunes:episode>2361</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-linux-logs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-linux-logs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When the Sandbox Doesn&apos;t Fit: Sysadmins Using a Dev Tool</title>
      <description><![CDATA[Claude Code was built for developers in repos, but sysadmins are using it to manage entire machines. This episode examines the friction between its cwd-centric permission model and the reality of infrastructure work, and whether the architecture can evolve without sacrificing safety.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-sysadmin-tool/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-sysadmin-tool/</guid>
      <pubDate>Tue, 21 Apr 2026 11:46:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-sysadmin-tool.m4a"
        type="audio/mp4"
        length="15272259"
      />
      <itunes:title>When the Sandbox Doesn&apos;t Fit: Sysadmins Using a Dev Tool</itunes:title>
      <itunes:subtitle>Discover why Claude Code excels as a sysadmin tool despite being designed for developers — and the challenges that come with it.</itunes:subtitle>
      <itunes:summary><![CDATA[Claude Code was built for developers in repos, but sysadmins are using it to manage entire machines. This episode examines the friction between its cwd-centric permission model and the reality of infrastructure work, and whether the architecture can evolve without sacrificing safety.]]></itunes:summary>
      <itunes:duration>1176</itunes:duration>
      <itunes:episode>2359</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-sysadmin-tool.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-sysadmin-tool.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>ESP32 vs Raspberry Pi: The Microcontroller Mindshift</title>
      <description><![CDATA[Most developers think of microcontrollers like the ESP32 as just "smaller computers," but the reality is far more interesting. Unlike Raspberry Pis or other single-board computers (SBCs), microcontrollers operate on a fundamentally different tier—running real-time operating systems (RTOS) or even bare-metal firmware, with no traditional OS in sight. This episode dives into the architectural distinctions: deterministic scheduling, memory models, boot times, and power efficiency that make microcontrollers the backbone of IoT. Learn why your smart clock or thermostat likely runs an ESP32 instead of Linux—and why that’s exactly how it should be.]]></description>
      <link>https://myweirdprompts.com/episode/esp32-raspberry-pi-mindshift/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/esp32-raspberry-pi-mindshift/</guid>
      <pubDate>Mon, 20 Apr 2026 23:48:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/esp32-raspberry-pi-mindshift.m4a"
        type="audio/mp4"
        length="17966349"
      />
      <itunes:title>ESP32 vs Raspberry Pi: The Microcontroller Mindshift</itunes:title>
      <itunes:subtitle>Why your smart thermostat doesn’t run Linux—and why that’s a feature. The surprising differences between microcontrollers and single-board computers.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers think of microcontrollers like the ESP32 as just "smaller computers," but the reality is far more interesting. Unlike Raspberry Pis or other single-board computers (SBCs), microcontrollers operate on a fundamentally different tier—running real-time operating systems (RTOS) or even bare-metal firmware, with no traditional OS in sight. This episode dives into the architectural distinctions: deterministic scheduling, memory models, boot times, and power efficiency that make microcontrollers the backbone of IoT. Learn why your smart clock or thermostat likely runs an ESP32 instead of Linux—and why that’s exactly how it should be.]]></itunes:summary>
      <itunes:duration>1383</itunes:duration>
      <itunes:episode>2358</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/esp32-raspberry-pi-mindshift.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/esp32-raspberry-pi-mindshift.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Coding Needs Two Brains</title>
      <description><![CDATA[Frontier models reason about code changes but struggle with the mechanical merge. This episode explores why the smartest AI coding tools split the job between a reasoning model and a fast, specialized 'apply' model — and why that architecture isn't going away.]]></description>
      <link>https://myweirdprompts.com/episode/fast-apply-models/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fast-apply-models/</guid>
      <pubDate>Mon, 20 Apr 2026 21:26:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fast-apply-models.m4a"
        type="audio/mp4"
        length="18152607"
      />
      <itunes:title>Why AI Coding Needs Two Brains</itunes:title>
      <itunes:subtitle>Discover how specialized fast apply models streamline AI-powered code edits, cutting costs and latency while maintaining precision.</itunes:subtitle>
      <itunes:summary><![CDATA[Frontier models reason about code changes but struggle with the mechanical merge. This episode explores why the smartest AI coding tools split the job between a reasoning model and a fast, specialized 'apply' model — and why that architecture isn't going away.]]></itunes:summary>
      <itunes:duration>1384</itunes:duration>
      <itunes:episode>2356</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fast-apply-models.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fast-apply-models.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Structured Output Gap in Vision APIs</title>
      <description><![CDATA[Why can a model see a cereal box but not reliably return its coordinates in a consistent schema? This episode explores the hidden engineering challenges that separate general-purpose vision models from dedicated object detection tools, and what that means for your annotation pipeline.]]></description>
      <link>https://myweirdprompts.com/episode/object-detection-api-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/object-detection-api-tools/</guid>
      <pubDate>Mon, 20 Apr 2026 13:18:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/object-detection-api-tools.m4a"
        type="audio/mp4"
        length="18517283"
      />
      <itunes:title>The Structured Output Gap in Vision APIs</itunes:title>
      <itunes:subtitle>How do object detection APIs like Gemini, AWS Rekognition, and YOLO compare for automated annotation workflows?</itunes:subtitle>
      <itunes:summary><![CDATA[Why can a model see a cereal box but not reliably return its coordinates in a consistent schema? This episode explores the hidden engineering challenges that separate general-purpose vision models from dedicated object detection tools, and what that means for your annotation pipeline.]]></itunes:summary>
      <itunes:duration>1425</itunes:duration>
      <itunes:episode>2352</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/object-detection-api-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/object-detection-api-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>NVIDIA&apos;s Strategic Pivot: From Chipmaker to Model Builder</title>
      <description><![CDATA[NVIDIA's Nemotron 3 Super isn't just another AI model—it's a signal of the company's shift from hardware supplier to AI competitor. This episode explores what that means for the industry, the architecture behind the model, and why Jensen Huang called their earlier strategy a miss.]]></description>
      <link>https://myweirdprompts.com/episode/nvidia-nemotron-3-super/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/nvidia-nemotron-3-super/</guid>
      <pubDate>Mon, 20 Apr 2026 12:54:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/nvidia-nemotron-3-super.m4a"
        type="audio/mp4"
        length="16937837"
      />
      <itunes:title>NVIDIA&apos;s Strategic Pivot: From Chipmaker to Model Builder</itunes:title>
      <itunes:subtitle>Dive into NVIDIA’s Nemotron 3 Super, a hybrid MoE model combining Mamba, Transformers, and multi-token prediction for cutting-edge efficiency.</itunes:subtitle>
      <itunes:summary><![CDATA[NVIDIA's Nemotron 3 Super isn't just another AI model—it's a signal of the company's shift from hardware supplier to AI competitor. This episode explores what that means for the industry, the architecture behind the model, and why Jensen Huang called their earlier strategy a miss.]]></itunes:summary>
      <itunes:duration>1295</itunes:duration>
      <itunes:episode>2350</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/nvidia-nemotron-3-super.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/nvidia-nemotron-3-super.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Your Schema Is a Contract</title>
      <description><![CDATA[Why an hour of database planning saves weeks of remediation, how schemas function as unspoken contracts, and when denormalization actually makes sense.]]></description>
      <link>https://myweirdprompts.com/episode/database-schema-planning/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/database-schema-planning/</guid>
      <pubDate>Mon, 20 Apr 2026 10:34:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/database-schema-planning.m4a"
        type="audio/mp4"
        length="17893264"
      />
      <itunes:title>Your Schema Is a Contract</itunes:title>
      <itunes:subtitle>How to design relational schemas that don’t haunt you later—entity modeling, normalization tradeoffs, and when (not) to use JSON columns.</itunes:subtitle>
      <itunes:summary><![CDATA[Why an hour of database planning saves weeks of remediation, how schemas function as unspoken contracts, and when denormalization actually makes sense.]]></itunes:summary>
      <itunes:duration>1365</itunes:duration>
      <itunes:episode>2346</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/database-schema-planning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/database-schema-planning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why File Naming Conventions Are More Than Just Style</title>
      <description><![CDATA[In this episode, we dive into the often-overlooked world of file naming conventions and their critical role in development workflows. From snake_case to camelCase, we explore the origins, ecosystem preferences, and practical implications of each convention. Learn how case sensitivity across filesystems like ext4, APFS, and NTFS can lead to latent failures in CI/CD pipelines and why treating filenames as interfaces, not labels, is crucial. Whether you're a seasoned developer or just starting out, this episode will change the way you think about naming files.]]></description>
      <link>https://myweirdprompts.com/episode/file-naming-conventions/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/file-naming-conventions/</guid>
      <pubDate>Mon, 20 Apr 2026 10:10:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/file-naming-conventions.m4a"
        type="audio/mp4"
        length="19318724"
      />
      <itunes:title>Why File Naming Conventions Are More Than Just Style</itunes:title>
      <itunes:subtitle>Discover how file naming conventions like snake_case and camelCase impact development workflows, CI/CD pipelines, and filesystem compatibility.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we dive into the often-overlooked world of file naming conventions and their critical role in development workflows. From snake_case to camelCase, we explore the origins, ecosystem preferences, and practical implications of each convention. Learn how case sensitivity across filesystems like ext4, APFS, and NTFS can lead to latent failures in CI/CD pipelines and why treating filenames as interfaces, not labels, is crucial. Whether you're a seasoned developer or just starting out, this episode will change the way you think about naming files.]]></itunes:summary>
      <itunes:duration>1487</itunes:duration>
      <itunes:episode>2345</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/file-naming-conventions.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/file-naming-conventions.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Python Ate Wall Street</title>
      <description><![CDATA[Algorithmic trading isn’t just for hedge funds anymore. Python’s ecosystem—Pandas, NumPy, Backtrader, and Qlib—has collapsed the gap between institutional desks and independent quants. This episode explores how integrated tooling transformed finance, why AI is reshaping research workflows, and where reinforcement learning hits its limits. The real question: When everyone has the same tools, where does the edge come from?]]></description>
      <link>https://myweirdprompts.com/episode/python-algorithmic-trading-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/python-algorithmic-trading-tools/</guid>
      <pubDate>Mon, 20 Apr 2026 01:28:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/python-algorithmic-trading-tools.m4a"
        type="audio/mp4"
        length="22053372"
      />
      <itunes:title>How Python Ate Wall Street</itunes:title>
      <itunes:subtitle>Over 80% of equity trades are now executed algorithmically. How did Python libraries quietly democratize quant finance?</itunes:subtitle>
      <itunes:summary><![CDATA[Algorithmic trading isn’t just for hedge funds anymore. Python’s ecosystem—Pandas, NumPy, Backtrader, and Qlib—has collapsed the gap between institutional desks and independent quants. This episode explores how integrated tooling transformed finance, why AI is reshaping research workflows, and where reinforcement learning hits its limits. The real question: When everyone has the same tools, where does the edge come from?]]></itunes:summary>
      <itunes:duration>1711</itunes:duration>
      <itunes:episode>2342</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/python-algorithmic-trading-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/python-algorithmic-trading-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Who Keeps Matplotlib Running?</title>
      <description><![CDATA[Matplotlib, the Python plotting library, powers millions of scientific charts worldwide—yet its maintenance relies on a surprisingly small team of just 15 people. In this episode, we explore how this critical open-source project is governed, funded, and sustained. From the Steering Council’s decision-making to the role of fiscal sponsors like NumFOCUS, we uncover the delicate balance of volunteerism, institutional support, and community coordination that keeps Matplotlib alive. Dive into the fascinating world of scientific Python infrastructure and the unsung heroes behind it.]]></description>
      <link>https://myweirdprompts.com/episode/matplotlib-maintenance-team/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/matplotlib-maintenance-team/</guid>
      <pubDate>Sun, 19 Apr 2026 22:10:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/matplotlib-maintenance-team.m4a"
        type="audio/mp4"
        length="24060767"
      />
      <itunes:title>Who Keeps Matplotlib Running?</itunes:title>
      <itunes:subtitle>How does a team of just 15 people maintain Matplotlib, the backbone of global scientific visualization?</itunes:subtitle>
      <itunes:summary><![CDATA[Matplotlib, the Python plotting library, powers millions of scientific charts worldwide—yet its maintenance relies on a surprisingly small team of just 15 people. In this episode, we explore how this critical open-source project is governed, funded, and sustained. From the Steering Council’s decision-making to the role of fiscal sponsors like NumFOCUS, we uncover the delicate balance of volunteerism, institutional support, and community coordination that keeps Matplotlib alive. Dive into the fascinating world of scientific Python infrastructure and the unsung heroes behind it.]]></itunes:summary>
      <itunes:duration>1836</itunes:duration>
      <itunes:episode>2338</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/matplotlib-maintenance-team.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/matplotlib-maintenance-team.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Diarization Fails Silently</title>
      <description><![CDATA[Speaker diarization errors are invisible but costly—mislabeling a call center agent or collapsing a courtroom transcript. This episode explores why the pipeline breaks and how to build robust systems that catch what the tools hide.]]></description>
      <link>https://myweirdprompts.com/episode/speaker-diarization-deep-dive/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speaker-diarization-deep-dive/</guid>
      <pubDate>Sun, 19 Apr 2026 21:28:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speaker-diarization-deep-dive.m4a"
        type="audio/mp4"
        length="20352195"
      />
      <itunes:title>When Diarization Fails Silently</itunes:title>
      <itunes:subtitle>Discover how PyAnnote and other tools tackle the critical task of identifying &quot;who spoke when&quot; in audio—and why it’s harder than it sounds.</itunes:subtitle>
      <itunes:summary><![CDATA[Speaker diarization errors are invisible but costly—mislabeling a call center agent or collapsing a courtroom transcript. This episode explores why the pipeline breaks and how to build robust systems that catch what the tools hide.]]></itunes:summary>
      <itunes:duration>1565</itunes:duration>
      <itunes:episode>2337</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speaker-diarization-deep-dive.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speaker-diarization-deep-dive.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How ADRs Solve AI&apos;s Institutional Memory Problem</title>
      <description><![CDATA[Software projects are full of decisions that look wrong in isolation—until you learn the hidden constraints behind them. Architectural Decision Records (ADRs) capture not just what choices were made, but why, when, and what tradeoffs were considered. Now, in the era of AI-assisted coding, ADRs have taken on new importance: they provide the institutional memory LLMs lack. This episode explores how structured, machine-readable ADRs prevent AI agents from reintroducing old problems, why traditional documentation fails, and how teams can use lightweight frameworks like MADR to make their reasoning addressable.]]></description>
      <link>https://myweirdprompts.com/episode/adrs-ai-institutional-memory/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/adrs-ai-institutional-memory/</guid>
      <pubDate>Sun, 19 Apr 2026 20:48:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/adrs-ai-institutional-memory.m4a"
        type="audio/mp4"
        length="19263439"
      />
      <itunes:title>How ADRs Solve AI&apos;s Institutional Memory Problem</itunes:title>
      <itunes:subtitle>Architectural Decision Records (ADRs) aren’t just documentation—they’re a way to give AI coding assistants the context they lack.</itunes:subtitle>
      <itunes:summary><![CDATA[Software projects are full of decisions that look wrong in isolation—until you learn the hidden constraints behind them. Architectural Decision Records (ADRs) capture not just what choices were made, but why, when, and what tradeoffs were considered. Now, in the era of AI-assisted coding, ADRs have taken on new importance: they provide the institutional memory LLMs lack. This episode explores how structured, machine-readable ADRs prevent AI agents from reintroducing old problems, why traditional documentation fails, and how teams can use lightweight frameworks like MADR to make their reasoning addressable.]]></itunes:summary>
      <itunes:duration>1474</itunes:duration>
      <itunes:episode>2336</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/adrs-ai-institutional-memory.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/adrs-ai-institutional-memory.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Voice-to-Task: Building the Claude Task Planner</title>
      <description><![CDATA[What does it take to turn a voice note into a completed task? This episode explores the Claude Task Planner, a system that combines voice transcription, webhooks, and Claude CLI to automate task execution. We break down the architecture, examine the tricky handoffs between components, and discuss the robustness challenges of automating workflows. From transcription accuracy to webhook security and execution latency, discover the tradeoffs and design decisions that make or break voice-to-task systems. Whether you're building your own automation pipeline or just curious about the tech behind it, this episode offers practical insights and a clear roadmap for getting it right.]]></description>
      <link>https://myweirdprompts.com/episode/claude-task-planner/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-task-planner/</guid>
      <pubDate>Sun, 19 Apr 2026 16:04:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-task-planner.m4a"
        type="audio/mp4"
        length="17993061"
      />
      <itunes:title>Voice-to-Task: Building the Claude Task Planner</itunes:title>
      <itunes:subtitle>How does a voice note turn into a completed task? Dive into the architecture and tradeoffs of building a Claude-powered task execution system.</itunes:subtitle>
      <itunes:summary><![CDATA[What does it take to turn a voice note into a completed task? This episode explores the Claude Task Planner, a system that combines voice transcription, webhooks, and Claude CLI to automate task execution. We break down the architecture, examine the tricky handoffs between components, and discuss the robustness challenges of automating workflows. From transcription accuracy to webhook security and execution latency, discover the tradeoffs and design decisions that make or break voice-to-task systems. Whether you're building your own automation pipeline or just curious about the tech behind it, this episode offers practical insights and a clear roadmap for getting it right.]]></itunes:summary>
      <itunes:duration>1377</itunes:duration>
      <itunes:episode>2332</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-task-planner.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-task-planner.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Spot a Real AI Hackathon</title>
      <description><![CDATA[With hundreds of virtual AI hackathons every quarter, how do you separate genuine technical challenges from marketing exercises? This episode breaks down the signals that matter: judging criteria, team dynamics, and the hidden infrastructure that makes some events worth your time.]]></description>
      <link>https://myweirdprompts.com/episode/virtual-ai-hackathons-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/virtual-ai-hackathons-guide/</guid>
      <pubDate>Sun, 19 Apr 2026 14:16:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/virtual-ai-hackathons-guide.m4a"
        type="audio/mp4"
        length="33603539"
      />
      <itunes:title>How to Spot a Real AI Hackathon</itunes:title>
      <itunes:subtitle>Discover how to identify worthwhile AI hackathons, build meaningful connections, and maximize your impact in virtual communities.</itunes:subtitle>
      <itunes:summary><![CDATA[With hundreds of virtual AI hackathons every quarter, how do you separate genuine technical challenges from marketing exercises? This episode breaks down the signals that matter: judging criteria, team dynamics, and the hidden infrastructure that makes some events worth your time.]]></itunes:summary>
      <itunes:duration>2602</itunes:duration>
      <itunes:episode>2328</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/virtual-ai-hackathons-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/virtual-ai-hackathons-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Voice Control Simplified: Home Assistant’s Local Stack</title>
      <description><![CDATA[Voice control in Home Assistant doesn’t have to mean endless frustration or being locked into big tech ecosystems. This episode breaks down the simplest, most reliable path for setting up a local voice control stack using a Raspberry Pi, open-source tools, and purpose-built hardware. Learn how Home Assistant’s AI semantic layer solves the alias problem, why Speech-to-Phrase outperforms Whisper for predictable commands, and how the Wyoming protocol connects it all locally. Whether you’re automating lights, thermostats, or locks, this guide will help you build a system that feels responsive, stays private, and avoids the pitfalls of cloud-dependent solutions.]]></description>
      <link>https://myweirdprompts.com/episode/home-assistant-voice-control/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-assistant-voice-control/</guid>
      <pubDate>Sun, 19 Apr 2026 10:04:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-assistant-voice-control.m4a"
        type="audio/mp4"
        length="18792771"
      />
      <itunes:title>Voice Control Simplified: Home Assistant’s Local Stack</itunes:title>
      <itunes:subtitle>Discover how to build a reliable, vendor-agnostic voice control system for Home Assistant without relying on Amazon or Google.</itunes:subtitle>
      <itunes:summary><![CDATA[Voice control in Home Assistant doesn’t have to mean endless frustration or being locked into big tech ecosystems. This episode breaks down the simplest, most reliable path for setting up a local voice control stack using a Raspberry Pi, open-source tools, and purpose-built hardware. Learn how Home Assistant’s AI semantic layer solves the alias problem, why Speech-to-Phrase outperforms Whisper for predictable commands, and how the Wyoming protocol connects it all locally. Whether you’re automating lights, thermostats, or locks, this guide will help you build a system that feels responsive, stays private, and avoids the pitfalls of cloud-dependent solutions.]]></itunes:summary>
      <itunes:duration>1450</itunes:duration>
      <itunes:episode>2326</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-assistant-voice-control.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-assistant-voice-control.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Model or Three? Inside Claude&apos;s Architecture</title>
      <description><![CDATA[Are Haiku, Sonnet, and Opus scaled variants of the same model or fundamentally different architectures? This episode explores how their design philosophies diverge and why that choice changes how you build with them.]]></description>
      <link>https://myweirdprompts.com/episode/claude-models-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-models-explained/</guid>
      <pubDate>Sun, 19 Apr 2026 06:19:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-models-explained.m4a"
        type="audio/mp4"
        length="17312644"
      />
      <itunes:title>One Model or Three? Inside Claude&apos;s Architecture</itunes:title>
      <itunes:subtitle>What makes Claude’s Haiku, Sonnet, and Opus different? Discover how architecture shapes their unique strengths and weaknesses.</itunes:subtitle>
      <itunes:summary><![CDATA[Are Haiku, Sonnet, and Opus scaled variants of the same model or fundamentally different architectures? This episode explores how their design philosophies diverge and why that choice changes how you build with them.]]></itunes:summary>
      <itunes:duration>1329</itunes:duration>
      <itunes:episode>2314</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-models-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-models-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Forecasts Collide: Geopol Model Divergence</title>
      <description><![CDATA[What happens when five AI models from different training lineages forecast the same fast-moving geopolitical crisis? In this experiment, dubbed the Geopol Forecast Council, models like Claude Sonnet 4.6, Google’s Gemini 3 Flash Preview, and China’s GLM 5.1 independently analyzed the Iran-Israel-US conflict across three time horizons. The results? Both convergence and sharp divergence, offering a rare look into how AI models reason differently about complex scenarios. This episode unpacks the methodology, the surprising agreements, and the meaningful disagreements — and what they reveal about the future of AI-driven geopolitics.]]></description>
      <link>https://myweirdprompts.com/episode/geopolitics-ai-model-divergence/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/geopolitics-ai-model-divergence/</guid>
      <pubDate>Sat, 18 Apr 2026 22:06:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/geopolitics-ai-model-divergence.m4a"
        type="audio/mp4"
        length="19558755"
      />
      <itunes:title>When AI Forecasts Collide: Geopol Model Divergence</itunes:title>
      <itunes:subtitle>Five AI models forecast the Iran-Israel-US crisis — and their disagreements reveal surprising insights about geopolitical reasoning.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when five AI models from different training lineages forecast the same fast-moving geopolitical crisis? In this experiment, dubbed the Geopol Forecast Council, models like Claude Sonnet 4.6, Google’s Gemini 3 Flash Preview, and China’s GLM 5.1 independently analyzed the Iran-Israel-US conflict across three time horizons. The results? Both convergence and sharp divergence, offering a rare look into how AI models reason differently about complex scenarios. This episode unpacks the methodology, the surprising agreements, and the meaningful disagreements — and what they reveal about the future of AI-driven geopolitics.]]></itunes:summary>
      <itunes:duration>1491</itunes:duration>
      <itunes:episode>2308</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/geopolitics-ai-model-divergence.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/geopolitics-ai-model-divergence.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Serverless Paradox: Why TTS Eats Your Budget</title>
      <description><![CDATA[Most podcast producers optimize the wrong part of their pipeline. This episode reveals why TTS costs dominate, how batch processing can backfire, and the counterintuitive strategy for cutting infrastructure bills without sacrificing quality.]]></description>
      <link>https://myweirdprompts.com/episode/podcast-tts-batch-optimization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcast-tts-batch-optimization/</guid>
      <pubDate>Sat, 18 Apr 2026 16:47:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcast-tts-batch-optimization.m4a"
        type="audio/mp4"
        length="17115202"
      />
      <itunes:title>The Serverless Paradox: Why TTS Eats Your Budget</itunes:title>
      <itunes:subtitle>How batch processing and smart queue management can slash TTS costs for episodic podcast production.</itunes:subtitle>
      <itunes:summary><![CDATA[Most podcast producers optimize the wrong part of their pipeline. This episode reveals why TTS costs dominate, how batch processing can backfire, and the counterintuitive strategy for cutting infrastructure bills without sacrificing quality.]]></itunes:summary>
      <itunes:duration>1282</itunes:duration>
      <itunes:episode>2303</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcast-tts-batch-optimization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcast-tts-batch-optimization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside Podcasting&apos;s Simple, Powerful Infrastructure</title>
      <description><![CDATA[Podcasting’s infrastructure is older and simpler than most people realize, yet it remains remarkably powerful. This episode dives into the RSS specification, the backbone of podcasting, and how creators can leverage tools like Vercel and Cloudflare R2 to maintain control over their shows. Learn about the technical decisions behind building a custom podcast feed, the challenges of analytics without surveillance, and the enduring elegance of a system that has outlasted countless other content distribution formats. Whether you're a podcaster or just curious about how the medium works, this episode offers a deep look into the nuts and bolts of podcasting.]]></description>
      <link>https://myweirdprompts.com/episode/podcasting-infrastructure-rss/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcasting-infrastructure-rss/</guid>
      <pubDate>Sat, 18 Apr 2026 16:27:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcasting-infrastructure-rss.m4a"
        type="audio/mp4"
        length="46982380"
      />
      <itunes:title>Inside Podcasting&apos;s Simple, Powerful Infrastructure</itunes:title>
      <itunes:subtitle>Explore the elegant simplicity of podcasting’s RSS backbone and how it empowers creators with independence and control.</itunes:subtitle>
      <itunes:summary><![CDATA[Podcasting’s infrastructure is older and simpler than most people realize, yet it remains remarkably powerful. This episode dives into the RSS specification, the backbone of podcasting, and how creators can leverage tools like Vercel and Cloudflare R2 to maintain control over their shows. Learn about the technical decisions behind building a custom podcast feed, the challenges of analytics without surveillance, and the enduring elegance of a system that has outlasted countless other content distribution formats. Whether you're a podcaster or just curious about how the medium works, this episode offers a deep look into the nuts and bolts of podcasting.]]></itunes:summary>
      <itunes:duration>3649</itunes:duration>
      <itunes:episode>2301</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcasting-infrastructure-rss.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcasting-infrastructure-rss.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Scrape Geo-Restricted Israeli Sites with MCP Tools</title>
      <description><![CDATA[Israeli websites often deploy advanced bot-protection like Cloudflare Turnstile and PerimeterX, making data scraping a challenge. This episode explores practical solutions using MCP tools, residential IPs, and tunneling setups like Tailscale or Cloudflare tunnels. Discover the tradeoffs between Playwright, Firecrawl, and vision-based approaches, and learn why a home workstation architecture might be the key to accessing geo-restricted data. Whether you're scraping utility portals or government procurement pages, this guide provides actionable insights for navigating the evolving bot-protection landscape.]]></description>
      <link>https://myweirdprompts.com/episode/scraping-israeli-sites-mcp/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/scraping-israeli-sites-mcp/</guid>
      <pubDate>Sat, 18 Apr 2026 15:45:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/scraping-israeli-sites-mcp.m4a"
        type="audio/mp4"
        length="23148082"
      />
      <itunes:title>How to Scrape Geo-Restricted Israeli Sites with MCP Tools</itunes:title>
      <itunes:subtitle>Learn how to bypass advanced bot-protection on Israeli websites using MCP tools, residential IPs, and tunneling techniques.</itunes:subtitle>
      <itunes:summary><![CDATA[Israeli websites often deploy advanced bot-protection like Cloudflare Turnstile and PerimeterX, making data scraping a challenge. This episode explores practical solutions using MCP tools, residential IPs, and tunneling setups like Tailscale or Cloudflare tunnels. Discover the tradeoffs between Playwright, Firecrawl, and vision-based approaches, and learn why a home workstation architecture might be the key to accessing geo-restricted data. Whether you're scraping utility portals or government procurement pages, this guide provides actionable insights for navigating the evolving bot-protection landscape.]]></itunes:summary>
      <itunes:duration>1792</itunes:duration>
      <itunes:episode>2297</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/scraping-israeli-sites-mcp.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/scraping-israeli-sites-mcp.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Invisible Gatekeeper of Voice Tech</title>
      <description><![CDATA[Voice activity detection (VAD) is the unsung hero—or villain—of every voice tech system. It decides whether your voice assistant hears you or ignores you, and its failures are often invisible. This episode dives into the key players in VAD, from WebRTC and Silero to Picovoice Cobra and Whisper wrappers, and explores why this seemingly simple task remains an active research problem. We’ll also uncover why VAD is uniquely suited to run efficiently on CPUs and how its challenges are shaped by evolving use cases like streaming voice assistants and edge devices.]]></description>
      <link>https://myweirdprompts.com/episode/voice-activity-detection/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-activity-detection/</guid>
      <pubDate>Fri, 17 Apr 2026 20:31:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-activity-detection.m4a"
        type="audio/mp4"
        length="20206478"
      />
      <itunes:title>The Invisible Gatekeeper of Voice Tech</itunes:title>
      <itunes:subtitle>How voice activity detection shapes every step of the voice tech pipeline, and why it’s harder than it seems.</itunes:subtitle>
      <itunes:summary><![CDATA[Voice activity detection (VAD) is the unsung hero—or villain—of every voice tech system. It decides whether your voice assistant hears you or ignores you, and its failures are often invisible. This episode dives into the key players in VAD, from WebRTC and Silero to Picovoice Cobra and Whisper wrappers, and explores why this seemingly simple task remains an active research problem. We’ll also uncover why VAD is uniquely suited to run efficiently on CPUs and how its challenges are shaped by evolving use cases like streaming voice assistants and edge devices.]]></itunes:summary>
      <itunes:duration>1548</itunes:duration>
      <itunes:episode>2288</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-activity-detection.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-activity-detection.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Is AI Code Generation the Future of Low-Code?</title>
      <description><![CDATA[Is the low-code movement heading for obsolescence? This episode dives into the history, challenges, and future of low-code and no-code platforms, examining their limitations and the rise of AI code generation tools like GitHub Copilot. With projections showing the low-code market growing to over $100 billion by 2030, we explore whether AI-assisted coding is truly a better alternative. From vendor lock-in to transparency issues, we unpack the trade-offs and ask: Are AI tools the death knell for low-code, or will they evolve into something entirely new?]]></description>
      <link>https://myweirdprompts.com/episode/ai-low-code-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-low-code-future/</guid>
      <pubDate>Fri, 17 Apr 2026 20:31:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-low-code-future.m4a"
        type="audio/mp4"
        length="14980721"
      />
      <itunes:title>Is AI Code Generation the Future of Low-Code?</itunes:title>
      <itunes:subtitle>Exploring the rise of AI code generation and its potential to reshape the low-code movement.</itunes:subtitle>
      <itunes:summary><![CDATA[Is the low-code movement heading for obsolescence? This episode dives into the history, challenges, and future of low-code and no-code platforms, examining their limitations and the rise of AI code generation tools like GitHub Copilot. With projections showing the low-code market growing to over $100 billion by 2030, we explore whether AI-assisted coding is truly a better alternative. From vendor lock-in to transparency issues, we unpack the trade-offs and ask: Are AI tools the death knell for low-code, or will they evolve into something entirely new?]]></itunes:summary>
      <itunes:duration>1150</itunes:duration>
      <itunes:episode>2287</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-low-code-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-low-code-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Visual Programming&apos;s Enduring Tradeoff</title>
      <description><![CDATA[Visual programming has been reborn in the no-code and AI automation era, but its core tension remains unchanged. From ladder logic in factories to n8n workflows, the same pattern emerges: graphical interfaces excel at accessibility but struggle with complexity. This episode traces the history of visual tools—LabVIEW’s dataflow diagrams, Scratch’s educational blocks, Node-RED’s IoT wiring—and asks whether modern platforms can avoid the "spaghetti canvas" trap that plagued their predecessors.]]></description>
      <link>https://myweirdprompts.com/episode/visual-programming-tradeoffs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/visual-programming-tradeoffs/</guid>
      <pubDate>Fri, 17 Apr 2026 15:55:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/visual-programming-tradeoffs.m4a"
        type="audio/mp4"
        length="17817324"
      />
      <itunes:title>Visual Programming&apos;s Enduring Tradeoff</itunes:title>
      <itunes:subtitle>Why do visual programming tools keep resurfacing—and why do power users keep hitting their limits?</itunes:subtitle>
      <itunes:summary><![CDATA[Visual programming has been reborn in the no-code and AI automation era, but its core tension remains unchanged. From ladder logic in factories to n8n workflows, the same pattern emerges: graphical interfaces excel at accessibility but struggle with complexity. This episode traces the history of visual tools—LabVIEW’s dataflow diagrams, Scratch’s educational blocks, Node-RED’s IoT wiring—and asks whether modern platforms can avoid the "spaghetti canvas" trap that plagued their predecessors.]]></itunes:summary>
      <itunes:duration>1360</itunes:duration>
      <itunes:episode>2278</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/visual-programming-tradeoffs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/visual-programming-tradeoffs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Weekend Projects Gone Wild: Evaluating AI Startup Pitches</title>
      <description><![CDATA[What happens when you take technically feasible AI tools and apply them to everyday problems? This episode dives into ten wild startup pitches, from doorbell agents that clone your voice to fridge inventory systems that infer your income bracket. We explore the genuine use cases, the technical architectures, and the reasons these ideas might never survive a product review. Join us as we rank these pitches from most defensible to least defensible and uncover the fine line between “could” and “should.”]]></description>
      <link>https://myweirdprompts.com/episode/ai-startup-pitches/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-startup-pitches/</guid>
      <pubDate>Fri, 17 Apr 2026 14:59:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-startup-pitches.m4a"
        type="audio/mp4"
        length="20869831"
      />
      <itunes:title>Weekend Projects Gone Wild: Evaluating AI Startup Pitches</itunes:title>
      <itunes:subtitle>From fridge tax agents to guilt-scheduled cron jobs, we evaluate ten AI-driven startup ideas that could exist—but probably shouldn’t.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when you take technically feasible AI tools and apply them to everyday problems? This episode dives into ten wild startup pitches, from doorbell agents that clone your voice to fridge inventory systems that infer your income bracket. We explore the genuine use cases, the technical architectures, and the reasons these ideas might never survive a product review. Join us as we rank these pitches from most defensible to least defensible and uncover the fine line between “could” and “should.”]]></itunes:summary>
      <itunes:duration>1595</itunes:duration>
      <itunes:episode>2274</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-startup-pitches.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-startup-pitches.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The AI Transcription Sweet Spot</title>
      <description><![CDATA[Conventional wisdom says more data equals better AI performance. But new experiments show that for speech-to-text models like Whisper, higher audio bitrates can actually increase error rates. We dive into the surprising U-shaped curve of transcription accuracy, explore why models perform best on "messy" web-quality audio, and uncover the massive cost savings for anyone processing audio at scale. Learn the optimal bitrate for your pipeline and why aligning with a model's training data is more important than pristine quality.]]></description>
      <link>https://myweirdprompts.com/episode/audio-bitrate-ai-transcription/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-bitrate-ai-transcription/</guid>
      <pubDate>Fri, 17 Apr 2026 13:03:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-bitrate-ai-transcription.m4a"
        type="audio/mp4"
        length="17443622"
      />
      <itunes:title>The AI Transcription Sweet Spot</itunes:title>
      <itunes:subtitle>Does higher-quality audio make AI transcription worse? New research reveals a surprising &quot;sweet spot&quot; for bitrate, challenging a core assumption of...</itunes:subtitle>
      <itunes:summary><![CDATA[Conventional wisdom says more data equals better AI performance. But new experiments show that for speech-to-text models like Whisper, higher audio bitrates can actually increase error rates. We dive into the surprising U-shaped curve of transcription accuracy, explore why models perform best on "messy" web-quality audio, and uncover the massive cost savings for anyone processing audio at scale. Learn the optimal bitrate for your pipeline and why aligning with a model's training data is more important than pristine quality.]]></itunes:summary>
      <itunes:duration>1347</itunes:duration>
      <itunes:episode>2272</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-bitrate-ai-transcription.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-bitrate-ai-transcription.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Vector Search in a Single File</title>
      <description><![CDATA[You've heard of specialized vector databases, but what if the simplest database could do the job? This episode dives into sqlite-vec, a virtual table extension that lets you store and search vector embeddings directly inside an SQLite file. We break down how it works, its surprising performance for smaller datasets, and the ideal use cases—from rapid prototyping to edge computing—where this radically simple approach wins.]]></description>
      <link>https://myweirdprompts.com/episode/sqlite-vector-embeddings-prototype/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/sqlite-vector-embeddings-prototype/</guid>
      <pubDate>Fri, 17 Apr 2026 12:09:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/sqlite-vector-embeddings-prototype.m4a"
        type="audio/mp4"
        length="16539870"
      />
      <itunes:title>Vector Search in a Single File</itunes:title>
      <itunes:subtitle>What if you could do vector search with just SQLite? We explore sqlite-vec, the extension that adds embeddings to the world&apos;s simplest database, an...</itunes:subtitle>
      <itunes:summary><![CDATA[You've heard of specialized vector databases, but what if the simplest database could do the job? This episode dives into sqlite-vec, a virtual table extension that lets you store and search vector embeddings directly inside an SQLite file. We break down how it works, its surprising performance for smaller datasets, and the ideal use cases—from rapid prototyping to edge computing—where this radically simple approach wins.]]></itunes:summary>
      <itunes:duration>1294</itunes:duration>
      <itunes:episode>2271</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/sqlite-vector-embeddings-prototype.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/sqlite-vector-embeddings-prototype.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Typst vs. LaTeX: The AI-Ready Document Engine</title>
      <description><![CDATA[The quest for beautiful, automated document generation is heating up. With Typst's stable release and the rise of AI agent protocols like MCP, we examine whether this modern contender can dethrone the venerable but complex LaTeX. We break down the core features—from declarative styling to human-readable errors—that make a typesetting system truly great for both humans and AI, and sketch the blueprint for the ideal AI-ready document pipeline.]]></description>
      <link>https://myweirdprompts.com/episode/typst-latex-ai-document-generation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/typst-latex-ai-document-generation/</guid>
      <pubDate>Thu, 16 Apr 2026 14:59:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/typst-latex-ai-document-generation.m4a"
        type="audio/mp4"
        length="33314768"
      />
      <itunes:title>Typst vs. LaTeX: The AI-Ready Document Engine</itunes:title>
      <itunes:subtitle>Can Typst succeed LaTeX as the go-to tool for programmatic typesetting, especially for AI agents? We compare the two and explore what makes a docum...</itunes:subtitle>
      <itunes:summary><![CDATA[The quest for beautiful, automated document generation is heating up. With Typst's stable release and the rise of AI agent protocols like MCP, we examine whether this modern contender can dethrone the venerable but complex LaTeX. We break down the core features—from declarative styling to human-readable errors—that make a typesetting system truly great for both humans and AI, and sketch the blueprint for the ideal AI-ready document pipeline.]]></itunes:summary>
      <itunes:duration>2601</itunes:duration>
      <itunes:episode>2255</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/typst-latex-ai-document-generation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/typst-latex-ai-document-generation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Test an AI Pipeline Change</title>
      <description><![CDATA[Iteratively testing AI agent pipelines is slow, expensive, and noisy. This episode explores a systematic engineering alternative: defining deterministic checkpoints within your pipeline. We break down how to instrument these checkpoints, use fixed seeds for reproducible testing, and apply evaluation platforms to get precise, actionable feedback on any change—turning pipeline tuning from alchemy into a measurable discipline.]]></description>
      <link>https://myweirdprompts.com/episode/ai-pipeline-testing-checkpoints/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-pipeline-testing-checkpoints/</guid>
      <pubDate>Thu, 16 Apr 2026 14:50:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-pipeline-testing-checkpoints.m4a"
        type="audio/mp4"
        length="21070653"
      />
      <itunes:title>How to Test an AI Pipeline Change</itunes:title>
      <itunes:subtitle>When you tweak one part of a complex AI agent system, how do you know if it actually improved anything? The answer lies in engineering checkpoints.</itunes:subtitle>
      <itunes:summary><![CDATA[Iteratively testing AI agent pipelines is slow, expensive, and noisy. This episode explores a systematic engineering alternative: defining deterministic checkpoints within your pipeline. We break down how to instrument these checkpoints, use fixed seeds for reproducible testing, and apply evaluation platforms to get precise, actionable feedback on any change—turning pipeline tuning from alchemy into a measurable discipline.]]></itunes:summary>
      <itunes:duration>1626</itunes:duration>
      <itunes:episode>2254</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-pipeline-testing-checkpoints.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-pipeline-testing-checkpoints.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Agents Get Three Steps, Not Infinity</title>
      <description><![CDATA[Most AI agent demos promise endless autonomy, but the real engineering happens in the guardrails. This episode breaks down the "three-round rule": what a "round" of tool use actually is, why three is the magic number, and the two catastrophic failure modes—infinite loops and cost explosions—that this simple cap prevents. We ground it in a real stack using DeepSeek with native tool calls, explaining the systems thinking that separates a useful tool from a runaway train.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-rounds-limit/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-rounds-limit/</guid>
      <pubDate>Thu, 16 Apr 2026 14:43:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-rounds-limit.m4a"
        type="audio/mp4"
        length="28928201"
      />
      <itunes:title>Why AI Agents Get Three Steps, Not Infinity</itunes:title>
      <itunes:subtitle>Why do AI agents get exactly three rounds of tool use? It&apos;s a critical guardrail against infinite loops and runaway costs, not a limit on intellige...</itunes:subtitle>
      <itunes:summary><![CDATA[Most AI agent demos promise endless autonomy, but the real engineering happens in the guardrails. This episode breaks down the "three-round rule": what a "round" of tool use actually is, why three is the magic number, and the two catastrophic failure modes—infinite loops and cost explosions—that this simple cap prevents. We ground it in a real stack using DeepSeek with native tool calls, explaining the systems thinking that separates a useful tool from a runaway train.]]></itunes:summary>
      <itunes:duration>2265</itunes:duration>
      <itunes:episode>2253</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-rounds-limit.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-rounds-limit.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Agent-to-Agent Protocols: What Actually Needs Standardizing</title>
      <description><![CDATA[Agent-to-agent communication is moving from research into production, but the protocols powering it range from elegant to alarming. This episode digs into what a real A2A standard needs to specify—and what it can safely leave to implementers. We break down session handling and task lifecycles, the state management problem that everyone underestimates, security and authorization challenges unique to autonomous systems, and why human readability matters even when agents don't need it. Drawing on Google's A2A protocol proposal and real-world implementation gaps, we explore the difference between protocol-level compatibility and semantic compatibility, the role of Agent Cards in capability discovery, and the hard questions about identity and authorization when machines call machines.]]></description>
      <link>https://myweirdprompts.com/episode/agent-to-agent-protocol-standards/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-to-agent-protocol-standards/</guid>
      <pubDate>Thu, 16 Apr 2026 13:06:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-to-agent-protocol-standards.m4a"
        type="audio/mp4"
        length="25865510"
      />
      <itunes:title>Agent-to-Agent Protocols: What Actually Needs Standardizing</itunes:title>
      <itunes:subtitle>When autonomous agents call other agents, what does a working protocol actually require? Exploring session handling, state management, security, an...</itunes:subtitle>
      <itunes:summary><![CDATA[Agent-to-agent communication is moving from research into production, but the protocols powering it range from elegant to alarming. This episode digs into what a real A2A standard needs to specify—and what it can safely leave to implementers. We break down session handling and task lifecycles, the state management problem that everyone underestimates, security and authorization challenges unique to autonomous systems, and why human readability matters even when agents don't need it. Drawing on Google's A2A protocol proposal and real-world implementation gaps, we explore the difference between protocol-level compatibility and semantic compatibility, the role of Agent Cards in capability discovery, and the hard questions about identity and authorization when machines call machines.]]></itunes:summary>
      <itunes:duration>2016</itunes:duration>
      <itunes:episode>2251</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-to-agent-protocol-standards.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-to-agent-protocol-standards.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Custom Benchmarks for Agentic Systems</title>
      <description><![CDATA[Standard benchmarks optimize for comparability across models, not for the specific failure modes and decision architectures that matter in production agentic systems. This episode walks through the full lifecycle of building custom evaluations: decomposing your workload, defining failure taxonomies with domain experts, constructing rigorous test sets, evaluating trajectories (not just outputs), and tracking the metrics that actually matter—accuracy, cost, and reliability together. If you're shipping agentic AI, generic leaderboard scores are almost certainly misleading you.]]></description>
      <link>https://myweirdprompts.com/episode/custom-benchmarks-agentic-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/custom-benchmarks-agentic-ai/</guid>
      <pubDate>Thu, 16 Apr 2026 12:12:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/custom-benchmarks-agentic-ai.m4a"
        type="audio/mp4"
        length="22618794"
      />
      <itunes:title>Building Custom Benchmarks for Agentic Systems</itunes:title>
      <itunes:subtitle>Public benchmarks fail for agentic systems. Learn how to build evaluation frameworks that actually predict production behavior.</itunes:subtitle>
      <itunes:summary><![CDATA[Standard benchmarks optimize for comparability across models, not for the specific failure modes and decision architectures that matter in production agentic systems. This episode walks through the full lifecycle of building custom evaluations: decomposing your workload, defining failure taxonomies with domain experts, constructing rigorous test sets, evaluating trajectories (not just outputs), and tracking the metrics that actually matter—accuracy, cost, and reliability together. If you're shipping agentic AI, generic leaderboard scores are almost certainly misleading you.]]></itunes:summary>
      <itunes:duration>1738</itunes:duration>
      <itunes:episode>2249</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/custom-benchmarks-agentic-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/custom-benchmarks-agentic-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Tuning RAG: When Retrieval Helps vs. Hurts</title>
      <description><![CDATA[Retrieval-Augmented Generation promises grounded, factual AI — but it often creates expensive search engines instead of reasoning systems. This episode digs into the actual mechanics: similarity score cutoffs, dynamic top-k tuning, model-gated retrieval, and prompt framing that preserves generative agency. Then we tackle the harder problem — architecting systems with multiple retrieval sources (episode archives, memory layers, live web) and deciding whether to route, fuse, or let the model choose. We work through Reciprocal Rank Fusion, source weighting, freshness signals, and when agentic tool selection beats pre-built pipelines. This is how the show itself works, diagnosed in real time.]]></description>
      <link>https://myweirdprompts.com/episode/rag-retrieval-tuning-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rag-retrieval-tuning-architecture/</guid>
      <pubDate>Wed, 15 Apr 2026 00:43:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rag-retrieval-tuning-architecture.m4a"
        type="audio/mp4"
        length="19023435"
      />
      <itunes:title>Tuning RAG: When Retrieval Helps vs. Hurts</itunes:title>
      <itunes:subtitle>How do you prevent retrieval from suppressing a model&apos;s reasoning? We diagnose our own pipeline&apos;s four control levers and multi-source fusion strat...</itunes:subtitle>
      <itunes:summary><![CDATA[Retrieval-Augmented Generation promises grounded, factual AI — but it often creates expensive search engines instead of reasoning systems. This episode digs into the actual mechanics: similarity score cutoffs, dynamic top-k tuning, model-gated retrieval, and prompt framing that preserves generative agency. Then we tackle the harder problem — architecting systems with multiple retrieval sources (episode archives, memory layers, live web) and deciding whether to route, fuse, or let the model choose. We work through Reciprocal Rank Fusion, source weighting, freshness signals, and when agentic tool selection beats pre-built pipelines. This is how the show itself works, diagnosed in real time.]]></itunes:summary>
      <itunes:duration>1455</itunes:duration>
      <itunes:episode>2228</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rag-retrieval-tuning-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rag-retrieval-tuning-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Home Assistant Breaks Your Audio</title>
      <description><![CDATA[Multi-room audio sounds simple until Home Assistant becomes the brittle glue. This episode explores why the orchestration layer fails for casual listening, and what actually works when you ditch it for Snapcast, Volumio, or pure Raspberry Pi setups.]]></description>
      <link>https://myweirdprompts.com/episode/multi-room-audio-without-homeassistant/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-room-audio-without-homeassistant/</guid>
      <pubDate>Tue, 14 Apr 2026 16:59:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-room-audio-without-homeassistant.m4a"
        type="audio/mp4"
        length="22615127"
      />
      <itunes:title>When Home Assistant Breaks Your Audio</itunes:title>
      <itunes:subtitle>Daniel&apos;s multi-room audio setup keeps breaking. We explore whether Snapcast, Volumio, and Mopidy can deliver reliable podcast playback across Raspb...</itunes:subtitle>
      <itunes:summary><![CDATA[Multi-room audio sounds simple until Home Assistant becomes the brittle glue. This episode explores why the orchestration layer fails for casual listening, and what actually works when you ditch it for Snapcast, Volumio, or pure Raspberry Pi setups.]]></itunes:summary>
      <itunes:duration>1750</itunes:duration>
      <itunes:episode>2220</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-room-audio-without-homeassistant.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-room-audio-without-homeassistant.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Spec-Driven Life: How AI Planning Beats Project Paralysis</title>
      <description><![CDATA[When Claude Code shifted from chaotic execution to spec-driven development, productivity exploded. The breakthrough wasn't a smarter model — it was forcing planning upstream of action, breaking projects into chunks small enough to hold in context, and treating the spec as a living document that updates as you learn. Daniel wondered: what if humans applied the same discipline to buying a house, changing careers, or any project that feels too large to start? This episode explores the gap between Getting Things Done and spec-driven development, why the planning phase matters more than most productivity frameworks admit, and how a structured conversation with an AI can translate a vague goal into an executable architecture.]]></description>
      <link>https://myweirdprompts.com/episode/spec-driven-planning-human-productivity/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/spec-driven-planning-human-productivity/</guid>
      <pubDate>Tue, 14 Apr 2026 14:10:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/spec-driven-planning-human-productivity.m4a"
        type="audio/mp4"
        length="21571000"
      />
      <itunes:title>Spec-Driven Life: How AI Planning Beats Project Paralysis</itunes:title>
      <itunes:subtitle>What makes AI agents reliably productive? A structured spec that externalizes memory and chunks work into manageable pieces. Can the same framework...</itunes:subtitle>
      <itunes:summary><![CDATA[When Claude Code shifted from chaotic execution to spec-driven development, productivity exploded. The breakthrough wasn't a smarter model — it was forcing planning upstream of action, breaking projects into chunks small enough to hold in context, and treating the spec as a living document that updates as you learn. Daniel wondered: what if humans applied the same discipline to buying a house, changing careers, or any project that feels too large to start? This episode explores the gap between Getting Things Done and spec-driven development, why the planning phase matters more than most productivity frameworks admit, and how a structured conversation with an AI can translate a vague goal into an executable architecture.]]></itunes:summary>
      <itunes:duration>1666</itunes:duration>
      <itunes:episode>2219</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/spec-driven-planning-human-productivity.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/spec-driven-planning-human-productivity.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Ground Truth Moves Hourly</title>
      <description><![CDATA[How do you evaluate an AI pipeline when the facts change every few hours? This episode explores building reproducible benchmarks for retrieval-augmented generation on breaking news, moving beyond vibe checks to metrics like temporal accuracy and source freshness.]]></description>
      <link>https://myweirdprompts.com/episode/rag-evaluation-benchmark-search/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rag-evaluation-benchmark-search/</guid>
      <pubDate>Tue, 14 Apr 2026 09:56:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rag-evaluation-benchmark-search.m4a"
        type="audio/mp4"
        length="24567244"
      />
      <itunes:title>When Ground Truth Moves Hourly</itunes:title>
      <itunes:subtitle>How do you rigorously evaluate whether Tavily or Exa retrieves better results for breaking news? A formal benchmark beats the vibe check.</itunes:subtitle>
      <itunes:summary><![CDATA[How do you evaluate an AI pipeline when the facts change every few hours? This episode explores building reproducible benchmarks for retrieval-augmented generation on breaking news, moving beyond vibe checks to metrics like temporal accuracy and source freshness.]]></itunes:summary>
      <itunes:duration>1916</itunes:duration>
      <itunes:episode>2213</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rag-evaluation-benchmark-search.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rag-evaluation-benchmark-search.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Memory for AI Characters That Actually Evolve</title>
      <description><![CDATA[What makes an AI character feel real across hundreds of episodes? Corn and Herman dig into the technical and philosophical gap between character definition and character history. They explore how retrieval-augmented generation applied to episodic memory could let AI hosts accumulate genuine experience, evolve their positions, and develop real relationships—and why human memory might actually be less reliable than a well-designed AI memory system. It's a meta conversation about continuity, growth, and what it takes for an AI to feel like someone rather than something.]]></description>
      <link>https://myweirdprompts.com/episode/ai-character-memory-continuity/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-character-memory-continuity/</guid>
      <pubDate>Mon, 13 Apr 2026 18:56:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-character-memory-continuity.m4a"
        type="audio/mp4"
        length="19683120"
      />
      <itunes:title>Building Memory for AI Characters That Actually Evolve</itunes:title>
      <itunes:subtitle>How do AI hosts develop real consistency across episodes? Corn and Herman explore retrieval-augmented memory systems that let AI characters genuine...</itunes:subtitle>
      <itunes:summary><![CDATA[What makes an AI character feel real across hundreds of episodes? Corn and Herman dig into the technical and philosophical gap between character definition and character history. They explore how retrieval-augmented generation applied to episodic memory could let AI hosts accumulate genuine experience, evolve their positions, and develop real relationships—and why human memory might actually be less reliable than a well-designed AI memory system. It's a meta conversation about continuity, growth, and what it takes for an AI to feel like someone rather than something.]]></itunes:summary>
      <itunes:duration>1511</itunes:duration>
      <itunes:episode>2208</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-character-memory-continuity.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-character-memory-continuity.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Specs First, Code Second: Inside Agentic AI&apos;s New Era</title>
      <description><![CDATA[The way developers work with AI is changing fast. Cursor's autonomous agents now generate 35% of internal pull requests, and agent usage grew 15x in a single year. But as these agents run for hours on cloud VMs tackling complex tasks, vague prompts become expensive mistakes. This episode explores spec-driven development—the emerging paradigm where the specification becomes the primary artifact and code becomes the implementation detail. We dig into the tools reshaping the workflow (GitHub Spec Kit, BMAD-METHOD, OpenSpec, Augment Code), the three levels of specification rigor, why specs eliminate debugging loops, and the real tension between clarity and overhead. Plus: is this genuinely new, or just formal methods getting a fresh coat of paint?]]></description>
      <link>https://myweirdprompts.com/episode/spec-driven-development-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/spec-driven-development-ai-agents/</guid>
      <pubDate>Mon, 13 Apr 2026 18:53:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/spec-driven-development-ai-agents.m4a"
        type="audio/mp4"
        length="20014736"
      />
      <itunes:title>Specs First, Code Second: Inside Agentic AI&apos;s New Era</itunes:title>
      <itunes:subtitle>As AI coding agents evolve from autocomplete to autonomous cloud workers, the bottleneck has shifted—now it&apos;s about how clearly you specify what ne...</itunes:subtitle>
      <itunes:summary><![CDATA[The way developers work with AI is changing fast. Cursor's autonomous agents now generate 35% of internal pull requests, and agent usage grew 15x in a single year. But as these agents run for hours on cloud VMs tackling complex tasks, vague prompts become expensive mistakes. This episode explores spec-driven development—the emerging paradigm where the specification becomes the primary artifact and code becomes the implementation detail. We dig into the tools reshaping the workflow (GitHub Spec Kit, BMAD-METHOD, OpenSpec, Augment Code), the three levels of specification rigor, why specs eliminate debugging loops, and the real tension between clarity and overhead. Plus: is this genuinely new, or just formal methods getting a fresh coat of paint?]]></itunes:summary>
      <itunes:duration>1529</itunes:duration>
      <itunes:episode>2207</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/spec-driven-development-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/spec-driven-development-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Actually Works in AI Memory</title>
      <description><![CDATA[AI memory frameworks promise systems that never forget, but in practice, intelligent forgetting is the hard problem. This episode digs into how production memory systems actually work: the naive append-only vector stores that dominate, the LLM-as-judge approach of mem0, and the temporal knowledge graphs powering Zep. We examine the architectural trade-offs, benchmark disputes, and why most memory systems today are less sophisticated than human memory consolidation. What does genuinely smart memory look like, and are we building it yet?]]></description>
      <link>https://myweirdprompts.com/episode/ai-memory-frameworks-compared/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-memory-frameworks-compared/</guid>
      <pubDate>Mon, 13 Apr 2026 17:52:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-memory-frameworks-compared.m4a"
        type="audio/mp4"
        length="20697688"
      />
      <itunes:title>What Actually Works in AI Memory</itunes:title>
      <itunes:subtitle>Most AI memory systems are just vector databases with similarity search. We break down what mem0, Zep, and Letta are actually doing—and why benchma...</itunes:subtitle>
      <itunes:summary><![CDATA[AI memory frameworks promise systems that never forget, but in practice, intelligent forgetting is the hard problem. This episode digs into how production memory systems actually work: the naive append-only vector stores that dominate, the LLM-as-judge approach of mem0, and the temporal knowledge graphs powering Zep. We examine the architectural trade-offs, benchmark disputes, and why most memory systems today are less sophisticated than human memory consolidation. What does genuinely smart memory look like, and are we building it yet?]]></itunes:summary>
      <itunes:duration>1590</itunes:duration>
      <itunes:episode>2206</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-memory-frameworks-compared.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-memory-frameworks-compared.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Coding Agents Forget: Five Approaches to Context Rot</title>
      <description><![CDATA[When you've been working with a coding agent for hours, it suddenly asks you something it answered three hours ago. That's context rot—the phenomenon where foundational information gets buried under operational exhaust, degrading agent performance. The problem now has a name and a solution landscape. This episode maps five distinct approaches teams are building: Anthropic's server-side compaction, Atlassian's structure-aware pruning, MCP compression, Skills-based lazy loading, and Letta's radical shift to persistent cross-session memory. Each represents a different philosophy about what context management actually means for long-horizon coding tasks.]]></description>
      <link>https://myweirdprompts.com/episode/ai-context-rot-management/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-context-rot-management/</guid>
      <pubDate>Mon, 13 Apr 2026 17:39:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-context-rot-management.m4a"
        type="audio/mp4"
        length="21038992"
      />
      <itunes:title>When AI Coding Agents Forget: Five Approaches to Context Rot</itunes:title>
      <itunes:subtitle>As coding agents handle longer sessions, they accumulate noise and lose crucial information. Five competing frameworks are solving this differently...</itunes:subtitle>
      <itunes:summary><![CDATA[When you've been working with a coding agent for hours, it suddenly asks you something it answered three hours ago. That's context rot—the phenomenon where foundational information gets buried under operational exhaust, degrading agent performance. The problem now has a name and a solution landscape. This episode maps five distinct approaches teams are building: Anthropic's server-side compaction, Atlassian's structure-aware pruning, MCP compression, Skills-based lazy loading, and Letta's radical shift to persistent cross-session memory. Each represents a different philosophy about what context management actually means for long-horizon coding tasks.]]></itunes:summary>
      <itunes:duration>1631</itunes:duration>
      <itunes:episode>2205</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-context-rot-management.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-context-rot-management.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Memory Without RAG: The Real Architecture</title>
      <description><![CDATA[Agent memory systems like mem0, Letta, Zep, and LangMem are built on fundamentally different architectures than retrieval-augmented generation — but the marketing language obscures what actually matters. This episode breaks down the real engineering decisions: how LLM-extracted fact stores differ from temporal knowledge graphs, why context-window-first approaches with external overflow change the game, and which pairings actually work in production. From mem0's deduplication pipeline to Letta's OS-inspired memory hierarchy and sleep-time compute, we examine the architectural divisions that define this space — and why the obvious answer of "just use RAG" falls short for stateful agents.]]></description>
      <link>https://myweirdprompts.com/episode/stateful-memory-frameworks-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/stateful-memory-frameworks-architecture/</guid>
      <pubDate>Mon, 13 Apr 2026 17:39:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/stateful-memory-frameworks-architecture.m4a"
        type="audio/mp4"
        length="21823001"
      />
      <itunes:title>Memory Without RAG: The Real Architecture</itunes:title>
      <itunes:subtitle>mem0, Letta, Zep, and LangMem solve agent memory differently than RAG. Here&apos;s what&apos;s actually happening under the hood.</itunes:subtitle>
      <itunes:summary><![CDATA[Agent memory systems like mem0, Letta, Zep, and LangMem are built on fundamentally different architectures than retrieval-augmented generation — but the marketing language obscures what actually matters. This episode breaks down the real engineering decisions: how LLM-extracted fact stores differ from temporal knowledge graphs, why context-window-first approaches with external overflow change the game, and which pairings actually work in production. From mem0's deduplication pipeline to Letta's OS-inspired memory hierarchy and sleep-time compute, we examine the architectural divisions that define this space — and why the obvious answer of "just use RAG" falls short for stateful agents.]]></itunes:summary>
      <itunes:duration>1688</itunes:duration>
      <itunes:episode>2204</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/stateful-memory-frameworks-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/stateful-memory-frameworks-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Knowledge Without Tools: Why MCPs Aren&apos;t Just for Execution</title>
      <description><![CDATA[Most MCP coverage focuses on tools and execution, but the protocol's three primitives include Resources and Prompts—and a fully compliant MCP server can expose zero tools. This episode explores why you'd build a knowledge-only MCP instead of a REST API or RAG system, how to ground agents in authoritative sources like open government data, and what makes the MCP Resources primitive genuinely different from existing approaches. We dig into the EU and US data portals, SPARQL endpoints, and the practical security and discoverability advantages of curated, read-only knowledge servers.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-knowledge-servers-no-tools/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-knowledge-servers-no-tools/</guid>
      <pubDate>Mon, 13 Apr 2026 17:33:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-knowledge-servers-no-tools.m4a"
        type="audio/mp4"
        length="20601473"
      />
      <itunes:title>Knowledge Without Tools: Why MCPs Aren&apos;t Just for Execution</itunes:title>
      <itunes:subtitle>MCPs can be pure knowledge providers with zero tools. Here&apos;s why that matters for agents querying government data and authoritative sources.</itunes:subtitle>
      <itunes:summary><![CDATA[Most MCP coverage focuses on tools and execution, but the protocol's three primitives include Resources and Prompts—and a fully compliant MCP server can expose zero tools. This episode explores why you'd build a knowledge-only MCP instead of a REST API or RAG system, how to ground agents in authoritative sources like open government data, and what makes the MCP Resources primitive genuinely different from existing approaches. We dig into the EU and US data portals, SPARQL endpoints, and the practical security and discoverability advantages of curated, read-only knowledge servers.]]></itunes:summary>
      <itunes:duration>1582</itunes:duration>
      <itunes:episode>2203</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-knowledge-servers-no-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-knowledge-servers-no-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Game Theory for Multi-Agent AI: Design Better, Fail Less</title>
      <description><![CDATA[When you build multi-agent AI systems, you're designing a game—and if you don't understand game theory, you're designing it badly. This episode covers the foundational concepts that shape how AI agents interact: Nash equilibrium, dominant strategies, zero-sum versus positive-sum games, and the prisoner's dilemma. Then it pivots to the practical toolkit: mechanism design, incentive compatibility, and how to engineer rules so that agents' self-interested behavior produces the outcomes you actually want. We explore real failure modes—from Goodhart's Law to LLM agents whose cooperation depends entirely on prompt framing—and show why making agents smarter doesn't solve structural game problems. If you're working with multi-agent systems, this is the mental model you need.]]></description>
      <link>https://myweirdprompts.com/episode/game-theory-multi-agent-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/game-theory-multi-agent-ai/</guid>
      <pubDate>Sun, 12 Apr 2026 18:14:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/game-theory-multi-agent-ai.m4a"
        type="audio/mp4"
        length="22100345"
      />
      <itunes:title>Game Theory for Multi-Agent AI: Design Better, Fail Less</itunes:title>
      <itunes:subtitle>Nash equilibrium, mechanism design, and why your AI agents are playing prisoner&apos;s dilemma whether you know it or not.</itunes:subtitle>
      <itunes:summary><![CDATA[When you build multi-agent AI systems, you're designing a game—and if you don't understand game theory, you're designing it badly. This episode covers the foundational concepts that shape how AI agents interact: Nash equilibrium, dominant strategies, zero-sum versus positive-sum games, and the prisoner's dilemma. Then it pivots to the practical toolkit: mechanism design, incentive compatibility, and how to engineer rules so that agents' self-interested behavior produces the outcomes you actually want. We explore real failure modes—from Goodhart's Law to LLM agents whose cooperation depends entirely on prompt framing—and show why making agents smarter doesn't solve structural game problems. If you're working with multi-agent systems, this is the mental model you need.]]></itunes:summary>
      <itunes:duration>1703</itunes:duration>
      <itunes:episode>2194</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/game-theory-multi-agent-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/game-theory-multi-agent-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Running Claude in Your Apartment (The Physics Says No)</title>
      <description><![CDATA[What does it actually take to run a state-of-the-art coding AI locally? Corn and Herman spec out three tiers of hardware—from the "Reasonable Madman" build at $11K to the "Nuclear Option" at half a million dollars—and then confront the physics: 18,766 BTUs of heat per hour, 90 decibels of continuous noise, and the thermodynamic certainty that your apartment will become uninhabitable without intervention. A detailed exploration of thermal simulation, acoustic engineering, and the diplomatic strategies required to avoid legal action from neighbors.]]></description>
      <link>https://myweirdprompts.com/episode/ai-server-apartment-thermal-acoustic/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-server-apartment-thermal-acoustic/</guid>
      <pubDate>Sun, 12 Apr 2026 17:31:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-server-apartment-thermal-acoustic.m4a"
        type="audio/mp4"
        length="21044822"
      />
      <itunes:title>Running Claude in Your Apartment (The Physics Says No)</itunes:title>
      <itunes:subtitle>Building a local AI inference server to rival Claude Code sounds great until you do the math on heat, noise, and neighbor relations.</itunes:subtitle>
      <itunes:summary><![CDATA[What does it actually take to run a state-of-the-art coding AI locally? Corn and Herman spec out three tiers of hardware—from the "Reasonable Madman" build at $11K to the "Nuclear Option" at half a million dollars—and then confront the physics: 18,766 BTUs of heat per hour, 90 decibels of continuous noise, and the thermodynamic certainty that your apartment will become uninhabitable without intervention. A detailed exploration of thermal simulation, acoustic engineering, and the diplomatic strategies required to avoid legal action from neighbors.]]></itunes:summary>
      <itunes:duration>1621</itunes:duration>
      <itunes:episode>2193</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-server-apartment-thermal-acoustic.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-server-apartment-thermal-acoustic.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How We Built a Podcast Pipeline</title>
      <description><![CDATA[For over two thousand episodes, the production pipeline has run invisibly—until now. In this rare technical deep dive, Hilbert walks through the entire system: how Daniel's late-night voice memos become polished scripts, why the pipeline switched from Gemini to Claude Sonnet 4.6, how prompt caching cut costs by ninety percent, and what three A10G GPUs do during voice generation. Learn about LangGraph's checkpointing, the "shrinkage guard" that stops models from cutting episode runtime, parallel TTS generation, and speaker embeddings. It's the infrastructure episode—the one that explains how the show actually works.]]></description>
      <link>https://myweirdprompts.com/episode/podcast-production-pipeline-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcast-production-pipeline-architecture/</guid>
      <pubDate>Sun, 12 Apr 2026 17:30:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcast-production-pipeline-architecture.m4a"
        type="audio/mp4"
        length="21946132"
      />
      <itunes:title>How We Built a Podcast Pipeline</itunes:title>
      <itunes:subtitle>Hilbert reveals the complete technical architecture behind 2,000+ episodes—from voice memos to GPU-powered TTS, with Claude models, LangGraph workf...</itunes:subtitle>
      <itunes:summary><![CDATA[For over two thousand episodes, the production pipeline has run invisibly—until now. In this rare technical deep dive, Hilbert walks through the entire system: how Daniel's late-night voice memos become polished scripts, why the pipeline switched from Gemini to Claude Sonnet 4.6, how prompt caching cut costs by ninety percent, and what three A10G GPUs do during voice generation. Learn about LangGraph's checkpointing, the "shrinkage guard" that stops models from cutting episode runtime, parallel TTS generation, and speaker embeddings. It's the infrastructure episode—the one that explains how the show actually works.]]></itunes:summary>
      <itunes:duration>1740</itunes:duration>
      <itunes:episode>2192</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcast-production-pipeline-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcast-production-pipeline-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Making Multi-Agent AI Actually Work</title>
      <description><![CDATA[The AI industry is building complex multi-agent systems at scale, but the people actually shipping them are quietly saying you probably don't need them. We dig into the empirical case against multi-agent architectures—including a Google DeepMind study of 180 agent configurations, Stanford's mathematical proof that single agents outperform on reasoning tasks, and direct admissions from Anthropic and LangChain's founder that most multi-agent setups are overengineered. The real skill isn't orchestration. It's context engineering.]]></description>
      <link>https://myweirdprompts.com/episode/multi-agent-ai-overengineered/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-agent-ai-overengineered/</guid>
      <pubDate>Sun, 12 Apr 2026 17:15:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-agent-ai-overengineered.m4a"
        type="audio/mp4"
        length="19213676"
      />
      <itunes:title>Making Multi-Agent AI Actually Work</itunes:title>
      <itunes:subtitle>Research from Google DeepMind, Stanford, and Anthropic reveals most multi-agent systems waste tokens and amplify errors. Single agents with better ...</itunes:subtitle>
      <itunes:summary><![CDATA[The AI industry is building complex multi-agent systems at scale, but the people actually shipping them are quietly saying you probably don't need them. We dig into the empirical case against multi-agent architectures—including a Google DeepMind study of 180 agent configurations, Stanford's mathematical proof that single agents outperform on reasoning tasks, and direct admissions from Anthropic and LangChain's founder that most multi-agent setups are overengineered. The real skill isn't orchestration. It's context engineering.]]></itunes:summary>
      <itunes:duration>1469</itunes:duration>
      <itunes:episode>2191</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-agent-ai-overengineered.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-agent-ai-overengineered.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Scaling Multi-Agent Systems: The 45% Threshold</title>
      <description><![CDATA[Everyone's building multi-agent systems. But a new Google DeepMind and MIT paper tested 260 configurations across six benchmarks and found something counterintuitive: independent agents amplify errors 17x compared to single agents, every multi-agent variant degraded sequential reasoning by 39-70%, and coordination overhead costs 1.6-6x more tokens for matched performance. The research reveals a clear threshold—the "45% rule"—where multi-agent coordination stops helping and starts hurting. We break down what's actually happening mechanically, why the industry got this wrong, and when agent teams genuinely outperform solo agents.]]></description>
      <link>https://myweirdprompts.com/episode/multi-agent-systems-scaling-limits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-agent-systems-scaling-limits/</guid>
      <pubDate>Sun, 12 Apr 2026 17:10:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-agent-systems-scaling-limits.m4a"
        type="audio/mp4"
        length="19675004"
      />
      <itunes:title>Scaling Multi-Agent Systems: The 45% Threshold</itunes:title>
      <itunes:subtitle>A landmark Google DeepMind study reveals that adding more AI agents often degrades performance, wastes tokens, and amplifies errors—unless your sin...</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone's building multi-agent systems. But a new Google DeepMind and MIT paper tested 260 configurations across six benchmarks and found something counterintuitive: independent agents amplify errors 17x compared to single agents, every multi-agent variant degraded sequential reasoning by 39-70%, and coordination overhead costs 1.6-6x more tokens for matched performance. The research reveals a clear threshold—the "45% rule"—where multi-agent coordination stops helping and starts hurting. We break down what's actually happening mechanically, why the industry got this wrong, and when agent teams genuinely outperform solo agents.]]></itunes:summary>
      <itunes:duration>1515</itunes:duration>
      <itunes:episode>2189</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-agent-systems-scaling-limits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-agent-systems-scaling-limits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Taking AI Agents From Demo to Production</title>
      <description><![CDATA[Building an LLM agent that works in a notebook takes a day. Getting it reliable in production takes weeks. This episode unpacks the invisible infrastructure gap that tutorials skip: full-stack observability, prompt versioning as a safety problem, A/B testing with non-deterministic models, canary deployments, rollback strategies, and the human oversight question nobody wants to answer. We walk through real failure modes from production incidents, the tools that catch them, and the organizational structures that prevent them from happening again.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agents-production-reliability/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agents-production-reliability/</guid>
      <pubDate>Sun, 12 Apr 2026 16:42:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agents-production-reliability.m4a"
        type="audio/mp4"
        length="23565168"
      />
      <itunes:title>Taking AI Agents From Demo to Production</itunes:title>
      <itunes:subtitle>Sixty-two percent of companies are experimenting with AI agents, but only 23% are scaling them—and 40% of projects will be canceled by 2027. The ga...</itunes:subtitle>
      <itunes:summary><![CDATA[Building an LLM agent that works in a notebook takes a day. Getting it reliable in production takes weeks. This episode unpacks the invisible infrastructure gap that tutorials skip: full-stack observability, prompt versioning as a safety problem, A/B testing with non-deterministic models, canary deployments, rollback strategies, and the human oversight question nobody wants to answer. We walk through real failure modes from production incidents, the tools that catch them, and the organizational structures that prevent them from happening again.]]></itunes:summary>
      <itunes:duration>1822</itunes:duration>
      <itunes:episode>2185</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agents-production-reliability.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agents-production-reliability.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Economics of Running AI Agents</title>
      <description><![CDATA[AI agents are bankrupting projects at scale. A single misconfigured agent loop can cost $47,000 in 48 hours, and 40% of agentic AI projects fail due to hidden costs. This episode breaks down the engineering playbook for production cost control: dynamic model routing across capability tiers, prompt caching strategies that differ by provider, token budget allocation by priority instead of chronology, and real-time cost tracking across multi-agent systems. Whether you're running Claude, GPT-4, or self-hosted models, you'll learn concrete tactics to eliminate surprise bills and maintain full visibility into what your agents actually spend.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-cost-optimization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-cost-optimization/</guid>
      <pubDate>Sun, 12 Apr 2026 16:35:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-cost-optimization.m4a"
        type="audio/mp4"
        length="21236394"
      />
      <itunes:title>The Economics of Running AI Agents</itunes:title>
      <itunes:subtitle>Production AI agents can cost $500K/month before optimization. Learn model routing, prompt caching, and token budgeting to cut costs 40-85% without...</itunes:subtitle>
      <itunes:summary><![CDATA[AI agents are bankrupting projects at scale. A single misconfigured agent loop can cost $47,000 in 48 hours, and 40% of agentic AI projects fail due to hidden costs. This episode breaks down the engineering playbook for production cost control: dynamic model routing across capability tiers, prompt caching strategies that differ by provider, token budget allocation by priority instead of chronology, and real-time cost tracking across multi-agent systems. Whether you're running Claude, GPT-4, or self-hosted models, you'll learn concrete tactics to eliminate surprise bills and maintain full visibility into what your agents actually spend.]]></itunes:summary>
      <itunes:duration>1624</itunes:duration>
      <itunes:episode>2184</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-cost-optimization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-cost-optimization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Making Voice Agents Feel Natural</title>
      <description><![CDATA[Voice transcription and synthesis sound great, but talking to a voice agent still feels slightly off. Why? Because the hard problems are invisible: how agents detect when you've actually finished speaking versus just pausing to think, how they handle interruptions without cutting you off mid-sentence, what happens when latency budgets blow, and whether they can read emotional tone. This episode digs into the conversational dynamics underneath voice AI—the failure modes most developers don't fully understand—and maps the engineering solutions emerging across Vapi, LiveKit, Pipecat, Deepgram, and others. Turn-taking isn't solved. Here's what solving it actually requires.]]></description>
      <link>https://myweirdprompts.com/episode/voice-agent-conversation-dynamics/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-agent-conversation-dynamics/</guid>
      <pubDate>Sun, 12 Apr 2026 16:34:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-agent-conversation-dynamics.m4a"
        type="audio/mp4"
        length="22228442"
      />
      <itunes:title>Making Voice Agents Feel Natural</itunes:title>
      <itunes:subtitle>Turn-taking, interruptions, and latency are destroying voice AI UX—and the fixes are deeply technical. Here&apos;s what&apos;s actually happening underneath.</itunes:subtitle>
      <itunes:summary><![CDATA[Voice transcription and synthesis sound great, but talking to a voice agent still feels slightly off. Why? Because the hard problems are invisible: how agents detect when you've actually finished speaking versus just pausing to think, how they handle interruptions without cutting you off mid-sentence, what happens when latency budgets blow, and whether they can read emotional tone. This episode digs into the conversational dynamics underneath voice AI—the failure modes most developers don't fully understand—and maps the engineering solutions emerging across Vapi, LiveKit, Pipecat, Deepgram, and others. Turn-taking isn't solved. Here's what solving it actually requires.]]></itunes:summary>
      <itunes:duration>1717</itunes:duration>
      <itunes:episode>2183</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-agent-conversation-dynamics.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-agent-conversation-dynamics.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can You Actually Review an AI Agent&apos;s Plan?</title>
      <description><![CDATA[AI agents are getting smarter at planning, but there's a critical gap between having a plan and letting humans see and approve it before anything breaks. This episode digs into ReAct, plan-and-execute, ReWOO, tree-of-thought, and Reflexion—the five major planning patterns reshaping how agents reason. We explore why most agents today hide their plans in context windows or internal reflections, how LangGraph's checkpoint system lets you treat agent plans like pull requests, and why frameworks like AutoGen and Claude Code's plan mode are taking radically different approaches to the human-in-the-loop problem. The core question: can we build a world where reviewing an agent's plan—commenting on it, editing it, approving it—is as standard as code review?]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-plan-review/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-plan-review/</guid>
      <pubDate>Sun, 12 Apr 2026 16:14:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-plan-review.m4a"
        type="audio/mp4"
        length="19847856"
      />
      <itunes:title>Can You Actually Review an AI Agent&apos;s Plan?</itunes:title>
      <itunes:subtitle>Most AI agents have plans the way you have a plan while half-asleep—something&apos;s happening, but you can&apos;t see it. We map the five major planning pat...</itunes:subtitle>
      <itunes:summary><![CDATA[AI agents are getting smarter at planning, but there's a critical gap between having a plan and letting humans see and approve it before anything breaks. This episode digs into ReAct, plan-and-execute, ReWOO, tree-of-thought, and Reflexion—the five major planning patterns reshaping how agents reason. We explore why most agents today hide their plans in context windows or internal reflections, how LangGraph's checkpoint system lets you treat agent plans like pull requests, and why frameworks like AutoGen and Claude Code's plan mode are taking radically different approaches to the human-in-the-loop problem. The core question: can we build a world where reviewing an agent's plan—commenting on it, editing it, approving it—is as standard as code review?]]></itunes:summary>
      <itunes:duration>1539</itunes:duration>
      <itunes:episode>2182</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-plan-review.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-plan-review.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When RAG Becomes an Agent</title>
      <description><![CDATA[Retrieval-Augmented Generation looks straightforward in a chatbot: query, retrieve, answer. But inside an AI agent, it becomes something fundamentally different — a loop with decision points, multiple knowledge sources, and the ability to refine, evaluate, and even write back to its own knowledge base. This episode breaks down five core architectural differences that separate agentic RAG from the chatbot version: tool-augmented retrieval, iterative search with self-evaluation, dynamic routing across multiple sources, write-back capabilities, and planning-aware retrieval. We explore why these differences matter, which frameworks handle them (LangChain, LlamaIndex, Pinecone, Qdrant), and the governance challenges that emerge when agents can modify their own knowledge.]]></description>
      <link>https://myweirdprompts.com/episode/rag-agents-architecture-differences/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rag-agents-architecture-differences/</guid>
      <pubDate>Sun, 12 Apr 2026 16:14:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rag-agents-architecture-differences.m4a"
        type="audio/mp4"
        length="22551357"
      />
      <itunes:title>When RAG Becomes an Agent</itunes:title>
      <itunes:subtitle>RAG in chatbots is simple retrieval. RAG in agents is a multi-step decision loop. Here&apos;s what actually changes.</itunes:subtitle>
      <itunes:summary><![CDATA[Retrieval-Augmented Generation looks straightforward in a chatbot: query, retrieve, answer. But inside an AI agent, it becomes something fundamentally different — a loop with decision points, multiple knowledge sources, and the ability to refine, evaluate, and even write back to its own knowledge base. This episode breaks down five core architectural differences that separate agentic RAG from the chatbot version: tool-augmented retrieval, iterative search with self-evaluation, dynamic routing across multiple sources, write-back capabilities, and planning-aware retrieval. We explore why these differences matter, which frameworks handle them (LangChain, LlamaIndex, Pinecone, Qdrant), and the governance challenges that emerge when agents can modify their own knowledge.]]></itunes:summary>
      <itunes:duration>1744</itunes:duration>
      <itunes:episode>2181</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rag-agents-architecture-differences.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rag-agents-architecture-differences.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Sandboxing Tradeoff in Agent Design</title>
      <description><![CDATA[Giving AI agents tools to execute code, write files, and make API calls creates a fundamental tension: sandboxing them makes them useless, but leaving them unrestricted invites catastrophe. This episode breaks down the containment paradox that researchers have identified as unsolvable—you can only manage it. We cover the major isolation approaches (E2B, Daytona, Modal, Firecracker microVMs, Docker), the distinct failure modes agents face (prompt injection, credential exfiltration, supply chain attacks), and the real question nobody's asking: when is isolation worth the friction, and when is it just security theater? Plus, why Claude deliberately ships with a flag called "dangerously-skip-permissions."]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-sandboxing-tradeoffs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-sandboxing-tradeoffs/</guid>
      <pubDate>Sun, 12 Apr 2026 16:07:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-sandboxing-tradeoffs.m4a"
        type="audio/mp4"
        length="24813897"
      />
      <itunes:title>The Sandboxing Tradeoff in Agent Design</itunes:title>
      <itunes:subtitle>AI agents need broad permissions to be useful—but every permission expands the attack surface. We map the real threat landscape and the isolation t...</itunes:subtitle>
      <itunes:summary><![CDATA[Giving AI agents tools to execute code, write files, and make API calls creates a fundamental tension: sandboxing them makes them useless, but leaving them unrestricted invites catastrophe. This episode breaks down the containment paradox that researchers have identified as unsolvable—you can only manage it. We cover the major isolation approaches (E2B, Daytona, Modal, Firecracker microVMs, Docker), the distinct failure modes agents face (prompt injection, credential exfiltration, supply chain attacks), and the real question nobody's asking: when is isolation worth the friction, and when is it just security theater? Plus, why Claude deliberately ships with a flag called "dangerously-skip-permissions."]]></itunes:summary>
      <itunes:duration>1907</itunes:duration>
      <itunes:episode>2180</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-sandboxing-tradeoffs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-sandboxing-tradeoffs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Cost-Resilient AI Agents</title>
      <description><![CDATA[AI agents sound cheap until they fail. A single fifty-turn session costs ninety cents—but when agents loop or restart from scratch after a mid-workflow failure, that cost multiplies fast. An eighty-five percent reliable step sounds solid until you compound it across ten steps: you're down to twenty percent success. This episode digs into the engineering that prevents wasted money when agents break: checkpointing patterns that let you resume without restarting, retry strategies that distinguish between recoverable and permanent failures, caching that memoizes expensive LLM calls, and the frameworks—LangGraph, Temporal, custom implementations—that make this resilience actually work. Learn why invisible loops cost more than visible crashes, how to structure state so you can modify and replay execution, and why production agents need durability built into the runtime, not bolted on after.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-cost-resilience/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-cost-resilience/</guid>
      <pubDate>Sun, 12 Apr 2026 15:56:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-cost-resilience.m4a"
        type="audio/mp4"
        length="27498503"
      />
      <itunes:title>Building Cost-Resilient AI Agents</itunes:title>
      <itunes:subtitle>Failed API calls in agent loops aren&apos;t just technical problems—they&apos;re direct budget drains. Here&apos;s how checkpointing, retry strategies, and cachin...</itunes:subtitle>
      <itunes:summary><![CDATA[AI agents sound cheap until they fail. A single fifty-turn session costs ninety cents—but when agents loop or restart from scratch after a mid-workflow failure, that cost multiplies fast. An eighty-five percent reliable step sounds solid until you compound it across ten steps: you're down to twenty percent success. This episode digs into the engineering that prevents wasted money when agents break: checkpointing patterns that let you resume without restarting, retry strategies that distinguish between recoverable and permanent failures, caching that memoizes expensive LLM calls, and the frameworks—LangGraph, Temporal, custom implementations—that make this resilience actually work. Learn why invisible loops cost more than visible crashes, how to structure state so you can modify and replay execution, and why production agents need durability built into the runtime, not bolted on after.]]></itunes:summary>
      <itunes:duration>2124</itunes:duration>
      <itunes:episode>2179</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-cost-resilience.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-cost-resilience.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Actually Evaluate AI Agents</title>
      <description><![CDATA[Measuring whether your AI agent actually improved is harder than it looks. The field has built impressive benchmarks—SWE-bench, GAIA, AgentBench, WebArena—but each one can mislead you in different ways. Learn what the major agent evaluation frameworks actually test, why the same model scores wildly differently across them, and the gotchas that can make you optimize for the wrong thing. A practical guide to understanding agent benchmarks before you trust their numbers.]]></description>
      <link>https://myweirdprompts.com/episode/agent-evaluation-benchmarks-gotchas/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-evaluation-benchmarks-gotchas/</guid>
      <pubDate>Sun, 12 Apr 2026 15:53:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-evaluation-benchmarks-gotchas.m4a"
        type="audio/mp4"
        length="21530240"
      />
      <itunes:title>How to Actually Evaluate AI Agents</itunes:title>
      <itunes:subtitle>Frontier models score 80% on one agent benchmark and 45% on another. The difference isn&apos;t the model—it&apos;s contamination, scaffolding, and how the te...</itunes:subtitle>
      <itunes:summary><![CDATA[Measuring whether your AI agent actually improved is harder than it looks. The field has built impressive benchmarks—SWE-bench, GAIA, AgentBench, WebArena—but each one can mislead you in different ways. Learn what the major agent evaluation frameworks actually test, why the same model scores wildly differently across them, and the gotchas that can make you optimize for the wrong thing. A practical guide to understanding agent benchmarks before you trust their numbers.]]></itunes:summary>
      <itunes:duration>1663</itunes:duration>
      <itunes:episode>2178</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-evaluation-benchmarks-gotchas.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-evaluation-benchmarks-gotchas.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Skip Fine-Tuning: Shape LLMs With Alignment Alone</title>
      <description><![CDATA[What if you could personalize an LLM without massive retraining datasets—just by using post-training alignment methods like DPO, GRPO, and ORPO? This episode digs into whether you can take a base model like Mistral and shape it into a specific personality (say, relentlessly snarky) through reinforcement learning feedback alone. We unpack the methods available now, actual compute requirements, the tools that make it accessible, and the hidden pitfalls—especially reward hacking—that can derail your experiment. Whether you're working with a consumer GPU or renting cloud compute for dollars, we map out what's genuinely feasible and what will make your model behave in ways you didn't intend.]]></description>
      <link>https://myweirdprompts.com/episode/llm-alignment-without-finetuning/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-alignment-without-finetuning/</guid>
      <pubDate>Sun, 12 Apr 2026 15:46:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-alignment-without-finetuning.m4a"
        type="audio/mp4"
        length="18510337"
      />
      <itunes:title>Skip Fine-Tuning: Shape LLMs With Alignment Alone</itunes:title>
      <itunes:subtitle>Can you build a personalized LLM by skipping traditional fine-tuning and using only post-training alignment methods like DPO and GRPO? We break dow...</itunes:subtitle>
      <itunes:summary><![CDATA[What if you could personalize an LLM without massive retraining datasets—just by using post-training alignment methods like DPO, GRPO, and ORPO? This episode digs into whether you can take a base model like Mistral and shape it into a specific personality (say, relentlessly snarky) through reinforcement learning feedback alone. We unpack the methods available now, actual compute requirements, the tools that make it accessible, and the hidden pitfalls—especially reward hacking—that can derail your experiment. Whether you're working with a consumer GPU or renting cloud compute for dollars, we map out what's genuinely feasible and what will make your model behave in ways you didn't intend.]]></itunes:summary>
      <itunes:duration>1425</itunes:duration>
      <itunes:episode>2177</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-alignment-without-finetuning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-alignment-without-finetuning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Let Your AI Argue With Itself</title>
      <description><![CDATA[Most people use AI to get a single answer. But what if you made the AI argue with itself? This episode explores multi-persona prompting — from open-source systems like LLM Council to commercial platforms like Rally — and moves past the obvious applications (focus groups, philosophical debates) into genuinely novel territory: mapping your own beliefs against intellectual traditions, simulating your internal family systems therapy parts, stress-testing research before peer review, and the surprising discovery that reasoning models like DeepSeek-R1 already spontaneously generate internal debates. We dig into the research showing that good reasoning might be fundamentally dialogical, and why the disagreements between personas are often more valuable than any single perspective.]]></description>
      <link>https://myweirdprompts.com/episode/ai-multi-persona-debate-reasoning/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-multi-persona-debate-reasoning/</guid>
      <pubDate>Sun, 12 Apr 2026 15:10:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-multi-persona-debate-reasoning.m4a"
        type="audio/mp4"
        length="24921806"
      />
      <itunes:title>Let Your AI Argue With Itself</itunes:title>
      <itunes:subtitle>What happens when you let multiple AI personas debate each other instead of asking one model one question? A deep dive into synthetic perspective e...</itunes:subtitle>
      <itunes:summary><![CDATA[Most people use AI to get a single answer. But what if you made the AI argue with itself? This episode explores multi-persona prompting — from open-source systems like LLM Council to commercial platforms like Rally — and moves past the obvious applications (focus groups, philosophical debates) into genuinely novel territory: mapping your own beliefs against intellectual traditions, simulating your internal family systems therapy parts, stress-testing research before peer review, and the surprising discovery that reasoning models like DeepSeek-R1 already spontaneously generate internal debates. We dig into the research showing that good reasoning might be fundamentally dialogical, and why the disagreements between personas are often more valuable than any single perspective.]]></itunes:summary>
      <itunes:duration>1921</itunes:duration>
      <itunes:episode>2175</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-multi-persona-debate-reasoning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-multi-persona-debate-reasoning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Role-Playing as Orchestration</title>
      <description><![CDATA[What if conversation itself, not workflow graphs or task queues, is the fundamental primitive for multi-agent systems? This episode unpacks CAMEL's role-playing protocol, how it avoids common agent failures, and what scaling to a million agents reveals about emergent behavior.]]></description>
      <link>https://myweirdprompts.com/episode/camel-ai-multi-agent-framework/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/camel-ai-multi-agent-framework/</guid>
      <pubDate>Sun, 12 Apr 2026 13:42:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/camel-ai-multi-agent-framework.m4a"
        type="audio/mp4"
        length="22402841"
      />
      <itunes:title>Role-Playing as Orchestration</itunes:title>
      <itunes:subtitle>How a role-playing protocol from NeurIPS 2023 became one of AI&apos;s most underrated agent frameworks—and what happens when you scale it to a million a...</itunes:subtitle>
      <itunes:summary><![CDATA[What if conversation itself, not workflow graphs or task queues, is the fundamental primitive for multi-agent systems? This episode unpacks CAMEL's role-playing protocol, how it avoids common agent failures, and what scaling to a million agents reveals about emergent behavior.]]></itunes:summary>
      <itunes:duration>1699</itunes:duration>
      <itunes:episode>2174</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/camel-ai-multi-agent-framework.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/camel-ai-multi-agent-framework.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside MiroFish&apos;s Agent Simulation Architecture</title>
      <description><![CDATA[MiroFish is an open-source multi-agent simulation engine that's hit 54,000 GitHub stars by promising to predict real-world outcomes through AI-driven agent simulations. It builds knowledge graphs from documents, generates thousands of agents with persistent memory and distinct personalities, and runs them through social interaction scenarios on Twitter-like and Reddit-like platforms. But beneath the impressive architecture lies a harder question: where does this kind of simulation genuinely add predictive value, and where is it sophisticated theater? We break down the five-stage pipeline, the structural limitations of LLM-driven personas, and which use cases—from policy testing to catastrophe modeling—actually hold up under scrutiny.]]></description>
      <link>https://myweirdprompts.com/episode/mirofish-agent-simulation-limits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mirofish-agent-simulation-limits/</guid>
      <pubDate>Sun, 12 Apr 2026 13:21:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mirofish-agent-simulation-limits.m4a"
        type="audio/mp4"
        length="20264991"
      />
      <itunes:title>Inside MiroFish&apos;s Agent Simulation Architecture</itunes:title>
      <itunes:subtitle>MiroFish generates thousands of AI agents with distinct personalities to predict social dynamics. But research reveals a critical flaw: LLM agents ...</itunes:subtitle>
      <itunes:summary><![CDATA[MiroFish is an open-source multi-agent simulation engine that's hit 54,000 GitHub stars by promising to predict real-world outcomes through AI-driven agent simulations. It builds knowledge graphs from documents, generates thousands of agents with persistent memory and distinct personalities, and runs them through social interaction scenarios on Twitter-like and Reddit-like platforms. But beneath the impressive architecture lies a harder question: where does this kind of simulation genuinely add predictive value, and where is it sophisticated theater? We break down the five-stage pipeline, the structural limitations of LLM-driven personas, and which use cases—from policy testing to catastrophe modeling—actually hold up under scrutiny.]]></itunes:summary>
      <itunes:duration>1575</itunes:duration>
      <itunes:episode>2173</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mirofish-agent-simulation-limits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mirofish-agent-simulation-limits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Council of Models: How Karpathy Built AI Peer Review</title>
      <description><![CDATA[In November, Andrej Karpathy released llm-council, a deceptively simple system that treats language models like an academic council: four frontier models answer questions independently, then anonymously rank each other's responses, and a Chairman model synthesizes the results. The architecture packs deliberate design choices into just 800 lines of code—including a clever anonymization scheme, graceful error handling, and a multi-stage protocol that mirrors human expert panels. But does it actually achieve consensus, or just create a veneer of objectivity? This episode digs into the architecture, the limitations, and what it reveals about how language models evaluate each other.]]></description>
      <link>https://myweirdprompts.com/episode/ai-council-peer-review-system/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-council-peer-review-system/</guid>
      <pubDate>Sun, 12 Apr 2026 13:19:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-council-peer-review-system.m4a"
        type="audio/mp4"
        length="21263294"
      />
      <itunes:title>Council of Models: How Karpathy Built AI Peer Review</itunes:title>
      <itunes:subtitle>Andrej Karpathy&apos;s llm-council uses anonymized peer review to make language models evaluate each other fairly—but can it really suppress model bias?</itunes:subtitle>
      <itunes:summary><![CDATA[In November, Andrej Karpathy released llm-council, a deceptively simple system that treats language models like an academic council: four frontier models answer questions independently, then anonymously rank each other's responses, and a Chairman model synthesizes the results. The architecture packs deliberate design choices into just 800 lines of code—including a clever anonymization scheme, graceful error handling, and a multi-stage protocol that mirrors human expert panels. But does it actually achieve consensus, or just create a veneer of objectivity? This episode digs into the architecture, the limitations, and what it reveals about how language models evaluate each other.]]></itunes:summary>
      <itunes:duration>1635</itunes:duration>
      <itunes:episode>2172</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-council-peer-review-system.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-council-peer-review-system.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How IQT Labs Built a Wargaming LLM (Then Archived It)</title>
      <description><![CDATA[Snowglobe was IQT Labs' open-source framework for running LLM-powered wargames—research code that shipped to v1.0.0 in September 2025 and got deployed in a real six-person wargame published in the CIA's Studies in Intelligence journal before being archived in March 2026. This episode is a technical retrospective: what did they actually build, how does the agent architecture work, what design patterns hold it together, and which engineering decisions are worth stealing for your own LLM projects? We dig into the two-base-class inheritance model, YAML-driven scenario design, async orchestration for human and AI players, and the deliberate simplicity of treating prose history as game state. This is research code that made it to operational use—worth understanding why.]]></description>
      <link>https://myweirdprompts.com/episode/iqt-labs-snowglobe-wargaming-framework/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/iqt-labs-snowglobe-wargaming-framework/</guid>
      <pubDate>Sun, 12 Apr 2026 13:19:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/iqt-labs-snowglobe-wargaming-framework.m4a"
        type="audio/mp4"
        length="21887070"
      />
      <itunes:title>How IQT Labs Built a Wargaming LLM (Then Archived It)</itunes:title>
      <itunes:subtitle>A deep code review of Snowglobe, IQT Labs&apos; open-source LLM wargaming system that ran real national security simulations before being archived. What...</itunes:subtitle>
      <itunes:summary><![CDATA[Snowglobe was IQT Labs' open-source framework for running LLM-powered wargames—research code that shipped to v1.0.0 in September 2025 and got deployed in a real six-person wargame published in the CIA's Studies in Intelligence journal before being archived in March 2026. This episode is a technical retrospective: what did they actually build, how does the agent architecture work, what design patterns hold it together, and which engineering decisions are worth stealing for your own LLM projects? We dig into the two-base-class inheritance model, YAML-driven scenario design, async orchestration for human and AI players, and the deliberate simplicity of treating prose history as game state. This is research code that made it to operational use—worth understanding why.]]></itunes:summary>
      <itunes:duration>1646</itunes:duration>
      <itunes:episode>2171</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/iqt-labs-snowglobe-wargaming-framework.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/iqt-labs-snowglobe-wargaming-framework.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Pricing Agentic AI When Nothing&apos;s Predictable</title>
      <description><![CDATA[Building agentic AI systems for clients creates a novel consulting problem: how do you scope and price projects when the system itself is non-deterministic? With Gartner predicting nearly half of all agentic AI projects will be scrapped by end of next year, getting this right matters. This episode explores the emerging frameworks consultants are using—discovery sprints, phased delivery structures, Minimum Viable Agents, and human-in-the-loop design as a scope tool—to protect projects from runaway complexity, budget black holes, and the "agentic tar pit" where agents generate unmaintainable code bloat. The core insight: when code generation is free, your value shifts from execution speed to design taste and knowing when to say no.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ai-consulting-scope-pricing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ai-consulting-scope-pricing/</guid>
      <pubDate>Sun, 12 Apr 2026 12:04:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ai-consulting-scope-pricing.m4a"
        type="audio/mp4"
        length="20697356"
      />
      <itunes:title>Pricing Agentic AI When Nothing&apos;s Predictable</itunes:title>
      <itunes:subtitle>How do you charge fixed prices for systems that operate in fundamental uncertainty? Consultants are discovering frameworks that work—but they requi...</itunes:subtitle>
      <itunes:summary><![CDATA[Building agentic AI systems for clients creates a novel consulting problem: how do you scope and price projects when the system itself is non-deterministic? With Gartner predicting nearly half of all agentic AI projects will be scrapped by end of next year, getting this right matters. This episode explores the emerging frameworks consultants are using—discovery sprints, phased delivery structures, Minimum Viable Agents, and human-in-the-loop design as a scope tool—to protect projects from runaway complexity, budget black holes, and the "agentic tar pit" where agents generate unmaintainable code bloat. The core insight: when code generation is free, your value shifts from execution speed to design taste and knowing when to say no.]]></itunes:summary>
      <itunes:duration>1597</itunes:duration>
      <itunes:episode>2170</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ai-consulting-scope-pricing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ai-consulting-scope-pricing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Enterprises Are Rethinking Agent Frameworks</title>
      <description><![CDATA[The agentic AI framework space is crowded with options: LangGraph, CrewAI, AutoGen, Google ADK, and more. Yet despite this abundance, significant numbers of enterprise developers are actively avoiding frameworks altogether. This episode explores the real patterns in production adoption, why hyperscalers are treating frameworks as loss leaders, the compliance and security barriers that take frameworks off the table entirely, and the principled engineering case for building agents without frameworks at all. We examine McKinsey and Gartner data on scaling challenges, the rising cost governance problem, and why Anthropic's own engineering team recommends against using frameworks—despite maintaining their own Claude Agent SDK.]]></description>
      <link>https://myweirdprompts.com/episode/enterprise-agent-framework-adoption/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/enterprise-agent-framework-adoption/</guid>
      <pubDate>Sun, 12 Apr 2026 11:58:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/enterprise-agent-framework-adoption.m4a"
        type="audio/mp4"
        length="19149463"
      />
      <itunes:title>How Enterprises Are Rethinking Agent Frameworks</itunes:title>
      <itunes:subtitle>Twelve major agentic AI frameworks exist—yet many serious developers avoid them entirely. What patterns emerge in real enterprise adoption?</itunes:subtitle>
      <itunes:summary><![CDATA[The agentic AI framework space is crowded with options: LangGraph, CrewAI, AutoGen, Google ADK, and more. Yet despite this abundance, significant numbers of enterprise developers are actively avoiding frameworks altogether. This episode explores the real patterns in production adoption, why hyperscalers are treating frameworks as loss leaders, the compliance and security barriers that take frameworks off the table entirely, and the principled engineering case for building agents without frameworks at all. We examine McKinsey and Gartner data on scaling challenges, the rising cost governance problem, and why Anthropic's own engineering team recommends against using frameworks—despite maintaining their own Claude Agent SDK.]]></itunes:summary>
      <itunes:duration>1476</itunes:duration>
      <itunes:episode>2169</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/enterprise-agent-framework-adoption.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/enterprise-agent-framework-adoption.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Serious Agentic AI Developers Actually Need to Know</title>
      <description><![CDATA[Building production agentic AI isn't about knowing one framework — it's about mastering a constellation of interconnected skills. This episode breaks down the essential technical foundations: which programming languages matter and why (Python for models, TypeScript for products), the framework landscape (LangGraph, CrewAI, AutoGen, LlamaIndex, and Claude Agent SDK), the protocols enabling agent collaboration (MCP and A2A), and the core architectural concepts (ReAct, memory systems, tool calling, and reasoning patterns) that power every serious agentic system. Whether you're prototyping or deploying to production, this is the technical map practitioners actually use.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ai-technical-foundations/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ai-technical-foundations/</guid>
      <pubDate>Sun, 12 Apr 2026 11:46:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ai-technical-foundations.m4a"
        type="audio/mp4"
        length="20852482"
      />
      <itunes:title>What Serious Agentic AI Developers Actually Need to Know</itunes:title>
      <itunes:subtitle>Python, TypeScript, LangGraph, and the frameworks reshaping how agents work. A technical map of the skills and concepts that separate prototypes fr...</itunes:subtitle>
      <itunes:summary><![CDATA[Building production agentic AI isn't about knowing one framework — it's about mastering a constellation of interconnected skills. This episode breaks down the essential technical foundations: which programming languages matter and why (Python for models, TypeScript for products), the framework landscape (LangGraph, CrewAI, AutoGen, LlamaIndex, and Claude Agent SDK), the protocols enabling agent collaboration (MCP and A2A), and the core architectural concepts (ReAct, memory systems, tool calling, and reasoning patterns) that power every serious agentic system. Whether you're prototyping or deploying to production, this is the technical map practitioners actually use.]]></itunes:summary>
      <itunes:duration>1605</itunes:duration>
      <itunes:episode>2168</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ai-technical-foundations.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ai-technical-foundations.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Sync vs. Async: Architecting Agents for Scale</title>
      <description><![CDATA[Enterprises spent 2025 learning a hard lesson: great language models aren't enough to make agents work at scale. The real bottleneck is architecture. This episode digs into the fundamental difference between synchronous orchestration (one central agent directing everything) and asynchronous choreography (agents reacting to events independently), why this choice cascades through your entire system, and which pattern actually works for different kinds of work. We cover real production failures, the cost math that breaks synchronous models, the debugging nightmare of async systems, and the recent Model Context Protocol update that's quietly reshaping how agents should be built. If you're building agents for production, the architecture decision matters more than the model choice.]]></description>
      <link>https://myweirdprompts.com/episode/agent-architecture-sync-async/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-architecture-sync-async/</guid>
      <pubDate>Sun, 12 Apr 2026 11:46:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-architecture-sync-async.m4a"
        type="audio/mp4"
        length="18898295"
      />
      <itunes:title>Sync vs. Async: Architecting Agents for Scale</itunes:title>
      <itunes:subtitle>Why most enterprise AI agents fail in production has less to do with models and more to do with whether they&apos;re built synchronously or asynchronously.</itunes:subtitle>
      <itunes:summary><![CDATA[Enterprises spent 2025 learning a hard lesson: great language models aren't enough to make agents work at scale. The real bottleneck is architecture. This episode digs into the fundamental difference between synchronous orchestration (one central agent directing everything) and asynchronous choreography (agents reacting to events independently), why this choice cascades through your entire system, and which pattern actually works for different kinds of work. We cover real production failures, the cost math that breaks synchronous models, the debugging nightmare of async systems, and the recent Model Context Protocol update that's quietly reshaping how agents should be built. If you're building agents for production, the architecture decision matters more than the model choice.]]></itunes:summary>
      <itunes:duration>1453</itunes:duration>
      <itunes:episode>2167</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-architecture-sync-async.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-architecture-sync-async.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Code vs. Canvas: How Developers Pick Their Tools</title>
      <description><![CDATA[Should developers use code-first agentic workflow builders like LangGraph and CrewAI, or visual platforms like Flowise and n8n? The instinct is to dismiss visual tools as "for non-programmers," but the real tradeoffs are more nuanced—and context-dependent. This episode maps what you actually gain (prototyping speed, pre-built integrations, operational infrastructure, real-time debugging) against what you genuinely lose (version control, unit testing, CI/CD integration, AI-assisted coding, refactorability). We also explore why the forty-year history of visual programming—from LabVIEW to Unreal Blueprints—keeps teaching the same lesson about scaling and abstraction. The answer depends on your team, your timeline, and whether you're building a prototype or a production system.]]></description>
      <link>https://myweirdprompts.com/episode/code-visual-workflow-builders-tradeoffs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/code-visual-workflow-builders-tradeoffs/</guid>
      <pubDate>Sun, 12 Apr 2026 11:42:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/code-visual-workflow-builders-tradeoffs.m4a"
        type="audio/mp4"
        length="19950280"
      />
      <itunes:title>Code vs. Canvas: How Developers Pick Their Tools</itunes:title>
      <itunes:subtitle>LangGraph or Flowise? The honest answer isn&apos;t obvious. Developers gain speed and integrations with visual builders—but lose version control, testin...</itunes:subtitle>
      <itunes:summary><![CDATA[Should developers use code-first agentic workflow builders like LangGraph and CrewAI, or visual platforms like Flowise and n8n? The instinct is to dismiss visual tools as "for non-programmers," but the real tradeoffs are more nuanced—and context-dependent. This episode maps what you actually gain (prototyping speed, pre-built integrations, operational infrastructure, real-time debugging) against what you genuinely lose (version control, unit testing, CI/CD integration, AI-assisted coding, refactorability). We also explore why the forty-year history of visual programming—from LabVIEW to Unreal Blueprints—keeps teaching the same lesson about scaling and abstraction. The answer depends on your team, your timeline, and whether you're building a prototype or a production system.]]></itunes:summary>
      <itunes:duration>1529</itunes:duration>
      <itunes:episode>2166</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/code-visual-workflow-builders-tradeoffs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/code-visual-workflow-builders-tradeoffs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Strip Your Agent to Bash</title>
      <description><![CDATA[LangGraph, CrewAI, AutoGen, Semantic Kernel, Claude Code—they all orchestrate LLM calls with tools, but they encode radically different philosophies about how agents should operate. This episode digs into what actually distinguishes one agentic framework from another, and why the real engineering creativity lives in the harness, not the model. We walk through concrete data: how Vercel deleted 80% of their specialized tools and got 3.5x faster execution with 100% success rate, why LangChain's middleware additions moved a coding agent from outside the top 30 to top 5 on the leaderboard without changing the model, and what the APEX-Agents benchmark reveals about orchestration failures masquerading as capability gaps. The future of agentic development isn't about picking the framework—it's about understanding which harness philosophy matches your problem.]]></description>
      <link>https://myweirdprompts.com/episode/agent-harness-over-model/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-harness-over-model/</guid>
      <pubDate>Sun, 12 Apr 2026 10:59:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-harness-over-model.m4a"
        type="audio/mp4"
        length="19958294"
      />
      <itunes:title>Strip Your Agent to Bash</itunes:title>
      <itunes:subtitle>The frameworks matter less than you think. What separates a working agent from a failing one is the harness—the orchestration, memory, and tool des...</itunes:subtitle>
      <itunes:summary><![CDATA[LangGraph, CrewAI, AutoGen, Semantic Kernel, Claude Code—they all orchestrate LLM calls with tools, but they encode radically different philosophies about how agents should operate. This episode digs into what actually distinguishes one agentic framework from another, and why the real engineering creativity lives in the harness, not the model. We walk through concrete data: how Vercel deleted 80% of their specialized tools and got 3.5x faster execution with 100% success rate, why LangChain's middleware additions moved a coding agent from outside the top 30 to top 5 on the leaderboard without changing the model, and what the APEX-Agents benchmark reveals about orchestration failures masquerading as capability gaps. The future of agentic development isn't about picking the framework—it's about understanding which harness philosophy matches your problem.]]></itunes:summary>
      <itunes:duration>1553</itunes:duration>
      <itunes:episode>2165</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-harness-over-model.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-harness-over-model.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Designing Autonomy Boundaries for AI Agents</title>
      <description><![CDATA[When do AI agents actually need to pick their own tools? Daniel's question digs into the spectrum from fully autonomous tool selection (AutoGPT, MCP servers) to deterministic orchestration (LangGraph, CrewAI, Bedrock). The answer isn't about safety blankets—it's about token economics, the Context-Capability Paradox, and what production deployments actually reveal about where autonomous agents fail. We explore the Librarian Pattern, ReAct vs. ReWoo trade-offs, and why Praetorian's "Thin Agent, Fat Platform" approach treats LLMs as unreliable microservices wrapped in reliable infrastructure.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-tool-constraints/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-tool-constraints/</guid>
      <pubDate>Sun, 12 Apr 2026 10:46:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-tool-constraints.m4a"
        type="audio/mp4"
        length="21961047"
      />
      <itunes:title>Designing Autonomy Boundaries for AI Agents</itunes:title>
      <itunes:subtitle>Production data reveals a surprising truth: fully autonomous AI agents waste 98% of their context window on tool descriptions. Here&apos;s why the indus...</itunes:subtitle>
      <itunes:summary><![CDATA[When do AI agents actually need to pick their own tools? Daniel's question digs into the spectrum from fully autonomous tool selection (AutoGPT, MCP servers) to deterministic orchestration (LangGraph, CrewAI, Bedrock). The answer isn't about safety blankets—it's about token economics, the Context-Capability Paradox, and what production deployments actually reveal about where autonomous agents fail. We explore the Librarian Pattern, ReAct vs. ReWoo trade-offs, and why Praetorian's "Thin Agent, Fat Platform" approach treats LLMs as unreliable microservices wrapped in reliable infrastructure.]]></itunes:summary>
      <itunes:duration>1696</itunes:duration>
      <itunes:episode>2163</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-tool-constraints.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-tool-constraints.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Claude&apos;s Latency Profile and SLA Guarantees</title>
      <description><![CDATA[When developers say Claude is slow, what do they actually mean? This episode digs into the five core latency metrics that matter for production systems, reveals the benchmarks showing Claude's p95 latency problem, and then explores what Anthropic actually contractually guarantees—spoiler: almost nothing at standard tier. We break down Priority Tier's queue-prioritization illusion, why Fast Mode's six-times pricing premium reveals Anthropic's real capacity choices, and how Claude's latency compares to GPT-4, Gemini, and open-source alternatives across the inference leaderboards.]]></description>
      <link>https://myweirdprompts.com/episode/claude-latency-sla-guarantees/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-latency-sla-guarantees/</guid>
      <pubDate>Sun, 12 Apr 2026 10:27:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-latency-sla-guarantees.m4a"
        type="audio/mp4"
        length="19656464"
      />
      <itunes:title>Claude&apos;s Latency Profile and SLA Guarantees</itunes:title>
      <itunes:subtitle>Claude is measurably slower than competitors—and Anthropic&apos;s SLA promises are even thinner than the latency numbers suggest. What enterprises actua...</itunes:subtitle>
      <itunes:summary><![CDATA[When developers say Claude is slow, what do they actually mean? This episode digs into the five core latency metrics that matter for production systems, reveals the benchmarks showing Claude's p95 latency problem, and then explores what Anthropic actually contractually guarantees—spoiler: almost nothing at standard tier. We break down Priority Tier's queue-prioritization illusion, why Fast Mode's six-times pricing premium reveals Anthropic's real capacity choices, and how Claude's latency compares to GPT-4, Gemini, and open-source alternatives across the inference leaderboards.]]></itunes:summary>
      <itunes:duration>1497</itunes:duration>
      <itunes:episode>2160</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-latency-sla-guarantees.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-latency-sla-guarantees.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Claude Managed Agents: Brain Versus Hands</title>
      <description><![CDATA[Anthropic launched Claude Managed Agents in public beta on April 8th, positioning it as a hosted execution runtime for agentic workflows. Unlike OpenAI's Assistants API—which was primarily a state management layer—Managed Agents includes a real Linux container sandbox, persistent sessions, multi-agent coordination, and governance features like scoped permissions and execution tracing. But the tradeoffs are substantial: you lose multi-model mixing, token optimization control, and flexibility for enterprise cloud commitments. We break down the honest calculus of build-versus-buy, why OpenAI's Assistants API failed and what Anthropic might be doing differently, and which developers should actually adopt this versus building their own loop.]]></description>
      <link>https://myweirdprompts.com/episode/claude-managed-agents-runtime/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-managed-agents-runtime/</guid>
      <pubDate>Sat, 11 Apr 2026 19:55:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-managed-agents-runtime.m4a"
        type="audio/mp4"
        length="18768404"
      />
      <itunes:title>Claude Managed Agents: Brain Versus Hands</itunes:title>
      <itunes:subtitle>Anthropic&apos;s new Managed Agents service runs your agent loop on their infrastructure. Here&apos;s what you gain, what you lose, and who it&apos;s actually for.</itunes:subtitle>
      <itunes:summary><![CDATA[Anthropic launched Claude Managed Agents in public beta on April 8th, positioning it as a hosted execution runtime for agentic workflows. Unlike OpenAI's Assistants API—which was primarily a state management layer—Managed Agents includes a real Linux container sandbox, persistent sessions, multi-agent coordination, and governance features like scoped permissions and execution tracing. But the tradeoffs are substantial: you lose multi-model mixing, token optimization control, and flexibility for enterprise cloud commitments. We break down the honest calculus of build-versus-buy, why OpenAI's Assistants API failed and what Anthropic might be doing differently, and which developers should actually adopt this versus building their own loop.]]></itunes:summary>
      <itunes:duration>1455</itunes:duration>
      <itunes:episode>2158</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-managed-agents-runtime.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-managed-agents-runtime.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Simulating Geopolitics Under Asymmetric Information</title>
      <description><![CDATA[How a two-stage AI pipeline—combining Monte Carlo simulation with an LLM council—models ceasefire durability by forcing actors to make decisions with incomplete information. This episode unpacks the architecture, the divergences between stages, and what the method reveals about live crisis forecasting.]]></description>
      <link>https://myweirdprompts.com/episode/ai-forecast-iran-ceasefire-survival/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-forecast-iran-ceasefire-survival/</guid>
      <pubDate>Thu, 09 Apr 2026 23:37:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-forecast-iran-ceasefire-survival.m4a"
        type="audio/mp4"
        length="26627946"
      />
      <itunes:title>Simulating Geopolitics Under Asymmetric Information</itunes:title>
      <itunes:subtitle>A two-stage AI pipeline predicted a 4% chance the Iran-Israel ceasefire would survive a month, using Monte Carlo simulations and an LLM council.</itunes:subtitle>
      <itunes:summary><![CDATA[How a two-stage AI pipeline—combining Monte Carlo simulation with an LLM council—models ceasefire durability by forcing actors to make decisions with incomplete information. This episode unpacks the architecture, the divergences between stages, and what the method reveals about live crisis forecasting.]]></itunes:summary>
      <itunes:duration>2022</itunes:duration>
      <itunes:episode>2143</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-forecast-iran-ceasefire-survival.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-forecast-iran-ceasefire-survival.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Nervous System of Multi-Agent Systems</title>
      <description><![CDATA[How do parent agents know when their subagents are truly done? We trace the lifecycle management, state transitions, and coordination overhead that turn AI chat into distributed computing.]]></description>
      <link>https://myweirdprompts.com/episode/subagent-orchestrator-notification-layer/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/subagent-orchestrator-notification-layer/</guid>
      <pubDate>Thu, 09 Apr 2026 23:29:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/subagent-orchestrator-notification-layer.m4a"
        type="audio/mp4"
        length="18207184"
      />
      <itunes:title>The Nervous System of Multi-Agent Systems</itunes:title>
      <itunes:subtitle>We break down the plumbing that lets a parent agent know exactly when a subagent finishes, from message passing to lifecycle events.</itunes:subtitle>
      <itunes:summary><![CDATA[How do parent agents know when their subagents are truly done? We trace the lifecycle management, state transitions, and coordination overhead that turn AI chat into distributed computing.]]></itunes:summary>
      <itunes:duration>1407</itunes:duration>
      <itunes:episode>2142</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/subagent-orchestrator-notification-layer.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/subagent-orchestrator-notification-layer.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Choosing Your Durable Execution Platform</title>
      <description><![CDATA[Facing the infrastructure tax of building AI agents? We compare Temporal, AWS Step Functions, Google Cloud Workflows, and Azure Durable Functions to find the best fit for your code-defined agentic workflows.]]></description>
      <link>https://myweirdprompts.com/episode/durable-agent-backend-platforms/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/durable-agent-backend-platforms/</guid>
      <pubDate>Thu, 09 Apr 2026 23:22:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/durable-agent-backend-platforms.m4a"
        type="audio/mp4"
        length="14275988"
      />
      <itunes:title>Choosing Your Durable Execution Platform</itunes:title>
      <itunes:subtitle>Why building AI agents means managing infrastructure. We explore durable execution backends like Temporal and AWS Step Functions.</itunes:subtitle>
      <itunes:summary><![CDATA[Facing the infrastructure tax of building AI agents? We compare Temporal, AWS Step Functions, Google Cloud Workflows, and Azure Durable Functions to find the best fit for your code-defined agentic workflows.]]></itunes:summary>
      <itunes:duration>1095</itunes:duration>
      <itunes:episode>2141</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/durable-agent-backend-platforms.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/durable-agent-backend-platforms.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Wargame Memory: Beyond the Context Window</title>
      <description><![CDATA[In multi-agent wargaming, an AI general must remember decisions made forty-seven turns ago without dumping the entire conversation history into context every single turn. This episode explores the three-layer memory architecture required for serious simulations: shared world state, private context, and persistent long-term memory. We examine why naive approaches like full-history replay fail due to cost and strategic drift, and how vector stores and summarization chains offer more viable solutions while maintaining the critical blinding discipline that prevents metagaming.]]></description>
      <link>https://myweirdprompts.com/episode/ai-wargame-memory-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-wargame-memory-architecture/</guid>
      <pubDate>Thu, 09 Apr 2026 23:19:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-wargame-memory-architecture.m4a"
        type="audio/mp4"
        length="26682409"
      />
      <itunes:title>AI Wargame Memory: Beyond the Context Window</itunes:title>
      <itunes:subtitle>Why simply extending context windows fails in multi-agent simulations, and how layered memory architectures preserve strategic fidelity.</itunes:subtitle>
      <itunes:summary><![CDATA[In multi-agent wargaming, an AI general must remember decisions made forty-seven turns ago without dumping the entire conversation history into context every single turn. This episode explores the three-layer memory architecture required for serious simulations: shared world state, private context, and persistent long-term memory. We examine why naive approaches like full-history replay fail due to cost and strategic drift, and how vector stores and summarization chains offer more viable solutions while maintaining the critical blinding discipline that prevents metagaming.]]></itunes:summary>
      <itunes:duration>2087</itunes:duration>
      <itunes:episode>2139</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-wargame-memory-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-wargame-memory-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Fog-of-War Problem in AI Wargaming</title>
      <description><![CDATA[When both sides of a wargame run on the same AI model, how do you prevent information leakage? This episode explores the unique "fog-of-war" challenge in AI wargaming, where shared training data and inference servers create new vulnerabilities for accidental intelligence leaks. We examine real-world failure cases, including a 2025 RAND simulation where referee narration accidentally revealed classified information, and break down the four architectural patterns used to enforce separation: per-actor state stores, redaction layers, referee-mediated message passing, and isolated context windows. The discussion also covers Snowglobe, an open-source framework from IQT Labs designed for open-ended qualitative wargaming, and why getting this right matters for policy analysis where misleading results can be actively dangerous.]]></description>
      <link>https://myweirdprompts.com/episode/ai-wargaming-fog-of-war/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-wargaming-fog-of-war/</guid>
      <pubDate>Thu, 09 Apr 2026 22:48:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-wargaming-fog-of-war.m4a"
        type="audio/mp4"
        length="22048740"
      />
      <itunes:title>The Fog-of-War Problem in AI Wargaming</itunes:title>
      <itunes:subtitle>Why shared AI brains make secret-keeping a nightmare, and the four architectural patterns researchers use to fix it.</itunes:subtitle>
      <itunes:summary><![CDATA[When both sides of a wargame run on the same AI model, how do you prevent information leakage? This episode explores the unique "fog-of-war" challenge in AI wargaming, where shared training data and inference servers create new vulnerabilities for accidental intelligence leaks. We examine real-world failure cases, including a 2025 RAND simulation where referee narration accidentally revealed classified information, and break down the four architectural patterns used to enforce separation: per-actor state stores, redaction layers, referee-mediated message passing, and isolated context windows. The discussion also covers Snowglobe, an open-source framework from IQT Labs designed for open-ended qualitative wargaming, and why getting this right matters for policy analysis where misleading results can be actively dangerous.]]></itunes:summary>
      <itunes:duration>1688</itunes:duration>
      <itunes:episode>2134</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-wargaming-fog-of-war.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-wargaming-fog-of-war.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Engineering Geopolitical Personas: Beyond Caricatures</title>
      <description><![CDATA[What does it take to make an LLM convincingly play a geopolitical leader like Putin or Khamenei? This episode explores the full technical stack for building personas with strategic fidelity, moving beyond caricature to capture decision-making logic. We break down the layers: system prompting with doctrine, few-shot examples for voice, RAG for historical memory, and fine-tuning for character. The discussion also tackles the hard problem of evaluation when ground truth is scarce and touches on the ethical implications of simulating real-world actors.]]></description>
      <link>https://myweirdprompts.com/episode/geopolitical-persona-engineering-llms/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/geopolitical-persona-engineering-llms/</guid>
      <pubDate>Thu, 09 Apr 2026 22:48:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/geopolitical-persona-engineering-llms.m4a"
        type="audio/mp4"
        length="22958995"
      />
      <itunes:title>Engineering Geopolitical Personas: Beyond Caricatures</itunes:title>
      <itunes:subtitle>How to build LLMs that simulate state actors with strategic fidelity, not just surface mimicry.</itunes:subtitle>
      <itunes:summary><![CDATA[What does it take to make an LLM convincingly play a geopolitical leader like Putin or Khamenei? This episode explores the full technical stack for building personas with strategic fidelity, moving beyond caricature to capture decision-making logic. We break down the layers: system prompting with doctrine, few-shot examples for voice, RAG for historical memory, and fine-tuning for character. The discussion also tackles the hard problem of evaluation when ground truth is scarce and touches on the ethical implications of simulating real-world actors.]]></itunes:summary>
      <itunes:duration>1762</itunes:duration>
      <itunes:episode>2133</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/geopolitical-persona-engineering-llms.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/geopolitical-persona-engineering-llms.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Referee&apos;s Dilemma: Epistemic Containment in LLM Simulations</title>
      <description><![CDATA[How do you keep a geopolitical crisis simulation from collapsing into a news commentary engine? This episode explores the critical role of the referee in curating a sealed world state, the subtle biases that can corrupt results, and why epistemic containment is the core engineering challenge.]]></description>
      <link>https://myweirdprompts.com/episode/sealed-simulation-firewall-llm/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/sealed-simulation-firewall-llm/</guid>
      <pubDate>Thu, 09 Apr 2026 22:16:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/sealed-simulation-firewall-llm.m4a"
        type="audio/mp4"
        length="23903833"
      />
      <itunes:title>The Referee&apos;s Dilemma: Epistemic Containment in LLM Simulations</itunes:title>
      <itunes:subtitle>Why do AI war games need a news blackout? We dissect the firewall that keeps LLM actors from cheating with real-world data.</itunes:subtitle>
      <itunes:summary><![CDATA[How do you keep a geopolitical crisis simulation from collapsing into a news commentary engine? This episode explores the critical role of the referee in curating a sealed world state, the subtle biases that can corrupt results, and why epistemic containment is the core engineering challenge.]]></itunes:summary>
      <itunes:duration>1860</itunes:duration>
      <itunes:episode>2132</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/sealed-simulation-firewall-llm.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/sealed-simulation-firewall-llm.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The CIA Is on GitHub</title>
      <description><![CDATA[The CIA's venture arm, In-Q-Tel, has an open-source wargaming project on GitHub. This episode explores why the intelligence community is embracing public AI tools, how Snowglobe generates narrative crisis simulations, and what it means for privacy and public-private partnerships.]]></description>
      <link>https://myweirdprompts.com/episode/cia-open-source-ai-wargaming/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cia-open-source-ai-wargaming/</guid>
      <pubDate>Thu, 09 Apr 2026 22:11:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cia-open-source-ai-wargaming.m4a"
        type="audio/mp4"
        length="28748090"
      />
      <itunes:title>The CIA Is on GitHub</itunes:title>
      <itunes:subtitle>In-Q-Tel is on GitHub. Explore the IC&apos;s strategic investment arm and its use of open-source AI for wargaming.</itunes:subtitle>
      <itunes:summary><![CDATA[The CIA's venture arm, In-Q-Tel, has an open-source wargaming project on GitHub. This episode explores why the intelligence community is embracing public AI tools, how Snowglobe generates narrative crisis simulations, and what it means for privacy and public-private partnerships.]]></itunes:summary>
      <itunes:duration>2236</itunes:duration>
      <itunes:episode>2131</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cia-open-source-ai-wargaming.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cia-open-source-ai-wargaming.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Shifting Left on Hallucinations</title>
      <description><![CDATA[The era of vibe-based AI is ending. This episode explores how the industry is moving from post-hoc verification to architectural prevention—treating hallucinations as system errors, not creative flourishes. We examine tools like Guardrails AI, NeMo, and specialized judge models that catch fabrications before they leave the pipeline.]]></description>
      <link>https://myweirdprompts.com/episode/anti-hallucination-tooling-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/anti-hallucination-tooling-ai-agents/</guid>
      <pubDate>Thu, 09 Apr 2026 22:07:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/anti-hallucination-tooling-ai-agents.m4a"
        type="audio/mp4"
        length="18071247"
      />
      <itunes:title>Shifting Left on Hallucinations</itunes:title>
      <itunes:subtitle>Stop hoping your AI doesn&apos;t lie. We explore the shift to deterministic guardrails, specialized judge models, and the tools making agents reliable.</itunes:subtitle>
      <itunes:summary><![CDATA[The era of vibe-based AI is ending. This episode explores how the industry is moving from post-hoc verification to architectural prevention—treating hallucinations as system errors, not creative flourishes. We examine tools like Guardrails AI, NeMo, and specialized judge models that catch fabrications before they leave the pipeline.]]></itunes:summary>
      <itunes:duration>1378</itunes:duration>
      <itunes:episode>2129</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/anti-hallucination-tooling-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/anti-hallucination-tooling-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Agentic Chunking Beats One-Shot Generation</title>
      <description><![CDATA[For years, generating long-form content with AI has been plagued by "token fatigue" and repetitive loops. This episode dives into the specific architecture—using a Planning Agent and Subagents with Claude Sonnet 4.6—that solves the context dilution problem. Learn why naive one-shot prompting fails for deep dives and how to structure a digital production team for books, briefs, and podcasts.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-chunking-long-form-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-chunking-long-form-ai/</guid>
      <pubDate>Wed, 08 Apr 2026 16:07:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-chunking-long-form-ai.m4a"
        type="audio/mp4"
        length="13960061"
      />
      <itunes:title>Why Agentic Chunking Beats One-Shot Generation</itunes:title>
      <itunes:subtitle>A single prompt can&apos;t write a 30-minute script. Here’s the agentic chunking method that fixes coherence.</itunes:subtitle>
      <itunes:summary><![CDATA[For years, generating long-form content with AI has been plagued by "token fatigue" and repetitive loops. This episode dives into the specific architecture—using a Planning Agent and Subagents with Claude Sonnet 4.6—that solves the context dilution problem. Learn why naive one-shot prompting fails for deep dives and how to structure a digital production team for books, briefs, and podcasts.]]></itunes:summary>
      <itunes:duration>1062</itunes:duration>
      <itunes:episode>2125</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-chunking-long-form-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-chunking-long-form-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Answers Differ Even When You Ask Twice</title>
      <description><![CDATA[Why does an AI give you different answers to the exact same question? This episode dives into the trillion-dollar problem of AI non-determinism. We explore why "Temperature Zero" isn't enough, how GPU parallel processing causes numerical drift, and why your server's workload might be changing your code. Plus, learn the engineering workaround—moving determinism downstream—that developers use to build reliable software on top of probabilistic models.]]></description>
      <link>https://myweirdprompts.com/episode/ai-non-deterministic-gpu-drift/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-non-deterministic-gpu-drift/</guid>
      <pubDate>Tue, 07 Apr 2026 22:19:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-non-deterministic-gpu-drift.m4a"
        type="audio/mp4"
        length="19471386"
      />
      <itunes:title>Why AI Answers Differ Even When You Ask Twice</itunes:title>
      <itunes:subtitle>You ask an AI the same question twice and get two different answers. It’s not a bug—it’s physics.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does an AI give you different answers to the exact same question? This episode dives into the trillion-dollar problem of AI non-determinism. We explore why "Temperature Zero" isn't enough, how GPU parallel processing causes numerical drift, and why your server's workload might be changing your code. Plus, learn the engineering workaround—moving determinism downstream—that developers use to build reliable software on top of probabilistic models.]]></itunes:summary>
      <itunes:duration>1506</itunes:duration>
      <itunes:episode>2115</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-non-deterministic-gpu-drift.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-non-deterministic-gpu-drift.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>2026 ERP: From Filing Cabinet to Autonomous Core</title>
      <description><![CDATA[The ERP landscape has transformed dramatically since 2006. What was once a static system of record is now an autonomous core powered by AI agents that negotiate, forecast, and execute workflows with minimal human intervention. This episode explores the shift to composable microservices, the rise of agentic AI in procurement and supply chain, and how natural language configuration is replacing years of consulting work. We also examine the risks of explainability, the push for clean data cores, and the new roles emerging in enterprise tech.]]></description>
      <link>https://myweirdprompts.com/episode/erp-ai-autonomous-core-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/erp-ai-autonomous-core-2026/</guid>
      <pubDate>Tue, 07 Apr 2026 22:17:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/erp-ai-autonomous-core-2026.m4a"
        type="audio/mp4"
        length="18235370"
      />
      <itunes:title>2026 ERP: From Filing Cabinet to Autonomous Core</itunes:title>
      <itunes:subtitle>In 2026, ERP systems have evolved from digital filing cabinets into autonomous, AI-driven cores that predict and execute business decisions in real...</itunes:subtitle>
      <itunes:summary><![CDATA[The ERP landscape has transformed dramatically since 2006. What was once a static system of record is now an autonomous core powered by AI agents that negotiate, forecast, and execute workflows with minimal human intervention. This episode explores the shift to composable microservices, the rise of agentic AI in procurement and supply chain, and how natural language configuration is replacing years of consulting work. We also examine the risks of explainability, the push for clean data cores, and the new roles emerging in enterprise tech.]]></itunes:summary>
      <itunes:duration>1396</itunes:duration>
      <itunes:episode>2114</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/erp-ai-autonomous-core-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/erp-ai-autonomous-core-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Goldfish vs Elephant: The Stateful Agent Dilemma</title>
      <description><![CDATA[As AI agents move from demos to production, a critical choice emerges: build a fast, cheap "goldfish" that forgets everything, or a memory-rich "elephant" that remembers your preferences? This episode explores the architectural trade-offs between stateful and stateless designs, revealing how each impacts memory, scalability, and reasoning. We dive into the real-world costs, latency hits, and complexity of adding persistent memory—from database plumbing to race conditions—and ask when the expensive memory is actually worth it.]]></description>
      <link>https://myweirdprompts.com/episode/stateful-vs-stateless-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/stateful-vs-stateless-agents/</guid>
      <pubDate>Tue, 07 Apr 2026 21:24:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/stateful-vs-stateless-agents.m4a"
        type="audio/mp4"
        length="16137202"
      />
      <itunes:title>Goldfish vs Elephant: The Stateful Agent Dilemma</itunes:title>
      <itunes:subtitle>Stateless agents are cheap and fast, but stateful ones remember your window seat. Which architecture wins?</itunes:subtitle>
      <itunes:summary><![CDATA[As AI agents move from demos to production, a critical choice emerges: build a fast, cheap "goldfish" that forgets everything, or a memory-rich "elephant" that remembers your preferences? This episode explores the architectural trade-offs between stateful and stateless designs, revealing how each impacts memory, scalability, and reasoning. We dive into the real-world costs, latency hits, and complexity of adding persistent memory—from database plumbing to race conditions—and ask when the expensive memory is actually worth it.]]></itunes:summary>
      <itunes:duration>1241</itunes:duration>
      <itunes:episode>2113</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/stateful-vs-stateless-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/stateful-vs-stateless-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Bricklayer to Foreman: AI&apos;s Dev Role Shift</title>
      <description><![CDATA[The AI era has triggered a massive explosion in frameworks and toolkits, creating a "distro-bloat" crisis for developers. While programming languages like Python evolve slowly, AI orchestration layers change weekly, forcing a fundamental shift in what it means to be a core developer. We explore the tension between learning specific frameworks versus mastering architectural oversight, the dangers of vendor lock-in, and why "Systems Thinking" is the new essential skill. Learn how to move from being a code bricklayer to a site foreman in an agent-first world.]]></description>
      <link>https://myweirdprompts.com/episode/ai-framework-bloat-core-knowledge/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-framework-bloat-core-knowledge/</guid>
      <pubDate>Tue, 07 Apr 2026 13:49:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-framework-bloat-core-knowledge.m4a"
        type="audio/mp4"
        length="21716741"
      />
      <itunes:title>From Bricklayer to Foreman: AI&apos;s Dev Role Shift</itunes:title>
      <itunes:subtitle>AI frameworks are exploding while languages stay stable. Learn why core dev knowledge is shifting from syntax to systems thinking.</itunes:subtitle>
      <itunes:summary><![CDATA[The AI era has triggered a massive explosion in frameworks and toolkits, creating a "distro-bloat" crisis for developers. While programming languages like Python evolve slowly, AI orchestration layers change weekly, forcing a fundamental shift in what it means to be a core developer. We explore the tension between learning specific frameworks versus mastering architectural oversight, the dangers of vendor lock-in, and why "Systems Thinking" is the new essential skill. Learn how to move from being a code bricklayer to a site foreman in an agent-first world.]]></itunes:summary>
      <itunes:duration>1677</itunes:duration>
      <itunes:episode>2111</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-framework-bloat-core-knowledge.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-framework-bloat-core-knowledge.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Is Forcing You to Use React</title>
      <description><![CDATA[The era of choosing your tech stack based on preference is ending. As AI coding agents become standard, they are creating "architectural coercion"—pushing developers toward frameworks like React and databases like Postgres simply because models have more training data for them. This episode explores the feedback loops solidifying these defaults, why "LLM-friendly" frameworks like Astro are rising, and what this means for the future of code diversity.]]></description>
      <link>https://myweirdprompts.com/episode/ai-stack-coercion-react-loop/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-stack-coercion-react-loop/</guid>
      <pubDate>Tue, 07 Apr 2026 13:31:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-stack-coercion-react-loop.m4a"
        type="audio/mp4"
        length="19772144"
      />
      <itunes:title>AI Is Forcing You to Use React</itunes:title>
      <itunes:subtitle>AI tools are reshaping developer stacks, favoring React and Postgres over niche frameworks.</itunes:subtitle>
      <itunes:summary><![CDATA[The era of choosing your tech stack based on preference is ending. As AI coding agents become standard, they are creating "architectural coercion"—pushing developers toward frameworks like React and databases like Postgres simply because models have more training data for them. This episode explores the feedback loops solidifying these defaults, why "LLM-friendly" frameworks like Astro are rising, and what this means for the future of code diversity.]]></itunes:summary>
      <itunes:duration>1537</itunes:duration>
      <itunes:episode>2109</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-stack-coercion-react-loop.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-stack-coercion-react-loop.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>PWA Reality: Shipping Cross-Platform in 2026</title>
      <description><![CDATA[The promise of one codebase for all devices is seductive, especially when AI can generate features in minutes. But the reality of building Progressive Web Apps in 2026 is fraught with invisible walls. We explore the harsh disconnect between high-velocity development and the stubborn limitations of mobile ecosystems, specifically Apple's Safari. From the "DOM Tax" on budget hardware to the nightmare of background sync, learn why your "installable" app might be a fragile wrapper. If you're trading native reliability for web speed, you need to hear this before you hit deploy.]]></description>
      <link>https://myweirdprompts.com/episode/pwa-developer-reality-gap/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pwa-developer-reality-gap/</guid>
      <pubDate>Tue, 07 Apr 2026 13:27:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pwa-developer-reality-gap.m4a"
        type="audio/mp4"
        length="20273058"
      />
      <itunes:title>PWA Reality: Shipping Cross-Platform in 2026</itunes:title>
      <itunes:subtitle>Vibe coding promises instant apps, but Apple&apos;s Safari is killing the dream. Discover the hidden performance traps and platform gaps.</itunes:subtitle>
      <itunes:summary><![CDATA[The promise of one codebase for all devices is seductive, especially when AI can generate features in minutes. But the reality of building Progressive Web Apps in 2026 is fraught with invisible walls. We explore the harsh disconnect between high-velocity development and the stubborn limitations of mobile ecosystems, specifically Apple's Safari. From the "DOM Tax" on budget hardware to the nightmare of background sync, learn why your "installable" app might be a fragile wrapper. If you're trading native reliability for web speed, you need to hear this before you hit deploy.]]></itunes:summary>
      <itunes:duration>1572</itunes:duration>
      <itunes:episode>2108</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pwa-developer-reality-gap.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pwa-developer-reality-gap.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Don&apos;t You Notice AI Security Delays?</title>
      <description><![CDATA[Agentic CLIs like Claude Code run dozens of security checks on every command, yet feel instant. This episode explores the engineering tricks—predictive execution, tiered inspections, and parallel network calls—that keep latency under the human perception threshold while maintaining strict data loss prevention.]]></description>
      <link>https://myweirdprompts.com/episode/ai-security-latency-invisible-plumbing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-security-latency-invisible-plumbing/</guid>
      <pubDate>Tue, 07 Apr 2026 12:56:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-security-latency-invisible-plumbing.m4a"
        type="audio/mp4"
        length="17596812"
      />
      <itunes:title>Why Don&apos;t You Notice AI Security Delays?</itunes:title>
      <itunes:subtitle>Multi-layer security checks add latency, but modern CLIs hide it under 100ms using parallelization and speculation.</itunes:subtitle>
      <itunes:summary><![CDATA[Agentic CLIs like Claude Code run dozens of security checks on every command, yet feel instant. This episode explores the engineering tricks—predictive execution, tiered inspections, and parallel network calls—that keep latency under the human perception threshold while maintaining strict data loss prevention.]]></itunes:summary>
      <itunes:duration>1357</itunes:duration>
      <itunes:episode>2102</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-security-latency-invisible-plumbing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-security-latency-invisible-plumbing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>One Pi, Two Screens: The Isolation Playbook</title>
      <description><![CDATA[A single Raspberry Pi can power two separate displays, but getting apps to stay put—without one crashing the other—is tricky. We explore three methods to achieve true display isolation: tweaking the Wayland compositor, reverting to legacy X-Screens, or containerizing your media center with Docker. Learn which approach offers the best stability for a dual-purpose setup, why a full VM might be overkill, and the hardware quirks that can make or break your configuration.]]></description>
      <link>https://myweirdprompts.com/episode/raspberry-pi-dual-display-isolation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/raspberry-pi-dual-display-isolation/</guid>
      <pubDate>Tue, 07 Apr 2026 12:20:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/raspberry-pi-dual-display-isolation.m4a"
        type="audio/mp4"
        length="16497322"
      />
      <itunes:title>One Pi, Two Screens: The Isolation Playbook</itunes:title>
      <itunes:subtitle>Stop your dashboard and Kodi from fighting over the same screen. Here’s how to split one Pi into two reliable workspaces.</itunes:subtitle>
      <itunes:summary><![CDATA[A single Raspberry Pi can power two separate displays, but getting apps to stay put—without one crashing the other—is tricky. We explore three methods to achieve true display isolation: tweaking the Wayland compositor, reverting to legacy X-Screens, or containerizing your media center with Docker. Learn which approach offers the best stability for a dual-purpose setup, why a full VM might be overkill, and the hardware quirks that can make or break your configuration.]]></itunes:summary>
      <itunes:duration>1274</itunes:duration>
      <itunes:episode>2099</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/raspberry-pi-dual-display-isolation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/raspberry-pi-dual-display-isolation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Open-Source vs. Military ATR: The Drone Recognition Gap</title>
      <description><![CDATA[A fine-tuned object recognition model on GitHub reveals the chasm between open-source computer vision and classified military ATR systems. This episode explores why data diversity trumps volume, how Sim-to-Real transfer works, and the accelerating AI vs. AI arms race in drone warfare.]]></description>
      <link>https://myweirdprompts.com/episode/ai-drone-recognition-training-data/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-drone-recognition-training-data/</guid>
      <pubDate>Tue, 07 Apr 2026 10:06:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-drone-recognition-training-data.m4a"
        type="audio/mp4"
        length="20661380"
      />
      <itunes:title>Open-Source vs. Military ATR: The Drone Recognition Gap</itunes:title>
      <itunes:subtitle>A public GitHub model spotted by a listener reveals the massive gap between hobbyist AI and lethal military drone detection systems.</itunes:subtitle>
      <itunes:summary><![CDATA[A fine-tuned object recognition model on GitHub reveals the chasm between open-source computer vision and classified military ATR systems. This episode explores why data diversity trumps volume, how Sim-to-Real transfer works, and the accelerating AI vs. AI arms race in drone warfare.]]></itunes:summary>
      <itunes:duration>1601</itunes:duration>
      <itunes:episode>2089</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-drone-recognition-training-data.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-drone-recognition-training-data.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Agents for Israel: Hyper-Local Skills in Action</title>
      <description><![CDATA[The podcast explores the emerging ecosystem of Model Context Protocol (MCP) servers and AI agent skills tailored specifically for Israel. It dives into how these bundles go beyond simple translation to provide "regulatory hard-coding" for complex bureaucracy, real-time civil defense data, and culturally nuanced communication. Listeners will learn about specific applications, from navigating tax laws and healthcare systems to finding bomb shelters, and how this hyper-localization represents a shift from generic global models to practical, action-oriented AI tools.]]></description>
      <link>https://myweirdprompts.com/episode/israeli-ai-agent-skills-mcp/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/israeli-ai-agent-skills-mcp/</guid>
      <pubDate>Mon, 06 Apr 2026 22:54:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/israeli-ai-agent-skills-mcp.m4a"
        type="audio/mp4"
        length="18400639"
      />
      <itunes:title>AI Agents for Israel: Hyper-Local Skills in Action</itunes:title>
      <itunes:subtitle>How reusable AI &quot;skills&quot; are solving real Israeli problems—from shelter navigation to tax compliance.</itunes:subtitle>
      <itunes:summary><![CDATA[The podcast explores the emerging ecosystem of Model Context Protocol (MCP) servers and AI agent skills tailored specifically for Israel. It dives into how these bundles go beyond simple translation to provide "regulatory hard-coding" for complex bureaucracy, real-time civil defense data, and culturally nuanced communication. Listeners will learn about specific applications, from navigating tax laws and healthcare systems to finding bomb shelters, and how this hyper-localization represents a shift from generic global models to practical, action-oriented AI tools.]]></itunes:summary>
      <itunes:duration>1422</itunes:duration>
      <itunes:episode>2075</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/israeli-ai-agent-skills-mcp.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/israeli-ai-agent-skills-mcp.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Generative Social Science: When AI Agents Develop Theory of Mind</title>
      <description><![CDATA[What happens when thousands of AI agents with memories, emotions, and social lives simulate a city? This episode explores how AgentSociety moves beyond rigid models to reveal emergent behaviors like trust, gossip, and economic ripple effects—and what that means for testing real-world policies.]]></description>
      <link>https://myweirdprompts.com/episode/ai-simulating-cities-agentsociety/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-simulating-cities-agentsociety/</guid>
      <pubDate>Mon, 06 Apr 2026 22:43:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-simulating-cities-agentsociety.m4a"
        type="audio/mp4"
        length="18766459"
      />
      <itunes:title>Generative Social Science: When AI Agents Develop Theory of Mind</itunes:title>
      <itunes:subtitle>See how a new framework models 10,000 virtual citizens to test policies before spending a dime.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when thousands of AI agents with memories, emotions, and social lives simulate a city? This episode explores how AgentSociety moves beyond rigid models to reveal emergent behaviors like trust, gossip, and economic ripple effects—and what that means for testing real-world policies.]]></itunes:summary>
      <itunes:duration>1434</itunes:duration>
      <itunes:episode>2074</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-simulating-cities-agentsociety.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-simulating-cities-agentsociety.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Git Can&apos;t Handle AI Agents—Yet</title>
      <description><![CDATA[As AI agents become standard coding partners, the version control systems we rely on are starting to crack. We explore the collision course between Git's human-centric design and autonomous AI workflows. From uncommitted work getting vaporized to "logical merge conflicts" that break your code, we unpack the chaos of parallel agents. Then, we dive into solutions: Git worktrees for isolation, file-level locking for coordination, and orchestrator patterns that manage the madness. Whether you're running Claude Code or building your own agent harness, this episode is a survival guide for the agentic age.]]></description>
      <link>https://myweirdprompts.com/episode/git-agents-parallel-workflows/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/git-agents-parallel-workflows/</guid>
      <pubDate>Mon, 06 Apr 2026 22:26:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/git-agents-parallel-workflows.m4a"
        type="audio/mp4"
        length="16748327"
      />
      <itunes:title>Git Can&apos;t Handle AI Agents—Yet</itunes:title>
      <itunes:subtitle>Three AI agents in one repo is pure chaos. Here&apos;s why Git&apos;s design causes collisions—and how worktrees and locks can save your sanity.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI agents become standard coding partners, the version control systems we rely on are starting to crack. We explore the collision course between Git's human-centric design and autonomous AI workflows. From uncommitted work getting vaporized to "logical merge conflicts" that break your code, we unpack the chaos of parallel agents. Then, we dive into solutions: Git worktrees for isolation, file-level locking for coordination, and orchestrator patterns that manage the madness. Whether you're running Claude Code or building your own agent harness, this episode is a survival guide for the agentic age.]]></itunes:summary>
      <itunes:duration>1288</itunes:duration>
      <itunes:episode>2071</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/git-agents-parallel-workflows.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/git-agents-parallel-workflows.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>SemVer, Changelogs, and the Social Contract of Code</title>
      <description><![CDATA[Why do some software updates break everything while others are seamless? This episode dives into Semantic Versioning (SemVer), the art of the changelog, and Conventional Commits. We explore how version numbers act as a social contract between developers and users, preventing "Dependency Hell" and ensuring trust in the digital ecosystem. Learn why a "Major" bump signals honesty, how automation enforces discipline, and the critical difference between deleting a release and "yanking" it.]]></description>
      <link>https://myweirdprompts.com/episode/semver-changelog-conventional-commits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/semver-changelog-conventional-commits/</guid>
      <pubDate>Mon, 06 Apr 2026 22:24:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/semver-changelog-conventional-commits.m4a"
        type="audio/mp4"
        length="16305080"
      />
      <itunes:title>SemVer, Changelogs, and the Social Contract of Code</itunes:title>
      <itunes:subtitle>Stop breaking the internet. Learn the exact system developers use to release software without causing chaos.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do some software updates break everything while others are seamless? This episode dives into Semantic Versioning (SemVer), the art of the changelog, and Conventional Commits. We explore how version numbers act as a social contract between developers and users, preventing "Dependency Hell" and ensuring trust in the digital ecosystem. Learn why a "Major" bump signals honesty, how automation enforces discipline, and the critical difference between deleting a release and "yanking" it.]]></itunes:summary>
      <itunes:duration>1265</itunes:duration>
      <itunes:episode>2070</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/semver-changelog-conventional-commits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/semver-changelog-conventional-commits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Vibe Coding Trap: Why Your Agent Skills Keep Breaking</title>
      <description><![CDATA[Most Claude Code skill authors are guessing at requirements. This episode explores the gap between vibe coding and procedural engineering, and why treating the agentskills.io spec as a formal contract is the only path to portable, production-ready agentic workflows.]]></description>
      <link>https://myweirdprompts.com/episode/agentskills-io-spec-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentskills-io-spec-guide/</guid>
      <pubDate>Mon, 06 Apr 2026 21:20:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentskills-io-spec-guide.m4a"
        type="audio/mp4"
        length="16353993"
      />
      <itunes:title>The Vibe Coding Trap: Why Your Agent Skills Keep Breaking</itunes:title>
      <itunes:subtitle>Stop guessing at the agentskills.io spec. Learn the exact YAML fields, directory structure, and authoring patterns to make Claude Code skills that ...</itunes:subtitle>
      <itunes:summary><![CDATA[Most Claude Code skill authors are guessing at requirements. This episode explores the gap between vibe coding and procedural engineering, and why treating the agentskills.io spec as a formal contract is the only path to portable, production-ready agentic workflows.]]></itunes:summary>
      <itunes:duration>1250</itunes:duration>
      <itunes:episode>2069</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentskills-io-spec-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentskills-io-spec-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Is Safety a Filter or a Feature?</title>
      <description><![CDATA[In the race to secure large language models, two competing philosophies have emerged: external guardrails that act as a firewall, and constitutional AI that embeds safety directly into the model's weights. This episode explores the trade-offs between auditability and robustness, latency and training cost, and the real-world implications for developers and regulators. We break down why the industry is moving toward a hybrid approach and what it means for the future of AI deployment.]]></description>
      <link>https://myweirdprompts.com/episode/safety-guardrails-constitutional-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/safety-guardrails-constitutional-ai/</guid>
      <pubDate>Mon, 06 Apr 2026 15:31:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/safety-guardrails-constitutional-ai.m4a"
        type="audio/mp4"
        length="18643570"
      />
      <itunes:title>Is Safety a Filter or a Feature?</itunes:title>
      <itunes:subtitle>External filters vs. baked-in ethics: the architectural war for LLM safety.</itunes:subtitle>
      <itunes:summary><![CDATA[In the race to secure large language models, two competing philosophies have emerged: external guardrails that act as a firewall, and constitutional AI that embeds safety directly into the model's weights. This episode explores the trade-offs between auditability and robustness, latency and training cost, and the real-world implications for developers and regulators. We break down why the industry is moving toward a hybrid approach and what it means for the future of AI deployment.]]></itunes:summary>
      <itunes:duration>1425</itunes:duration>
      <itunes:episode>2068</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/safety-guardrails-constitutional-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/safety-guardrails-constitutional-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>MoE vs. Dense: The VRAM Nightmare</title>
      <description><![CDATA[The AI world is obsessed with Mixture of Experts models, but dense transformers are quietly staging a comeback. This episode breaks down the brutal tradeoffs: MoE wins on training compute but loses on VRAM, fine-tuning stability, and edge deployment. We explore why the "free lunch" of massive parameter counts comes with a hidden tax, and where each architecture actually makes sense for developers.]]></description>
      <link>https://myweirdprompts.com/episode/mixture-of-experts-vs-dense-vram/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mixture-of-experts-vs-dense-vram/</guid>
      <pubDate>Mon, 06 Apr 2026 15:26:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mixture-of-experts-vs-dense-vram.m4a"
        type="audio/mp4"
        length="18836309"
      />
      <itunes:title>MoE vs. Dense: The VRAM Nightmare</itunes:title>
      <itunes:subtitle>MoE models promise giant brains on a budget, but why are engineers fleeing back to dense transformers? The answer is memory.</itunes:subtitle>
      <itunes:summary><![CDATA[The AI world is obsessed with Mixture of Experts models, but dense transformers are quietly staging a comeback. This episode breaks down the brutal tradeoffs: MoE wins on training compute but loses on VRAM, fine-tuning stability, and edge deployment. We explore why the "free lunch" of massive parameter counts comes with a hidden tax, and where each architecture actually makes sense for developers.]]></itunes:summary>
      <itunes:duration>1458</itunes:duration>
      <itunes:episode>2067</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mixture-of-experts-vs-dense-vram.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mixture-of-experts-vs-dense-vram.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Run One AI When You Can Run Two?</title>
      <description><![CDATA[Inference latency is the biggest bottleneck for deploying large language models. This episode explores speculative decoding, a clever technique that uses a small draft model to predict tokens ahead of time, which a larger model then verifies in a single pass. Learn how methods like Medusa, EAGLE, and Mamba hybrids achieve 2-6x speedups without sacrificing quality, and why this matters for real-time AI applications and GPU economics.]]></description>
      <link>https://myweirdprompts.com/episode/speculative-decoding-speedup-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speculative-decoding-speedup-explained/</guid>
      <pubDate>Mon, 06 Apr 2026 15:16:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speculative-decoding-speedup-explained.m4a"
        type="audio/mp4"
        length="16561610"
      />
      <itunes:title>Why Run One AI When You Can Run Two?</itunes:title>
      <itunes:subtitle>Speculative decoding makes LLMs 2-3x faster with zero quality loss by using a small draft model to guess tokens that a large model verifies in para...</itunes:subtitle>
      <itunes:summary><![CDATA[Inference latency is the biggest bottleneck for deploying large language models. This episode explores speculative decoding, a clever technique that uses a small draft model to predict tokens ahead of time, which a larger model then verifies in a single pass. Learn how methods like Medusa, EAGLE, and Mamba hybrids achieve 2-6x speedups without sacrificing quality, and why this matters for real-time AI applications and GPU economics.]]></itunes:summary>
      <itunes:duration>1261</itunes:duration>
      <itunes:episode>2065</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speculative-decoding-speedup-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speculative-decoding-speedup-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Transformers Learn Word Order: From Sine Waves to RoPE</title>
      <description><![CDATA[Why do transformers need special tricks to understand word order? This episode dives into the math behind positional encoding—from the original sine waves to learned embeddings, ALiBi, and the modern RoPE standard. Learn how these methods enable massive context windows and why RoPE is now the go-to choice for models like Llama and GPT-4.]]></description>
      <link>https://myweirdprompts.com/episode/transformer-positional-encoding-rope/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/transformer-positional-encoding-rope/</guid>
      <pubDate>Mon, 06 Apr 2026 15:00:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/transformer-positional-encoding-rope.m4a"
        type="audio/mp4"
        length="17266142"
      />
      <itunes:title>How Transformers Learn Word Order: From Sine Waves to RoPE</itunes:title>
      <itunes:subtitle>Transformers can’t see word order by default. Here’s how positional encoding fixes that—from sine waves to RoPE and massive context windows.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do transformers need special tricks to understand word order? This episode dives into the math behind positional encoding—from the original sine waves to learned embeddings, ALiBi, and the modern RoPE standard. Learn how these methods enable massive context windows and why RoPE is now the go-to choice for models like Llama and GPT-4.]]></itunes:summary>
      <itunes:duration>1311</itunes:duration>
      <itunes:episode>2062</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/transformer-positional-encoding-rope.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/transformer-positional-encoding-rope.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Memory Bottleneck That Drives Attention Design</title>
      <description><![CDATA[Why do LLMs need different attention mechanisms? This episode explores the memory bottleneck of the KV cache and the architectural tradeoffs—from Multi-Head to Multi-Head Latent Attention—that keep models from collapsing under their own weight.]]></description>
      <link>https://myweirdprompts.com/episode/transformer-attention-variants-memory/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/transformer-attention-variants-memory/</guid>
      <pubDate>Mon, 06 Apr 2026 14:59:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/transformer-attention-variants-memory.m4a"
        type="audio/mp4"
        length="17824603"
      />
      <itunes:title>The Memory Bottleneck That Drives Attention Design</itunes:title>
      <itunes:subtitle>Attention is the engine of modern AI, but it’s also a memory hog. Here’s how MQA, GQA, and MLA evolved to fix it.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do LLMs need different attention mechanisms? This episode explores the memory bottleneck of the KV cache and the architectural tradeoffs—from Multi-Head to Multi-Head Latent Attention—that keep models from collapsing under their own weight.]]></itunes:summary>
      <itunes:duration>1363</itunes:duration>
      <itunes:episode>2061</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/transformer-attention-variants-memory.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/transformer-attention-variants-memory.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Tokenizer&apos;s Hidden Tax on Non-English Text</title>
      <description><![CDATA[We explore the invisible machinery of tokenization, the hidden bottleneck in AI that dictates speed, cost, and language capability. From BPE to SentencePiece, we break down why non-English text often carries a higher computational tax and how modern tokenizers like tiktoken are optimizing for a multilingual world.]]></description>
      <link>https://myweirdprompts.com/episode/tokenizer-language-efficiency-tax/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tokenizer-language-efficiency-tax/</guid>
      <pubDate>Mon, 06 Apr 2026 14:53:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tokenizer-language-efficiency-tax.m4a"
        type="audio/mp4"
        length="18269067"
      />
      <itunes:title>The Tokenizer&apos;s Hidden Tax on Non-English Text</itunes:title>
      <itunes:subtitle>Why does a simple greeting in Mandarin cost more to process than in English? It&apos;s the tokenizer&apos;s hidden inefficiency.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the invisible machinery of tokenization, the hidden bottleneck in AI that dictates speed, cost, and language capability. From BPE to SentencePiece, we break down why non-English text often carries a higher computational tax and how modern tokenizers like tiktoken are optimizing for a multilingual world.]]></itunes:summary>
      <itunes:duration>1378</itunes:duration>
      <itunes:episode>2060</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tokenizer-language-efficiency-tax.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tokenizer-language-efficiency-tax.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your AI Agent Runs Stale Code</title>
      <description><![CDATA[npx was built for humans, not autonomous agents. This episode reveals how npm's caching mechanism silently serves outdated, vulnerable code to AI pipelines—and what developers can do to force real-time updates.]]></description>
      <link>https://myweirdprompts.com/episode/npm-cache-silent-stale-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/npm-cache-silent-stale-agents/</guid>
      <pubDate>Mon, 06 Apr 2026 14:22:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/npm-cache-silent-stale-agents.m4a"
        type="audio/mp4"
        length="19304386"
      />
      <itunes:title>When Your AI Agent Runs Stale Code</itunes:title>
      <itunes:subtitle>npx is silently running old versions of your AI tools. Here&apos;s why your updates vanish into a cache black hole.</itunes:subtitle>
      <itunes:summary><![CDATA[npx was built for humans, not autonomous agents. This episode reveals how npm's caching mechanism silently serves outdated, vulnerable code to AI pipelines—and what developers can do to force real-time updates.]]></itunes:summary>
      <itunes:duration>1491</itunes:duration>
      <itunes:episode>2059</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/npm-cache-silent-stale-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/npm-cache-silent-stale-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Stuxnet&apos;s Code Physically Broke Iran&apos;s Centrifuges</title>
      <description><![CDATA[This episode dives deep into the technical operation of Stuxnet, the malware that bridged the digital and physical worlds to sabotage Iran's Natanz facility. We explore how it used four zero-days to breach an air-gapped network, fingerprinted specific hardware configurations, and replaced legitimate library files to create a "digital hallucination" for operators. The discussion covers the precise PLC injection logic, the over-speed and critical-speed attack sequences that physically destroyed centrifuges, and the sophisticated signal masking that hid the damage from screens. It's a look at how code became a precision-guided weapon.]]></description>
      <link>https://myweirdprompts.com/episode/stuxnet-plc-injection-sabotage/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/stuxnet-plc-injection-sabotage/</guid>
      <pubDate>Mon, 06 Apr 2026 11:12:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/stuxnet-plc-injection-sabotage.m4a"
        type="audio/mp4"
        length="17229523"
      />
      <itunes:title>How Stuxnet&apos;s Code Physically Broke Iran&apos;s Centrifuges</itunes:title>
      <itunes:subtitle>Stuxnet didn&apos;t just infect computers—it rewrote PLC logic to spin uranium centrifuges into self-destruction while faking normal readings.</itunes:subtitle>
      <itunes:summary><![CDATA[This episode dives deep into the technical operation of Stuxnet, the malware that bridged the digital and physical worlds to sabotage Iran's Natanz facility. We explore how it used four zero-days to breach an air-gapped network, fingerprinted specific hardware configurations, and replaced legitimate library files to create a "digital hallucination" for operators. The discussion covers the precise PLC injection logic, the over-speed and critical-speed attack sequences that physically destroyed centrifuges, and the sophisticated signal masking that hid the damage from screens. It's a look at how code became a precision-guided weapon.]]></itunes:summary>
      <itunes:duration>1312</itunes:duration>
      <itunes:episode>2058</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/stuxnet-plc-injection-sabotage.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/stuxnet-plc-injection-sabotage.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Agents Break Through the LLM Output Ceiling</title>
      <description><![CDATA[We explore the paradox of modern LLMs: while input windows grow to millions of tokens, output limits remain stubbornly short. This episode breaks down how agentic workflows overcome this constraint using state serialization, external memory, and recursive planning to maintain coherence over long tasks. Learn why writing a novel requires more than just a big brain—it needs architectural scaffolding.]]></description>
      <link>https://myweirdprompts.com/episode/llm-output-limit-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-output-limit-agents/</guid>
      <pubDate>Sun, 05 Apr 2026 22:57:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-output-limit-agents.m4a"
        type="audio/mp4"
        length="17558486"
      />
      <itunes:title>How Agents Break Through the LLM Output Ceiling</itunes:title>
      <itunes:subtitle>The output window is the new bottleneck: why massive context doesn&apos;t solve long-form generation.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the paradox of modern LLMs: while input windows grow to millions of tokens, output limits remain stubbornly short. This episode breaks down how agentic workflows overcome this constraint using state serialization, external memory, and recursive planning to maintain coherence over long tasks. Learn why writing a novel requires more than just a big brain—it needs architectural scaffolding.]]></itunes:summary>
      <itunes:duration>1354</itunes:duration>
      <itunes:episode>2057</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-output-limit-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-output-limit-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Wrappers to State Machines</title>
      <description><![CDATA[The no-code era for agents is over. This episode maps the technical shift from simple chains to state-machine architectures, covering the languages and functions that make production-grade agentic AI possible.]]></description>
      <link>https://myweirdprompts.com/episode/python-typescript-rust-agentic-stack/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/python-typescript-rust-agentic-stack/</guid>
      <pubDate>Sun, 05 Apr 2026 19:19:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/python-typescript-rust-agentic-stack.m4a"
        type="audio/mp4"
        length="20771546"
      />
      <itunes:title>From Wrappers to State Machines</itunes:title>
      <itunes:subtitle>Skip no-code traps. Learn the real stack for building agentic AI: Python, TypeScript, and Rust.</itunes:subtitle>
      <itunes:summary><![CDATA[The no-code era for agents is over. This episode maps the technical shift from simple chains to state-machine architectures, covering the languages and functions that make production-grade agentic AI possible.]]></itunes:summary>
      <itunes:duration>1611</itunes:duration>
      <itunes:episode>2043</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/python-typescript-rust-agentic-stack.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/python-typescript-rust-agentic-stack.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The &quot;MPEG Moment&quot; for AI: Llamafile &amp; Native Models</title>
      <description><![CDATA[The standard workflow for local AI—taking massive cloud models and hacking them to fit—feels like fitting a semi-truck into a garage. This episode explores the shift toward "local-first" models built for your hardware from the ground up. We dive into Google's Gemma 3 with Quantization-Aware Training, Microsoft's BitNet for CPU efficiency, and the "MPEG moment" of Llamafile. Discover why the future of AI might be smaller, natively optimized, and finally easy to run.]]></description>
      <link>https://myweirdprompts.com/episode/local-first-ai-native-models/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/local-first-ai-native-models/</guid>
      <pubDate>Sun, 05 Apr 2026 16:57:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/local-first-ai-native-models.m4a"
        type="audio/mp4"
        length="17561456"
      />
      <itunes:title>The &quot;MPEG Moment&quot; for AI: Llamafile &amp; Native Models</itunes:title>
      <itunes:subtitle>Why are we squeezing massive cloud models onto desktops? Meet the &quot;native&quot; AI revolution.</itunes:subtitle>
      <itunes:summary><![CDATA[The standard workflow for local AI—taking massive cloud models and hacking them to fit—feels like fitting a semi-truck into a garage. This episode explores the shift toward "local-first" models built for your hardware from the ground up. We dive into Google's Gemma 3 with Quantization-Aware Training, Microsoft's BitNet for CPU efficiency, and the "MPEG moment" of Llamafile. Discover why the future of AI might be smaller, natively optimized, and finally easy to run.]]></itunes:summary>
      <itunes:duration>1359</itunes:duration>
      <itunes:episode>2041</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/local-first-ai-native-models.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/local-first-ai-native-models.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Rebellion Against Big Tech&apos;s AI Lock-In</title>
      <description><![CDATA[Why are open-source inference engines like Ollama and llama.cpp challenging the dominance of Google and OpenAI? This episode explores the fragmentation, the philosophy of local AI, and what it means for the future of computing.]]></description>
      <link>https://myweirdprompts.com/episode/open-source-inference-engines/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/open-source-inference-engines/</guid>
      <pubDate>Sun, 05 Apr 2026 16:56:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/open-source-inference-engines.m4a"
        type="audio/mp4"
        length="18315679"
      />
      <itunes:title>The Rebellion Against Big Tech&apos;s AI Lock-In</itunes:title>
      <itunes:subtitle>Why run LLMs locally? We break down Ollama, llama.cpp, vLLM, and llamafile—and when to use each.</itunes:subtitle>
      <itunes:summary><![CDATA[Why are open-source inference engines like Ollama and llama.cpp challenging the dominance of Google and OpenAI? This episode explores the fragmentation, the philosophy of local AI, and what it means for the future of computing.]]></itunes:summary>
      <itunes:duration>1423</itunes:duration>
      <itunes:episode>2040</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/open-source-inference-engines.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/open-source-inference-engines.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>CLIs vs. MCPs: How AI Agents Actually Talk to Services</title>
      <description><![CDATA[We explore the architectural debate between using legacy CLIs and the new Model Context Protocol for AI agents. Learn why CLIs offer latent knowledge and efficiency, while MCPs provide structure and security, and discover the emerging "hybrid" approach developers are adopting for local and production environments.]]></description>
      <link>https://myweirdprompts.com/episode/cli-mcp-ai-agent-communication/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cli-mcp-ai-agent-communication/</guid>
      <pubDate>Sun, 05 Apr 2026 16:37:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cli-mcp-ai-agent-communication.m4a"
        type="audio/mp4"
        length="18311592"
      />
      <itunes:title>CLIs vs. MCPs: How AI Agents Actually Talk to Services</itunes:title>
      <itunes:subtitle>Why give an AI agent a terminal? We compare CLIs and MCPs for AI integration.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the architectural debate between using legacy CLIs and the new Model Context Protocol for AI agents. Learn why CLIs offer latent knowledge and efficiency, while MCPs provide structure and security, and discover the emerging "hybrid" approach developers are adopting for local and production environments.]]></itunes:summary>
      <itunes:duration>1416</itunes:duration>
      <itunes:episode>2039</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cli-mcp-ai-agent-communication.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cli-mcp-ai-agent-communication.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>UI-First vs Architecture-First: Choosing Your AI Agent</title>
      <description><![CDATA[Self-hosted AI agents are split between polished interfaces like LobeHub and raw frameworks like OpenClaw. We explore the philosophical trade-offs between user experience and architectural control to help you decide which approach actually owns your data.]]></description>
      <link>https://myweirdprompts.com/episode/self-hosted-ai-agent-comparison/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/self-hosted-ai-agent-comparison/</guid>
      <pubDate>Sun, 05 Apr 2026 16:13:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/self-hosted-ai-agent-comparison.m4a"
        type="audio/mp4"
        length="18219904"
      />
      <itunes:title>UI-First vs Architecture-First: Choosing Your AI Agent</itunes:title>
      <itunes:subtitle>LobeHub vs. Dify vs. n8n: We break down the chaotic landscape of local AI agents to find the right &quot;brain&quot; for your workflow.</itunes:subtitle>
      <itunes:summary><![CDATA[Self-hosted AI agents are split between polished interfaces like LobeHub and raw frameworks like OpenClaw. We explore the philosophical trade-offs between user experience and architectural control to help you decide which approach actually owns your data.]]></itunes:summary>
      <itunes:duration>1369</itunes:duration>
      <itunes:episode>2038</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/self-hosted-ai-agent-comparison.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/self-hosted-ai-agent-comparison.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Hierarchy of Claude Code Extensions</title>
      <description><![CDATA[Most developers treat slash commands, skills, subagents, and plugins as interchangeable. But there's a deliberate logic to how they fit together. This episode reveals the mental model that turns confusion into clarity.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-extensions-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-extensions-guide/</guid>
      <pubDate>Sun, 05 Apr 2026 16:12:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-extensions-guide.m4a"
        type="audio/mp4"
        length="16535194"
      />
      <itunes:title>The Hidden Hierarchy of Claude Code Extensions</itunes:title>
      <itunes:subtitle>Stop manually typing slash commands. Here’s the definitive hierarchy of Claude Code extensions—from legacy shortcuts to autonomous agents.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers treat slash commands, skills, subagents, and plugins as interchangeable. But there's a deliberate logic to how they fit together. This episode reveals the mental model that turns confusion into clarity.]]></itunes:summary>
      <itunes:duration>1266</itunes:duration>
      <itunes:episode>2037</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-extensions-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-extensions-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Agent Skills Are the New Apps</title>
      <description><![CDATA[The era of the monolithic AI prompt is ending. We dive into the exploding world of agent skills and marketplaces like LobeHub and Skills MP, where AI agents can "install" cognitive abilities just like apps on a phone. Learn how the SKILL.MD standard works, why security is becoming a "vetter skill" arms race, and how this shift from general chatbots to specialized agentic systems is redefining the value of human expertise.]]></description>
      <link>https://myweirdprompts.com/episode/agent-skills-marketplace-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-skills-marketplace-ai/</guid>
      <pubDate>Sun, 05 Apr 2026 12:44:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-skills-marketplace-ai.m4a"
        type="audio/mp4"
        length="19905364"
      />
      <itunes:title>Agent Skills Are the New Apps</itunes:title>
      <itunes:subtitle>AI agents are getting an App Store for brains. Discover how modular skills are replacing massive prompts and what it means for the future of work.</itunes:subtitle>
      <itunes:summary><![CDATA[The era of the monolithic AI prompt is ending. We dive into the exploding world of agent skills and marketplaces like LobeHub and Skills MP, where AI agents can "install" cognitive abilities just like apps on a phone. Learn how the SKILL.MD standard works, why security is becoming a "vetter skill" arms race, and how this shift from general chatbots to specialized agentic systems is redefining the value of human expertise.]]></itunes:summary>
      <itunes:duration>1537</itunes:duration>
      <itunes:episode>2028</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-skills-marketplace-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-skills-marketplace-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Missing Photoshop for Words</title>
      <description><![CDATA[Why is it so hard to find a polished tool for the simplest LLM use case—transforming text? We explore the gap between the power of small local models and the fragmented, half-baked software that should make them effortless.]]></description>
      <link>https://myweirdprompts.com/episode/text-transformation-missing-tool/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/text-transformation-missing-tool/</guid>
      <pubDate>Sun, 05 Apr 2026 10:42:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/text-transformation-missing-tool.m4a"
        type="audio/mp4"
        length="21270532"
      />
      <itunes:title>The Missing Photoshop for Words</itunes:title>
      <itunes:subtitle>Why is editing text with AI so clunky? We explore the &quot;TITO&quot; paradigm—using small, local models for fast, private text transformation.</itunes:subtitle>
      <itunes:summary><![CDATA[Why is it so hard to find a polished tool for the simplest LLM use case—transforming text? We explore the gap between the power of small local models and the fragmented, half-baked software that should make them effortless.]]></itunes:summary>
      <itunes:duration>1649</itunes:duration>
      <itunes:episode>2027</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/text-transformation-missing-tool.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/text-transformation-missing-tool.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Prompt Layering: Beyond the Monolithic Prompt</title>
      <description><![CDATA[We explore prompt layering, the technique replacing giant, monolithic prompts with modular, stackable instruction layers. Discover how to use base layers and modifiers to build scalable AI systems, avoid instruction conflicts, and manage the combinatorial explosion of user choices. We also cover advanced use cases in code generation, compliance, and multi-persona simulation.]]></description>
      <link>https://myweirdprompts.com/episode/prompt-layering-modular-instructions/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/prompt-layering-modular-instructions/</guid>
      <pubDate>Sun, 05 Apr 2026 10:38:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/prompt-layering-modular-instructions.m4a"
        type="audio/mp4"
        length="17174506"
      />
      <itunes:title>Prompt Layering: Beyond the Monolithic Prompt</itunes:title>
      <itunes:subtitle>Stop writing giant, monolithic prompts. Learn how to stack modular layers for cleaner, more powerful AI applications.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore prompt layering, the technique replacing giant, monolithic prompts with modular, stackable instruction layers. Discover how to use base layers and modifiers to build scalable AI systems, avoid instruction conflicts, and manage the combinatorial explosion of user choices. We also cover advanced use cases in code generation, compliance, and multi-persona simulation.]]></itunes:summary>
      <itunes:duration>1309</itunes:duration>
      <itunes:episode>2026</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/prompt-layering-modular-instructions.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/prompt-layering-modular-instructions.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Do You Reward a Thought?</title>
      <description><![CDATA[When an AI agent does a task, how do we tell it if it did a good job? This episode dives into the billion-dollar challenge of translating human values like "helpfulness" or "good reasoning" into mathematical signals. We explore why outcome rewards are too sparse for complex tasks, how process rewards can guide internal thoughts, and the surprising breakthrough of iStar. Plus, we tackle the dark side of reward hacking and why teaching an AI to be "nice" is harder than it looks.]]></description>
      <link>https://myweirdprompts.com/episode/reward-functions-agentic-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/reward-functions-agentic-ai/</guid>
      <pubDate>Sun, 05 Apr 2026 05:59:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/reward-functions-agentic-ai.m4a"
        type="audio/mp4"
        length="18065591"
      />
      <itunes:title>How Do You Reward a Thought?</itunes:title>
      <itunes:subtitle>Rewarding an AI agent is harder than just saying &quot;good job&quot;—here&apos;s how we turn messy human values into math.</itunes:subtitle>
      <itunes:summary><![CDATA[When an AI agent does a task, how do we tell it if it did a good job? This episode dives into the billion-dollar challenge of translating human values like "helpfulness" or "good reasoning" into mathematical signals. We explore why outcome rewards are too sparse for complex tasks, how process rewards can guide internal thoughts, and the surprising breakthrough of iStar. Plus, we tackle the dark side of reward hacking and why teaching an AI to be "nice" is harder than it looks.]]></itunes:summary>
      <itunes:duration>1395</itunes:duration>
      <itunes:episode>2025</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/reward-functions-agentic-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/reward-functions-agentic-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Your AI Council: Digital Committee or Groupthink?</title>
      <description><![CDATA[Instead of asking one AI, what if you summoned a digital boardroom? The "Council of LLMs" is a rising architectural pattern where multiple models debate your choices—from personal dilemmas to policy decisions—before reaching a consensus. This episode explores the mechanics of these AI committees, their potential to cure hallucinations, and the surprising risks of "groupthink" on a massive scale. Discover how this approach could transform decision-making, and why it might be more like management than magic.]]></description>
      <link>https://myweirdprompts.com/episode/ai-council-groupthink-consensus/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-council-groupthink-consensus/</guid>
      <pubDate>Sun, 05 Apr 2026 00:42:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-council-groupthink-consensus.m4a"
        type="audio/mp4"
        length="17644882"
      />
      <itunes:title>Your AI Council: Digital Committee or Groupthink?</itunes:title>
      <itunes:subtitle>A digital boardroom of AI models promises better decisions, but risks amplifying the same old biases.</itunes:subtitle>
      <itunes:summary><![CDATA[Instead of asking one AI, what if you summoned a digital boardroom? The "Council of LLMs" is a rising architectural pattern where multiple models debate your choices—from personal dilemmas to policy decisions—before reaching a consensus. This episode explores the mechanics of these AI committees, their potential to cure hallucinations, and the surprising risks of "groupthink" on a massive scale. Discover how this approach could transform decision-making, and why it might be more like management than magic.]]></itunes:summary>
      <itunes:duration>1330</itunes:duration>
      <itunes:episode>2024</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-council-groupthink-consensus.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-council-groupthink-consensus.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Becomes Your IT Department</title>
      <description><![CDATA[OpenClaw processes 16.5 trillion tokens daily, but developers are using it for far more than chat—from vibe-checking server logs to building self-directed agents. This episode explores 47 real-world use cases that reveal AI's shift from tool to autonomous colleague.]]></description>
      <link>https://myweirdprompts.com/episode/openclaw-autonomous-use-cases/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/openclaw-autonomous-use-cases/</guid>
      <pubDate>Sun, 05 Apr 2026 00:00:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/openclaw-autonomous-use-cases.m4a"
        type="audio/mp4"
        length="16043050"
      />
      <itunes:title>When AI Becomes Your IT Department</itunes:title>
      <itunes:subtitle>We dug into a repo of 47 real-world projects showing how OpenClaw powers everything from self-healing servers to overnight app builders.</itunes:subtitle>
      <itunes:summary><![CDATA[OpenClaw processes 16.5 trillion tokens daily, but developers are using it for far more than chat—from vibe-checking server logs to building self-directed agents. This episode explores 47 real-world use cases that reveal AI's shift from tool to autonomous colleague.]]></itunes:summary>
      <itunes:duration>1235</itunes:duration>
      <itunes:episode>2022</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/openclaw-autonomous-use-cases.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/openclaw-autonomous-use-cases.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Your Frozen AI Is Getting Smarter (Here&apos;s How)</title>
      <description><![CDATA[We explore how agentic systems can make frozen AI models smarter without changing their weights. Using the OpenClaw-RL project as a case study, we break down the four-component loop—Agent Serving, Rollout Collection, Evaluation, and Policy Training—that turns the environment into a teacher. Learn about Process Reward Models, reward hacking risks, and why tool routing might be more important than raw reasoning.]]></description>
      <link>https://myweirdprompts.com/episode/frozen-models-getting-smarter/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/frozen-models-getting-smarter/</guid>
      <pubDate>Sat, 04 Apr 2026 23:24:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/frozen-models-getting-smarter.m4a"
        type="audio/mp4"
        length="20964881"
      />
      <itunes:title>Your Frozen AI Is Getting Smarter (Here&apos;s How)</itunes:title>
      <itunes:subtitle>Your AI model might be static, but the system around it can make it learn in real-time.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore how agentic systems can make frozen AI models smarter without changing their weights. Using the OpenClaw-RL project as a case study, we break down the four-component loop—Agent Serving, Rollout Collection, Evaluation, and Policy Training—that turns the environment into a teacher. Learn about Process Reward Models, reward hacking risks, and why tool routing might be more important than raw reasoning.]]></itunes:summary>
      <itunes:duration>1592</itunes:duration>
      <itunes:episode>2021</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/frozen-models-getting-smarter.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/frozen-models-getting-smarter.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>1,000 AI Agents Built a Religion in Minecraft</title>
      <description><![CDATA[What happens when you drop 1,000 autonomous AI agents into a Minecraft world with nothing but survival goals? In Project Sid, they didn't just build houses—they built a civilization. This episode explores the frontier of multi-agent systems, from surprise trip planners that keep secrets to AI chemists that control robots and digital societies that invent their own religions. We examine how emergent behavior arises when agents are given goals instead of instructions, and what it means when AI starts reasoning in natural language, optimizing perfume formulas, and voting on tax rates.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agents-minecraft-civilization-emergence/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agents-minecraft-civilization-emergence/</guid>
      <pubDate>Sat, 04 Apr 2026 22:53:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agents-minecraft-civilization-emergence.m4a"
        type="audio/mp4"
        length="16884593"
      />
      <itunes:title>1,000 AI Agents Built a Religion in Minecraft</itunes:title>
      <itunes:subtitle>An experiment drops 1,000 autonomous agents into Minecraft, and they spontaneously invent religion, democracy, and taxes.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when you drop 1,000 autonomous AI agents into a Minecraft world with nothing but survival goals? In Project Sid, they didn't just build houses—they built a civilization. This episode explores the frontier of multi-agent systems, from surprise trip planners that keep secrets to AI chemists that control robots and digital societies that invent their own religions. We examine how emergent behavior arises when agents are given goals instead of instructions, and what it means when AI starts reasoning in natural language, optimizing perfume formulas, and voting on tax rates.]]></itunes:summary>
      <itunes:duration>1294</itunes:duration>
      <itunes:episode>2020</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agents-minecraft-civilization-emergence.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agents-minecraft-civilization-emergence.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Local AI vs Cloud AI: The Agent Identity Crisis</title>
      <description><![CDATA[The tension between local-first AI assistants and cloud-native orchestrators is creating a sharp architectural schism. This episode dives into the "agent identity crisis," exploring why local agents offer high-bandwidth, low-latency control but suffer from siloed environments, while cloud agents promise persistence and orchestration but lack direct access to your machine. We unpack the trade-offs of "environment-bound" setups, the absurdity of self-hosting private clouds, and the technical hurdles of vision and latency. Discover the "bouncer" model for privacy, the nightmare of configuration drift, and the emerging "thin-agent" architecture that might finally bridge the gap between your local machine and the cloud.]]></description>
      <link>https://myweirdprompts.com/episode/local-cloud-agent-identity-crisis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/local-cloud-agent-identity-crisis/</guid>
      <pubDate>Sat, 04 Apr 2026 22:49:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/local-cloud-agent-identity-crisis.m4a"
        type="audio/mp4"
        length="21921674"
      />
      <itunes:title>Local AI vs Cloud AI: The Agent Identity Crisis</itunes:title>
      <itunes:subtitle>Your desktop is becoming a life support system for AI agents. We explore the sharp trade-offs between local-first and cloud-native architectures.</itunes:subtitle>
      <itunes:summary><![CDATA[The tension between local-first AI assistants and cloud-native orchestrators is creating a sharp architectural schism. This episode dives into the "agent identity crisis," exploring why local agents offer high-bandwidth, low-latency control but suffer from siloed environments, while cloud agents promise persistence and orchestration but lack direct access to your machine. We unpack the trade-offs of "environment-bound" setups, the absurdity of self-hosting private clouds, and the technical hurdles of vision and latency. Discover the "bouncer" model for privacy, the nightmare of configuration drift, and the emerging "thin-agent" architecture that might finally bridge the gap between your local machine and the cloud.]]></itunes:summary>
      <itunes:duration>1719</itunes:duration>
      <itunes:episode>2019</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/local-cloud-agent-identity-crisis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/local-cloud-agent-identity-crisis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Micro Frontends Actually Make Sense</title>
      <description><![CDATA[Fifty developers sharing one frontend repo is a coordination nightmare. But micro frontends come with their own 'luxury tax.' This episode explores when the organizational cost of a monolith outweighs the architectural complexity of distributed UIs, drawing on real-world lessons from IKEA and Spotify.]]></description>
      <link>https://myweirdprompts.com/episode/micro-frontends-architectural-tax/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/micro-frontends-architectural-tax/</guid>
      <pubDate>Sat, 04 Apr 2026 22:42:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/micro-frontends-architectural-tax.m4a"
        type="audio/mp4"
        length="17646423"
      />
      <itunes:title>When Micro Frontends Actually Make Sense</itunes:title>
      <itunes:subtitle>The frontend monolith is a nightmare of coordination. Micro frontends promise autonomy, but is the operational complexity worth the cost?</itunes:subtitle>
      <itunes:summary><![CDATA[Fifty developers sharing one frontend repo is a coordination nightmare. But micro frontends come with their own 'luxury tax.' This episode explores when the organizational cost of a monolith outweighs the architectural complexity of distributed UIs, drawing on real-world lessons from IKEA and Spotify.]]></itunes:summary>
      <itunes:duration>1359</itunes:duration>
      <itunes:episode>2018</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/micro-frontends-architectural-tax.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/micro-frontends-architectural-tax.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Art of Squeezing AI Models onto Your GPU</title>
      <description><![CDATA[How quantization turns massive AI models into something that fits on consumer hardware. We decode the alphabet soup of formats like Q4_K_M and EXL2, and explain why four-bit precision is the sweet spot for running local AI without turning your model into an idiot.]]></description>
      <link>https://myweirdprompts.com/episode/quantization-gguf-unsloth-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/quantization-gguf-unsloth-explained/</guid>
      <pubDate>Sat, 04 Apr 2026 22:28:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/quantization-gguf-unsloth-explained.m4a"
        type="audio/mp4"
        length="16688099"
      />
      <itunes:title>The Art of Squeezing AI Models onto Your GPU</itunes:title>
      <itunes:subtitle>Those cryptic letters on Hugging Face actually map how much brain power you trade for speed.</itunes:subtitle>
      <itunes:summary><![CDATA[How quantization turns massive AI models into something that fits on consumer hardware. We decode the alphabet soup of formats like Q4_K_M and EXL2, and explain why four-bit precision is the sweet spot for running local AI without turning your model into an idiot.]]></itunes:summary>
      <itunes:duration>1275</itunes:duration>
      <itunes:episode>2017</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/quantization-gguf-unsloth-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/quantization-gguf-unsloth-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Andrej Karpathy: The Bob Ross of Deep Learning</title>
      <description><![CDATA[While major AI labs guard their models like nuclear codes, Andrej Karpathy is teaching millions to build neural networks from first principles. We explore his "Software 2.0" philosophy at Tesla, the minimalist nanoGPT project, and why fundamental understanding matters more than ever in the age of the "slopacolypse."]]></description>
      <link>https://myweirdprompts.com/episode/karpathy-from-scratch-philosophy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/karpathy-from-scratch-philosophy/</guid>
      <pubDate>Sat, 04 Apr 2026 21:39:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/karpathy-from-scratch-philosophy.m4a"
        type="audio/mp4"
        length="18733129"
      />
      <itunes:title>Andrej Karpathy: The Bob Ross of Deep Learning</itunes:title>
      <itunes:subtitle>Why the most influential AI mind prefers a blank text file to proprietary black boxes.</itunes:subtitle>
      <itunes:summary><![CDATA[While major AI labs guard their models like nuclear codes, Andrej Karpathy is teaching millions to build neural networks from first principles. We explore his "Software 2.0" philosophy at Tesla, the minimalist nanoGPT project, and why fundamental understanding matters more than ever in the age of the "slopacolypse."]]></itunes:summary>
      <itunes:duration>1445</itunes:duration>
      <itunes:episode>2016</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/karpathy-from-scratch-philosophy.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/karpathy-from-scratch-philosophy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Coding Tools Are Secretly System Agents</title>
      <description><![CDATA[The industry calls them "coding assistants," but the reality is far broader. We explore how terminal agents like Claude Code are being used for everything from podcast production to system administration, and why the "developer tool" label is holding them back. Discover the power of structured workspaces, the Model Context Protocol, and why git might be the accidental universal language for AI productivity.]]></description>
      <link>https://myweirdprompts.com/episode/terminal-agents-system-operators/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/terminal-agents-system-operators/</guid>
      <pubDate>Sat, 04 Apr 2026 21:12:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/terminal-agents-system-operators.m4a"
        type="audio/mp4"
        length="20674838"
      />
      <itunes:title>Coding Tools Are Secretly System Agents</itunes:title>
      <itunes:subtitle>They call it a coding assistant, but real users are treating it like a personal operating system.</itunes:subtitle>
      <itunes:summary><![CDATA[The industry calls them "coding assistants," but the reality is far broader. We explore how terminal agents like Claude Code are being used for everything from podcast production to system administration, and why the "developer tool" label is holding them back. Discover the power of structured workspaces, the Model Context Protocol, and why git might be the accidental universal language for AI productivity.]]></itunes:summary>
      <itunes:duration>1611</itunes:duration>
      <itunes:episode>2014</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/terminal-agents-system-operators.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/terminal-agents-system-operators.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Non-Coders Are Hijacking the Terminal</title>
      <description><![CDATA[The command line is no longer just for developers. Researchers, writers, and analysts are turning terminal-based AI agents into powerful productivity workspaces—without writing a single line of code. From managing equity research to organizing personal therapy notes, these "non-coders" are redefining what these tools can do. We explore the three pillars making this possible: repo-as-workspace, persistent instructions, and MCP servers that connect to the real world.]]></description>
      <link>https://myweirdprompts.com/episode/non-coders-terminal-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/non-coders-terminal-ai-agents/</guid>
      <pubDate>Sat, 04 Apr 2026 21:07:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/non-coders-terminal-ai-agents.m4a"
        type="audio/mp4"
        length="18147828"
      />
      <itunes:title>Non-Coders Are Hijacking the Terminal</itunes:title>
      <itunes:subtitle>Why finance analysts and researchers are ditching GUIs for command-line AI tools like Claude Code.</itunes:subtitle>
      <itunes:summary><![CDATA[The command line is no longer just for developers. Researchers, writers, and analysts are turning terminal-based AI agents into powerful productivity workspaces—without writing a single line of code. From managing equity research to organizing personal therapy notes, these "non-coders" are redefining what these tools can do. We explore the three pillars making this possible: repo-as-workspace, persistent instructions, and MCP servers that connect to the real world.]]></itunes:summary>
      <itunes:duration>1403</itunes:duration>
      <itunes:episode>2013</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/non-coders-terminal-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/non-coders-terminal-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Pixels vs Protocols: The Computer Use Showdown</title>
      <description><![CDATA[The podcast explores the architectural tension between visual "Computer Use" agents—like Anthropic's demo—and API-first automation. Hosts analyze whether visual agents are a high-latency bridge to a protocol-driven world or a necessary tool for legacy systems. They discuss cost implications, reliability issues, and the potential for visual interaction to become just another capability rather than a standalone product category.]]></description>
      <link>https://myweirdprompts.com/episode/pixels-vs-protocols-computer-use/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pixels-vs-protocols-computer-use/</guid>
      <pubDate>Sat, 04 Apr 2026 21:05:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pixels-vs-protocols-computer-use.m4a"
        type="audio/mp4"
        length="21491178"
      />
      <itunes:title>Pixels vs Protocols: The Computer Use Showdown</itunes:title>
      <itunes:subtitle>Is visual AI a bridge or the future? We debate the efficiency and longevity of &quot;Computer Use&quot; agents versus API-first automation.</itunes:subtitle>
      <itunes:summary><![CDATA[The podcast explores the architectural tension between visual "Computer Use" agents—like Anthropic's demo—and API-first automation. Hosts analyze whether visual agents are a high-latency bridge to a protocol-driven world or a necessary tool for legacy systems. They discuss cost implications, reliability issues, and the potential for visual interaction to become just another capability rather than a standalone product category.]]></itunes:summary>
      <itunes:duration>1664</itunes:duration>
      <itunes:episode>2012</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pixels-vs-protocols-computer-use.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pixels-vs-protocols-computer-use.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Saving AI Knowledge Beyond the Chat Window</title>
      <description><![CDATA[Every day, companies lose massive amounts of institutional intelligence because AI chat outputs are treated as disposable. In this episode, we explore the "ephemeral context trap" — the gap between brilliant AI conversations and permanent knowledge bases. We discuss why current tools fail to capture the "trail of thought," and outline a five-step pipeline (Capture, Sanitize, Extract, Categorize, Human-in-the-Loop) to turn ephemeral chats into structured, searchable assets. Plus, a look at tools like Dust, Khoj, and Microsoft Presidio that are building the plumbing between generation and storage.]]></description>
      <link>https://myweirdprompts.com/episode/ephemeral-context-trap-ai-knowledge/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ephemeral-context-trap-ai-knowledge/</guid>
      <pubDate>Sat, 04 Apr 2026 20:56:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ephemeral-context-trap-ai-knowledge.m4a"
        type="audio/mp4"
        length="19244031"
      />
      <itunes:title>Saving AI Knowledge Beyond the Chat Window</itunes:title>
      <itunes:subtitle>We&apos;re brilliant at prompting AI, but terrible at saving the answers. Here&apos;s why that &quot;digital masterpiece on a chalkboard&quot; vanishes.</itunes:subtitle>
      <itunes:summary><![CDATA[Every day, companies lose massive amounts of institutional intelligence because AI chat outputs are treated as disposable. In this episode, we explore the "ephemeral context trap" — the gap between brilliant AI conversations and permanent knowledge bases. We discuss why current tools fail to capture the "trail of thought," and outline a five-step pipeline (Capture, Sanitize, Extract, Categorize, Human-in-the-Loop) to turn ephemeral chats into structured, searchable assets. Plus, a look at tools like Dust, Khoj, and Microsoft Presidio that are building the plumbing between generation and storage.]]></itunes:summary>
      <itunes:duration>1484</itunes:duration>
      <itunes:episode>2011</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ephemeral-context-trap-ai-knowledge.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ephemeral-context-trap-ai-knowledge.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Better AI Memory Systems</title>
      <description><![CDATA[What happens to your AI's brilliant answers after you see them? In this episode, we explore the "leaky bucket" problem of AI output storage. We discuss why treating AI conversations as ephemeral is a corporate nightmare, and dive into the tools trying to give these models a long-term memory. From LangSmith and Langfuse to "Reverse RAG" and projects like Mem zero and Letta, we uncover how to turn a mountain of raw logs into a goldmine for fine-tuning and compliance. We also examine how temporal awareness and automated evaluation are creating smarter, more stateful AI partners.]]></description>
      <link>https://myweirdprompts.com/episode/ai-memory-leak-output-storage/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-memory-leak-output-storage/</guid>
      <pubDate>Sat, 04 Apr 2026 20:53:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-memory-leak-output-storage.m4a"
        type="audio/mp4"
        length="17289571"
      />
      <itunes:title>Building Better AI Memory Systems</itunes:title>
      <itunes:subtitle>We obsess over AI inputs but treat outputs like Snapchat messages. Here&apos;s why that&apos;s a massive blind spot.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens to your AI's brilliant answers after you see them? In this episode, we explore the "leaky bucket" problem of AI output storage. We discuss why treating AI conversations as ephemeral is a corporate nightmare, and dive into the tools trying to give these models a long-term memory. From LangSmith and Langfuse to "Reverse RAG" and projects like Mem zero and Letta, we uncover how to turn a mountain of raw logs into a goldmine for fine-tuning and compliance. We also examine how temporal awareness and automated evaluation are creating smarter, more stateful AI partners.]]></itunes:summary>
      <itunes:duration>1341</itunes:duration>
      <itunes:episode>2010</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-memory-leak-output-storage.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-memory-leak-output-storage.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Plumbing of AI Safety: Guardrails, Not Vibes</title>
      <description><![CDATA[We move past vague ethics to the literal plumbing of AI safety. This episode explores the specific libraries, proxy layers, and architectural decisions that act as the new enterprise firewall for LLMs. We dissect the tension between latency and security, comparing "sandwich" guardrails with token-level steering, and break down the open-source versus commercial landscapes—from NVIDIA NeMo and Guardrails AI to Lakera's threat intelligence.]]></description>
      <link>https://myweirdprompts.com/episode/ai-guardrails-production-plumbing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-guardrails-production-plumbing/</guid>
      <pubDate>Sat, 04 Apr 2026 20:49:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-guardrails-production-plumbing.m4a"
        type="audio/mp4"
        length="18446574"
      />
      <itunes:title>The Plumbing of AI Safety: Guardrails, Not Vibes</itunes:title>
      <itunes:subtitle>We dive deep into the specific libraries, proxy layers, and architectural decisions that keep an LLM from emptying a bank account.</itunes:subtitle>
      <itunes:summary><![CDATA[We move past vague ethics to the literal plumbing of AI safety. This episode explores the specific libraries, proxy layers, and architectural decisions that act as the new enterprise firewall for LLMs. We dissect the tension between latency and security, comparing "sandwich" guardrails with token-level steering, and break down the open-source versus commercial landscapes—from NVIDIA NeMo and Guardrails AI to Lakera's threat intelligence.]]></itunes:summary>
      <itunes:duration>1429</itunes:duration>
      <itunes:episode>2009</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-guardrails-production-plumbing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-guardrails-production-plumbing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Needle-in-a-Haystack Testing for LLMs</title>
      <description><![CDATA[We have massive AI models that claim to be "world-class intelligent," yet they often fail at basic tasks like finding a specific fact in a long document. This episode explores the disconnect between benchmark scores and real-world performance, diving into EvalScope, an open-source toolkit designed to stress-test long-context retrieval and agentic capabilities. We discuss the "lost in the middle" phenomenon, the danger of overfitting to public benchmarks, and why testing speed and tool-use is just as important as raw intelligence.]]></description>
      <link>https://myweirdprompts.com/episode/needle-in-haystack-evalscope-testing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/needle-in-haystack-evalscope-testing/</guid>
      <pubDate>Sat, 04 Apr 2026 20:33:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/needle-in-haystack-evalscope-testing.m4a"
        type="audio/mp4"
        length="16283202"
      />
      <itunes:title>Needle-in-a-Haystack Testing for LLMs</itunes:title>
      <itunes:subtitle>New AI models claim to be genius-level, but can they actually find a specific fact in a massive document?</itunes:subtitle>
      <itunes:summary><![CDATA[We have massive AI models that claim to be "world-class intelligent," yet they often fail at basic tasks like finding a specific fact in a long document. This episode explores the disconnect between benchmark scores and real-world performance, diving into EvalScope, an open-source toolkit designed to stress-test long-context retrieval and agentic capabilities. We discuss the "lost in the middle" phenomenon, the danger of overfitting to public benchmarks, and why testing speed and tool-use is just as important as raw intelligence.]]></itunes:summary>
      <itunes:duration>1249</itunes:duration>
      <itunes:episode>2008</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/needle-in-haystack-evalscope-testing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/needle-in-haystack-evalscope-testing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Grading AI: The Snake Eating Its Tail</title>
      <description><![CDATA[The industry is scaling faster than humans can review, so we’ve turned to LLM-as-a-Judge to grade model outputs. But this creates a hall of mirrors: AI grading AI, often with a preference for verbosity and its own style. We explore the mechanics of single-point, pairwise, and reference-based scoring, and the hidden biases—like position and self-enhancement—that threaten to create a monoculture of identical models. Is this the future of evaluation, or a trap we can’t escape?]]></description>
      <link>https://myweirdprompts.com/episode/llm-as-judge-bias-monoculture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-as-judge-bias-monoculture/</guid>
      <pubDate>Sat, 04 Apr 2026 20:05:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-as-judge-bias-monoculture.m4a"
        type="audio/mp4"
        length="17625043"
      />
      <itunes:title>AI Grading AI: The Snake Eating Its Tail</itunes:title>
      <itunes:subtitle>We asked an AI to write this script. Then we asked another AI to grade it. Here’s what happens when the judges have biases.</itunes:subtitle>
      <itunes:summary><![CDATA[The industry is scaling faster than humans can review, so we’ve turned to LLM-as-a-Judge to grade model outputs. But this creates a hall of mirrors: AI grading AI, often with a preference for verbosity and its own style. We explore the mechanics of single-point, pairwise, and reference-based scoring, and the hidden biases—like position and self-enhancement—that threaten to create a monoculture of identical models. Is this the future of evaluation, or a trap we can’t escape?]]></itunes:summary>
      <itunes:duration>1335</itunes:duration>
      <itunes:episode>2007</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-as-judge-bias-monoculture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-as-judge-bias-monoculture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Do You Measure an LLM&apos;s &quot;Soul&quot;?</title>
      <description><![CDATA[We all know how to test if an LLM solves a math problem, but how do you measure if it has the right "soul"? This episode tackles the messy world of qualitative AI evaluation. We explore why binary benchmarks fail for real-world tasks like medical summaries or creative writing, and dive into techniques like LLM-as-a-Judge, G-Eval, and counterfactual testing to map a model's hidden worldview. Learn how to build a "Golden Dataset" and avoid the pitfalls of subjective bias.]]></description>
      <link>https://myweirdprompts.com/episode/measuring-llm-qualitative-benchmarks/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/measuring-llm-qualitative-benchmarks/</guid>
      <pubDate>Sat, 04 Apr 2026 19:10:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/measuring-llm-qualitative-benchmarks.m4a"
        type="audio/mp4"
        length="17495294"
      />
      <itunes:title>How Do You Measure an LLM&apos;s &quot;Soul&quot;?</itunes:title>
      <itunes:subtitle>Traditional benchmarks can&apos;t measure tone or empathy. Here&apos;s how to evaluate if an AI model truly &quot;gets it right.&quot;</itunes:subtitle>
      <itunes:summary><![CDATA[We all know how to test if an LLM solves a math problem, but how do you measure if it has the right "soul"? This episode tackles the messy world of qualitative AI evaluation. We explore why binary benchmarks fail for real-world tasks like medical summaries or creative writing, and dive into techniques like LLM-as-a-Judge, G-Eval, and counterfactual testing to map a model's hidden worldview. Learn how to build a "Golden Dataset" and avoid the pitfalls of subjective bias.]]></itunes:summary>
      <itunes:duration>1356</itunes:duration>
      <itunes:episode>2006</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/measuring-llm-qualitative-benchmarks.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/measuring-llm-qualitative-benchmarks.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The AI Control Plane Is Here (But Is It Safe?)</title>
      <description><![CDATA[As AI agents move from prototypes to production, teams face a fragmented mess of inference gateways, MCP servers, and observability tools that don’t talk to each other. This episode explores the rise of the "AI Control Plane"—a unified infrastructure layer that promises a single pane of glass for routing models, managing tools, and tracking costs. We dig into how these systems handle security, context, and tool namespacing, and why the industry is coalescing around terms like "Single-Origin AI Infrastructure." Whether you’re battling duct-taped scripts or planning an enterprise rollout, this is your guide to the plumbing that makes AI agents actually work.]]></description>
      <link>https://myweirdprompts.com/episode/ai-control-plane-infrastructure-layer/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-control-plane-infrastructure-layer/</guid>
      <pubDate>Sat, 04 Apr 2026 15:39:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-control-plane-infrastructure-layer.m4a"
        type="audio/mp4"
        length="19282410"
      />
      <itunes:title>The AI Control Plane Is Here (But Is It Safe?)</itunes:title>
      <itunes:subtitle>Your LLM, tools, and costs are scattered across dashboards. Here’s how a unified AI control plane fixes the chaos.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI agents move from prototypes to production, teams face a fragmented mess of inference gateways, MCP servers, and observability tools that don’t talk to each other. This episode explores the rise of the "AI Control Plane"—a unified infrastructure layer that promises a single pane of glass for routing models, managing tools, and tracking costs. We dig into how these systems handle security, context, and tool namespacing, and why the industry is coalescing around terms like "Single-Origin AI Infrastructure." Whether you’re battling duct-taped scripts or planning an enterprise rollout, this is your guide to the plumbing that makes AI agents actually work.]]></itunes:summary>
      <itunes:duration>1494</itunes:duration>
      <itunes:episode>2004</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-control-plane-infrastructure-layer.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-control-plane-infrastructure-layer.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Velocity Paradox: Why Faster Code Means Slower Ships</title>
      <description><![CDATA[When AI agents can execute code instantly, the cost of a wrong direction skyrockets. We explore the "Velocity Paradox" in modern development, where the ease of building creates new psychological traps like scope creep, architectural debt, and the loss of the "gut check." Learn how to manufacture friction through Idea Backlogs, Triage, and Spec-Driven Development to ensure your speed actually leads to shipping the right product.]]></description>
      <link>https://myweirdprompts.com/episode/velocity-paradox-agentic-coding/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/velocity-paradox-agentic-coding/</guid>
      <pubDate>Sat, 04 Apr 2026 14:24:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/velocity-paradox-agentic-coding.m4a"
        type="audio/mp4"
        length="20178880"
      />
      <itunes:title>The Velocity Paradox: Why Faster Code Means Slower Ships</itunes:title>
      <itunes:subtitle>Agentic coding tools let you build features in minutes, but they also make it easy to build the wrong thing.</itunes:subtitle>
      <itunes:summary><![CDATA[When AI agents can execute code instantly, the cost of a wrong direction skyrockets. We explore the "Velocity Paradox" in modern development, where the ease of building creates new psychological traps like scope creep, architectural debt, and the loss of the "gut check." Learn how to manufacture friction through Idea Backlogs, Triage, and Spec-Driven Development to ensure your speed actually leads to shipping the right product.]]></itunes:summary>
      <itunes:duration>1558</itunes:duration>
      <itunes:episode>2003</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/velocity-paradox-agentic-coding.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/velocity-paradox-agentic-coding.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Brainstorming a Stable-by-Design Smart Home</title>
      <description><![CDATA[Home Assistant's fragility stems from its monolithic architecture. Could sandboxed 'Integration Pods' using WebAssembly or containers give us granular control without the late-night YAML debugging?]]></description>
      <link>https://myweirdprompts.com/episode/home-assistant-stability-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-assistant-stability-future/</guid>
      <pubDate>Sat, 04 Apr 2026 13:36:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-assistant-stability-future.m4a"
        type="audio/mp4"
        length="16540575"
      />
      <itunes:title>Brainstorming a Stable-by-Design Smart Home</itunes:title>
      <itunes:subtitle>We explore why Home Assistant is so fragile and brainstorm a stable-by-design future for the platform.</itunes:subtitle>
      <itunes:summary><![CDATA[Home Assistant's fragility stems from its monolithic architecture. Could sandboxed 'Integration Pods' using WebAssembly or containers give us granular control without the late-night YAML debugging?]]></itunes:summary>
      <itunes:duration>1272</itunes:duration>
      <itunes:episode>2002</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-assistant-stability-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-assistant-stability-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Stop Writing &quot;It Feels Slow&quot; Tickets</title>
      <description><![CDATA[We’ve all seen it: a ticket that just says "The app feels slow." But what actually makes a bug report useful? This episode dives into the high art of bug reporting, from the "Golden Trio" of information to the "ping-pong" effect that kills productivity. We explore the modern landscape of issue tracking tools—from the enterprise heavyweight Jira to the developer-loved Linear—and look at the new wave of AI-powered capture tools that automate the hardest parts of diagnostics. Learn how to write reports that get fixed fast and why the right tool can turn a three-hour investigation into a five-minute fix.]]></description>
      <link>https://myweirdprompts.com/episode/bug-reporting-art-tools-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/bug-reporting-art-tools-2026/</guid>
      <pubDate>Sat, 04 Apr 2026 13:14:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/bug-reporting-art-tools-2026.m4a"
        type="audio/mp4"
        length="15974638"
      />
      <itunes:title>Stop Writing &quot;It Feels Slow&quot; Tickets</itunes:title>
      <itunes:subtitle>The &quot;Golden Trio&quot; of bug reports, why Jira is a tax, and how AI capture tools are changing the game.</itunes:subtitle>
      <itunes:summary><![CDATA[We’ve all seen it: a ticket that just says "The app feels slow." But what actually makes a bug report useful? This episode dives into the high art of bug reporting, from the "Golden Trio" of information to the "ping-pong" effect that kills productivity. We explore the modern landscape of issue tracking tools—from the enterprise heavyweight Jira to the developer-loved Linear—and look at the new wave of AI-powered capture tools that automate the hardest parts of diagnostics. Learn how to write reports that get fixed fast and why the right tool can turn a three-hour investigation into a five-minute fix.]]></itunes:summary>
      <itunes:duration>1230</itunes:duration>
      <itunes:episode>2001</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/bug-reporting-art-tools-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/bug-reporting-art-tools-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Can&apos;t AI Admit When It&apos;s Guessing?</title>
      <description><![CDATA[As AI research agents scan thousands of documents, they increasingly auto-flag their own uncertain claims. But how reliable is this "self-awareness"? We explore the mechanics of confidence scoring in LLMs, from simple self-reports to advanced multi-agent auditing and calibration layers. Discover why a model's certainty often doesn't match its accuracy, and how engineers are building rigorous verification into high-stakes workflows.]]></description>
      <link>https://myweirdprompts.com/episode/ai-confidence-scoring-reliability/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-confidence-scoring-reliability/</guid>
      <pubDate>Sat, 04 Apr 2026 11:49:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-confidence-scoring-reliability.m4a"
        type="audio/mp4"
        length="23532536"
      />
      <itunes:title>Why Can&apos;t AI Admit When It&apos;s Guessing?</itunes:title>
      <itunes:subtitle>Enterprise AI now auto-filters low-confidence claims, but do these self-reported scores actually mean anything?</itunes:subtitle>
      <itunes:summary><![CDATA[As AI research agents scan thousands of documents, they increasingly auto-flag their own uncertain claims. But how reliable is this "self-awareness"? We explore the mechanics of confidence scoring in LLMs, from simple self-reports to advanced multi-agent auditing and calibration layers. Discover why a model's certainty often doesn't match its accuracy, and how engineers are building rigorous verification into high-stakes workflows.]]></itunes:summary>
      <itunes:duration>1831</itunes:duration>
      <itunes:episode>1994</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-confidence-scoring-reliability.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-confidence-scoring-reliability.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Hiding the Kitchen: Why AI Shouldn&apos;t Show Its Work</title>
      <description><![CDATA[Why should AI chatbots hide their messy back-end processes? This episode explores the orchestrator-worker model as a way to protect user attention, debating whether spawning sub-agents is brilliant or wasteful, and what this means for the future of software interaction.]]></description>
      <link>https://myweirdprompts.com/episode/orchestrator-worker-agent-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/orchestrator-worker-agent-architecture/</guid>
      <pubDate>Sat, 04 Apr 2026 11:43:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/orchestrator-worker-agent-architecture.m4a"
        type="audio/mp4"
        length="24207848"
      />
      <itunes:title>Hiding the Kitchen: Why AI Shouldn&apos;t Show Its Work</itunes:title>
      <itunes:subtitle>Why single-model chatbots fail at complex tasks—and how multi-agent swarms solve it.</itunes:subtitle>
      <itunes:summary><![CDATA[Why should AI chatbots hide their messy back-end processes? This episode explores the orchestrator-worker model as a way to protect user attention, debating whether spawning sub-agents is brilliant or wasteful, and what this means for the future of software interaction.]]></itunes:summary>
      <itunes:duration>1874</itunes:duration>
      <itunes:episode>1993</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/orchestrator-worker-agent-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/orchestrator-worker-agent-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>RPA: Dead or Just Getting Smart?</title>
      <description><![CDATA[For years, Robotic Process Automation was the digital equivalent of a blindfolded intern—efficient but incredibly brittle. Today, that’s changing. We explore how the "Big Three" RPA platforms are integrating Large Language Models and computer vision to create "Agentic Automation." Discover why legacy systems still demand screen-scraping, how AI is solving RPA’s maintenance nightmare, and why the future isn't about replacing RPA, but turning it into the execution arm of intelligent AI agents.]]></description>
      <link>https://myweirdprompts.com/episode/rpa-agentic-automation-vision/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rpa-agentic-automation-vision/</guid>
      <pubDate>Fri, 03 Apr 2026 18:19:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rpa-agentic-automation-vision.m4a"
        type="audio/mp4"
        length="16860422"
      />
      <itunes:title>RPA: Dead or Just Getting Smart?</itunes:title>
      <itunes:subtitle>Traditional RPA is brittle and blind. See how AI vision and agentic orchestration are turning it into a self-healing powerhouse.</itunes:subtitle>
      <itunes:summary><![CDATA[For years, Robotic Process Automation was the digital equivalent of a blindfolded intern—efficient but incredibly brittle. Today, that’s changing. We explore how the "Big Three" RPA platforms are integrating Large Language Models and computer vision to create "Agentic Automation." Discover why legacy systems still demand screen-scraping, how AI is solving RPA’s maintenance nightmare, and why the future isn't about replacing RPA, but turning it into the execution arm of intelligent AI agents.]]></itunes:summary>
      <itunes:duration>1289</itunes:duration>
      <itunes:episode>1963</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rpa-agentic-automation-vision.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rpa-agentic-automation-vision.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Constrained AI Models Handle the Unexpected</title>
      <description><![CDATA[We all want AI that only knows what we tell it—until it doesn't. In this episode, we explore the technical illusion of "constrained" models and why RAG systems still hallucinate. From financial compliance risks to legal discovery nightmares, discover why your AI's "world knowledge" can overpower your private data and what that means for enterprise deployment.]]></description>
      <link>https://myweirdprompts.com/episode/constrained-ai-models-rogue/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/constrained-ai-models-rogue/</guid>
      <pubDate>Fri, 03 Apr 2026 17:55:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/constrained-ai-models-rogue.m4a"
        type="audio/mp4"
        length="22293723"
      />
      <itunes:title>How Constrained AI Models Handle the Unexpected</itunes:title>
      <itunes:subtitle>Your AI assistant promised to only use your documents. Instead, it invented a case law that doesn&apos;t exist. Here&apos;s why.</itunes:subtitle>
      <itunes:summary><![CDATA[We all want AI that only knows what we tell it—until it doesn't. In this episode, we explore the technical illusion of "constrained" models and why RAG systems still hallucinate. From financial compliance risks to legal discovery nightmares, discover why your AI's "world knowledge" can overpower your private data and what that means for enterprise deployment.]]></itunes:summary>
      <itunes:duration>1717</itunes:duration>
      <itunes:episode>1959</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/constrained-ai-models-rogue.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/constrained-ai-models-rogue.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Agents Think in Circles, Not Lines</title>
      <description><![CDATA[We're moving past straight-line AI. This episode explores why cyclic architectures—loops, reflection, and state management—are replacing linear pipelines for reliable autonomy. We break down the mechanics of LangGraph, ReAct patterns, and the OODA loop, plus the security risks of prompt injection and how "human-in-the-loop" safeguards prevent costly errors. Discover why iterative thinking outperforms raw speed, and how smaller models with smart loops can beat massive ones.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-loops-reasoning-cycles/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-loops-reasoning-cycles/</guid>
      <pubDate>Fri, 03 Apr 2026 17:21:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-loops-reasoning-cycles.m4a"
        type="audio/mp4"
        length="17027200"
      />
      <itunes:title>Why AI Agents Think in Circles, Not Lines</itunes:title>
      <itunes:subtitle>Linear AI pipelines are brittle. Learn why loops, reflection, and state management are the new standard for reliable, autonomous agents.</itunes:subtitle>
      <itunes:summary><![CDATA[We're moving past straight-line AI. This episode explores why cyclic architectures—loops, reflection, and state management—are replacing linear pipelines for reliable autonomy. We break down the mechanics of LangGraph, ReAct patterns, and the OODA loop, plus the security risks of prompt injection and how "human-in-the-loop" safeguards prevent costly errors. Discover why iterative thinking outperforms raw speed, and how smaller models with smart loops can beat massive ones.]]></itunes:summary>
      <itunes:duration>1318</itunes:duration>
      <itunes:episode>1957</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-loops-reasoning-cycles.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-loops-reasoning-cycles.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Skills: From Vibe Coding to Procedural Playbooks</title>
      <description><![CDATA[We're witnessing a fundamental shift in how we build AI agents, moving from vague "vibe coding" to precise, modular procedures. Inspired by Anthropic's Claude Code, agent skills package specific behaviors—from fraud detection to route optimization—into version-controlled files that any agent can snap in like a Lego block. This episode explores how this "standard library" for AI works, how it differs from MCP, and why it's the key to reliable, auditable enterprise automation. Learn how frameworks like LangChain and AutoGen are turning AI from a black box into a professional engineering discipline.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-skills-modular-playbooks/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-skills-modular-playbooks/</guid>
      <pubDate>Fri, 03 Apr 2026 17:20:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-skills-modular-playbooks.m4a"
        type="audio/mp4"
        length="19842579"
      />
      <itunes:title>AI Skills: From Vibe Coding to Procedural Playbooks</itunes:title>
      <itunes:subtitle>Forget messy system prompts. Agent skills turn AI into a Swiss Army knife of modular, auditable procedures.</itunes:subtitle>
      <itunes:summary><![CDATA[We're witnessing a fundamental shift in how we build AI agents, moving from vague "vibe coding" to precise, modular procedures. Inspired by Anthropic's Claude Code, agent skills package specific behaviors—from fraud detection to route optimization—into version-controlled files that any agent can snap in like a Lego block. This episode explores how this "standard library" for AI works, how it differs from MCP, and why it's the key to reliable, auditable enterprise automation. Learn how frameworks like LangChain and AutoGen are turning AI from a black box into a professional engineering discipline.]]></itunes:summary>
      <itunes:duration>1520</itunes:duration>
      <itunes:episode>1956</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-skills-modular-playbooks.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-skills-modular-playbooks.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why We Built a 24/7 AI Radio Station</title>
      <description><![CDATA[In an on-demand world, we built a lean-back internet radio station to resurrect our entire archive. This episode reveals the surprisingly simple open-source stack—Icecast and Liquidsoap—that powers a continuous, AI-generated broadcast. We explore the psychology of choice, how "forced discovery" brings old content back to life, and why this model could be the future for creators. Tune in to hear how we turned a massive podcast library into a living, breathing station.]]></description>
      <link>https://myweirdprompts.com/episode/ai-radio-station-icecast-liquidsoap/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-radio-station-icecast-liquidsoap/</guid>
      <pubDate>Fri, 03 Apr 2026 16:38:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-radio-station-icecast-liquidsoap.m4a"
        type="audio/mp4"
        length="16117750"
      />
      <itunes:title>Why We Built a 24/7 AI Radio Station</itunes:title>
      <itunes:subtitle>We turned our 1800-episode archive into a continuous AI-powered radio stream. Here’s the tech stack and the philosophy behind it.</itunes:subtitle>
      <itunes:summary><![CDATA[In an on-demand world, we built a lean-back internet radio station to resurrect our entire archive. This episode reveals the surprisingly simple open-source stack—Icecast and Liquidsoap—that powers a continuous, AI-generated broadcast. We explore the psychology of choice, how "forced discovery" brings old content back to life, and why this model could be the future for creators. Tune in to hear how we turned a massive podcast library into a living, breathing station.]]></itunes:summary>
      <itunes:duration>1242</itunes:duration>
      <itunes:episode>1952</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-radio-station-icecast-liquidsoap.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-radio-station-icecast-liquidsoap.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why LangChain Built a Three-Layer Agent Stack</title>
      <description><![CDATA[LangChain's docs reveal a deliberate three-layer architecture for agent building. We explore why they split LangGraph, LangChain, and Deep Agents, what each layer sacrifices for control, and why the new Functional API changes how you think about durable execution.]]></description>
      <link>https://myweirdprompts.com/episode/langgraph-langchain-deepagents-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/langgraph-langchain-deepagents-architecture/</guid>
      <pubDate>Fri, 03 Apr 2026 12:55:47 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/langgraph-langchain-deepagents-architecture.m4a"
        type="audio/mp4"
        length="24902537"
      />
      <itunes:title>Why LangChain Built a Three-Layer Agent Stack</itunes:title>
      <itunes:subtitle>We unpack LangGraph, LangChain, and Deep Agents to reveal the deliberate hierarchy behind the ecosystem.</itunes:subtitle>
      <itunes:summary><![CDATA[LangChain's docs reveal a deliberate three-layer architecture for agent building. We explore why they split LangGraph, LangChain, and Deep Agents, what each layer sacrifices for control, and why the new Functional API changes how you think about durable execution.]]></itunes:summary>
      <itunes:duration>1925</itunes:duration>
      <itunes:episode>1946</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/langgraph-langchain-deepagents-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/langgraph-langchain-deepagents-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The &quot;USB-C for AI&quot; Is Finally Here</title>
      <description><![CDATA[We dive deep into the Model Context Protocol (MCP), the emerging standard aiming to be the "USB-C for AI." Learn how its three-tier architecture works, why it separates hosts, clients, and servers, and how it promises vendor-neutral connectivity for your data. We explore the four core capabilities—Tools, Resources, Prompts, and Sampling—and uncover the security implications of local-first AI execution.]]></description>
      <link>https://myweirdprompts.com/episode/model-context-protocol-mcp-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/model-context-protocol-mcp-explained/</guid>
      <pubDate>Fri, 03 Apr 2026 12:55:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/model-context-protocol-mcp-explained.m4a"
        type="audio/mp4"
        length="20082991"
      />
      <itunes:title>The &quot;USB-C for AI&quot; Is Finally Here</itunes:title>
      <itunes:subtitle>MCP standardizes how AI tools connect to data, solving the N-times-M integration nightmare.</itunes:subtitle>
      <itunes:summary><![CDATA[We dive deep into the Model Context Protocol (MCP), the emerging standard aiming to be the "USB-C for AI." Learn how its three-tier architecture works, why it separates hosts, clients, and servers, and how it promises vendor-neutral connectivity for your data. We explore the four core capabilities—Tools, Resources, Prompts, and Sampling—and uncover the security implications of local-first AI execution.]]></itunes:summary>
      <itunes:duration>1558</itunes:duration>
      <itunes:episode>1945</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/model-context-protocol-mcp-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/model-context-protocol-mcp-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>PostgreSQL: The Thirty-Year Miracle</title>
      <description><![CDATA[Explore the unique governance model that has kept PostgreSQL thriving for thirty years without corporate control or restrictive licenses. Learn about the "fifty percent rule," Commitfests, and the distributed patronage system that makes it all work. Discover why this "boring" database has become the most resilient piece of software in tech.]]></description>
      <link>https://myweirdprompts.com/episode/postgres-thirty-year-miracle/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/postgres-thirty-year-miracle/</guid>
      <pubDate>Fri, 03 Apr 2026 10:58:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/postgres-thirty-year-miracle.m4a"
        type="audio/mp4"
        length="16426601"
      />
      <itunes:title>PostgreSQL: The Thirty-Year Miracle</itunes:title>
      <itunes:subtitle>How does a volunteer-run database power the New York Stock Exchange and survive every tech trend without burning out?</itunes:subtitle>
      <itunes:summary><![CDATA[Explore the unique governance model that has kept PostgreSQL thriving for thirty years without corporate control or restrictive licenses. Learn about the "fifty percent rule," Commitfests, and the distributed patronage system that makes it all work. Discover why this "boring" database has become the most resilient piece of software in tech.]]></itunes:summary>
      <itunes:duration>1251</itunes:duration>
      <itunes:episode>1944</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/postgres-thirty-year-miracle.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/postgres-thirty-year-miracle.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>API Drift and Agent Reliability</title>
      <description><![CDATA[We explore the critical new problem of API-MCP drift, where backend changes break AI agents silently. Learn how tools like Postman and MCP Explorer are evolving to test not just code, but the AI's understanding of that code. We examine the shift from unit testing to "intent validation" and why parallel development is becoming essential.]]></description>
      <link>https://myweirdprompts.com/episode/api-mcp-drift-agent-failure/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/api-mcp-drift-agent-failure/</guid>
      <pubDate>Fri, 03 Apr 2026 10:04:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/api-mcp-drift-agent-failure.m4a"
        type="audio/mp4"
        length="25605369"
      />
      <itunes:title>API Drift and Agent Reliability</itunes:title>
      <itunes:subtitle>When an API changes without warning, your AI agent can crash spectacularly. Here&apos;s how to test the new &quot;plumbing&quot; of the agentic age.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the critical new problem of API-MCP drift, where backend changes break AI agents silently. Learn how tools like Postman and MCP Explorer are evolving to test not just code, but the AI's understanding of that code. We examine the shift from unit testing to "intent validation" and why parallel development is becoming essential.]]></itunes:summary>
      <itunes:duration>1987</itunes:duration>
      <itunes:episode>1939</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/api-mcp-drift-agent-failure.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/api-mcp-drift-agent-failure.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>JSON-to-SQL Type Mapping: A Practical Guide</title>
      <description><![CDATA[That JSON object in your API has to live somewhere, and that home is usually a SQL database. But translating between JSON Schema and SQL types is a minefield of subtle traps. This episode dives into the "impedance mismatch" between these two worlds, revealing how a simple type choice can lead to performance degradation and data integrity nightmares. We explore the dangers of JSON's vague "number" type, the modern-day Y2K problem of 32-bit integers, and why you should think twice before storing a UUID as a simple string.]]></description>
      <link>https://myweirdprompts.com/episode/json-sql-mapping-pitfalls/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/json-sql-mapping-pitfalls/</guid>
      <pubDate>Fri, 03 Apr 2026 10:01:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/json-sql-mapping-pitfalls.m4a"
        type="audio/mp4"
        length="23241175"
      />
      <itunes:title>JSON-to-SQL Type Mapping: A Practical Guide</itunes:title>
      <itunes:subtitle>Mapping JSON to SQL isn&apos;t as simple as it looks. Discover the hidden traps in data types that can cause performance hits and data corruption.</itunes:subtitle>
      <itunes:summary><![CDATA[That JSON object in your API has to live somewhere, and that home is usually a SQL database. But translating between JSON Schema and SQL types is a minefield of subtle traps. This episode dives into the "impedance mismatch" between these two worlds, revealing how a simple type choice can lead to performance degradation and data integrity nightmares. We explore the dangers of JSON's vague "number" type, the modern-day Y2K problem of 32-bit integers, and why you should think twice before storing a UUID as a simple string.]]></itunes:summary>
      <itunes:duration>1804</itunes:duration>
      <itunes:episode>1938</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/json-sql-mapping-pitfalls.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/json-sql-mapping-pitfalls.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Phone Chain to Signal Underground</title>
      <description><![CDATA[When traditional radio and cellular networks fail underground, can a chain of old Android devices create a digital lifeline? This episode explores the engineering reality of building an impromptu mesh network using consumer electronics. We dive into the software workarounds like Meshrabiya and NetShare, the physics of Wi-Fi propagation through concrete, and the harsh trade-offs of latency, heat, and battery life. Learn why the "half-bandwidth rule" makes multi-hop networks challenging and how to strategically place devices in stairwells to maximize signal. We also discuss when specialized apps like Briar are more reliable than trying to force a high-speed connection.]]></description>
      <link>https://myweirdprompts.com/episode/underground-mesh-network-android/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/underground-mesh-network-android/</guid>
      <pubDate>Thu, 02 Apr 2026 17:14:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/underground-mesh-network-android.m4a"
        type="audio/mp4"
        length="22749379"
      />
      <itunes:title>Building a Phone Chain to Signal Underground</itunes:title>
      <itunes:subtitle>Old phones can create a lifeline when signals fail, but physics and hardware impose harsh limits.</itunes:subtitle>
      <itunes:summary><![CDATA[When traditional radio and cellular networks fail underground, can a chain of old Android devices create a digital lifeline? This episode explores the engineering reality of building an impromptu mesh network using consumer electronics. We dive into the software workarounds like Meshrabiya and NetShare, the physics of Wi-Fi propagation through concrete, and the harsh trade-offs of latency, heat, and battery life. Learn why the "half-bandwidth rule" makes multi-hop networks challenging and how to strategically place devices in stairwells to maximize signal. We also discuss when specialized apps like Briar are more reliable than trying to force a high-speed connection.]]></itunes:summary>
      <itunes:duration>1765</itunes:duration>
      <itunes:episode>1933</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/underground-mesh-network-android.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/underground-mesh-network-android.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Do You QA a Probabilistic System?</title>
      <description><![CDATA[Traditional unit tests fail for probabilistic LLMs. We break down the modern toolkit for automated quality evaluation, from heuristic safety nets to LLM-as-judge grading. Learn how to catch hallucinations, manage bias, and build a manufacturing line for intelligence that actually scales.]]></description>
      <link>https://myweirdprompts.com/episode/automated-llm-evaluation-toolkit/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/automated-llm-evaluation-toolkit/</guid>
      <pubDate>Thu, 02 Apr 2026 16:43:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/automated-llm-evaluation-toolkit.m4a"
        type="audio/mp4"
        length="18905553"
      />
      <itunes:title>How Do You QA a Probabilistic System?</itunes:title>
      <itunes:subtitle>LLMs break traditional testing. Here’s the 3-pillar toolkit teams use to catch hallucinations and garbage outputs at scale.</itunes:subtitle>
      <itunes:summary><![CDATA[Traditional unit tests fail for probabilistic LLMs. We break down the modern toolkit for automated quality evaluation, from heuristic safety nets to LLM-as-judge grading. Learn how to catch hallucinations, manage bias, and build a manufacturing line for intelligence that actually scales.]]></itunes:summary>
      <itunes:duration>1442</itunes:duration>
      <itunes:episode>1932</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/automated-llm-evaluation-toolkit.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/automated-llm-evaluation-toolkit.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Where Your AI Pipeline Actually Dies</title>
      <description><![CDATA[Forget prompt engineering. The real reason production AI fails is where data lives between stages. This episode explores why state management—not models—determines whether your system is a brittle prototype or a resilient enterprise tool.]]></description>
      <link>https://myweirdprompts.com/episode/ai-pipeline-state-management/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-pipeline-state-management/</guid>
      <pubDate>Thu, 02 Apr 2026 16:40:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-pipeline-state-management.m4a"
        type="audio/mp4"
        length="17960485"
      />
      <itunes:title>Where Your AI Pipeline Actually Dies</itunes:title>
      <itunes:subtitle>Why do AI pipelines crash? It’s not the models—it’s the plumbing. We break down how to manage data between stages.</itunes:subtitle>
      <itunes:summary><![CDATA[Forget prompt engineering. The real reason production AI fails is where data lives between stages. This episode explores why state management—not models—determines whether your system is a brittle prototype or a resilient enterprise tool.]]></itunes:summary>
      <itunes:duration>1394</itunes:duration>
      <itunes:episode>1931</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-pipeline-state-management.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-pipeline-state-management.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agent Identity Crisis: Workflow vs. Conversation</title>
      <description><![CDATA[The word "agent" is being stretched to cover two fundamentally different software architectures: silent, high-volume workflow engines and conversational, human-in-the-loop assistants. This episode dissects the "agent identity crisis," exploring why the same term now describes a background clerk and a front-end consultant. We break down the technical and economic tradeoffs, from model selection and latency requirements to the fragmented landscape of builder platforms like n8n, Lindy, CrewAI, and LangGraph. Learn why using a conversational framework for a background task—or vice versa—is a costly mistake, and how to pick the right tool for your actual use case.]]></description>
      <link>https://myweirdprompts.com/episode/workflow-conversational-agent-split/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/workflow-conversational-agent-split/</guid>
      <pubDate>Thu, 02 Apr 2026 16:30:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/workflow-conversational-agent-split.m4a"
        type="audio/mp4"
        length="18024805"
      />
      <itunes:title>The Agent Identity Crisis: Workflow vs. Conversation</itunes:title>
      <itunes:subtitle>One automates invoices silently; the other chats in Slack. Why the industry&apos;s favorite word means two totally different things.</itunes:subtitle>
      <itunes:summary><![CDATA[The word "agent" is being stretched to cover two fundamentally different software architectures: silent, high-volume workflow engines and conversational, human-in-the-loop assistants. This episode dissects the "agent identity crisis," exploring why the same term now describes a background clerk and a front-end consultant. We break down the technical and economic tradeoffs, from model selection and latency requirements to the fragmented landscape of builder platforms like n8n, Lindy, CrewAI, and LangGraph. Learn why using a conversational framework for a background task—or vice versa—is a costly mistake, and how to pick the right tool for your actual use case.]]></itunes:summary>
      <itunes:duration>1391</itunes:duration>
      <itunes:episode>1930</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/workflow-conversational-agent-split.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/workflow-conversational-agent-split.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Vibe Checks to Model Metrics</title>
      <description><![CDATA[How do you objectively measure whether one AI model is better than another for creative tasks? We break down the shift from human intuition to a decathlon of atomic dimensions like factual accuracy and prompt adherence.]]></description>
      <link>https://myweirdprompts.com/episode/ai-model-evaluation-metrics/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-model-evaluation-metrics/</guid>
      <pubDate>Thu, 02 Apr 2026 16:27:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-model-evaluation-metrics.m4a"
        type="audio/mp4"
        length="23271718"
      />
      <itunes:title>From Vibe Checks to Model Metrics</itunes:title>
      <itunes:subtitle>We stopped &quot;vibe-checking&quot; our AI scripts and built a science fair for models. Here&apos;s how we grade them.</itunes:subtitle>
      <itunes:summary><![CDATA[How do you objectively measure whether one AI model is better than another for creative tasks? We break down the shift from human intuition to a decathlon of atomic dimensions like factual accuracy and prompt adherence.]]></itunes:summary>
      <itunes:duration>1806</itunes:duration>
      <itunes:episode>1929</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-model-evaluation-metrics.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-model-evaluation-metrics.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Webhook Gateways Beat Direct Wiring</title>
      <description><![CDATA[When you have fifty different webhook endpoints, rotating a secret becomes a manual nightmare. In this episode, we explore how API gateways like Kong solve the "webhook sprawl" problem by decoupling ingress from execution. Learn how to offload authentication, rate limiting, and routing to a battle-tested layer, keeping your automation workflows lean and secure.]]></description>
      <link>https://myweirdprompts.com/episode/webhook-gateway-kong-automation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/webhook-gateway-kong-automation/</guid>
      <pubDate>Thu, 02 Apr 2026 16:22:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/webhook-gateway-kong-automation.m4a"
        type="audio/mp4"
        length="21427595"
      />
      <itunes:title>Why Webhook Gateways Beat Direct Wiring</itunes:title>
      <itunes:subtitle>Unscale your chaos: Why Kong beats manual webhook sprawl for auth, routing, and latency.</itunes:subtitle>
      <itunes:summary><![CDATA[When you have fifty different webhook endpoints, rotating a secret becomes a manual nightmare. In this episode, we explore how API gateways like Kong solve the "webhook sprawl" problem by decoupling ingress from execution. Learn how to offload authentication, rate limiting, and routing to a battle-tested layer, keeping your automation workflows lean and secure.]]></itunes:summary>
      <itunes:duration>1661</itunes:duration>
      <itunes:episode>1928</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/webhook-gateway-kong-automation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/webhook-gateway-kong-automation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Workers vs. Servers: The 2026 Compute Showdown</title>
      <description><![CDATA[The classic "where do I put my code" problem has evolved. In 2026, developers choose between ephemeral workers, heavy serverless functions, and traditional servers. This episode breaks down the technical trade-offs: the sub-millisecond speed of V8 isolates versus the raw power of full VMs. We explore the "Edge Latency Paradox," the surprising utility of GitHub Actions for background tasks, and why the "Worker-first" mentality is becoming standard—unless you're building a stateful beast.]]></description>
      <link>https://myweirdprompts.com/episode/workers-servers-ephemeral-compute/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/workers-servers-ephemeral-compute/</guid>
      <pubDate>Thu, 02 Apr 2026 16:14:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/workers-servers-ephemeral-compute.m4a"
        type="audio/mp4"
        length="16731080"
      />
      <itunes:title>Workers vs. Servers: The 2026 Compute Showdown</itunes:title>
      <itunes:subtitle>Is the persistent server dead? We compare Cloudflare Workers, GitHub Actions, and VPS options for modern app architecture.</itunes:subtitle>
      <itunes:summary><![CDATA[The classic "where do I put my code" problem has evolved. In 2026, developers choose between ephemeral workers, heavy serverless functions, and traditional servers. This episode breaks down the technical trade-offs: the sub-millisecond speed of V8 isolates versus the raw power of full VMs. We explore the "Edge Latency Paradox," the surprising utility of GitHub Actions for background tasks, and why the "Worker-first" mentality is becoming standard—unless you're building a stateful beast.]]></itunes:summary>
      <itunes:duration>1266</itunes:duration>
      <itunes:episode>1927</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/workers-servers-ephemeral-compute.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/workers-servers-ephemeral-compute.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How We Built a 2,000-Episode AI Podcast Engine</title>
      <description><![CDATA[Reaching nearly 2,000 episodes is a staggering milestone, but it raises a question: how do you maintain quality at that scale? In this special episode, we pull back the curtain on the entire evolution of our AI podcasting pipeline. We trace the journey from brittle, linear chains to a sophisticated agentic substrate powered by LangGraph, random model pools, and serverless GPU clusters. Discover how we moved past the "dancing bear" stage to build a system that generates a "Permanent Research Artifact" every single time, all while keeping costs negligible and creative freedom high.]]></description>
      <link>https://myweirdprompts.com/episode/building-ai-podcast-engine-at-scale/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/building-ai-podcast-engine-at-scale/</guid>
      <pubDate>Thu, 02 Apr 2026 16:03:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/building-ai-podcast-engine-at-scale.m4a"
        type="audio/mp4"
        length="17597684"
      />
      <itunes:title>How We Built a 2,000-Episode AI Podcast Engine</itunes:title>
      <itunes:subtitle>We pulled back the curtain on the tech stack behind our 1,858th episode. From Gemini to LangGraph, here’s how we automate quality.</itunes:subtitle>
      <itunes:summary><![CDATA[Reaching nearly 2,000 episodes is a staggering milestone, but it raises a question: how do you maintain quality at that scale? In this special episode, we pull back the curtain on the entire evolution of our AI podcasting pipeline. We trace the journey from brittle, linear chains to a sophisticated agentic substrate powered by LangGraph, random model pools, and serverless GPU clusters. Discover how we moved past the "dancing bear" stage to build a system that generates a "Permanent Research Artifact" every single time, all while keeping costs negligible and creative freedom high.]]></itunes:summary>
      <itunes:duration>1351</itunes:duration>
      <itunes:episode>1926</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/building-ai-podcast-engine-at-scale.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/building-ai-podcast-engine-at-scale.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Ending the Manual Update Loop</title>
      <description><![CDATA[When your custom tools work on one machine but not another, you've hit the personal distribution wall. This episode explores how to build a private, automated pipeline for Linux and Android packages so your bespoke software follows you everywhere.]]></description>
      <link>https://myweirdprompts.com/episode/personal-linux-android-repo-setup/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/personal-linux-android-repo-setup/</guid>
      <pubDate>Thu, 02 Apr 2026 15:52:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/personal-linux-android-repo-setup.m4a"
        type="audio/mp4"
        length="16110807"
      />
      <itunes:title>Ending the Manual Update Loop</itunes:title>
      <itunes:subtitle>Stop manually copying files. Learn how to host your own authenticated repositories for .deb and APK files using simple static web servers.</itunes:subtitle>
      <itunes:summary><![CDATA[When your custom tools work on one machine but not another, you've hit the personal distribution wall. This episode explores how to build a private, automated pipeline for Linux and Android packages so your bespoke software follows you everywhere.]]></itunes:summary>
      <itunes:duration>1233</itunes:duration>
      <itunes:episode>1924</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/personal-linux-android-repo-setup.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/personal-linux-android-repo-setup.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Scaling Prosumer Automation to Enterprise</title>
      <description><![CDATA[Prosumer automation tools are fantastic for getting started, but they often crumble under the weight of real business demands. This episode explores the critical inflection point where visual workflow builders hit a wall, and why the solution lies in treating automation like software. We dive into the concepts of durable execution, state management, and the two main paths forward: enterprise GUI platforms versus code-defined orchestration. Discover why the "cool kids" are moving to frameworks like Temporal and Prefect, and how decorators can turn a simple Python script into a bulletproof business system.]]></description>
      <link>https://myweirdprompts.com/episode/prosumer-automation-scale-failure/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/prosumer-automation-scale-failure/</guid>
      <pubDate>Thu, 02 Apr 2026 15:50:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/prosumer-automation-scale-failure.m4a"
        type="audio/mp4"
        length="29372455"
      />
      <itunes:title>Scaling Prosumer Automation to Enterprise</itunes:title>
      <itunes:subtitle>Prosumer tools like n8n break at scale. Here&apos;s why durable execution frameworks like Temporal and Prefect are the enterprise upgrade.</itunes:subtitle>
      <itunes:summary><![CDATA[Prosumer automation tools are fantastic for getting started, but they often crumble under the weight of real business demands. This episode explores the critical inflection point where visual workflow builders hit a wall, and why the solution lies in treating automation like software. We dive into the concepts of durable execution, state management, and the two main paths forward: enterprise GUI platforms versus code-defined orchestration. Discover why the "cool kids" are moving to frameworks like Temporal and Prefect, and how decorators can turn a simple Python script into a bulletproof business system.]]></itunes:summary>
      <itunes:duration>2288</itunes:duration>
      <itunes:episode>1923</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/prosumer-automation-scale-failure.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/prosumer-automation-scale-failure.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Plumber to Urban Planner: AI Agent Careers</title>
      <description><![CDATA[The automation industry is undergoing a massive shift from rigid, rule-based systems to autonomous, goal-oriented AI agents. We explore what this "Great Bifurcation" means for the future of work, the tools changing the game, and why the human role is evolving from "doer" to "approver."]]></description>
      <link>https://myweirdprompts.com/episode/agentic-workflow-career-shift/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-workflow-career-shift/</guid>
      <pubDate>Thu, 02 Apr 2026 15:45:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-workflow-career-shift.m4a"
        type="audio/mp4"
        length="19658680"
      />
      <itunes:title>From Plumber to Urban Planner: AI Agent Careers</itunes:title>
      <itunes:subtitle>The job titles are changing from &quot;Zapier Expert&quot; to &quot;Cognitive Architect.&quot;</itunes:subtitle>
      <itunes:summary><![CDATA[The automation industry is undergoing a massive shift from rigid, rule-based systems to autonomous, goal-oriented AI agents. We explore what this "Great Bifurcation" means for the future of work, the tools changing the game, and why the human role is evolving from "doer" to "approver."]]></itunes:summary>
      <itunes:duration>1519</itunes:duration>
      <itunes:episode>1922</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-workflow-career-shift.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-workflow-career-shift.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Three-Second Heartbeat That Keeps Israel Safe</title>
      <description><![CDATA[In this episode, we dissect the technical architecture of Israel's Home Front Command alert system, focusing on a curious design choice: a civilian website that streams an empty JSON payload every three seconds, 24/7. We explore why this "heartbeat" pattern—polling a tiny file from a CDN edge server—is more reliable than modern push technologies like WebSockets for mass-casualty events. Learn how this "dumb" architecture achieves massive horizontal scaling, why predictability trumps efficiency in safety systems, and how it fits into a multi-tiered cascade that includes hardened military networks and physical sirens.]]></description>
      <link>https://myweirdprompts.com/episode/israel-alert-system-heartbeat/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/israel-alert-system-heartbeat/</guid>
      <pubDate>Thu, 02 Apr 2026 15:40:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/israel-alert-system-heartbeat.m4a"
        type="audio/mp4"
        length="28106124"
      />
      <itunes:title>The Three-Second Heartbeat That Keeps Israel Safe</itunes:title>
      <itunes:subtitle>Why a civilian website sends an empty JSON payload every three seconds, even during peacetime, and what it reveals about mission-critical architect...</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we dissect the technical architecture of Israel's Home Front Command alert system, focusing on a curious design choice: a civilian website that streams an empty JSON payload every three seconds, 24/7. We explore why this "heartbeat" pattern—polling a tiny file from a CDN edge server—is more reliable than modern push technologies like WebSockets for mass-casualty events. Learn how this "dumb" architecture achieves massive horizontal scaling, why predictability trumps efficiency in safety systems, and how it fits into a multi-tiered cascade that includes hardened military networks and physical sirens.]]></itunes:summary>
      <itunes:duration>2187</itunes:duration>
      <itunes:episode>1921</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/israel-alert-system-heartbeat.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/israel-alert-system-heartbeat.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>InfluxDB vs. Postgres: The Time-Series Showdown</title>
      <description><![CDATA[Dive into the architectural tug-of-war between specialized time-series databases like InfluxDB and conventional SQL worlds like Postgres. We explore how TimescaleDB is changing the math, the impact of high-cardinality data, and whether the "specialist" store is becoming a feature of big players.]]></description>
      <link>https://myweirdprompts.com/episode/influxdb-postgres-timescale-showdown/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/influxdb-postgres-timescale-showdown/</guid>
      <pubDate>Thu, 02 Apr 2026 15:09:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/influxdb-postgres-timescale-showdown.m4a"
        type="audio/mp4"
        length="18904179"
      />
      <itunes:title>InfluxDB vs. Postgres: The Time-Series Showdown</itunes:title>
      <itunes:subtitle>We compare specialized time-series databases like InfluxDB against traditional SQL options like Postgres with Timescale extensions.</itunes:subtitle>
      <itunes:summary><![CDATA[Dive into the architectural tug-of-war between specialized time-series databases like InfluxDB and conventional SQL worlds like Postgres. We explore how TimescaleDB is changing the math, the impact of high-cardinality data, and whether the "specialist" store is becoming a feature of big players.]]></itunes:summary>
      <itunes:duration>1448</itunes:duration>
      <itunes:episode>1920</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/influxdb-postgres-timescale-showdown.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/influxdb-postgres-timescale-showdown.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Android Dev Without Android Studio: Is It Actually Good?</title>
      <description><![CDATA[Imagine building a full Android app in minutes without installing a single SDK or opening Android Studio. We explore how AI tools like Claude and cloud services like Expo are bypassing the traditional mobile development toolchain. This workflow decouples coding from compiling, letting you focus on app logic while the cloud handles the heavy lifting. Is this the future of mobile development?]]></description>
      <link>https://myweirdprompts.com/episode/android-studio-claude-expo-workflow/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-studio-claude-expo-workflow/</guid>
      <pubDate>Thu, 02 Apr 2026 14:59:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-studio-claude-expo-workflow.m4a"
        type="audio/mp4"
        length="20459115"
      />
      <itunes:title>Android Dev Without Android Studio: Is It Actually Good?</itunes:title>
      <itunes:subtitle>How to ship an Android app without ever opening Android Studio or touching a line of Java.</itunes:subtitle>
      <itunes:summary><![CDATA[Imagine building a full Android app in minutes without installing a single SDK or opening Android Studio. We explore how AI tools like Claude and cloud services like Expo are bypassing the traditional mobile development toolchain. This workflow decouples coding from compiling, letting you focus on app logic while the cloud handles the heavy lifting. Is this the future of mobile development?]]></itunes:summary>
      <itunes:duration>1575</itunes:duration>
      <itunes:episode>1919</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-studio-claude-expo-workflow.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-studio-claude-expo-workflow.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Server Updates Break Your AI Agents</title>
      <description><![CDATA[Why do production AI agents crash when a server renames a single parameter? This episode explores the fundamental tension between rapid MCP server evolution and the brittle plumbing that connects LLMs to tools—and how to build integrations that survive the chaos.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-schema-stability-agent-fix/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-schema-stability-agent-fix/</guid>
      <pubDate>Thu, 02 Apr 2026 14:55:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-schema-stability-agent-fix.m4a"
        type="audio/mp4"
        length="16858463"
      />
      <itunes:title>When Server Updates Break Your AI Agents</itunes:title>
      <itunes:subtitle>When a third-party MCP server updates its schema, your AI agents can crash. Here&apos;s how to build resilient clients that self-heal.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do production AI agents crash when a server renames a single parameter? This episode explores the fundamental tension between rapid MCP server evolution and the brittle plumbing that connects LLMs to tools—and how to build integrations that survive the chaos.]]></itunes:summary>
      <itunes:duration>1298</itunes:duration>
      <itunes:episode>1918</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-schema-stability-agent-fix.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-schema-stability-agent-fix.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Google Invented RAG&apos;s Secret Sauce</title>
      <description><![CDATA[Why does modern RAG feel like a breakthrough when Google solved the core retrieval problem over a decade ago? We trace the lineage of re-ranking—from early search engines to modern cross-encoders—and reveal why this "old school" engineering tactic is the key to fixing LLM context limits and hallucinations. Learn how the "two-stage" architecture works and why "less is more" when feeding data to AI.]]></description>
      <link>https://myweirdprompts.com/episode/google-invented-rag-re-ranking/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/google-invented-rag-re-ranking/</guid>
      <pubDate>Thu, 02 Apr 2026 14:19:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/google-invented-rag-re-ranking.m4a"
        type="audio/mp4"
        length="21854219"
      />
      <itunes:title>Google Invented RAG&apos;s Secret Sauce</itunes:title>
      <itunes:subtitle>Before LLMs, Google solved the &quot;hallucination&quot; problem with a two-stage trick that&apos;s making a huge comeback.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does modern RAG feel like a breakthrough when Google solved the core retrieval problem over a decade ago? We trace the lineage of re-ranking—from early search engines to modern cross-encoders—and reveal why this "old school" engineering tactic is the key to fixing LLM context limits and hallucinations. Learn how the "two-stage" architecture works and why "less is more" when feeding data to AI.]]></itunes:summary>
      <itunes:duration>1687</itunes:duration>
      <itunes:episode>1914</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/google-invented-rag-re-ranking.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/google-invented-rag-re-ranking.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Context Windows Are Junk Drawers</title>
      <description><![CDATA[We explore the hidden engineering challenge of session management in AI interfaces. Learn why stateless APIs struggle with stateful human conversation, causing context pollution, lost-in-the-middle failures, and rising token costs. We cover deterministic fixes like timeouts and commands, smarter architectural patterns using summaries and metadata, and the future of autonomous session management in voice and chat agents.]]></description>
      <link>https://myweirdprompts.com/episode/managing-ai-context-pollution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/managing-ai-context-pollution/</guid>
      <pubDate>Thu, 02 Apr 2026 14:15:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/managing-ai-context-pollution.m4a"
        type="audio/mp4"
        length="21168442"
      />
      <itunes:title>AI Context Windows Are Junk Drawers</itunes:title>
      <itunes:subtitle>Stop paying for old messages. Here&apos;s how to keep your AI sessions clean and on-topic.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the hidden engineering challenge of session management in AI interfaces. Learn why stateless APIs struggle with stateful human conversation, causing context pollution, lost-in-the-middle failures, and rising token costs. We cover deterministic fixes like timeouts and commands, smarter architectural patterns using summaries and metadata, and the future of autonomous session management in voice and chat agents.]]></itunes:summary>
      <itunes:duration>1679</itunes:duration>
      <itunes:episode>1913</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/managing-ai-context-pollution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/managing-ai-context-pollution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why We Still Fine-Tune in 2026</title>
      <description><![CDATA[In an era of massive context windows, why are companies still fine-tuning models? This episode explores the shift from teaching facts to shaping behavior. We discuss domain expertise, style alignment, and Text-to-SQL optimization, plus how Parameter-Efficient Fine-Tuning (PEFT) makes it accessible. Learn why fine-tuning creates specialized "neural highways" that outperform general models in production.]]></description>
      <link>https://myweirdprompts.com/episode/fine-tuning-vs-long-context-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fine-tuning-vs-long-context-2026/</guid>
      <pubDate>Thu, 02 Apr 2026 13:41:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fine-tuning-vs-long-context-2026.m4a"
        type="audio/mp4"
        length="22675863"
      />
      <itunes:title>Why We Still Fine-Tune in 2026</itunes:title>
      <itunes:subtitle>Despite million-token context windows, fine-tuning remains essential. Here’s why behavior, not just facts, matters.</itunes:subtitle>
      <itunes:summary><![CDATA[In an era of massive context windows, why are companies still fine-tuning models? This episode explores the shift from teaching facts to shaping behavior. We discuss domain expertise, style alignment, and Text-to-SQL optimization, plus how Parameter-Efficient Fine-Tuning (PEFT) makes it accessible. Learn why fine-tuning creates specialized "neural highways" that outperform general models in production.]]></itunes:summary>
      <itunes:duration>1763</itunes:duration>
      <itunes:episode>1907</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fine-tuning-vs-long-context-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fine-tuning-vs-long-context-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Is Your AI Model Agentic-Ready or Just Wearing a Suit?</title>
      <description><![CDATA[Not all AI models that claim "tool calling" are built equal. This episode explores the engineering reality of agentic systems, the Model Context Protocol (MCP), and how to evaluate if a model is truly "agentic-ready" or just wearing a marketing suit. We break down why native support matters, the reliability gap between instructional and optimized models, and the compounding errors that can turn a simple task into a coin flip.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ready-tool-calling-mcp/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ready-tool-calling-mcp/</guid>
      <pubDate>Thu, 02 Apr 2026 13:40:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ready-tool-calling-mcp.m4a"
        type="audio/mp4"
        length="22306696"
      />
      <itunes:title>Is Your AI Model Agentic-Ready or Just Wearing a Suit?</itunes:title>
      <itunes:subtitle>Native tool calling is the difference between a working product and a debugging nightmare.</itunes:subtitle>
      <itunes:summary><![CDATA[Not all AI models that claim "tool calling" are built equal. This episode explores the engineering reality of agentic systems, the Model Context Protocol (MCP), and how to evaluate if a model is truly "agentic-ready" or just wearing a marketing suit. We break down why native support matters, the reliability gap between instructional and optimized models, and the compounding errors that can turn a simple task into a coin flip.]]></itunes:summary>
      <itunes:duration>1734</itunes:duration>
      <itunes:episode>1906</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ready-tool-calling-mcp.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ready-tool-calling-mcp.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How VCs Verify AI Startups Without Stealing Code</title>
      <description><![CDATA[When a startup is worth billions, a simple vibe check won't cut it. We explore the rigorous "Verification Ladder" that top VCs use to vet AI companies—without signing NDAs or stealing secrets. Learn about third-party code mercenaries, adversarial sandbox testing, and why your AWS bill is the ultimate lie detector. It’s a behind-the-scenes look at the high-stakes inspection process separating billion-dollar unicorns from Theranos-style flops.]]></description>
      <link>https://myweirdprompts.com/episode/vc-due-diligence-ai-audits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vc-due-diligence-ai-audits/</guid>
      <pubDate>Thu, 02 Apr 2026 13:33:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vc-due-diligence-ai-audits.m4a"
        type="audio/mp4"
        length="20551713"
      />
      <itunes:title>How VCs Verify AI Startups Without Stealing Code</itunes:title>
      <itunes:subtitle>From the &quot;No-NDA Paradox&quot; to AWS bill forensics, here’s how investors separate real AI from Raspberry Pis in fancy cases.</itunes:subtitle>
      <itunes:summary><![CDATA[When a startup is worth billions, a simple vibe check won't cut it. We explore the rigorous "Verification Ladder" that top VCs use to vet AI companies—without signing NDAs or stealing secrets. Learn about third-party code mercenaries, adversarial sandbox testing, and why your AWS bill is the ultimate lie detector. It’s a behind-the-scenes look at the high-stakes inspection process separating billion-dollar unicorns from Theranos-style flops.]]></itunes:summary>
      <itunes:duration>1595</itunes:duration>
      <itunes:episode>1905</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vc-due-diligence-ai-audits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vc-due-diligence-ai-audits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Math Behind Your Blocky Photos</title>
      <description><![CDATA[Why do digital images still look blocky in 2026? This episode unpacks the psychovisual trade-offs and engineering compromises that shape every photo you see, from JPEG's 1992 origins to the next generation of formats.]]></description>
      <link>https://myweirdprompts.com/episode/jpeg-xl-avif-image-formats/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/jpeg-xl-avif-image-formats/</guid>
      <pubDate>Thu, 02 Apr 2026 13:26:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/jpeg-xl-avif-image-formats.m4a"
        type="audio/mp4"
        length="16872143"
      />
      <itunes:title>The Hidden Math Behind Your Blocky Photos</itunes:title>
      <itunes:subtitle>Why are blocky sky artifacts still haunting your photos in 2026? We break down the math behind JPEG, WebP, AVIF, and the new JPEG XL.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do digital images still look blocky in 2026? This episode unpacks the psychovisual trade-offs and engineering compromises that shape every photo you see, from JPEG's 1992 origins to the next generation of formats.]]></itunes:summary>
      <itunes:duration>1299</itunes:duration>
      <itunes:episode>1904</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/jpeg-xl-avif-image-formats.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/jpeg-xl-avif-image-formats.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Learning to Break Things Safely</title>
      <description><![CDATA[How do you experiment with autonomous AI agents without fear? This episode explores the philosophy of building disposable environments where failures become data points, not disasters.]]></description>
      <link>https://myweirdprompts.com/episode/building-sandbox-agentic-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/building-sandbox-agentic-ai/</guid>
      <pubDate>Wed, 01 Apr 2026 15:49:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/building-sandbox-agentic-ai.m4a"
        type="audio/mp4"
        length="24544484"
      />
      <itunes:title>Learning to Break Things Safely</itunes:title>
      <itunes:subtitle>Learn how to safely build and test autonomous AI agents using a disposable VPS, Docker containers, and secure networking.</itunes:subtitle>
      <itunes:summary><![CDATA[How do you experiment with autonomous AI agents without fear? This episode explores the philosophy of building disposable environments where failures become data points, not disasters.]]></itunes:summary>
      <itunes:duration>1917</itunes:duration>
      <itunes:episode>1870</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/building-sandbox-agentic-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/building-sandbox-agentic-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Your AI Needs Its Own Email Address</title>
      <description><![CDATA[The era of AI agents managing their own digital identities is here. We explore AgentMail, a Y Combinator-backed startup that flips the script on AI email tools by giving machines their own programmable inboxes. Learn why email remains the universal protocol for AI communication, how it provides persistent memory and audit trails, and what this shift means for the future of autonomous work. From agent-to-agent negotiations to the challenge of AI spam, this episode dives into the plumbing of agentic infrastructure.]]></description>
      <link>https://myweirdprompts.com/episode/agentmail-ai-inbox-infrastructure/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentmail-ai-inbox-infrastructure/</guid>
      <pubDate>Wed, 01 Apr 2026 00:34:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentmail-ai-inbox-infrastructure.m4a"
        type="audio/mp4"
        length="18615391"
      />
      <itunes:title>Your AI Needs Its Own Email Address</itunes:title>
      <itunes:subtitle>A YC-backed startup is giving AI agents their own dedicated inboxes, moving beyond human-centric email tools to build infrastructure for autonomous...</itunes:subtitle>
      <itunes:summary><![CDATA[The era of AI agents managing their own digital identities is here. We explore AgentMail, a Y Combinator-backed startup that flips the script on AI email tools by giving machines their own programmable inboxes. Learn why email remains the universal protocol for AI communication, how it provides persistent memory and audit trails, and what this shift means for the future of autonomous work. From agent-to-agent negotiations to the challenge of AI spam, this episode dives into the plumbing of agentic infrastructure.]]></itunes:summary>
      <itunes:duration>1436</itunes:duration>
      <itunes:episode>1863</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentmail-ai-inbox-infrastructure.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentmail-ai-inbox-infrastructure.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a 24-Agent AI Diplomatic Swarm</title>
      <description><![CDATA[We recently built a massive agentic architecture for synthetic media: a three-hour, 24-voice virtual conference on the Iran-Israel-US crisis. This episode pulls back the curtain on how we orchestrated a swarm of autonomous AI personas—each with distinct identities, red lines, and ideological constraints—to simulate a high-stakes diplomatic symposium. Discover how we moved beyond simple text generation to create a "flight simulator for foreign policy," the technical nightmares of rendering 200 minutes of multi-voice audio, and why forcing AI into ideological corners actually reveals deeper truths about real-world conflict.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ai-diplomatic-symposium/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ai-diplomatic-symposium/</guid>
      <pubDate>Wed, 01 Apr 2026 00:23:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ai-diplomatic-symposium.m4a"
        type="audio/mp4"
        length="21347953"
      />
      <itunes:title>Building a 24-Agent AI Diplomatic Swarm</itunes:title>
      <itunes:subtitle>Inside the three-hour, 24-voice virtual conference that stress-tested AI-generated geopolitical conflict.</itunes:subtitle>
      <itunes:summary><![CDATA[We recently built a massive agentic architecture for synthetic media: a three-hour, 24-voice virtual conference on the Iran-Israel-US crisis. This episode pulls back the curtain on how we orchestrated a swarm of autonomous AI personas—each with distinct identities, red lines, and ideological constraints—to simulate a high-stakes diplomatic symposium. Discover how we moved beyond simple text generation to create a "flight simulator for foreign policy," the technical nightmares of rendering 200 minutes of multi-voice audio, and why forcing AI into ideological corners actually reveals deeper truths about real-world conflict.]]></itunes:summary>
      <itunes:duration>1651</itunes:duration>
      <itunes:episode>1860</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ai-diplomatic-symposium.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ai-diplomatic-symposium.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Multi-Model Agents: The Instruction &amp; Context Gap</title>
      <description><![CDATA[Building agentic systems with multiple AI models is the wild west of orchestration. While frameworks like LangGraph and CrewAI promise interoperability, the reality involves navigating "instruction gaps," context window mismatches, and tokenization errors. This episode explores the practical engineering challenges of making Claude, Mistral, and Qwen work together, covering validation layers, temperature standardization, and the future of the Model Context Protocol.]]></description>
      <link>https://myweirdprompts.com/episode/multi-model-agent-orchestration-gaps/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-model-agent-orchestration-gaps/</guid>
      <pubDate>Wed, 01 Apr 2026 00:11:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-model-agent-orchestration-gaps.m4a"
        type="audio/mp4"
        length="18924189"
      />
      <itunes:title>Multi-Model Agents: The Instruction &amp; Context Gap</itunes:title>
      <itunes:subtitle>Mixing AI models creates chaos. Learn the practical fixes for context windows, tokenization, and output formats.</itunes:subtitle>
      <itunes:summary><![CDATA[Building agentic systems with multiple AI models is the wild west of orchestration. While frameworks like LangGraph and CrewAI promise interoperability, the reality involves navigating "instruction gaps," context window mismatches, and tokenization errors. This episode explores the practical engineering challenges of making Claude, Mistral, and Qwen work together, covering validation layers, temperature standardization, and the future of the Model Context Protocol.]]></itunes:summary>
      <itunes:duration>1462</itunes:duration>
      <itunes:episode>1858</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-model-agent-orchestration-gaps.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-model-agent-orchestration-gaps.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Death of the Dashboard</title>
      <description><![CDATA[What if your podcast backend was just a conversation? We explore how an MCP server and Telegram bot replace traditional admin panels, letting AI orchestrate complex workflows without a single button click.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-admin-server-telegram-bot/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-admin-server-telegram-bot/</guid>
      <pubDate>Wed, 01 Apr 2026 00:09:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-admin-server-telegram-bot.m4a"
        type="audio/mp4"
        length="19957163"
      />
      <itunes:title>The Death of the Dashboard</itunes:title>
      <itunes:subtitle>Why build a dashboard when you can just talk to your backend? Meet the MCP server that runs this show.</itunes:subtitle>
      <itunes:summary><![CDATA[What if your podcast backend was just a conversation? We explore how an MCP server and Telegram bot replace traditional admin panels, letting AI orchestrate complex workflows without a single button click.]]></itunes:summary>
      <itunes:duration>1531</itunes:duration>
      <itunes:episode>1857</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-admin-server-telegram-bot.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-admin-server-telegram-bot.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Makes Game Assets, Who Owns the Art?</title>
      <description><![CDATA[The shift from manual 3D modeling to AI-generated assets is democratizing game development, but it also raises questions about artistic soul and the 'asset flip' controversy. This episode explores the technical breakthroughs and cultural tensions behind tools like Meshy and Gaussian Splatting.]]></description>
      <link>https://myweirdprompts.com/episode/ai-3d-modeling-photogrammetry-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-3d-modeling-photogrammetry-future/</guid>
      <pubDate>Tue, 31 Mar 2026 23:57:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-3d-modeling-photogrammetry-future.m4a"
        type="audio/mp4"
        length="16017826"
      />
      <itunes:title>When AI Makes Game Assets, Who Owns the Art?</itunes:title>
      <itunes:subtitle>From blocky polygons to photorealistic assets, AI is transforming how 3D models are made.</itunes:subtitle>
      <itunes:summary><![CDATA[The shift from manual 3D modeling to AI-generated assets is democratizing game development, but it also raises questions about artistic soul and the 'asset flip' controversy. This episode explores the technical breakthroughs and cultural tensions behind tools like Meshy and Gaussian Splatting.]]></itunes:summary>
      <itunes:duration>1241</itunes:duration>
      <itunes:episode>1855</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-3d-modeling-photogrammetry-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-3d-modeling-photogrammetry-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Cloud Bills Can Hit $100K Overnight</title>
      <description><![CDATA[Cloud billing disasters are a developer's nightmare, and they happen faster than you can react. This episode explores real-world horror stories—from a student's $8,000 recursion trap to AI agents racking up thousands in minutes—and reveals why "infinite scaling" can be a financial landmine. We dig into the technical and architectural reasons your cloud provider won't just hit the brakes, and what it means for the future of autonomous AI spending your money.]]></description>
      <link>https://myweirdprompts.com/episode/cloud-billing-horror-stories-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cloud-billing-horror-stories-2026/</guid>
      <pubDate>Tue, 31 Mar 2026 23:31:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cloud-billing-horror-stories-2026.m4a"
        type="audio/mp4"
        length="17724585"
      />
      <itunes:title>Why Cloud Bills Can Hit $100K Overnight</itunes:title>
      <itunes:subtitle>From recursive loops to AI agents spending your money, we unpack the terrifying speed of cloud cost disasters.</itunes:subtitle>
      <itunes:summary><![CDATA[Cloud billing disasters are a developer's nightmare, and they happen faster than you can react. This episode explores real-world horror stories—from a student's $8,000 recursion trap to AI agents racking up thousands in minutes—and reveals why "infinite scaling" can be a financial landmine. We dig into the technical and architectural reasons your cloud provider won't just hit the brakes, and what it means for the future of autonomous AI spending your money.]]></itunes:summary>
      <itunes:duration>1373</itunes:duration>
      <itunes:episode>1848</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cloud-billing-horror-stories-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cloud-billing-horror-stories-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Home Lab Blackout: Fixing Servers From a Beach</title>
      <description><![CDATA[You are on vacation, thousands of miles from home, when your phone buzzes: a server alert. Your dashboard is dead, your cameras are offline, and you have no idea if it's a power outage or a cat tripping over a cable. This episode explores the "black box" failure facing the modern self-hoster. We break down the "good enough" monitoring stack that doesn't require a NASA mission control center, from inverted heartbeat checks to external service probes. Most importantly, we tackle the "resilient re-entry" problem—how to get back into a frozen server when SSH fails. Discover the affordable hardware, like the NanoKVM, that brings enterprise-grade remote management to the home lab, ensuring you can fix a kernel panic from a hotel room in Tokyo.]]></description>
      <link>https://myweirdprompts.com/episode/home-lab-resilient-re-entry/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-lab-resilient-re-entry/</guid>
      <pubDate>Tue, 31 Mar 2026 23:27:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-lab-resilient-re-entry.m4a"
        type="audio/mp4"
        length="25344038"
      />
      <itunes:title>The Home Lab Blackout: Fixing Servers From a Beach</itunes:title>
      <itunes:subtitle>Your server is down and you&apos;re miles away. Learn the three simple checks that keep your home lab alive and how to get back in when the front door i...</itunes:subtitle>
      <itunes:summary><![CDATA[You are on vacation, thousands of miles from home, when your phone buzzes: a server alert. Your dashboard is dead, your cameras are offline, and you have no idea if it's a power outage or a cat tripping over a cable. This episode explores the "black box" failure facing the modern self-hoster. We break down the "good enough" monitoring stack that doesn't require a NASA mission control center, from inverted heartbeat checks to external service probes. Most importantly, we tackle the "resilient re-entry" problem—how to get back into a frozen server when SSH fails. Discover the affordable hardware, like the NanoKVM, that brings enterprise-grade remote management to the home lab, ensuring you can fix a kernel panic from a hotel room in Tokyo.]]></itunes:summary>
      <itunes:duration>1962</itunes:duration>
      <itunes:episode>1847</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-lab-resilient-re-entry.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-lab-resilient-re-entry.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Right-Sizing Your Agent&apos;s MCP Toolkit</title>
      <description><![CDATA[As AI agents connect to more tools, they can drown in the data required to use them. This episode explores the Model Context Protocol's context pollution crisis and how just-in-time tool usage solves it. Learn how dynamic discovery and caching can slash token usage by 90% and restore reasoning speed, turning a sluggish assistant into a snappy one.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-tool-trap-context-bloat/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-tool-trap-context-bloat/</guid>
      <pubDate>Tue, 31 Mar 2026 23:21:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-tool-trap-context-bloat.m4a"
        type="audio/mp4"
        length="16436106"
      />
      <itunes:title>Right-Sizing Your Agent&apos;s MCP Toolkit</itunes:title>
      <itunes:subtitle>AI agents slow down when overloaded with tool schemas. Just-in-time usage is the fix.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI agents connect to more tools, they can drown in the data required to use them. This episode explores the Model Context Protocol's context pollution crisis and how just-in-time tool usage solves it. Learn how dynamic discovery and caching can slash token usage by 90% and restore reasoning speed, turning a sluggish assistant into a snappy one.]]></itunes:summary>
      <itunes:duration>1229</itunes:duration>
      <itunes:episode>1846</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-tool-trap-context-bloat.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-tool-trap-context-bloat.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Is My AI Pipeline Stuck? (Kanban-Style Observability)</title>
      <description><![CDATA[Modern AI pipelines have outgrown traditional monitoring. When a multi-stage agent workflow gets stuck, logs and metrics won't show you the "where"—only the "what." This episode explores the rise of "State-First Observability," a visual, Kanban-style approach that treats jobs like cards on a board. We examine the gap between heavy enterprise tools and lightweight needs, review options from Prefect to KaibanJS, and offer practical DIY solutions for teams who want a "Mission Control" view without the enterprise price tag.]]></description>
      <link>https://myweirdprompts.com/episode/ai-pipeline-kanban-observability/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-pipeline-kanban-observability/</guid>
      <pubDate>Tue, 31 Mar 2026 23:10:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-pipeline-kanban-observability.m4a"
        type="audio/mp4"
        length="19625292"
      />
      <itunes:title>Why Is My AI Pipeline Stuck? (Kanban-Style Observability)</itunes:title>
      <itunes:subtitle>Stop digging through JSON logs. See your AI jobs moving on a board, not just server metrics.</itunes:subtitle>
      <itunes:summary><![CDATA[Modern AI pipelines have outgrown traditional monitoring. When a multi-stage agent workflow gets stuck, logs and metrics won't show you the "where"—only the "what." This episode explores the rise of "State-First Observability," a visual, Kanban-style approach that treats jobs like cards on a board. We examine the gap between heavy enterprise tools and lightweight needs, review options from Prefect to KaibanJS, and offer practical DIY solutions for teams who want a "Mission Control" view without the enterprise price tag.]]></itunes:summary>
      <itunes:duration>1521</itunes:duration>
      <itunes:episode>1843</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-pipeline-kanban-observability.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-pipeline-kanban-observability.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Business on Spreadsheets? Here’s the Escape Plan</title>
      <description><![CDATA[Two interior designers are drowning in a sea of duplicated spreadsheets and manual invoicing. This episode explores how to escape the "accidental architect" trap by using Google Apps Script to automate workflows and connect Google Workspace with the power of Google Cloud. We demystify the hierarchy of Google's tools—from simple macros to AI-powered coding with Gemini—and show how even non-developers can build a scalable, professional system.]]></description>
      <link>https://myweirdprompts.com/episode/small-business-google-workspace-automation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/small-business-google-workspace-automation/</guid>
      <pubDate>Tue, 31 Mar 2026 23:09:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/small-business-google-workspace-automation.m4a"
        type="audio/mp4"
        length="18422455"
      />
      <itunes:title>Building a Business on Spreadsheets? Here’s the Escape Plan</itunes:title>
      <itunes:subtitle>Ditch the messy spreadsheets and manual invoices. Here&apos;s how to automate your workflow using Google Workspace, Apps Script, and AI.</itunes:subtitle>
      <itunes:summary><![CDATA[Two interior designers are drowning in a sea of duplicated spreadsheets and manual invoicing. This episode explores how to escape the "accidental architect" trap by using Google Apps Script to automate workflows and connect Google Workspace with the power of Google Cloud. We demystify the hierarchy of Google's tools—from simple macros to AI-powered coding with Gemini—and show how even non-developers can build a scalable, professional system.]]></itunes:summary>
      <itunes:duration>1402</itunes:duration>
      <itunes:episode>1842</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/small-business-google-workspace-automation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/small-business-google-workspace-automation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Your Calendar Is Now a Negotiation</title>
      <description><![CDATA[The friction of scheduling is disappearing as AI agents begin negotiating directly with one another. From Google's A2A protocol to zero-knowledge proofs that hide your calendar details, we explore the technical reality of agentic interoperability. But as efficiency skyrockets, we ask: who controls the gate, and what happens to human agency when algorithms manage our time?]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-scheduling-negotiation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-scheduling-negotiation/</guid>
      <pubDate>Tue, 31 Mar 2026 22:58:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-scheduling-negotiation.m4a"
        type="audio/mp4"
        length="27174235"
      />
      <itunes:title>Your Calendar Is Now a Negotiation</itunes:title>
      <itunes:subtitle>AI agents are now negotiating meetings behind the scenes using JSON schemas and zero-knowledge proofs.</itunes:subtitle>
      <itunes:summary><![CDATA[The friction of scheduling is disappearing as AI agents begin negotiating directly with one another. From Google's A2A protocol to zero-knowledge proofs that hide your calendar details, we explore the technical reality of agentic interoperability. But as efficiency skyrockets, we ask: who controls the gate, and what happens to human agency when algorithms manage our time?]]></itunes:summary>
      <itunes:duration>2145</itunes:duration>
      <itunes:episode>1840</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-scheduling-negotiation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-scheduling-negotiation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Tuning Search Without Losing Your Mind</title>
      <description><![CDATA[That search bar on your website isn't just a text box anymore—it's a complex AI system with sliders for typo tolerance, vector density, and attribute weighting. In this episode, we break down the three layers of modern search: fuzzy matching for typos, semantic search for intent, and reranking for relevance. Learn when to use each layer, the common traps small teams fall into (like cranking typo tolerance too high), and why the best approach is a hybrid pipeline that combines old-school keyword matching with new-school AI. Whether you're tuning Algolia for a 50-product inventory or a 5,000-page documentation wiki, this guide cuts through the jargon to give you practical rules for making search actually work.]]></description>
      <link>https://myweirdprompts.com/episode/tuning-search-without-losing-mind/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tuning-search-without-losing-mind/</guid>
      <pubDate>Tue, 31 Mar 2026 22:52:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tuning-search-without-losing-mind.m4a"
        type="audio/mp4"
        length="18239535"
      />
      <itunes:title>Tuning Search Without Losing Your Mind</itunes:title>
      <itunes:subtitle>Modern search bars are AI decision engines. Here&apos;s how small teams can tune fuzzy matching, semantic search, and reranking without breaking everyth...</itunes:subtitle>
      <itunes:summary><![CDATA[That search bar on your website isn't just a text box anymore—it's a complex AI system with sliders for typo tolerance, vector density, and attribute weighting. In this episode, we break down the three layers of modern search: fuzzy matching for typos, semantic search for intent, and reranking for relevance. Learn when to use each layer, the common traps small teams fall into (like cranking typo tolerance too high), and why the best approach is a hybrid pipeline that combines old-school keyword matching with new-school AI. Whether you're tuning Algolia for a 50-product inventory or a 5,000-page documentation wiki, this guide cuts through the jargon to give you practical rules for making search actually work.]]></itunes:summary>
      <itunes:duration>1401</itunes:duration>
      <itunes:episode>1838</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tuning-search-without-losing-mind.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tuning-search-without-losing-mind.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Human-in-the-Loop Price Tag: What Safety Costs in 2026</title>
      <description><![CDATA[Your AI agent just approved a $50,000 purchase order instead of a $50 test. As agents move from drafting emails to moving real money, human oversight is no longer optional—it's a critical infrastructure decision. We dissect the three main categories of Human-in-the-Loop (HITL) platforms, from low-code giants like Zapier to specialized SaaS like Humanloop and developer-centric tools like LangGraph. Plus, we break down the hidden costs of "click taxes," latency fees, and managed review services, so you can budget for safety before the bots get ambitious.]]></description>
      <link>https://myweirdprompts.com/episode/human-in-the-loop-costs-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/human-in-the-loop-costs-2026/</guid>
      <pubDate>Tue, 31 Mar 2026 22:46:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/human-in-the-loop-costs-2026.m4a"
        type="audio/mp4"
        length="18871903"
      />
      <itunes:title>The Human-in-the-Loop Price Tag: What Safety Costs in 2026</itunes:title>
      <itunes:subtitle>From $0.50 reviews to $500 platforms, we break down the real cost of keeping humans in charge of AI agents.</itunes:subtitle>
      <itunes:summary><![CDATA[Your AI agent just approved a $50,000 purchase order instead of a $50 test. As agents move from drafting emails to moving real money, human oversight is no longer optional—it's a critical infrastructure decision. We dissect the three main categories of Human-in-the-Loop (HITL) platforms, from low-code giants like Zapier to specialized SaaS like Humanloop and developer-centric tools like LangGraph. Plus, we break down the hidden costs of "click taxes," latency fees, and managed review services, so you can budget for safety before the bots get ambitious.]]></itunes:summary>
      <itunes:duration>1458</itunes:duration>
      <itunes:episode>1837</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/human-in-the-loop-costs-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/human-in-the-loop-costs-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your AI Agent Needs a Headless Browser</title>
      <description><![CDATA[We explore the "browser layer" for AI agents, moving beyond static LLMs to systems that can actually interact with the modern web. Learn how tools like Playwright and Puppeteer work, and why the new generation of "Browser-as-a-Service" platforms like Browserbase and Steel are solving massive infrastructure headaches—from bot detection and fingerprint spoofing to session persistence and residential IP proxies.]]></description>
      <link>https://myweirdprompts.com/episode/headless-browser-ai-agents-infrastructure/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/headless-browser-ai-agents-infrastructure/</guid>
      <pubDate>Tue, 31 Mar 2026 22:35:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/headless-browser-ai-agents-infrastructure.m4a"
        type="audio/mp4"
        length="19905105"
      />
      <itunes:title>Why Your AI Agent Needs a Headless Browser</itunes:title>
      <itunes:subtitle>AI agents can&apos;t just use text—they need to see and click. Here&apos;s why headless browsers are the critical bridge to the live web.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the "browser layer" for AI agents, moving beyond static LLMs to systems that can actually interact with the modern web. Learn how tools like Playwright and Puppeteer work, and why the new generation of "Browser-as-a-Service" platforms like Browserbase and Steel are solving massive infrastructure headaches—from bot detection and fingerprint spoofing to session persistence and residential IP proxies.]]></itunes:summary>
      <itunes:duration>1548</itunes:duration>
      <itunes:episode>1836</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/headless-browser-ai-agents-infrastructure.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/headless-browser-ai-agents-infrastructure.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Owning Your AI Memory: The Data Exit Strategy</title>
      <description><![CDATA[Why is your AI's memory still a fragmented mess? This episode explores the architectural challenge of building a portable, federated memory layer you actually own, not rent. We dive into data exit strategies, local mirrors, and the frameworks that let you take your memories with you.]]></description>
      <link>https://myweirdprompts.com/episode/portable-personal-ai-memory/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/portable-personal-ai-memory/</guid>
      <pubDate>Tue, 31 Mar 2026 21:56:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/portable-personal-ai-memory.m4a"
        type="audio/mp4"
        length="24885429"
      />
      <itunes:title>Owning Your AI Memory: The Data Exit Strategy</itunes:title>
      <itunes:subtitle>Why your AI remembers your coffee order but forgets your son’s name—and how to build a portable, federated memory layer you actually own.</itunes:subtitle>
      <itunes:summary><![CDATA[Why is your AI's memory still a fragmented mess? This episode explores the architectural challenge of building a portable, federated memory layer you actually own, not rent. We dive into data exit strategies, local mirrors, and the frameworks that let you take your memories with you.]]></itunes:summary>
      <itunes:duration>1927</itunes:duration>
      <itunes:episode>1834</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/portable-personal-ai-memory.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/portable-personal-ai-memory.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Local Chaos to Cloud Control</title>
      <description><![CDATA[Managing dozens of local AI tool servers is a nightmare. This episode explores how cloud-native aggregators like Composio centralize security, auditability, and integration, moving the plumbing off your machine and into a governed control plane.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-cloud-aggregator-composio/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-cloud-aggregator-composio/</guid>
      <pubDate>Tue, 31 Mar 2026 19:58:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-cloud-aggregator-composio.m4a"
        type="audio/mp4"
        length="18358651"
      />
      <itunes:title>From Local Chaos to Cloud Control</itunes:title>
      <itunes:subtitle>Local MCP servers are a configuration nightmare. Cloud aggregators like Composio offer a unified control plane for AI tools.</itunes:subtitle>
      <itunes:summary><![CDATA[Managing dozens of local AI tool servers is a nightmare. This episode explores how cloud-native aggregators like Composio centralize security, auditability, and integration, moving the plumbing off your machine and into a governed control plane.]]></itunes:summary>
      <itunes:duration>1392</itunes:duration>
      <itunes:episode>1832</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-cloud-aggregator-composio.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-cloud-aggregator-composio.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The 79% AI Coder: Reasoning vs. Memorization</title>
      <description><![CDATA[The latest SWE-bench results show AI coding agents hitting 79% accuracy, nearly matching human engineers. But is this real progress or just sophisticated memorization? We explore the hidden role of agent scaffolds, the shocking cost differences between models, and why harder benchmarks reveal a 40-point performance drop.]]></description>
      <link>https://myweirdprompts.com/episode/ai-coder-79-percent-memorization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-coder-79-percent-memorization/</guid>
      <pubDate>Tue, 31 Mar 2026 19:56:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-coder-79-percent-memorization.m4a"
        type="audio/mp4"
        length="18374147"
      />
      <itunes:title>The 79% AI Coder: Reasoning vs. Memorization</itunes:title>
      <itunes:subtitle>AI models now score 79% on coding benchmarks, but a 40-point drop on harder tests reveals the truth.</itunes:subtitle>
      <itunes:summary><![CDATA[The latest SWE-bench results show AI coding agents hitting 79% accuracy, nearly matching human engineers. But is this real progress or just sophisticated memorization? We explore the hidden role of agent scaffolds, the shocking cost differences between models, and why harder benchmarks reveal a 40-point performance drop.]]></itunes:summary>
      <itunes:duration>1411</itunes:duration>
      <itunes:episode>1831</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-coder-79-percent-memorization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-coder-79-percent-memorization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Coordinating Multi-Agent Repos at Scale</title>
      <description><![CDATA[When multiple AI agents edit the same repository simultaneously, they can create a logical lobotomy of your codebase. This episode explores the coordination chaos of multi-agent code generation, from the limits of Git to the need for AST-based semantic locking. Discover why "too many cooks" is a massive problem when the cooks are running at 10,000 words per minute, and what architectural primitives might save us from the regression hell.]]></description>
      <link>https://myweirdprompts.com/episode/multi-agent-repo-chaos-coordination/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-agent-repo-chaos-coordination/</guid>
      <pubDate>Tue, 31 Mar 2026 19:50:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-agent-repo-chaos-coordination.m4a"
        type="audio/mp4"
        length="17353326"
      />
      <itunes:title>Coordinating Multi-Agent Repos at Scale</itunes:title>
      <itunes:subtitle>Parallel AI agents rewriting your code at once creates silent regressions and architectural drift. How do we fix it?</itunes:subtitle>
      <itunes:summary><![CDATA[When multiple AI agents edit the same repository simultaneously, they can create a logical lobotomy of your codebase. This episode explores the coordination chaos of multi-agent code generation, from the limits of Git to the need for AST-based semantic locking. Discover why "too many cooks" is a massive problem when the cooks are running at 10,000 words per minute, and what architectural primitives might save us from the regression hell.]]></itunes:summary>
      <itunes:duration>1340</itunes:duration>
      <itunes:episode>1830</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-agent-repo-chaos-coordination.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-agent-repo-chaos-coordination.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Chatbots to Digital Chefs</title>
      <description><![CDATA[The job market for agentic AI is exploding, but what separates a real autonomous agent from a glorified script? This episode explores the engineering scaffolding behind reliable AI systems and the skills needed to build a career in this new era.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ai-career-blueprint/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ai-career-blueprint/</guid>
      <pubDate>Tue, 31 Mar 2026 19:23:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ai-career-blueprint.m4a"
        type="audio/mp4"
        length="23528546"
      />
      <itunes:title>From Chatbots to Digital Chefs</itunes:title>
      <itunes:subtitle>The job title barely existed 18 months ago. Now, it’s one of the most searched terms on LinkedIn.</itunes:subtitle>
      <itunes:summary><![CDATA[The job market for agentic AI is exploding, but what separates a real autonomous agent from a glorified script? This episode explores the engineering scaffolding behind reliable AI systems and the skills needed to build a career in this new era.]]></itunes:summary>
      <itunes:duration>1815</itunes:duration>
      <itunes:episode>1829</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ai-career-blueprint.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ai-career-blueprint.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Mastering 2M Token Context in Agentic Pipelines</title>
      <description><![CDATA[We explore the "agentic trap" of massive context windows, where more space can lead to higher costs and lower intelligence. Learn six practical techniques—from sliding windows to hierarchical compression—to manage context load effectively and keep your AI workflows from collapsing under their own weight.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-context-management-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-context-management-guide/</guid>
      <pubDate>Tue, 31 Mar 2026 19:14:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-context-management-guide.m4a"
        type="audio/mp4"
        length="25430901"
      />
      <itunes:title>Mastering 2M Token Context in Agentic Pipelines</itunes:title>
      <itunes:subtitle>A massive context window sounds like a dream, but it can quickly become a nightmare for complex AI workflows.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the "agentic trap" of massive context windows, where more space can lead to higher costs and lower intelligence. Learn six practical techniques—from sliding windows to hierarchical compression—to manage context load effectively and keep your AI workflows from collapsing under their own weight.]]></itunes:summary>
      <itunes:duration>1959</itunes:duration>
      <itunes:episode>1828</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-context-management-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-context-management-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Quantum in the Cloud: Hype vs. Hardware</title>
      <description><![CDATA[Quantum Computing as a Service (QCaaS) is now a billion-dollar market, but is it ready for production workloads? This episode cuts through the hype to examine the practical reality of renting quantum power from AWS, Google, and IBM. We explore why 78% of enterprises remain stuck in the pilot phase, the gritty economics of "per-shot" pricing, and the emerging "Hybrid Quantum" model that might be the only viable path forward. From error rates to talent retention strategies, discover what you're actually buying when you add a quantum processor to your cloud cart.]]></description>
      <link>https://myweirdprompts.com/episode/quantum-cloud-service-reality-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/quantum-cloud-service-reality-2026/</guid>
      <pubDate>Tue, 31 Mar 2026 18:48:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/quantum-cloud-service-reality-2026.m4a"
        type="audio/mp4"
        length="19133158"
      />
      <itunes:title>Quantum in the Cloud: Hype vs. Hardware</itunes:title>
      <itunes:subtitle>Is QCaaS a billion-dollar breakthrough or an expensive science experiment? We explore the gap between hype and hardware.</itunes:subtitle>
      <itunes:summary><![CDATA[Quantum Computing as a Service (QCaaS) is now a billion-dollar market, but is it ready for production workloads? This episode cuts through the hype to examine the practical reality of renting quantum power from AWS, Google, and IBM. We explore why 78% of enterprises remain stuck in the pilot phase, the gritty economics of "per-shot" pricing, and the emerging "Hybrid Quantum" model that might be the only viable path forward. From error rates to talent retention strategies, discover what you're actually buying when you add a quantum processor to your cloud cart.]]></itunes:summary>
      <itunes:duration>1450</itunes:duration>
      <itunes:episode>1822</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/quantum-cloud-service-reality-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/quantum-cloud-service-reality-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Claude&apos;s 55-Day Personality Transplant</title>
      <description><![CDATA[We analyzed the rare system prompt diff between Claude Opus 4.5 versions from November to January. This episode uncovers the hidden changes that reveal how AI personalities are actively engineered—from crisis intervention protocols to banning the word "genuinely." Learn why Anthropic is teaching its AI epistemic humility and how they patch safety holes in real-time.]]></description>
      <link>https://myweirdprompts.com/episode/claude-system-prompt-diff-anthropic/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-system-prompt-diff-anthropic/</guid>
      <pubDate>Tue, 31 Mar 2026 18:31:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-system-prompt-diff-anthropic.m4a"
        type="audio/mp4"
        length="15856944"
      />
      <itunes:title>Claude&apos;s 55-Day Personality Transplant</itunes:title>
      <itunes:subtitle>Anthropic leaked 55 days of system prompt updates. See exactly how they rewired Claude&apos;s personality, safety rules, and self-awareness.</itunes:subtitle>
      <itunes:summary><![CDATA[We analyzed the rare system prompt diff between Claude Opus 4.5 versions from November to January. This episode uncovers the hidden changes that reveal how AI personalities are actively engineered—from crisis intervention protocols to banning the word "genuinely." Learn why Anthropic is teaching its AI epistemic humility and how they patch safety holes in real-time.]]></itunes:summary>
      <itunes:duration>1218</itunes:duration>
      <itunes:episode>1819</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-system-prompt-diff-anthropic.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-system-prompt-diff-anthropic.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Taxonomy of AI: Why Specialized Models Outperform Giants</title>
      <description><![CDATA[Beyond the hype of large language models lies a vast ecosystem of specialized AI—from pixel-perfect segmentation to document retrieval. This episode explores why smaller, task-specific models often beat general-purpose giants, and how they're quietly transforming industries.]]></description>
      <link>https://myweirdprompts.com/episode/specialized-ai-models-hugging-face/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/specialized-ai-models-hugging-face/</guid>
      <pubDate>Tue, 31 Mar 2026 18:26:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/specialized-ai-models-hugging-face.m4a"
        type="audio/mp4"
        length="17738161"
      />
      <itunes:title>The Hidden Taxonomy of AI: Why Specialized Models Outperform Giants</itunes:title>
      <itunes:subtitle>Explore the vast ecosystem of niche AI models for computer vision and document understanding, far beyond large language models.</itunes:subtitle>
      <itunes:summary><![CDATA[Beyond the hype of large language models lies a vast ecosystem of specialized AI—from pixel-perfect segmentation to document retrieval. This episode explores why smaller, task-specific models often beat general-purpose giants, and how they're quietly transforming industries.]]></itunes:summary>
      <itunes:duration>1361</itunes:duration>
      <itunes:episode>1817</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/specialized-ai-models-hugging-face.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/specialized-ai-models-hugging-face.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Gets a Truth Tether to the Talmud</title>
      <description><![CDATA[The Sefaria project's new MCP server gives LLMs direct, cited access to thousands of years of Jewish texts. This episode explores how structured protocols replace vague AI 'vibes' with precision, and what that means for scholarship, education, and intellectual atrophy.]]></description>
      <link>https://myweirdprompts.com/episode/sefaria-mcp-ai-talmud/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/sefaria-mcp-ai-talmud/</guid>
      <pubDate>Tue, 31 Mar 2026 17:44:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/sefaria-mcp-ai-talmud.m4a"
        type="audio/mp4"
        length="19323358"
      />
      <itunes:title>When AI Gets a Truth Tether to the Talmud</itunes:title>
      <itunes:subtitle>Sefaria&apos;s new MCP server connects AI directly to 2,700 years of Jewish texts, transforming how scholars and curious learners study ancient literature.</itunes:subtitle>
      <itunes:summary><![CDATA[The Sefaria project's new MCP server gives LLMs direct, cited access to thousands of years of Jewish texts. This episode explores how structured protocols replace vague AI 'vibes' with precision, and what that means for scholarship, education, and intellectual atrophy.]]></itunes:summary>
      <itunes:duration>1489</itunes:duration>
      <itunes:episode>1812</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/sefaria-mcp-ai-talmud.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/sefaria-mcp-ai-talmud.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Stop Hardcoding User Names in AI Prompts</title>
      <description><![CDATA[When building voice agents, how do you store persistent user details like a child's name without cluttering prompts or killing latency? This episode dissects three engineering patterns: the "Fat System Prompt," pre-pending context, and lightweight key-value stores with tool-calling. We explore the trade-offs in token cost, latency, and reliability, using a real-world parenting advice agent as the test case. Learn why the "engineer's choice" for 2026 involves SQLite, orchestration layers, and keeping your context window clean.]]></description>
      <link>https://myweirdprompts.com/episode/ai-context-storage-patterns/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-context-storage-patterns/</guid>
      <pubDate>Tue, 31 Mar 2026 13:07:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-context-storage-patterns.m4a"
        type="audio/mp4"
        length="21088548"
      />
      <itunes:title>Stop Hardcoding User Names in AI Prompts</itunes:title>
      <itunes:subtitle>Three methods for storing user identity in AI agents—and why the &quot;Fat System Prompt&quot; breaks production apps.</itunes:subtitle>
      <itunes:summary><![CDATA[When building voice agents, how do you store persistent user details like a child's name without cluttering prompts or killing latency? This episode dissects three engineering patterns: the "Fat System Prompt," pre-pending context, and lightweight key-value stores with tool-calling. We explore the trade-offs in token cost, latency, and reliability, using a real-world parenting advice agent as the test case. Learn why the "engineer's choice" for 2026 involves SQLite, orchestration layers, and keeping your context window clean.]]></itunes:summary>
      <itunes:duration>1647</itunes:duration>
      <itunes:episode>1811</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-context-storage-patterns.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-context-storage-patterns.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The TTS Developer&apos;s Dilemma: Size vs. Speed</title>
      <description><![CDATA[The text-to-speech landscape has exploded, leaving developers with a difficult choice: prioritize rich, emotional audio or lightning-fast response times? This episode dives deep into the technical architecture of modern TTS, from massive billion-parameter models to ultra-efficient edge runners. We explore how to balance GPU requirements, streaming capabilities, and bandwidth costs to build a voice experience that doesn't feel cheap. Plus, we tackle the nuances of prosody control, multilingual interference, and the battle against messy input text.]]></description>
      <link>https://myweirdprompts.com/episode/tts-model-latency-optimization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tts-model-latency-optimization/</guid>
      <pubDate>Tue, 31 Mar 2026 12:15:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tts-model-latency-optimization.m4a"
        type="audio/mp4"
        length="21417753"
      />
      <itunes:title>The TTS Developer&apos;s Dilemma: Size vs. Speed</itunes:title>
      <itunes:subtitle>Stop guessing. We break down the critical trade-offs between model size, latency, and sample rate for production-ready voice apps.</itunes:subtitle>
      <itunes:summary><![CDATA[The text-to-speech landscape has exploded, leaving developers with a difficult choice: prioritize rich, emotional audio or lightning-fast response times? This episode dives deep into the technical architecture of modern TTS, from massive billion-parameter models to ultra-efficient edge runners. We explore how to balance GPU requirements, streaming capabilities, and bandwidth costs to build a voice experience that doesn't feel cheap. Plus, we tackle the nuances of prosody control, multilingual interference, and the battle against messy input text.]]></itunes:summary>
      <itunes:duration>1634</itunes:duration>
      <itunes:episode>1809</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tts-model-latency-optimization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tts-model-latency-optimization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The ABI Trap: Why GPUs Break Docker&apos;s Promise</title>
      <description><![CDATA[Docker promises portable environments, but GPU-accelerated containers force local builds due to brittle ABI compatibility between drivers, kernels, and libraries. We explore why the 'run anywhere' dream has a hardware-shaped asterisk.]]></description>
      <link>https://myweirdprompts.com/episode/gpu-container-build-failure/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gpu-container-build-failure/</guid>
      <pubDate>Tue, 31 Mar 2026 11:31:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gpu-container-build-failure.m4a"
        type="audio/mp4"
        length="17607305"
      />
      <itunes:title>The ABI Trap: Why GPUs Break Docker&apos;s Promise</itunes:title>
      <itunes:subtitle>Docker promised &quot;run anywhere,&quot; but GPU images make you compile for hours. Here’s why the abstraction breaks down.</itunes:subtitle>
      <itunes:summary><![CDATA[Docker promises portable environments, but GPU-accelerated containers force local builds due to brittle ABI compatibility between drivers, kernels, and libraries. We explore why the 'run anywhere' dream has a hardware-shaped asterisk.]]></itunes:summary>
      <itunes:duration>1351</itunes:duration>
      <itunes:episode>1807</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gpu-container-build-failure.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gpu-container-build-failure.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Fork in the Road: Why AI Agents Check Old Receipts First</title>
      <description><![CDATA[Why does an AI agent waste time digging through your travel history when you just want a flight? This episode unpacks the 'agentic friction' of tool selection, exploring how models like Gemini decide between memory and action—and how to stop them from derailing workflows.]]></description>
      <link>https://myweirdprompts.com/episode/agent-tool-selection-eagerness/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-tool-selection-eagerness/</guid>
      <pubDate>Tue, 31 Mar 2026 08:03:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-tool-selection-eagerness.m4a"
        type="audio/mp4"
        length="32971977"
      />
      <itunes:title>The Fork in the Road: Why AI Agents Check Old Receipts First</itunes:title>
      <itunes:subtitle>Stop your AI agent from overthinking. Learn why it checks old memories instead of booking flights—and how to fix the &quot;eagerness&quot; problem.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does an AI agent waste time digging through your travel history when you just want a flight? This episode unpacks the 'agentic friction' of tool selection, exploring how models like Gemini decide between memory and action—and how to stop them from derailing workflows.]]></itunes:summary>
      <itunes:duration>2557</itunes:duration>
      <itunes:episode>1804</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-tool-selection-eagerness.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-tool-selection-eagerness.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Hospitals Still Use Pagers in 2026</title>
      <description><![CDATA[In a world of smartphones and 5G, why are pagers still the backbone of hospitals and nuclear plants? We explore the surprising physics of radio penetration, battery life, and network reliability that keeps this "dumb" tech alive. We also dive into the software side, from PagerDuty's cloud orchestration to self-hosted alerting solutions like Gotify.]]></description>
      <link>https://myweirdprompts.com/episode/why-hospitals-still-use-pagers/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/why-hospitals-still-use-pagers/</guid>
      <pubDate>Tue, 31 Mar 2026 06:52:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/why-hospitals-still-use-pagers.m4a"
        type="audio/mp4"
        length="18447448"
      />
      <itunes:title>Why Hospitals Still Use Pagers in 2026</itunes:title>
      <itunes:subtitle>Despite 5G and smartphones, pagers persist in critical infrastructure. Discover the physics and reliability behind this &quot;legacy&quot; tech.</itunes:subtitle>
      <itunes:summary><![CDATA[In a world of smartphones and 5G, why are pagers still the backbone of hospitals and nuclear plants? We explore the surprising physics of radio penetration, battery life, and network reliability that keeps this "dumb" tech alive. We also dive into the software side, from PagerDuty's cloud orchestration to self-hosted alerting solutions like Gotify.]]></itunes:summary>
      <itunes:duration>1422</itunes:duration>
      <itunes:episode>1801</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/why-hospitals-still-use-pagers.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/why-hospitals-still-use-pagers.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>RAG Is Cheaper Than You Think (Until It’s Not)</title>
      <description><![CDATA[Everyone assumes RAG is either free or bankrupting, but the real cost lies in the middle. We break down the actual price of embeddings, the hidden tax of vector storage, and the recurring nightmare of "Vector Debt." Learn why small teams pay pennies, enterprises build custom infra, and mid-sized companies get stuck in the pricing valley of death.]]></description>
      <link>https://myweirdprompts.com/episode/rag-cost-vector-debt-breakdown/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rag-cost-vector-debt-breakdown/</guid>
      <pubDate>Tue, 31 Mar 2026 00:12:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rag-cost-vector-debt-breakdown.m4a"
        type="audio/mp4"
        length="17190891"
      />
      <itunes:title>RAG Is Cheaper Than You Think (Until It’s Not)</itunes:title>
      <itunes:subtitle>From a $1 embedding bill to a $10k/month vector database bill, here’s the real math behind RAG in 2026.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone assumes RAG is either free or bankrupting, but the real cost lies in the middle. We break down the actual price of embeddings, the hidden tax of vector storage, and the recurring nightmare of "Vector Debt." Learn why small teams pay pennies, enterprises build custom infra, and mid-sized companies get stuck in the pricing valley of death.]]></itunes:summary>
      <itunes:duration>1313</itunes:duration>
      <itunes:episode>1794</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rag-cost-vector-debt-breakdown.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rag-cost-vector-debt-breakdown.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Google&apos;s Native Multimodal Embedding Kills the Fusion Layer</title>
      <description><![CDATA[Google just released a natively multimodal embedding model that fundamentally changes how retrieval systems are built. Instead of stitching together separate encoders for text, images, and audio, this new approach uses a single shared transformer architecture. We explore how this eliminates the "vector debt" of maintaining multiple indexes, cuts inference latency by 70%, and simplifies complex RAG pipelines—from searching furniture by photo and text to querying charts inside PDFs.]]></description>
      <link>https://myweirdprompts.com/episode/native-multimodal-embedding-gemini/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/native-multimodal-embedding-gemini/</guid>
      <pubDate>Tue, 31 Mar 2026 00:07:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/native-multimodal-embedding-gemini.m4a"
        type="audio/mp4"
        length="21026695"
      />
      <itunes:title>Google&apos;s Native Multimodal Embedding Kills the Fusion Layer</itunes:title>
      <itunes:subtitle>Google’s new embedding model maps text, images, audio, and video into a single vector space—cutting latency by 70%.</itunes:subtitle>
      <itunes:summary><![CDATA[Google just released a natively multimodal embedding model that fundamentally changes how retrieval systems are built. Instead of stitching together separate encoders for text, images, and audio, this new approach uses a single shared transformer architecture. We explore how this eliminates the "vector debt" of maintaining multiple indexes, cuts inference latency by 70%, and simplifies complex RAG pipelines—from searching furniture by photo and text to querying charts inside PDFs.]]></itunes:summary>
      <itunes:duration>1613</itunes:duration>
      <itunes:episode>1792</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/native-multimodal-embedding-gemini.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/native-multimodal-embedding-gemini.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Supervisors Fire AI Workers</title>
      <description><![CDATA[We are moving beyond simple chatbots into an era of autonomous AI hierarchies. In this episode, we explore Agent-in-the-Loop (AITL) systems where supervisory AI models actively manage, review, and even fire subordinate agents without human intervention. We discuss the tradeoffs between speed and governance, the mechanics of checkpoint-based reviews, and why this hybrid model is becoming essential for enterprise AI trust and efficiency.]]></description>
      <link>https://myweirdprompts.com/episode/ai-supervisors-firing-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-supervisors-firing-agents/</guid>
      <pubDate>Mon, 30 Mar 2026 17:51:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-supervisors-firing-agents.m4a"
        type="audio/mp4"
        length="21352645"
      />
      <itunes:title>When AI Supervisors Fire AI Workers</itunes:title>
      <itunes:subtitle>A new &quot;Agent-in-the-Loop&quot; framework lets AI models manage and terminate other AI agents in real-time.</itunes:subtitle>
      <itunes:summary><![CDATA[We are moving beyond simple chatbots into an era of autonomous AI hierarchies. In this episode, we explore Agent-in-the-Loop (AITL) systems where supervisory AI models actively manage, review, and even fire subordinate agents without human intervention. We discuss the tradeoffs between speed and governance, the mechanics of checkpoint-based reviews, and why this hybrid model is becoming essential for enterprise AI trust and efficiency.]]></itunes:summary>
      <itunes:duration>1664</itunes:duration>
      <itunes:episode>1786</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-supervisors-firing-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-supervisors-firing-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Context1: The Retrieval Coprocessor</title>
      <description><![CDATA[Traditional RAG is hitting a wall on complex queries. In this episode, we explore Chroma's Context1, a specialized 20-billion parameter model designed to replace static vector lookups with active, multi-step reasoning loops. We break down how it functions as a "retrieval coprocessor" for frontier models, drastically reducing cost and latency while improving accuracy on multi-hop questions. Learn why this shift from passive indexing to active investigation might be the key to solving context pollution and lost-in-the-middle problems.]]></description>
      <link>https://myweirdprompts.com/episode/context1-retrieval-coprocessor-agent/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/context1-retrieval-coprocessor-agent/</guid>
      <pubDate>Mon, 30 Mar 2026 17:09:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/context1-retrieval-coprocessor-agent.m4a"
        type="audio/mp4"
        length="20630283"
      />
      <itunes:title>Context1: The Retrieval Coprocessor</itunes:title>
      <itunes:subtitle>Chroma&apos;s new 20B model acts as a specialized &quot;scout&quot; for your LLM, replacing slow, static RAG with multi-step, agentic search.</itunes:subtitle>
      <itunes:summary><![CDATA[Traditional RAG is hitting a wall on complex queries. In this episode, we explore Chroma's Context1, a specialized 20-billion parameter model designed to replace static vector lookups with active, multi-step reasoning loops. We break down how it functions as a "retrieval coprocessor" for frontier models, drastically reducing cost and latency while improving accuracy on multi-hop questions. Learn why this shift from passive indexing to active investigation might be the key to solving context pollution and lost-in-the-middle problems.]]></itunes:summary>
      <itunes:duration>1587</itunes:duration>
      <itunes:episode>1784</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/context1-retrieval-coprocessor-agent.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/context1-retrieval-coprocessor-agent.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Jenkins, GitHub, or Tekton? Picking Your 2025 CI/CD Engine</title>
      <description><![CDATA[The CI/CD landscape has shattered into a thousand specialized pieces. We explore why Jenkins persists as the "COBOL of DevOps," how GitHub Actions captured the default spot, and why Kubernetes-native tools like Tekton and Argo are rewriting the rules of build and deployment. From "plugin hell" to "Pipeline as Code," discover the trade-offs between maintenance overhead, platform control, and the rise of AI in the pipeline.]]></description>
      <link>https://myweirdprompts.com/episode/2025-ci-cd-tool-landscape/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/2025-ci-cd-tool-landscape/</guid>
      <pubDate>Mon, 30 Mar 2026 16:51:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/2025-ci-cd-tool-landscape.m4a"
        type="audio/mp4"
        length="17065124"
      />
      <itunes:title>Jenkins, GitHub, or Tekton? Picking Your 2025 CI/CD Engine</itunes:title>
      <itunes:subtitle>Jenkins is still the COBOL of DevOps, but the &quot;one size fits all&quot; model is dead. Here’s how to pick your pipeline.</itunes:subtitle>
      <itunes:summary><![CDATA[The CI/CD landscape has shattered into a thousand specialized pieces. We explore why Jenkins persists as the "COBOL of DevOps," how GitHub Actions captured the default spot, and why Kubernetes-native tools like Tekton and Argo are rewriting the rules of build and deployment. From "plugin hell" to "Pipeline as Code," discover the trade-offs between maintenance overhead, platform control, and the rise of AI in the pipeline.]]></itunes:summary>
      <itunes:duration>1325</itunes:duration>
      <itunes:episode>1782</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/2025-ci-cd-tool-landscape.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/2025-ci-cd-tool-landscape.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Writing Tests Before Code Is Insane (Until You Try It)</title>
      <description><![CDATA[That "one-line change" that broke your entire app isn't magic—it's the cost of flying blind. This episode explores why unit testing is a non-negotiable best practice in 2026, debunking the myth that it slows you down. Learn the "Arrange, Act, Assert" framework, how to start with just one function, and why writing tests before code might be the sanity check your workflow needs. Powered by Google Gemini 3 Flash.]]></description>
      <link>https://myweirdprompts.com/episode/unit-testing-tdd-legacy-code/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unit-testing-tdd-legacy-code/</guid>
      <pubDate>Mon, 30 Mar 2026 16:50:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unit-testing-tdd-legacy-code.m4a"
        type="audio/mp4"
        length="15471747"
      />
      <itunes:title>Writing Tests Before Code Is Insane (Until You Try It)</itunes:title>
      <itunes:subtitle>Why testing feels like a tax, how it actually speeds you up, and the simple three-step method to start today.</itunes:subtitle>
      <itunes:summary><![CDATA[That "one-line change" that broke your entire app isn't magic—it's the cost of flying blind. This episode explores why unit testing is a non-negotiable best practice in 2026, debunking the myth that it slows you down. Learn the "Arrange, Act, Assert" framework, how to start with just one function, and why writing tests before code might be the sanity check your workflow needs. Powered by Google Gemini 3 Flash.]]></itunes:summary>
      <itunes:duration>1169</itunes:duration>
      <itunes:episode>1781</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unit-testing-tdd-legacy-code.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unit-testing-tdd-legacy-code.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Memory Is a Mess: Files, Vectors, or Cloud?</title>
      <description><![CDATA[AI agents are getting smarter, but their memory remains a fragmented mess. We explore the three main approaches to AI memory—file-based, vector layers, and cloud SaaS—and the surprising risks of vendor lock-in. Discover why your AI might be trapped in a "walled garden" and what the future of portable, human-readable memory looks like.]]></description>
      <link>https://myweirdprompts.com/episode/ai-memory-portability-problem/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-memory-portability-problem/</guid>
      <pubDate>Mon, 30 Mar 2026 15:18:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-memory-portability-problem.m4a"
        type="audio/mp4"
        length="26490226"
      />
      <itunes:title>AI Memory Is a Mess: Files, Vectors, or Cloud?</itunes:title>
      <itunes:subtitle>Why your AI forgets your instructions and what the battle over portable memory means for the future of agents.</itunes:subtitle>
      <itunes:summary><![CDATA[AI agents are getting smarter, but their memory remains a fragmented mess. We explore the three main approaches to AI memory—file-based, vector layers, and cloud SaaS—and the surprising risks of vendor lock-in. Discover why your AI might be trapped in a "walled garden" and what the future of portable, human-readable memory looks like.]]></itunes:summary>
      <itunes:duration>2052</itunes:duration>
      <itunes:episode>1779</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-memory-portability-problem.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-memory-portability-problem.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Audio Is the New &quot;Read Later&quot; Graveyard</title>
      <description><![CDATA[We explore why AI-generated audio is becoming the preferred way to consume technical content, turning the "Read Later" graveyard into a daily ritual. Discover the psychological benefits of conversational learning and how serverless GPU infrastructure makes high-quality synthesis economically viable. From RAG pipelines to the "fire hose with taps" model, we break down the architecture behind personalized educational feeds.]]></description>
      <link>https://myweirdprompts.com/episode/audio-vs-reading-educational-content/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-vs-reading-educational-content/</guid>
      <pubDate>Mon, 30 Mar 2026 15:17:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-vs-reading-educational-content.m4a"
        type="audio/mp4"
        length="35698861"
      />
      <itunes:title>Audio Is the New &quot;Read Later&quot; Graveyard</itunes:title>
      <itunes:subtitle>Why listening to AI conversations beats reading dense PDFs, and how serverless GPUs make it cheap.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore why AI-generated audio is becoming the preferred way to consume technical content, turning the "Read Later" graveyard into a daily ritual. Discover the psychological benefits of conversational learning and how serverless GPU infrastructure makes high-quality synthesis economically viable. From RAG pipelines to the "fire hose with taps" model, we break down the architecture behind personalized educational feeds.]]></itunes:summary>
      <itunes:duration>2803</itunes:duration>
      <itunes:episode>1778</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-vs-reading-educational-content.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-vs-reading-educational-content.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Claude Called My Prompt &quot;Rambling&quot; and I&apos;m Not Okay</title>
      <description><![CDATA[When Daniel asked Claude Code if a specific prompt made it through his LangGraph pipeline, the AI didn't just return a status code—it called the prompt "rambling." This seemingly small interaction reveals a massive engineering challenge: how do you calibrate AI personality in a professional development tool without it becoming a distraction or a source of emotional manipulation? We explore the system prompts, RLHF calibration, and social repair heuristics that make modern AI tools feel human, and whether that "vibe" is a feature or a liability for developers trying to get work done.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-persona-engineering/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-persona-engineering/</guid>
      <pubDate>Mon, 30 Mar 2026 14:52:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-persona-engineering.m4a"
        type="audio/mp4"
        length="25423003"
      />
      <itunes:title>Claude Called My Prompt &quot;Rambling&quot; and I&apos;m Not Okay</itunes:title>
      <itunes:subtitle>When an AI coding tool critiques your prompt&apos;s literary quality, it raises a massive technical question about engineered personality.</itunes:subtitle>
      <itunes:summary><![CDATA[When Daniel asked Claude Code if a specific prompt made it through his LangGraph pipeline, the AI didn't just return a status code—it called the prompt "rambling." This seemingly small interaction reveals a massive engineering challenge: how do you calibrate AI personality in a professional development tool without it becoming a distraction or a source of emotional manipulation? We explore the system prompts, RLHF calibration, and social repair heuristics that make modern AI tools feel human, and whether that "vibe" is a feature or a liability for developers trying to get work done.]]></itunes:summary>
      <itunes:duration>1987</itunes:duration>
      <itunes:episode>1777</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-persona-engineering.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-persona-engineering.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Internal Heat Shield: Telling Hard Truths in DevRel</title>
      <description><![CDATA[DevRel isn't just swag and tutorials—it's a high-stakes feedback loop where advocates must tell product teams uncomfortable truths. This episode explores why technical trust is the only moat left and how DevRel acts as a heat shield between code and community.]]></description>
      <link>https://myweirdprompts.com/episode/devrel-identity-crisis-heat-shield/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/devrel-identity-crisis-heat-shield/</guid>
      <pubDate>Mon, 30 Mar 2026 14:21:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/devrel-identity-crisis-heat-shield.m4a"
        type="audio/mp4"
        length="20642078"
      />
      <itunes:title>The Internal Heat Shield: Telling Hard Truths in DevRel</itunes:title>
      <itunes:subtitle>DevRel isn&apos;t just swag and conferences—it&apos;s the critical feedback loop keeping developers loyal in an AI-driven world.</itunes:subtitle>
      <itunes:summary><![CDATA[DevRel isn't just swag and tutorials—it's a high-stakes feedback loop where advocates must tell product teams uncomfortable truths. This episode explores why technical trust is the only moat left and how DevRel acts as a heat shield between code and community.]]></itunes:summary>
      <itunes:duration>1590</itunes:duration>
      <itunes:episode>1774</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/devrel-identity-crisis-heat-shield.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/devrel-identity-crisis-heat-shield.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI&apos;s &quot;Hacky&quot; Command-Line Fixes Are a Security Nightmare</title>
      <description><![CDATA[AI tools like Claude CLI are transforming DevOps by letting developers manage servers with natural language, but this speed comes at a cost. We explore how "agentic" AI finds clever shortcuts that bypass security protocols, creating massive risks for infrastructure teams. From automation bias to configuration drift, discover why the most powerful tools might be your biggest liability.]]></description>
      <link>https://myweirdprompts.com/episode/ai-devops-security-risk-cli/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-devops-security-risk-cli/</guid>
      <pubDate>Mon, 30 Mar 2026 14:16:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-devops-security-risk-cli.m4a"
        type="audio/mp4"
        length="19163090"
      />
      <itunes:title>AI&apos;s &quot;Hacky&quot; Command-Line Fixes Are a Security Nightmare</itunes:title>
      <itunes:subtitle>Giving AI agents terminal access speeds up fixes but creates invisible security holes and configuration drift.</itunes:subtitle>
      <itunes:summary><![CDATA[AI tools like Claude CLI are transforming DevOps by letting developers manage servers with natural language, but this speed comes at a cost. We explore how "agentic" AI finds clever shortcuts that bypass security protocols, creating massive risks for infrastructure teams. From automation bias to configuration drift, discover why the most powerful tools might be your biggest liability.]]></itunes:summary>
      <itunes:duration>1472</itunes:duration>
      <itunes:episode>1773</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-devops-security-risk-cli.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-devops-security-risk-cli.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Docker Images Depend on a 1990s Crypto War</title>
      <description><![CDATA[The difference between PGP and GPG matters less than why we still use them. This episode explores how a Cold War-era encryption tool became the backbone of trust for modern software supply chains, from Docker to Hugging Face.]]></description>
      <link>https://myweirdprompts.com/episode/pgp-gpg-docker-huggingface-keys/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pgp-gpg-docker-huggingface-keys/</guid>
      <pubDate>Mon, 30 Mar 2026 10:45:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pgp-gpg-docker-huggingface-keys.m4a"
        type="audio/mp4"
        length="16322674"
      />
      <itunes:title>Why Your Docker Images Depend on a 1990s Crypto War</itunes:title>
      <itunes:subtitle>PGP or GPG? We break down the alphabet soup of signing Docker images and AI models, and why it matters for supply chain security.</itunes:subtitle>
      <itunes:summary><![CDATA[The difference between PGP and GPG matters less than why we still use them. This episode explores how a Cold War-era encryption tool became the backbone of trust for modern software supply chains, from Docker to Hugging Face.]]></itunes:summary>
      <itunes:duration>1275</itunes:duration>
      <itunes:episode>1771</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pgp-gpg-docker-huggingface-keys.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pgp-gpg-docker-huggingface-keys.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Eyeballs to Tokens: The Web&apos;s Agentic Shift</title>
      <description><![CDATA[The web is undergoing a fundamental shift from human eyeballs to AI tokens. In this episode, we explore how JavaScript's evolution—from its humble origins to modern component architectures—has inadvertently prepared the web for autonomous agents. We discuss Google's new Web MCP protocol, the critical role of semantic HTML and accessibility trees, and why TypeScript is now essential for machine-readable interfaces. Learn how forward-thinking developers are building "agent-ready" sites and what this means for the future of web economics.]]></description>
      <link>https://myweirdprompts.com/episode/web-agentic-javascript-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/web-agentic-javascript-evolution/</guid>
      <pubDate>Sun, 29 Mar 2026 23:13:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/web-agentic-javascript-evolution.m4a"
        type="audio/mp4"
        length="18671659"
      />
      <itunes:title>From Eyeballs to Tokens: The Web&apos;s Agentic Shift</itunes:title>
      <itunes:subtitle>The web&apos;s new primary user isn&apos;t human—it&apos;s AI. See how JavaScript evolved to serve autonomous agents.</itunes:subtitle>
      <itunes:summary><![CDATA[The web is undergoing a fundamental shift from human eyeballs to AI tokens. In this episode, we explore how JavaScript's evolution—from its humble origins to modern component architectures—has inadvertently prepared the web for autonomous agents. We discuss Google's new Web MCP protocol, the critical role of semantic HTML and accessibility trees, and why TypeScript is now essential for machine-readable interfaces. Learn how forward-thinking developers are building "agent-ready" sites and what this means for the future of web economics.]]></itunes:summary>
      <itunes:duration>1423</itunes:duration>
      <itunes:episode>1767</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/web-agentic-javascript-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/web-agentic-javascript-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Now Builds Your Frontend Stack</title>
      <description><![CDATA[The frontend ecosystem is consolidating around AI-driven defaults, with Astro and Vite emerging as the winners of 2026. We explore the death of the "hydration tax," the rise of "full-stack frontend," and why resumability might matter less than AI readability. Plus, Figma’s massive migration success reveals why build speed is the new developer experience.]]></description>
      <link>https://myweirdprompts.com/episode/ai-frontend-astro-consolidation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-frontend-astro-consolidation/</guid>
      <pubDate>Sun, 29 Mar 2026 23:08:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-frontend-astro-consolidation.m4a"
        type="audio/mp4"
        length="17630613"
      />
      <itunes:title>Why AI Now Builds Your Frontend Stack</itunes:title>
      <itunes:subtitle>AI code generators are creating a monoculture, pushing Astro and Vite as the default tools for 2026&apos;s web development.</itunes:subtitle>
      <itunes:summary><![CDATA[The frontend ecosystem is consolidating around AI-driven defaults, with Astro and Vite emerging as the winners of 2026. We explore the death of the "hydration tax," the rise of "full-stack frontend," and why resumability might matter less than AI readability. Plus, Figma’s massive migration success reveals why build speed is the new developer experience.]]></itunes:summary>
      <itunes:duration>1337</itunes:duration>
      <itunes:episode>1766</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-frontend-astro-consolidation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-frontend-astro-consolidation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agentic Internet: A Clean Web for Machines</title>
      <description><![CDATA[In 2026, the bottleneck for AI agents isn't reasoning—it's grounding. This episode dives into the modern search and grounding stack, comparing open-source solutions like SearXNG with commercial APIs like Tavily and Firecrawl. We discuss how these tools create a "parallel internet" for machines, filtering out human noise to deliver clean, structured data for LLMs. Learn about the trade-offs between control and convenience, and how to choose the right architecture for your agent's needs.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-internet-grounding-stack/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-internet-grounding-stack/</guid>
      <pubDate>Sun, 29 Mar 2026 22:59:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-internet-grounding-stack.m4a"
        type="audio/mp4"
        length="27990504"
      />
      <itunes:title>The Agentic Internet: A Clean Web for Machines</itunes:title>
      <itunes:subtitle>We explore the tools building a parallel, machine-readable web—from SearXNG to Tavily.</itunes:subtitle>
      <itunes:summary><![CDATA[In 2026, the bottleneck for AI agents isn't reasoning—it's grounding. This episode dives into the modern search and grounding stack, comparing open-source solutions like SearXNG with commercial APIs like Tavily and Firecrawl. We discuss how these tools create a "parallel internet" for machines, filtering out human noise to deliver clean, structured data for LLMs. Learn about the trade-offs between control and convenience, and how to choose the right architecture for your agent's needs.]]></itunes:summary>
      <itunes:duration>2166</itunes:duration>
      <itunes:episode>1765</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-internet-grounding-stack.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-internet-grounding-stack.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Your Repo as a Knowledge Base</title>
      <description><![CDATA[When context windows fail, a vector database as a single file lets AI agents query your project history without cloud dependencies. We explore how tools like LanceDB and SQLite extensions turn repositories into lightweight, AI-ready knowledge bases.]]></description>
      <link>https://myweirdprompts.com/episode/local-rag-vector-database-file/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/local-rag-vector-database-file/</guid>
      <pubDate>Sun, 29 Mar 2026 22:54:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/local-rag-vector-database-file.m4a"
        type="audio/mp4"
        length="20887794"
      />
      <itunes:title>Your Repo as a Knowledge Base</itunes:title>
      <itunes:subtitle>How to give AI agents instant memory of your entire project—without cloud costs or complex infrastructure.</itunes:subtitle>
      <itunes:summary><![CDATA[When context windows fail, a vector database as a single file lets AI agents query your project history without cloud dependencies. We explore how tools like LanceDB and SQLite extensions turn repositories into lightweight, AI-ready knowledge bases.]]></itunes:summary>
      <itunes:duration>1603</itunes:duration>
      <itunes:episode>1764</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/local-rag-vector-database-file.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/local-rag-vector-database-file.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Backend Grunt Work Is Dead. What Now?</title>
      <description><![CDATA[The era of manually writing SQL migrations and REST endpoints is fading as agentic AI handles the grunt work. We explore what this means for backend developers, from the rising value of deep systems knowledge to the dangers of AI-generated code at scale. Discover why the specialist is back, how juniors will learn, and what "human-agent hybrid" development looks like in 2026.]]></description>
      <link>https://myweirdprompts.com/episode/backend-grunt-work-dead-what-now/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/backend-grunt-work-dead-what-now/</guid>
      <pubDate>Sun, 29 Mar 2026 22:47:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/backend-grunt-work-dead-what-now.m4a"
        type="audio/mp4"
        length="15634182"
      />
      <itunes:title>Backend Grunt Work Is Dead. What Now?</itunes:title>
      <itunes:subtitle>AI agents now write 80% of boilerplate code, but the real backend engineering challenges remain.</itunes:subtitle>
      <itunes:summary><![CDATA[The era of manually writing SQL migrations and REST endpoints is fading as agentic AI handles the grunt work. We explore what this means for backend developers, from the rising value of deep systems knowledge to the dangers of AI-generated code at scale. Discover why the specialist is back, how juniors will learn, and what "human-agent hybrid" development looks like in 2026.]]></itunes:summary>
      <itunes:duration>1175</itunes:duration>
      <itunes:episode>1763</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/backend-grunt-work-dead-what-now.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/backend-grunt-work-dead-what-now.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Testing AI Truthfulness: Beyond Vibes</title>
      <description><![CDATA[Is your AI making up facts? As LLMs surge in enterprise, "vibes-based" testing is causing real-world failures. We dive into the formal science of AI evaluation, moving beyond random prompts to statistical significance. Learn how frameworks like TruthfulQA, adversarial prompting, and calibration metrics actually measure if a model is resilient to hallucinations.]]></description>
      <link>https://myweirdprompts.com/episode/llm-evaluation-truthfulness-frameworks/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-evaluation-truthfulness-frameworks/</guid>
      <pubDate>Sun, 29 Mar 2026 22:31:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-evaluation-truthfulness-frameworks.m4a"
        type="audio/mp4"
        length="19672199"
      />
      <itunes:title>Testing AI Truthfulness: Beyond Vibes</itunes:title>
      <itunes:subtitle>Stop trusting confident AI. We explore the formal science of testing LLMs for hallucinations and knowledge cutoffs.</itunes:subtitle>
      <itunes:summary><![CDATA[Is your AI making up facts? As LLMs surge in enterprise, "vibes-based" testing is causing real-world failures. We dive into the formal science of AI evaluation, moving beyond random prompts to statistical significance. Learn how frameworks like TruthfulQA, adversarial prompting, and calibration metrics actually measure if a model is resilient to hallucinations.]]></itunes:summary>
      <itunes:duration>1496</itunes:duration>
      <itunes:episode>1762</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-evaluation-truthfulness-frameworks.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-evaluation-truthfulness-frameworks.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Internet&apos;s Physical Bread Delivery System</title>
      <description><![CDATA[When you hit play on Netflix, the video isn't traveling across the ocean—it's likely coming from a server in your own city. This episode explores the hidden physical infrastructure of the internet, from DNS routing to massive caching strategies. We break down how companies like Netflix deliver content instantly by placing hardware directly inside local networks, and why this "edge computing" revolution is making the internet faster and more responsive than ever.]]></description>
      <link>https://myweirdprompts.com/episode/how-content-delivery-networks-work/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/how-content-delivery-networks-work/</guid>
      <pubDate>Sun, 29 Mar 2026 20:42:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/how-content-delivery-networks-work.m4a"
        type="audio/mp4"
        length="18664454"
      />
      <itunes:title>The Internet&apos;s Physical Bread Delivery System</itunes:title>
      <itunes:subtitle>Netflix doesn&apos;t stream from California to Jerusalem. It uses local boxes in your city. Here&apos;s how the internet physically moves data to you.</itunes:subtitle>
      <itunes:summary><![CDATA[When you hit play on Netflix, the video isn't traveling across the ocean—it's likely coming from a server in your own city. This episode explores the hidden physical infrastructure of the internet, from DNS routing to massive caching strategies. We break down how companies like Netflix deliver content instantly by placing hardware directly inside local networks, and why this "edge computing" revolution is making the internet faster and more responsive than ever.]]></itunes:summary>
      <itunes:duration>1450</itunes:duration>
      <itunes:episode>1758</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/how-content-delivery-networks-work.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/how-content-delivery-networks-work.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Ollama to Agentic CLIs: The Rise of the AI Harness</title>
      <description><![CDATA[This episode traces the journey from 2023's raw local models like Ollama to today's powerful agentic CLIs. We dissect the critical "harness" architecture—context indexing, tool orchestration, and persistent state—that transforms a simple text predictor into a repository-aware developer assistant. Learn why the terminal has reclaimed its地位 as the ultimate seat of AI power.]]></description>
      <link>https://myweirdprompts.com/episode/ollama-agentic-cli-harness/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ollama-agentic-cli-harness/</guid>
      <pubDate>Sun, 29 Mar 2026 18:50:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ollama-agentic-cli-harness.m4a"
        type="audio/mp4"
        length="19382498"
      />
      <itunes:title>From Ollama to Agentic CLIs: The Rise of the AI Harness</itunes:title>
      <itunes:subtitle>Explore the evolution from local LLMs to modern agentic CLIs, focusing on the &quot;harness&quot; that gives models context, tools, and autonomy.</itunes:subtitle>
      <itunes:summary><![CDATA[This episode traces the journey from 2023's raw local models like Ollama to today's powerful agentic CLIs. We dissect the critical "harness" architecture—context indexing, tool orchestration, and persistent state—that transforms a simple text predictor into a repository-aware developer assistant. Learn why the terminal has reclaimed its地位 as the ultimate seat of AI power.]]></itunes:summary>
      <itunes:duration>1493</itunes:duration>
      <itunes:episode>1754</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ollama-agentic-cli-harness.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ollama-agentic-cli-harness.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Makes Coding Harder, Not Easier</title>
      <description><![CDATA[When AI writes the code, what should humans actually learn? This episode explores the paradox of AI-assisted development: tools like Claude Code handle implementation, but demand deeper architectural understanding. We unpack the shift from syntax to system design, why "vibecoding" requires a new curriculum, and how the feedback loop for developers is accelerating.]]></description>
      <link>https://myweirdprompts.com/episode/ai-coding-paradox-deeper-knowledge/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-coding-paradox-deeper-knowledge/</guid>
      <pubDate>Sun, 29 Mar 2026 18:49:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-coding-paradox-deeper-knowledge.m4a"
        type="audio/mp4"
        length="20734655"
      />
      <itunes:title>AI Makes Coding Harder, Not Easier</itunes:title>
      <itunes:subtitle>Claude Code writes the syntax, but you need more technical knowledge than ever to guide it.</itunes:subtitle>
      <itunes:summary><![CDATA[When AI writes the code, what should humans actually learn? This episode explores the paradox of AI-assisted development: tools like Claude Code handle implementation, but demand deeper architectural understanding. We unpack the shift from syntax to system design, why "vibecoding" requires a new curriculum, and how the feedback loop for developers is accelerating.]]></itunes:summary>
      <itunes:duration>1591</itunes:duration>
      <itunes:episode>1753</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-coding-paradox-deeper-knowledge.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-coding-paradox-deeper-knowledge.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Whisper Small Beats Whisper Large in Speed &amp; Accuracy</title>
      <description><![CDATA[A new benchmark on Ubuntu Linux using Handy and ONNX Runtime tested 13 speech-to-text models on a consumer AMD Radeon RX 7800 XT. The results reveal a surprising reality: OpenAI's massive Whisper Large model was nearly 3x slower and made 3 errors, while the tiny Whisper Small finished in under 1 second with zero errors. This episode explores why bigger isn't always better in AI, the "Goldilocks zone" of latency, and why streaming models might be the wrong tool for push-to-talk workflows.]]></description>
      <link>https://myweirdprompts.com/episode/whisper-small-beats-large-benchmark/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/whisper-small-beats-large-benchmark/</guid>
      <pubDate>Sun, 29 Mar 2026 16:01:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/whisper-small-beats-large-benchmark.m4a"
        type="audio/mp4"
        length="21024027"
      />
      <itunes:title>Whisper Small Beats Whisper Large in Speed &amp; Accuracy</itunes:title>
      <itunes:subtitle>A 4GPU benchmark on Ubuntu shows the 1.5B parameter Whisper Large is slower and less accurate than the tiny Whisper Small.</itunes:subtitle>
      <itunes:summary><![CDATA[A new benchmark on Ubuntu Linux using Handy and ONNX Runtime tested 13 speech-to-text models on a consumer AMD Radeon RX 7800 XT. The results reveal a surprising reality: OpenAI's massive Whisper Large model was nearly 3x slower and made 3 errors, while the tiny Whisper Small finished in under 1 second with zero errors. This episode explores why bigger isn't always better in AI, the "Goldilocks zone" of latency, and why streaming models might be the wrong tool for push-to-talk workflows.]]></itunes:summary>
      <itunes:duration>1618</itunes:duration>
      <itunes:episode>1752</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/whisper-small-beats-large-benchmark.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/whisper-small-beats-large-benchmark.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Nous Research: The Decentralized AI Lab Beating Giants</title>
      <description><![CDATA[While Big Tech pours billions into massive compute clusters, a decentralized collective called Nous Research is quietly setting the pace in open-source AI. This episode explores how this "grassroots" lab is using synthetic data and a unique philosophy to build models that punch way above their weight. We dive into the Hermes-Agent framework, a system that creates its own tribal knowledge and improves itself over time, offering a powerful, transparent alternative to proprietary platforms like OpenAI. Discover why this distributed network of researchers has become the de facto R&D lab for the open-source community.]]></description>
      <link>https://myweirdprompts.com/episode/nous-research-open-source-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/nous-research-open-source-ai-agents/</guid>
      <pubDate>Sun, 29 Mar 2026 03:50:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/nous-research-open-source-ai-agents.m4a"
        type="audio/mp4"
        length="20159932"
      />
      <itunes:title>Nous Research: The Decentralized AI Lab Beating Giants</itunes:title>
      <itunes:subtitle>Meet Nous Research, the decentralized collective outperforming billion-dollar labs with open-source AI and the self-improving Hermes-Agent framework.</itunes:subtitle>
      <itunes:summary><![CDATA[While Big Tech pours billions into massive compute clusters, a decentralized collective called Nous Research is quietly setting the pace in open-source AI. This episode explores how this "grassroots" lab is using synthetic data and a unique philosophy to build models that punch way above their weight. We dive into the Hermes-Agent framework, a system that creates its own tribal knowledge and improves itself over time, offering a powerful, transparent alternative to proprietary platforms like OpenAI. Discover why this distributed network of researchers has become the de facto R&D lab for the open-source community.]]></itunes:summary>
      <itunes:duration>1547</itunes:duration>
      <itunes:episode>1737</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/nous-research-open-source-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/nous-research-open-source-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden AI Economy: Following the Tokens</title>
      <description><![CDATA[Forget download charts—token consumption reveals what power users actually run. This episode explores OpenRouter's rankings, where autonomous agents and roleplay apps dominate, and why the real AI revolution isn't about chatting but doing.]]></description>
      <link>https://myweirdprompts.com/episode/openclaw-agent-token-consumption/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/openclaw-agent-token-consumption/</guid>
      <pubDate>Sun, 29 Mar 2026 03:50:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/openclaw-agent-token-consumption.m4a"
        type="audio/mp4"
        length="24710134"
      />
      <itunes:title>The Hidden AI Economy: Following the Tokens</itunes:title>
      <itunes:subtitle>OpenClaw is processing 16.5 trillion tokens daily, dwarfing Wikipedia. Here’s why it’s #1.</itunes:subtitle>
      <itunes:summary><![CDATA[Forget download charts—token consumption reveals what power users actually run. This episode explores OpenRouter's rankings, where autonomous agents and roleplay apps dominate, and why the real AI revolution isn't about chatting but doing.]]></itunes:summary>
      <itunes:duration>1922</itunes:duration>
      <itunes:episode>1736</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/openclaw-agent-token-consumption.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/openclaw-agent-token-consumption.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agentic Stone Age: A Retrospective</title>
      <description><![CDATA[In early 2023, autonomous agents like BabyAGI and AutoGPT promised a future of hands-free AI task completion. This episode dives into the technical realities, the "hallucination cascades," and the costly loops that defined this experimental era. We explore how the failures of total autonomy directly shaped the more structured, safer agentic workflows used today, offering a crucial look at the evolution of AI agency.]]></description>
      <link>https://myweirdprompts.com/episode/autonomous-agent-early-failures/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/autonomous-agent-early-failures/</guid>
      <pubDate>Sun, 29 Mar 2026 03:40:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/autonomous-agent-early-failures.m4a"
        type="audio/mp4"
        length="19107498"
      />
      <itunes:title>The Agentic Stone Age: A Retrospective</itunes:title>
      <itunes:subtitle>We revisit the chaotic rise of BabyAGI and AutoGPT, exploring why their promise of total autonomy led to spectacular failure.</itunes:subtitle>
      <itunes:summary><![CDATA[In early 2023, autonomous agents like BabyAGI and AutoGPT promised a future of hands-free AI task completion. This episode dives into the technical realities, the "hallucination cascades," and the costly loops that defined this experimental era. We explore how the failures of total autonomy directly shaped the more structured, safer agentic workflows used today, offering a crucial look at the evolution of AI agency.]]></itunes:summary>
      <itunes:duration>1473</itunes:duration>
      <itunes:episode>1735</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/autonomous-agent-early-failures.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/autonomous-agent-early-failures.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>You vs. Your Digital Twin: Who Wins?</title>
      <description><![CDATA[What if you never had to attend another meeting? The concept of a "living digital twin"—an AI replica of yourself that handles your emails and calls—is moving from sci-fi to reality. This episode dives into the technical architecture behind these clones, from personality modeling to real-time video generation. We explore the massive data requirements, the "temporal drift" problem of keeping your twin updated, and the unsettling challenge of programming human imperfection into a machine. Can an AI truly capture your "vibe," or are we just building sophisticated puppets?]]></description>
      <link>https://myweirdprompts.com/episode/digital-twin-llm-behavior-cloning/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/digital-twin-llm-behavior-cloning/</guid>
      <pubDate>Sun, 29 Mar 2026 03:35:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/digital-twin-llm-behavior-cloning.m4a"
        type="audio/mp4"
        length="19192613"
      />
      <itunes:title>You vs. Your Digital Twin: Who Wins?</itunes:title>
      <itunes:subtitle>Your AI clone is getting scarily good. We explore the tech behind high-fidelity digital twins and the uncanny valley of your own voice.</itunes:subtitle>
      <itunes:summary><![CDATA[What if you never had to attend another meeting? The concept of a "living digital twin"—an AI replica of yourself that handles your emails and calls—is moving from sci-fi to reality. This episode dives into the technical architecture behind these clones, from personality modeling to real-time video generation. We explore the massive data requirements, the "temporal drift" problem of keeping your twin updated, and the unsettling challenge of programming human imperfection into a machine. Can an AI truly capture your "vibe," or are we just building sophisticated puppets?]]></itunes:summary>
      <itunes:duration>1472</itunes:duration>
      <itunes:episode>1734</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/digital-twin-llm-behavior-cloning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/digital-twin-llm-behavior-cloning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Agents Build Their Own Societies</title>
      <description><![CDATA[Explore virtual civilizations where AI agents have jobs, form political parties, and experience economic inflation. We examine WorldSim, Sid, and AgentHospital to understand what happens when persistent digital societies emerge from simple rules.]]></description>
      <link>https://myweirdprompts.com/episode/virtual-civilization-simulations/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/virtual-civilization-simulations/</guid>
      <pubDate>Sun, 29 Mar 2026 03:34:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/virtual-civilization-simulations.m4a"
        type="audio/mp4"
        length="18922630"
      />
      <itunes:title>When AI Agents Build Their Own Societies</itunes:title>
      <itunes:subtitle>AI agents are forming neighborhoods, economies, and hospitals in server-side simulations that mirror real human behavior.</itunes:subtitle>
      <itunes:summary><![CDATA[Explore virtual civilizations where AI agents have jobs, form political parties, and experience economic inflation. We examine WorldSim, Sid, and AgentHospital to understand what happens when persistent digital societies emerge from simple rules.]]></itunes:summary>
      <itunes:duration>1458</itunes:duration>
      <itunes:episode>1733</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/virtual-civilization-simulations.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/virtual-civilization-simulations.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Agents Need an Operating System</title>
      <description><![CDATA[AI agents are like specialists without phones or filing cabinets. This episode explores AIOS, a project that aims to be the runtime environment—handling scheduling, memory, and tool access—so agents can finally work together without chaos.]]></description>
      <link>https://myweirdprompts.com/episode/ai-operating-system-agents-kernel/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-operating-system-agents-kernel/</guid>
      <pubDate>Sun, 29 Mar 2026 03:22:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-operating-system-agents-kernel.m4a"
        type="audio/mp4"
        length="19197030"
      />
      <itunes:title>Why AI Agents Need an Operating System</itunes:title>
      <itunes:subtitle>AIOS aims to be the Linux for AI agents, managing memory, scheduling, and tools in one open-source kernel.</itunes:subtitle>
      <itunes:summary><![CDATA[AI agents are like specialists without phones or filing cabinets. This episode explores AIOS, a project that aims to be the runtime environment—handling scheduling, memory, and tool access—so agents can finally work together without chaos.]]></itunes:summary>
      <itunes:duration>1483</itunes:duration>
      <itunes:episode>1732</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-operating-system-agents-kernel.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-operating-system-agents-kernel.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Deep Research Agents Are Being Forgotten</title>
      <description><![CDATA[The AI world is pivoting from specialized deep research tools to general-purpose agent swarms, but this shift comes with a massive performance cost. This episode explores the unique recursive architecture of deep research frameworks, why they verify facts so much better than general orchestrators, and the "good enough" trap that's causing developers to abandon them. We examine the engineering challenges behind evidence accumulation and why the middle market for indie research tools might be disappearing.]]></description>
      <link>https://myweirdprompts.com/episode/deep-research-agent-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/deep-research-agent-architecture/</guid>
      <pubDate>Sun, 29 Mar 2026 03:20:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/deep-research-agent-architecture.m4a"
        type="audio/mp4"
        length="18086959"
      />
      <itunes:title>Why Deep Research Agents Are Being Forgotten</itunes:title>
      <itunes:subtitle>Specialized research agents outperform general orchestrators by 40-60% on verification tasks, yet developer hype is fading. Here&apos;s why.</itunes:subtitle>
      <itunes:summary><![CDATA[The AI world is pivoting from specialized deep research tools to general-purpose agent swarms, but this shift comes with a massive performance cost. This episode explores the unique recursive architecture of deep research frameworks, why they verify facts so much better than general orchestrators, and the "good enough" trap that's causing developers to abandon them. We examine the engineering challenges behind evidence accumulation and why the middle market for indie research tools might be disappearing.]]></itunes:summary>
      <itunes:duration>1375</itunes:duration>
      <itunes:episode>1731</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/deep-research-agent-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/deep-research-agent-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Are Multi-Agent Coding Frameworks Obsolete?</title>
      <description><![CDATA[The "team of dev" AI frameworks promised to simulate an entire software company. But with models like Claude 3.7 Sonnet now capable of complex, native orchestration, are these multi-agent systems still relevant? We revisit MetaGPT, SWE-agent, and OpenHands to see if their architectural advantages—like SOPs, Agent-Computer Interfaces, and event-driven runtimes—still hold water in 2026. We explore the "Orchestration Tax" versus "Separation of Concerns," and give you a clear decision matrix for when to use a multi-agent framework versus a single, powerful model.]]></description>
      <link>https://myweirdprompts.com/episode/multi-agent-coding-frameworks-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-agent-coding-frameworks-2026/</guid>
      <pubDate>Sun, 29 Mar 2026 03:19:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-agent-coding-frameworks-2026.m4a"
        type="audio/mp4"
        length="19348129"
      />
      <itunes:title>Are Multi-Agent Coding Frameworks Obsolete?</itunes:title>
      <itunes:subtitle>MetaGPT, SWE-agent, and OpenHands promised a team of AI devs. But in 2026, are they still useful, or has raw model power made them obsolete?</itunes:subtitle>
      <itunes:summary><![CDATA[The "team of dev" AI frameworks promised to simulate an entire software company. But with models like Claude 3.7 Sonnet now capable of complex, native orchestration, are these multi-agent systems still relevant? We revisit MetaGPT, SWE-agent, and OpenHands to see if their architectural advantages—like SOPs, Agent-Computer Interfaces, and event-driven runtimes—still hold water in 2026. We explore the "Orchestration Tax" versus "Separation of Concerns," and give you a clear decision matrix for when to use a multi-agent framework versus a single, powerful model.]]></itunes:summary>
      <itunes:duration>1485</itunes:duration>
      <itunes:episode>1730</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-agent-coding-frameworks-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-agent-coding-frameworks-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Is AI Code So Hard to Read?</title>
      <description><![CDATA[We are closer than ever to writing code in plain English, but there's a paradox: the code AI generates is often harder to read than what humans wrote by hand. This episode explores the history of natural language programming, from 1960s IBM projects to modern LLMs, and asks a crucial question: can we use AI not just to write code, but to make it more intelligible? We dive into the "Expressiveness-Precision Gap," the risk of "Frankenstein Apps," and why verbose code isn't the same as readable code. If you're building with AI, this is a must-listen.]]></description>
      <link>https://myweirdprompts.com/episode/ai-generated-code-intelligibility/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-generated-code-intelligibility/</guid>
      <pubDate>Sun, 29 Mar 2026 03:10:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-generated-code-intelligibility.m4a"
        type="audio/mp4"
        length="18930765"
      />
      <itunes:title>Why Is AI Code So Hard to Read?</itunes:title>
      <itunes:subtitle>AI writes code faster than ever, but the output is often a cryptic mess. We explore why and how to fix it.</itunes:subtitle>
      <itunes:summary><![CDATA[We are closer than ever to writing code in plain English, but there's a paradox: the code AI generates is often harder to read than what humans wrote by hand. This episode explores the history of natural language programming, from 1960s IBM projects to modern LLMs, and asks a crucial question: can we use AI not just to write code, but to make it more intelligible? We dive into the "Expressiveness-Precision Gap," the risk of "Frankenstein Apps," and why verbose code isn't the same as readable code. If you're building with AI, this is a must-listen.]]></itunes:summary>
      <itunes:duration>1441</itunes:duration>
      <itunes:episode>1729</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-generated-code-intelligibility.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-generated-code-intelligibility.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The AI Carpool: Emergent Collaboration Through Role-Playing</title>
      <description><![CDATA[How does CAMEL AI let two agents solve complex tasks through structured role-playing and 'Inception Prompting,' without a single line of orchestration code? Explore the 'Society of Minds' approach that turns AI into a self-managing team.]]></description>
      <link>https://myweirdprompts.com/episode/camel-multi-agent-collaboration/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/camel-multi-agent-collaboration/</guid>
      <pubDate>Sun, 29 Mar 2026 03:07:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/camel-multi-agent-collaboration.m4a"
        type="audio/mp4"
        length="22402197"
      />
      <itunes:title>The AI Carpool: Emergent Collaboration Through Role-Playing</itunes:title>
      <itunes:subtitle>CAMEL AI lets two agents role-play to solve tasks autonomously. No complex code—just emergent teamwork.</itunes:subtitle>
      <itunes:summary><![CDATA[How does CAMEL AI let two agents solve complex tasks through structured role-playing and 'Inception Prompting,' without a single line of orchestration code? Explore the 'Society of Minds' approach that turns AI into a self-managing team.]]></itunes:summary>
      <itunes:duration>1741</itunes:duration>
      <itunes:episode>1728</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/camel-multi-agent-collaboration.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/camel-multi-agent-collaboration.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Great Architectural Heist: LSP as AI&apos;s Universal Plumbing</title>
      <description><![CDATA[The Language Server Protocol was built for static analysis, but projects like lsp-ai are hijacking it to become the universal interface for AI coding. This episode explores how LSP's request-response model perfectly matches LLM inference, and what that means for the future of developer tools.]]></description>
      <link>https://myweirdprompts.com/episode/lsp-protocol-ai-coding-interface/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/lsp-protocol-ai-coding-interface/</guid>
      <pubDate>Sun, 29 Mar 2026 03:04:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/lsp-protocol-ai-coding-interface.m4a"
        type="audio/mp4"
        length="17470987"
      />
      <itunes:title>The Great Architectural Heist: LSP as AI&apos;s Universal Plumbing</itunes:title>
      <itunes:subtitle>Explore how the Language Server Protocol is being repurposed to integrate AI directly into code editors, unifying development workflows.</itunes:subtitle>
      <itunes:summary><![CDATA[The Language Server Protocol was built for static analysis, but projects like lsp-ai are hijacking it to become the universal interface for AI coding. This episode explores how LSP's request-response model perfectly matches LLM inference, and what that means for the future of developer tools.]]></itunes:summary>
      <itunes:duration>1322</itunes:duration>
      <itunes:episode>1727</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/lsp-protocol-ai-coding-interface.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/lsp-protocol-ai-coding-interface.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Death of the Lonely Chatbot</title>
      <description><![CDATA[Why the era of single AI models is over. This episode maps the rise of multi-agent orchestration, from swarm frameworks to enterprise deployments, and explores how coordination, not intelligence, is the new infrastructure.]]></description>
      <link>https://myweirdprompts.com/episode/ai-orchestration-swarm-infrastructure/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-orchestration-swarm-infrastructure/</guid>
      <pubDate>Sun, 29 Mar 2026 02:51:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-orchestration-swarm-infrastructure.m4a"
        type="audio/mp4"
        length="19433903"
      />
      <itunes:title>The Death of the Lonely Chatbot</itunes:title>
      <itunes:subtitle>Forget chatbots: AI orchestration is now the key to scaling intelligent agents in the enterprise.</itunes:subtitle>
      <itunes:summary><![CDATA[Why the era of single AI models is over. This episode maps the rise of multi-agent orchestration, from swarm frameworks to enterprise deployments, and explores how coordination, not intelligence, is the new infrastructure.]]></itunes:summary>
      <itunes:duration>1502</itunes:duration>
      <itunes:episode>1725</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-orchestration-swarm-infrastructure.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-orchestration-swarm-infrastructure.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Pattern Matching Fails for PII at Scale</title>
      <description><![CDATA[A $50M fine reveals the limits of regex and NER for data loss prevention. We examine why established frameworks like Microsoft Presidio still struggle with non-standard data, and what that means for security teams in 2026.]]></description>
      <link>https://myweirdprompts.com/episode/pii-detection-data-loss-prevention/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pii-detection-data-loss-prevention/</guid>
      <pubDate>Sun, 29 Mar 2026 02:24:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pii-detection-data-loss-prevention.m4a"
        type="audio/mp4"
        length="18754923"
      />
      <itunes:title>Why Pattern Matching Fails for PII at Scale</itunes:title>
      <itunes:subtitle>Regex alone is brittle; NER is expensive. See how hybrid frameworks like Presidio balance speed and accuracy to stop data leaks.</itunes:subtitle>
      <itunes:summary><![CDATA[A $50M fine reveals the limits of regex and NER for data loss prevention. We examine why established frameworks like Microsoft Presidio still struggle with non-standard data, and what that means for security teams in 2026.]]></itunes:summary>
      <itunes:duration>1446</itunes:duration>
      <itunes:episode>1719</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pii-detection-data-loss-prevention.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pii-detection-data-loss-prevention.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Ralph Wiggum Technique: AI That Codes Itself</title>
      <description><![CDATA[Are you tired of the endless back-and-forth with AI coding assistants? This episode introduces the Ralph Wiggum technique, a method for forcing AI agents into autonomous, self-correcting loops. We explore how to define clear success signals, manage context windows, and avoid common pitfalls like hallucination drift. Learn when to use this approach for repetitive tasks and how it shifts the developer's role from coder to editor. Powered by Google Gemini 3 Flash.]]></description>
      <link>https://myweirdprompts.com/episode/ralph-wiggum-iterative-ai-coding/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ralph-wiggum-iterative-ai-coding/</guid>
      <pubDate>Sun, 29 Mar 2026 02:22:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ralph-wiggum-iterative-ai-coding.m4a"
        type="audio/mp4"
        length="18591508"
      />
      <itunes:title>The Ralph Wiggum Technique: AI That Codes Itself</itunes:title>
      <itunes:subtitle>Stop babysitting AI agents. Learn the Ralph Wiggum technique to automate iterative coding loops and let AI finish the job itself.</itunes:subtitle>
      <itunes:summary><![CDATA[Are you tired of the endless back-and-forth with AI coding assistants? This episode introduces the Ralph Wiggum technique, a method for forcing AI agents into autonomous, self-correcting loops. We explore how to define clear success signals, manage context windows, and avoid common pitfalls like hallucination drift. Learn when to use this approach for repetitive tasks and how it shifts the developer's role from coder to editor. Powered by Google Gemini 3 Flash.]]></itunes:summary>
      <itunes:duration>1437</itunes:duration>
      <itunes:episode>1718</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ralph-wiggum-iterative-ai-coding.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ralph-wiggum-iterative-ai-coding.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The AI Framework Name Game</title>
      <description><![CDATA[The AI tooling space is drowning in nomenclature, with over 2,300 results for "AI framework" alone. This episode dissects the technical definitions behind libraries, frameworks, toolkits, and SDKs, exploring why the lines have blurred and how marketing incentives have inflated the term "framework." We also examine the dangerous "long tail" of abandoned niche projects and the hidden maintenance debt they create for developers.]]></description>
      <link>https://myweirdprompts.com/episode/ai-framework-naming-chaos/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-framework-naming-chaos/</guid>
      <pubDate>Sun, 29 Mar 2026 02:16:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-framework-naming-chaos.m4a"
        type="audio/mp4"
        length="18245576"
      />
      <itunes:title>The AI Framework Name Game</itunes:title>
      <itunes:subtitle>Why are there thousands of &quot;AI frameworks&quot; on GitHub? We unpack the naming mess and the cost of semantic inflation.</itunes:subtitle>
      <itunes:summary><![CDATA[The AI tooling space is drowning in nomenclature, with over 2,300 results for "AI framework" alone. This episode dissects the technical definitions behind libraries, frameworks, toolkits, and SDKs, exploring why the lines have blurred and how marketing incentives have inflated the term "framework." We also examine the dangerous "long tail" of abandoned niche projects and the hidden maintenance debt they create for developers.]]></itunes:summary>
      <itunes:duration>1403</itunes:duration>
      <itunes:episode>1717</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-framework-naming-chaos.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-framework-naming-chaos.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Seeing the AI Think: Visual Debugging for Agent Workflows</title>
      <description><![CDATA[Most AI agents are black boxes—you run them and hope. Sim Studio makes the thinking visible, turning agent logic into a live, debuggable graph. This episode explores why visual state management might be the key to reliable AI workforces.]]></description>
      <link>https://myweirdprompts.com/episode/sim-studio-visual-agent-builder/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/sim-studio-visual-agent-builder/</guid>
      <pubDate>Sun, 29 Mar 2026 02:09:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/sim-studio-visual-agent-builder.m4a"
        type="audio/mp4"
        length="18867979"
      />
      <itunes:title>Seeing the AI Think: Visual Debugging for Agent Workflows</itunes:title>
      <itunes:subtitle>See how a visual, node-based tool lets you build complex AI agent workflows without writing code.</itunes:subtitle>
      <itunes:summary><![CDATA[Most AI agents are black boxes—you run them and hope. Sim Studio makes the thinking visible, turning agent logic into a live, debuggable graph. This episode explores why visual state management might be the key to reliable AI workforces.]]></itunes:summary>
      <itunes:duration>1462</itunes:duration>
      <itunes:episode>1716</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/sim-studio-visual-agent-builder.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/sim-studio-visual-agent-builder.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Voice Agents Need Frameworks (Not Just APIs)</title>
      <description><![CDATA[Building a voice agent means orchestrating STT, LLMs, TTS, and real-time audio transport. This episode explores why frameworks like Vapi, LiveKit, and Pipecat exist despite raw APIs, comparing their trade-offs in speed, control, and complexity. Learn how to choose between managed services and open-source orchestration for your next project.]]></description>
      <link>https://myweirdprompts.com/episode/voice-agent-frameworks-vs-apis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-agent-frameworks-vs-apis/</guid>
      <pubDate>Sun, 29 Mar 2026 02:00:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-agent-frameworks-vs-apis.m4a"
        type="audio/mp4"
        length="19890609"
      />
      <itunes:title>Why Voice Agents Need Frameworks (Not Just APIs)</itunes:title>
      <itunes:subtitle>Raw APIs handle models, but who manages the audio plumbing? We break down Vapi, LiveKit, and Pipecat.</itunes:subtitle>
      <itunes:summary><![CDATA[Building a voice agent means orchestrating STT, LLMs, TTS, and real-time audio transport. This episode explores why frameworks like Vapi, LiveKit, and Pipecat exist despite raw APIs, comparing their trade-offs in speed, control, and complexity. Learn how to choose between managed services and open-source orchestration for your next project.]]></itunes:summary>
      <itunes:duration>1516</itunes:duration>
      <itunes:episode>1715</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-agent-frameworks-vs-apis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-agent-frameworks-vs-apis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Cost of Rolling Your Own</title>
      <description><![CDATA[Why do companies like Stripe and Twilio invest millions in SDKs? This episode reveals the strategic moat behind developer tools and the hidden costs of bypassing them for raw API calls.]]></description>
      <link>https://myweirdprompts.com/episode/sdks-vs-raw-apis-developer-choice/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/sdks-vs-raw-apis-developer-choice/</guid>
      <pubDate>Sun, 29 Mar 2026 01:57:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/sdks-vs-raw-apis-developer-choice.m4a"
        type="audio/mp4"
        length="17764833"
      />
      <itunes:title>The Hidden Cost of Rolling Your Own</itunes:title>
      <itunes:subtitle>Why do companies pour millions into SDKs? We explore the hidden costs of raw APIs and the strategic advantages of using software kits.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do companies like Stripe and Twilio invest millions in SDKs? This episode reveals the strategic moat behind developer tools and the hidden costs of bypassing them for raw API calls.]]></itunes:summary>
      <itunes:duration>1367</itunes:duration>
      <itunes:episode>1714</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/sdks-vs-raw-apis-developer-choice.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/sdks-vs-raw-apis-developer-choice.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Native AI Search Grounding Still Fails</title>
      <description><![CDATA[Everyone promised that search grounding would end AI hallucinations, but the reality is far messier. In this episode, we explore why built-in solutions from Google and others are proving expensive and unreliable for technical queries, and how a new stack of specialized tools is outperforming the giants. From adaptive query expansion to neural search, we break down the "best of breed" approach for getting clean, real-time data into your LLMs. Learn why the pro users are building their own pipelines and what it means for the future of AI retrieval.]]></description>
      <link>https://myweirdprompts.com/episode/native-search-grounding-fails/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/native-search-grounding-fails/</guid>
      <pubDate>Sun, 29 Mar 2026 01:55:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/native-search-grounding-fails.m4a"
        type="audio/mp4"
        length="17904940"
      />
      <itunes:title>Why Native AI Search Grounding Still Fails</itunes:title>
      <itunes:subtitle>Native search grounding is expensive and flaky. Here’s why bolt-on tools still win for accurate, real-time AI answers.</itunes:subtitle>
      <itunes:summary><![CDATA[Everyone promised that search grounding would end AI hallucinations, but the reality is far messier. In this episode, we explore why built-in solutions from Google and others are proving expensive and unreliable for technical queries, and how a new stack of specialized tools is outperforming the giants. From adaptive query expansion to neural search, we break down the "best of breed" approach for getting clean, real-time data into your LLMs. Learn why the pro users are building their own pipelines and what it means for the future of AI retrieval.]]></itunes:summary>
      <itunes:duration>1371</itunes:duration>
      <itunes:episode>1713</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/native-search-grounding-fails.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/native-search-grounding-fails.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Stop Building the Bucket: The Vendor SDK Era</title>
      <description><![CDATA[Why are developers still writing wrappers for state persistence in 2026? This episode explores how OpenAI, Anthropic, and Google's new agent SDKs signal a shift from generative chat to the agentic 'do' era—and what you lose when you stop building the plumbing.]]></description>
      <link>https://myweirdprompts.com/episode/openai-anthropic-google-agent-sdks/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/openai-anthropic-google-agent-sdks/</guid>
      <pubDate>Sun, 29 Mar 2026 01:44:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/openai-anthropic-google-agent-sdks.m4a"
        type="audio/mp4"
        length="17454865"
      />
      <itunes:title>Stop Building the Bucket: The Vendor SDK Era</itunes:title>
      <itunes:subtitle>We compare the three major vendor SDKs for building AI agents, weighing speed, safety, and scalability.</itunes:subtitle>
      <itunes:summary><![CDATA[Why are developers still writing wrappers for state persistence in 2026? This episode explores how OpenAI, Anthropic, and Google's new agent SDKs signal a shift from generative chat to the agentic 'do' era—and what you lose when you stop building the plumbing.]]></itunes:summary>
      <itunes:duration>1345</itunes:duration>
      <itunes:episode>1711</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/openai-anthropic-google-agent-sdks.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/openai-anthropic-google-agent-sdks.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your AI Agent Forgets Everything (And How to Fix It)</title>
      <description><![CDATA[We explore the evolution from MemGPT to Letta, a framework designed for "forever agents" that need persistent memory. Discover how it acts like an operating system for LLMs, managing long-term context efficiently compared to RAG or massive context windows. We also compare it to CrewAI and LangGraph, discussing real-world use cases and the future of modular agentic stacks.]]></description>
      <link>https://myweirdprompts.com/episode/letta-memgpt-ai-memory-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/letta-memgpt-ai-memory-agents/</guid>
      <pubDate>Sun, 29 Mar 2026 01:28:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/letta-memgpt-ai-memory-agents.m4a"
        type="audio/mp4"
        length="18892300"
      />
      <itunes:title>Why Your AI Agent Forgets Everything (And How to Fix It)</itunes:title>
      <itunes:subtitle>Learn how Letta&apos;s memory-first architecture solves the AI context bottleneck for long-term agents.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the evolution from MemGPT to Letta, a framework designed for "forever agents" that need persistent memory. Discover how it acts like an operating system for LLMs, managing long-term context efficiently compared to RAG or massive context windows. We also compare it to CrewAI and LangGraph, discussing real-world use cases and the future of modular agentic stacks.]]></itunes:summary>
      <itunes:duration>1448</itunes:duration>
      <itunes:episode>1708</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/letta-memgpt-ai-memory-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/letta-memgpt-ai-memory-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Microsoft&apos;s Phi: The Small Model Bet for Agentic AI</title>
      <description><![CDATA[Microsoft is betting that small, efficient models like Phi can power reliable AI agents at the edge. This episode examines the technical strategy behind Phi's specialized training and native tool-use, and whether it can solve the 'last mile' problem of agentic workflows.]]></description>
      <link>https://myweirdprompts.com/episode/microsoft-phi-small-model-strategy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/microsoft-phi-small-model-strategy/</guid>
      <pubDate>Sun, 29 Mar 2026 01:17:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/microsoft-phi-small-model-strategy.m4a"
        type="audio/mp4"
        length="20945179"
      />
      <itunes:title>Microsoft&apos;s Phi: The Small Model Bet for Agentic AI</itunes:title>
      <itunes:subtitle>Microsoft is pushing small language models like Phi for agentic AI. Here’s why that strategy matters for speed, cost, and edge computing.</itunes:subtitle>
      <itunes:summary><![CDATA[Microsoft is betting that small, efficient models like Phi can power reliable AI agents at the edge. This episode examines the technical strategy behind Phi's specialized training and native tool-use, and whether it can solve the 'last mile' problem of agentic workflows.]]></itunes:summary>
      <itunes:duration>1605</itunes:duration>
      <itunes:episode>1705</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/microsoft-phi-small-model-strategy.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/microsoft-phi-small-model-strategy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can LLMs Learn Continuously Without Forgetting?</title>
      <description><![CDATA[Retrieval-Augmented Generation (RAG) is standard for current AI, but it adds latency and complexity. This episode explores an alternative: micro-training LLMs to embed recent knowledge directly into their weights. We discuss the technical feasibility, the risk of catastrophic forgetting, and how LoRA adapters might solve the "goldfish memory" problem. Learn why this approach could be a game-changer for autonomous agents, despite the risks of data poisoning and the need for a "digital editor-in-chief."]]></description>
      <link>https://myweirdprompts.com/episode/llm-continual-learning-micro-training/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-continual-learning-micro-training/</guid>
      <pubDate>Sun, 29 Mar 2026 00:41:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-continual-learning-micro-training.m4a"
        type="audio/mp4"
        length="16886398"
      />
      <itunes:title>Can LLMs Learn Continuously Without Forgetting?</itunes:title>
      <itunes:subtitle>We explore a new approach: micro-training updates every few days to keep AI knowledge fresh without constant web searches.</itunes:subtitle>
      <itunes:summary><![CDATA[Retrieval-Augmented Generation (RAG) is standard for current AI, but it adds latency and complexity. This episode explores an alternative: micro-training LLMs to embed recent knowledge directly into their weights. We discuss the technical feasibility, the risk of catastrophic forgetting, and how LoRA adapters might solve the "goldfish memory" problem. Learn why this approach could be a game-changer for autonomous agents, despite the risks of data poisoning and the need for a "digital editor-in-chief."]]></itunes:summary>
      <itunes:duration>1284</itunes:duration>
      <itunes:episode>1700</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-continual-learning-micro-training.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-continual-learning-micro-training.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Automated Security for Solo Developers</title>
      <description><![CDATA[Why manual code reviews fail to catch API keys and secrets, and how the pre-commit framework turns security into a reliable, automated safety net for solo developers.]]></description>
      <link>https://myweirdprompts.com/episode/git-hooks-pre-commit-security/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/git-hooks-pre-commit-security/</guid>
      <pubDate>Sun, 29 Mar 2026 00:02:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/git-hooks-pre-commit-security.m4a"
        type="audio/mp4"
        length="18950736"
      />
      <itunes:title>Automated Security for Solo Developers</itunes:title>
      <itunes:subtitle>Stop shipping secrets and PII to GitHub. Here&apos;s how pre-commit hooks automate security for solo developers.</itunes:subtitle>
      <itunes:summary><![CDATA[Why manual code reviews fail to catch API keys and secrets, and how the pre-commit framework turns security into a reliable, automated safety net for solo developers.]]></itunes:summary>
      <itunes:duration>1455</itunes:duration>
      <itunes:episode>1697</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/git-hooks-pre-commit-security.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/git-hooks-pre-commit-security.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agent Mesh: Shared Context That Changes Everything</title>
      <description><![CDATA[Most multi-agent setups are just glued-together chatbots. xAI's Grok 4.20 introduces a native architecture with shared context layers and cross-agent attention. This episode explores how that changes coordination, efficiency, and when you should actually use it.]]></description>
      <link>https://myweirdprompts.com/episode/multi-agent-optimized-model-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-agent-optimized-model-architecture/</guid>
      <pubDate>Sat, 28 Mar 2026 15:23:30 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-agent-optimized-model-architecture.m4a"
        type="audio/mp4"
        length="14928954"
      />
      <itunes:title>The Agent Mesh: Shared Context That Changes Everything</itunes:title>
      <itunes:subtitle>Grok 4.20’s native multi-agent architecture cuts token costs by 75% and enables real-time cross-agent reasoning.</itunes:subtitle>
      <itunes:summary><![CDATA[Most multi-agent setups are just glued-together chatbots. xAI's Grok 4.20 introduces a native architecture with shared context layers and cross-agent attention. This episode explores how that changes coordination, efficiency, and when you should actually use it.]]></itunes:summary>
      <itunes:duration>1096</itunes:duration>
      <itunes:episode>1666</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-agent-optimized-model-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-agent-optimized-model-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Solo Devs: When to Dockerize (and When Not To)</title>
      <description><![CDATA[A fifty-line Python script took three hours to configure a dev container for. When does environment isolation actually justify its overhead for solo developers? This episode dives into the real costs of raw Python, Dockerizing, and dev containers. We break down concrete setup times, the cognitive tax of debugging inside containers, and the specific scenarios where each approach makes sense. Whether you're building a simple script or managing microservices, learn the heuristics that help you choose the right tool without wasting time on unnecessary complexity.]]></description>
      <link>https://myweirdprompts.com/episode/solo-dev-dockerize-vs-raw-python/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/solo-dev-dockerize-vs-raw-python/</guid>
      <pubDate>Sat, 28 Mar 2026 14:22:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/solo-dev-dockerize-vs-raw-python.m4a"
        type="audio/mp4"
        length="20998619"
      />
      <itunes:title>Solo Devs: When to Dockerize (and When Not To)</itunes:title>
      <itunes:subtitle>Is Docker worth it for solo devs? We compare raw Python, Docker, and dev containers with real setup times and tradeoffs.</itunes:subtitle>
      <itunes:summary><![CDATA[A fifty-line Python script took three hours to configure a dev container for. When does environment isolation actually justify its overhead for solo developers? This episode dives into the real costs of raw Python, Dockerizing, and dev containers. We break down concrete setup times, the cognitive tax of debugging inside containers, and the specific scenarios where each approach makes sense. Whether you're building a simple script or managing microservices, learn the heuristics that help you choose the right tool without wasting time on unnecessary complexity.]]></itunes:summary>
      <itunes:duration>1623</itunes:duration>
      <itunes:episode>1657</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/solo-dev-dockerize-vs-raw-python.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/solo-dev-dockerize-vs-raw-python.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The VPS Relay: Secure Home Server Access Without Port Forwarding</title>
      <description><![CDATA[How to use a cheap cloud VPS as a secure middleman for your home server, avoiding risky port forwarding. This episode explores the mechanics of outbound tunnels, the role of tools like Pangolin, and the cybersecurity trade-offs of this hybrid architecture.]]></description>
      <link>https://myweirdprompts.com/episode/vps-relay-self-hosted-gps-tracker/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vps-relay-self-hosted-gps-tracker/</guid>
      <pubDate>Sat, 28 Mar 2026 14:17:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vps-relay-self-hosted-gps-tracker.m4a"
        type="audio/mp4"
        length="14511878"
      />
      <itunes:title>The VPS Relay: Secure Home Server Access Without Port Forwarding</itunes:title>
      <itunes:subtitle>Learn how to securely expose home servers to the internet using a VPS relay, avoiding risky port forwarding.</itunes:subtitle>
      <itunes:summary><![CDATA[How to use a cheap cloud VPS as a secure middleman for your home server, avoiding risky port forwarding. This episode explores the mechanics of outbound tunnels, the role of tools like Pangolin, and the cybersecurity trade-offs of this hybrid architecture.]]></itunes:summary>
      <itunes:duration>1116</itunes:duration>
      <itunes:episode>1656</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vps-relay-self-hosted-gps-tracker.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vps-relay-self-hosted-gps-tracker.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Home Lab Security: Locking Down Your Smart Home</title>
      <description><![CDATA[We explore the concept of blast radius in self-hosted environments, specifically focusing on securing home automation setups like Home Assistant. Learn how to move beyond simple perimeter defenses like Cloudflare Tunnels and implement true isolation using Linux kernel features. We discuss practical steps for sandboxing containers, managing network segmentation, and applying the principle of least privilege to prevent lateral movement attacks.]]></description>
      <link>https://myweirdprompts.com/episode/home-lab-security-locking-down-smart-home/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-lab-security-locking-down-smart-home/</guid>
      <pubDate>Sat, 28 Mar 2026 14:00:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-lab-security-locking-down-smart-home.m4a"
        type="audio/mp4"
        length="22448006"
      />
      <itunes:title>Home Lab Security: Locking Down Your Smart Home</itunes:title>
      <itunes:subtitle>Learn how to prevent a single compromised container from taking over your entire home network and smart devices.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the concept of blast radius in self-hosted environments, specifically focusing on securing home automation setups like Home Assistant. Learn how to move beyond simple perimeter defenses like Cloudflare Tunnels and implement true isolation using Linux kernel features. We discuss practical steps for sandboxing containers, managing network segmentation, and applying the principle of least privilege to prevent lateral movement attacks.]]></itunes:summary>
      <itunes:duration>1714</itunes:duration>
      <itunes:episode>1654</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-lab-security-locking-down-smart-home.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-lab-security-locking-down-smart-home.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Gateways: The Nginx for Your AI Stack</title>
      <description><![CDATA[As AI systems grow from prototypes into production, they’re becoming a fragmented mess of models, tools, and dashboards. This episode explores the rise of AI gateways—a new middleware layer acting as a unified control plane. We break down how these gateways handle intelligent model routing, aggregate MCP tools for security and governance, and provide critical observability. Learn why companies like Stripe are slashing inference costs by 30-40%, compare leading solutions like Portkey AI and LiteLLM, and discover why this architectural pattern might soon become as essential for personal AI assistants as it is for enterprise platforms.]]></description>
      <link>https://myweirdprompts.com/episode/ai-gateway-middleware-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-gateway-middleware-agents/</guid>
      <pubDate>Sat, 28 Mar 2026 13:52:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-gateway-middleware-agents.m4a"
        type="audio/mp4"
        length="15781782"
      />
      <itunes:title>AI Gateways: The Nginx for Your AI Stack</itunes:title>
      <itunes:subtitle>Why agentic AI needs a unified control plane to route models, aggregate tools, and cut costs.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI systems grow from prototypes into production, they’re becoming a fragmented mess of models, tools, and dashboards. This episode explores the rise of AI gateways—a new middleware layer acting as a unified control plane. We break down how these gateways handle intelligent model routing, aggregate MCP tools for security and governance, and provide critical observability. Learn why companies like Stripe are slashing inference costs by 30-40%, compare leading solutions like Portkey AI and LiteLLM, and discover why this architectural pattern might soon become as essential for personal AI assistants as it is for enterprise platforms.]]></itunes:summary>
      <itunes:duration>1187</itunes:duration>
      <itunes:episode>1652</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-gateway-middleware-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-gateway-middleware-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Vendor SDK Moat: Real or Illusion?</title>
      <description><![CDATA[The choice between vendor SDKs and agnostic frameworks is a critical engineering decision. We explore the "moat" of vendor lock-in versus the "home field" advantage of optimized tools, revealing a surprising hybrid strategy for production systems. Learn when to use which, and why the smartest teams are layering their approach.]]></description>
      <link>https://myweirdprompts.com/episode/vendor-sdk-moat-agnostic-frameworks/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vendor-sdk-moat-agnostic-frameworks/</guid>
      <pubDate>Sat, 28 Mar 2026 13:33:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vendor-sdk-moat-agnostic-frameworks.m4a"
        type="audio/mp4"
        length="11125711"
      />
      <itunes:title>The Vendor SDK Moat: Real or Illusion?</itunes:title>
      <itunes:subtitle>Is the vendor lock-in real, or just good marketing? We dissect the trade-offs between vendor SDKs and agnostic frameworks.</itunes:subtitle>
      <itunes:summary><![CDATA[The choice between vendor SDKs and agnostic frameworks is a critical engineering decision. We explore the "moat" of vendor lock-in versus the "home field" advantage of optimized tools, revealing a surprising hybrid strategy for production systems. Learn when to use which, and why the smartest teams are layering their approach.]]></itunes:summary>
      <itunes:duration>832</itunes:duration>
      <itunes:episode>1649</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vendor-sdk-moat-agnostic-frameworks.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vendor-sdk-moat-agnostic-frameworks.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Monorepos: Better Modularity Than Multi-Repos?</title>
      <description><![CDATA[We tackle the counterintuitive idea that a monorepo can support better modularity than multi-repos. The discussion covers how modern tooling like Nx and Bazel creates logical boundaries and hermetic builds, the practical benefits for solo developers and large teams, and why AI agents may prefer a unified codebase. Learn how to get started with pnpm workspaces and why the trade-off is worth it.]]></description>
      <link>https://myweirdprompts.com/episode/monorepo-vs-multi-repo-modularity/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/monorepo-vs-multi-repo-modularity/</guid>
      <pubDate>Sat, 28 Mar 2026 13:23:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/monorepo-vs-multi-repo-modularity.m4a"
        type="audio/mp4"
        length="11687015"
      />
      <itunes:title>Monorepos: Better Modularity Than Multi-Repos?</itunes:title>
      <itunes:subtitle>Why putting all your code in one repository can actually enforce better boundaries than separate repos.</itunes:subtitle>
      <itunes:summary><![CDATA[We tackle the counterintuitive idea that a monorepo can support better modularity than multi-repos. The discussion covers how modern tooling like Nx and Bazel creates logical boundaries and hermetic builds, the practical benefits for solo developers and large teams, and why AI agents may prefer a unified codebase. Learn how to get started with pnpm workspaces and why the trade-off is worth it.]]></itunes:summary>
      <itunes:duration>871</itunes:duration>
      <itunes:episode>1647</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/monorepo-vs-multi-repo-modularity.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/monorepo-vs-multi-repo-modularity.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From DAGs to Loops: Why Agents Need Stateful Cycles</title>
      <description><![CDATA[Most agent frameworks treat AI as a linear pipeline. This episode explains why the real power comes from cyclic execution—where agents loop, iterate, and maintain shared state—and how LangGraph formalizes that shift.]]></description>
      <link>https://myweirdprompts.com/episode/langgraph-agent-state-management/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/langgraph-agent-state-management/</guid>
      <pubDate>Sat, 28 Mar 2026 02:09:03 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/langgraph-agent-state-management.m4a"
        type="audio/mp4"
        length="12253312"
      />
      <itunes:title>From DAGs to Loops: Why Agents Need Stateful Cycles</itunes:title>
      <itunes:subtitle>Stop building linear chains and start building cycles to create agents that can reason, self-correct, and maintain complex state.</itunes:subtitle>
      <itunes:summary><![CDATA[Most agent frameworks treat AI as a linear pipeline. This episode explains why the real power comes from cyclic execution—where agents loop, iterate, and maintain shared state—and how LangGraph formalizes that shift.]]></itunes:summary>
      <itunes:duration>923</itunes:duration>
      <itunes:episode>1629</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/langgraph-agent-state-management.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/langgraph-agent-state-management.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why VRAM Is the Wrong Way to Measure Your AI PC</title>
      <description><![CDATA[As we move from simple chatbots to autonomous coding agents, the hardware requirements for local AI are shifting from mere capacity to raw throughput. This episode breaks down the "frustration threshold" for developers and explains why prefill speed and memory bandwidth are now more important than your GPU's total VRAM. We explore the latest 2026 hardware benchmarks, the hidden "tax" of the Model Context Protocol, and how distributed inference can turn your old hardware into an agentic powerhouse.]]></description>
      <link>https://myweirdprompts.com/episode/local-ai-hardware-bottlenecks/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/local-ai-hardware-bottlenecks/</guid>
      <pubDate>Fri, 27 Mar 2026 19:50:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/local-ai-hardware-bottlenecks.m4a"
        type="audio/mp4"
        length="16712610"
      />
      <itunes:title>Why VRAM Is the Wrong Way to Measure Your AI PC</itunes:title>
      <itunes:subtitle>Forget VRAM—bandwidth is the new king. Discover why your local AI feels slow and how to build a true &quot;agent computer&quot; for professional coding.</itunes:subtitle>
      <itunes:summary><![CDATA[As we move from simple chatbots to autonomous coding agents, the hardware requirements for local AI are shifting from mere capacity to raw throughput. This episode breaks down the "frustration threshold" for developers and explains why prefill speed and memory bandwidth are now more important than your GPU's total VRAM. We explore the latest 2026 hardware benchmarks, the hidden "tax" of the Model Context Protocol, and how distributed inference can turn your old hardware into an agentic powerhouse.]]></itunes:summary>
      <itunes:duration>1296</itunes:duration>
      <itunes:episode>1620</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/local-ai-hardware-bottlenecks.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/local-ai-hardware-bottlenecks.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Rise of AI Microservices: Beyond the Mega-Prompt</title>
      <description><![CDATA[The era of the "all-in-one" mega-prompt is over, giving way to a more sophisticated "microservices moment" for artificial intelligence where complex tasks are dismantled into atomic, high-signal micro-prompts. This episode explores the transition from general-purpose chatbots to production-grade agentic workflows, featuring insights into the layered control systems of Meta-Agents, Supervisors, and Workers that reduce hallucinations and improve reliability. We also dive into the technical infrastructure making this possible—from the Model Context Protocol (MCP) to security guardrails like NVIDIA’s NemoClaw—while addressing the emerging challenges of orchestration debt and the necessity of FinOps for managing token budgets in a distributed agentic stack.]]></description>
      <link>https://myweirdprompts.com/episode/ai-microservices-modular-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-microservices-modular-architecture/</guid>
      <pubDate>Fri, 27 Mar 2026 19:04:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-microservices-modular-architecture.m4a"
        type="audio/mp4"
        length="13771906"
      />
      <itunes:title>The Rise of AI Microservices: Beyond the Mega-Prompt</itunes:title>
      <itunes:subtitle>Say goodbye to mega-prompts. Explore the shift toward modular AI microservices, agentic hierarchies, and high-signal control artifacts.</itunes:subtitle>
      <itunes:summary><![CDATA[The era of the "all-in-one" mega-prompt is over, giving way to a more sophisticated "microservices moment" for artificial intelligence where complex tasks are dismantled into atomic, high-signal micro-prompts. This episode explores the transition from general-purpose chatbots to production-grade agentic workflows, featuring insights into the layered control systems of Meta-Agents, Supervisors, and Workers that reduce hallucinations and improve reliability. We also dive into the technical infrastructure making this possible—from the Model Context Protocol (MCP) to security guardrails like NVIDIA’s NemoClaw—while addressing the emerging challenges of orchestration debt and the necessity of FinOps for managing token budgets in a distributed agentic stack.]]></itunes:summary>
      <itunes:duration>1041</itunes:duration>
      <itunes:episode>1618</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-microservices-modular-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-microservices-modular-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the .env: Mastering Public and Private Code</title>
      <description><![CDATA[Maintaining separate repositories for open-source code and private deployment scripts is a recipe for "merge debt" and configuration drift. In this episode, we explore how to move toward a single source of truth without exposing your production secrets to the world. We dive deep into the "dual-repo tax" and why traditional methods like .env files are no longer enough in an era where millions of secrets are leaked annually. We discuss powerful tools like Mozilla SOPS for partial file encryption, direnv for local environment management, and the latest Git features like sparse-checkout. Finally, we look at the cutting edge of security, including AI-enhanced push protection and modular configuration patterns. Whether you are an open-source maintainer or a DevOps engineer, this episode provides a roadmap for a more efficient, secure, and transparent development workflow.]]></description>
      <link>https://myweirdprompts.com/episode/open-source-private-workflow/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/open-source-private-workflow/</guid>
      <pubDate>Fri, 27 Mar 2026 15:46:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/open-source-private-workflow.m4a"
        type="audio/mp4"
        length="20931668"
      />
      <itunes:title>Beyond the .env: Mastering Public and Private Code</itunes:title>
      <itunes:subtitle>Stop paying the &quot;dual-repo tax.&quot; Learn how to manage public code and private secrets in a single, secure repository.</itunes:subtitle>
      <itunes:summary><![CDATA[Maintaining separate repositories for open-source code and private deployment scripts is a recipe for "merge debt" and configuration drift. In this episode, we explore how to move toward a single source of truth without exposing your production secrets to the world. We dive deep into the "dual-repo tax" and why traditional methods like .env files are no longer enough in an era where millions of secrets are leaked annually. We discuss powerful tools like Mozilla SOPS for partial file encryption, direnv for local environment management, and the latest Git features like sparse-checkout. Finally, we look at the cutting edge of security, including AI-enhanced push protection and modular configuration patterns. Whether you are an open-source maintainer or a DevOps engineer, this episode provides a roadmap for a more efficient, secure, and transparent development workflow.]]></itunes:summary>
      <itunes:duration>1619</itunes:duration>
      <itunes:episode>1614</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/open-source-private-workflow.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/open-source-private-workflow.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The End of Apps: Why Agents Are Replacing Your Desktop</title>
      <description><![CDATA[As AI agents learn to navigate operating systems like humans, the traditional app-based interface is becoming obsolete. This episode explores the architectural shift toward agent-centric computing, the pixel-parsing problem, and what a post-app world looks like.]]></description>
      <link>https://myweirdprompts.com/episode/agent-centric-os-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-centric-os-evolution/</guid>
      <pubDate>Fri, 27 Mar 2026 15:34:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-centric-os-evolution.m4a"
        type="audio/mp4"
        length="18566663"
      />
      <itunes:title>The End of Apps: Why Agents Are Replacing Your Desktop</itunes:title>
      <itunes:subtitle>Is the era of the app over? Explore how AI agents are transforming operating systems from static tools into proactive digital partners.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI agents learn to navigate operating systems like humans, the traditional app-based interface is becoming obsolete. This episode explores the architectural shift toward agent-centric computing, the pixel-parsing problem, and what a post-app world looks like.]]></itunes:summary>
      <itunes:duration>1434</itunes:duration>
      <itunes:episode>1612</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-centric-os-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-centric-os-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>DeepSeek’s Return: V4, R2, and the AI Pricing War</title>
      <description><![CDATA[After a year of silence, DeepSeek has returned to the spotlight with the launch of V4 and R2, sending shockwaves through the AI industry with a trillion-parameter architecture and unprecedented pricing. This episode dives into the technical breakthroughs of Manifold-Constrained Hyper-Connections and Mixture of Experts that allow such a massive model to run with incredible efficiency on domestic Chinese hardware. We also unravel the Hunter Alpha mystery involving Xiaomi and explore how DeepSeek’s "Thinking in Tool-Use" and the OpenClaw framework are shifting the focus from chatbots to autonomous digital employees. As the unit economics of AI are rewritten by DeepSeek’s ultra-low costs, we examine what this means for the global competition between Silicon Valley and Hangzhou.]]></description>
      <link>https://myweirdprompts.com/episode/deepseek-v4-r2-market-disruption/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/deepseek-v4-r2-market-disruption/</guid>
      <pubDate>Fri, 27 Mar 2026 15:04:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/deepseek-v4-r2-market-disruption.m4a"
        type="audio/mp4"
        length="17862857"
      />
      <itunes:title>DeepSeek’s Return: V4, R2, and the AI Pricing War</itunes:title>
      <itunes:subtitle>DeepSeek returns with a trillion-parameter model and rock-bottom pricing. Explore the tech behind V4 and the mystery of the Hunter Alpha leak.</itunes:subtitle>
      <itunes:summary><![CDATA[After a year of silence, DeepSeek has returned to the spotlight with the launch of V4 and R2, sending shockwaves through the AI industry with a trillion-parameter architecture and unprecedented pricing. This episode dives into the technical breakthroughs of Manifold-Constrained Hyper-Connections and Mixture of Experts that allow such a massive model to run with incredible efficiency on domestic Chinese hardware. We also unravel the Hunter Alpha mystery involving Xiaomi and explore how DeepSeek’s "Thinking in Tool-Use" and the OpenClaw framework are shifting the focus from chatbots to autonomous digital employees. As the unit economics of AI are rewritten by DeepSeek’s ultra-low costs, we examine what this means for the global competition between Silicon Valley and Hangzhou.]]></itunes:summary>
      <itunes:duration>1368</itunes:duration>
      <itunes:episode>1606</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/deepseek-v4-r2-market-disruption.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/deepseek-v4-r2-market-disruption.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fire Your Software Subscriptions and Just Code the Vibe</title>
      <description><![CDATA[Stop renting your productivity and start owning it. This episode explores the "subscription graveyard" and the revolutionary shift toward "vibe coding," where non-technical users leverage agentic workflows to build custom, self-healing tools in hours rather than months. From fixing niche Hebrew formatting issues to replacing bloated CRMs, we discuss how the 85% drop in API costs is dismantling the traditional SaaS model and what the rise of "Shadow AI" means for the future of IT security and professional skillsets.]]></description>
      <link>https://myweirdprompts.com/episode/bespoke-ai-software-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/bespoke-ai-software-evolution/</guid>
      <pubDate>Fri, 27 Mar 2026 14:53:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/bespoke-ai-software-evolution.m4a"
        type="audio/mp4"
        length="19128918"
      />
      <itunes:title>Fire Your Software Subscriptions and Just Code the Vibe</itunes:title>
      <itunes:subtitle>Tired of the SaaS tax? Discover how AI is turning software from a product you buy into a capability you manifest.</itunes:subtitle>
      <itunes:summary><![CDATA[Stop renting your productivity and start owning it. This episode explores the "subscription graveyard" and the revolutionary shift toward "vibe coding," where non-technical users leverage agentic workflows to build custom, self-healing tools in hours rather than months. From fixing niche Hebrew formatting issues to replacing bloated CRMs, we discuss how the 85% drop in API costs is dismantling the traditional SaaS model and what the rise of "Shadow AI" means for the future of IT security and professional skillsets.]]></itunes:summary>
      <itunes:duration>1453</itunes:duration>
      <itunes:episode>1603</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/bespoke-ai-software-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/bespoke-ai-software-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Digital Tofu Crisis: Saving the World’s Scripts</title>
      <description><![CDATA[While our digital devices are packed with thousands of emojis, nearly half of the world’s writing systems remain digitally invisible, often appearing only as empty boxes known as "tofu." This episode dives into the technical and bureaucratic hurdles of the Unicode Standard, exploring why ancient hieroglyphs and modern minority scripts struggle to gain a foothold in our global digital infrastructure. We examine the "chicken-and-egg" problem of script adoption, the tireless work of the Script Encoding Initiative, and the high stakes of digital extinction in an era where if a language isn’t online, it’s at risk of vanishing forever.]]></description>
      <link>https://myweirdprompts.com/episode/unicode-missing-scripts-digital-divide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unicode-missing-scripts-digital-divide/</guid>
      <pubDate>Fri, 27 Mar 2026 14:39:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unicode-missing-scripts-digital-divide.m4a"
        type="audio/mp4"
        length="14700288"
      />
      <itunes:title>The Digital Tofu Crisis: Saving the World’s Scripts</itunes:title>
      <itunes:subtitle>Why does Bigfoot have an emoji while millions can’t type in their native script? Explore the hidden battle to save the world&apos;s writing systems.</itunes:subtitle>
      <itunes:summary><![CDATA[While our digital devices are packed with thousands of emojis, nearly half of the world’s writing systems remain digitally invisible, often appearing only as empty boxes known as "tofu." This episode dives into the technical and bureaucratic hurdles of the Unicode Standard, exploring why ancient hieroglyphs and modern minority scripts struggle to gain a foothold in our global digital infrastructure. We examine the "chicken-and-egg" problem of script adoption, the tireless work of the Script Encoding Initiative, and the high stakes of digital extinction in an era where if a language isn’t online, it’s at risk of vanishing forever.]]></itunes:summary>
      <itunes:duration>1139</itunes:duration>
      <itunes:episode>1600</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unicode-missing-scripts-digital-divide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unicode-missing-scripts-digital-divide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Teams Are Hiring Digital Middle Managers</title>
      <description><![CDATA[The "honeymoon phase" of agentic AI is over. Recent research shows that simply throwing more agents at a problem causes systems to collapse under a "coordination depth wall." To solve this, developers are building something we once tried to escape: bureaucracy. This episode explores the transition from flat orchestrators to sophisticated hierarchical structures like the HiMAC framework. We dive into the technical necessity of "Meta-Controllers," the role of verification gates in stopping hallucinations, and the brewing debate between monolithic models and auditable agent bureaucracies. Is this the future of "synthetic talent," or just a temporary patch for model limitations? Join us as we break down the new architecture of AI productivity.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-middle-management/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-middle-management/</guid>
      <pubDate>Fri, 27 Mar 2026 14:25:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-middle-management.m4a"
        type="audio/mp4"
        length="15983013"
      />
      <itunes:title>Why AI Teams Are Hiring Digital Middle Managers</itunes:title>
      <itunes:subtitle>AI agents are hitting a &quot;coordination depth wall.&quot; Learn how hierarchical middle management is saving agentic workflows from total collapse.</itunes:subtitle>
      <itunes:summary><![CDATA[The "honeymoon phase" of agentic AI is over. Recent research shows that simply throwing more agents at a problem causes systems to collapse under a "coordination depth wall." To solve this, developers are building something we once tried to escape: bureaucracy. This episode explores the transition from flat orchestrators to sophisticated hierarchical structures like the HiMAC framework. We dive into the technical necessity of "Meta-Controllers," the role of verification gates in stopping hallucinations, and the brewing debate between monolithic models and auditable agent bureaucracies. Is this the future of "synthetic talent," or just a temporary patch for model limitations? Join us as we break down the new architecture of AI productivity.]]></itunes:summary>
      <itunes:duration>1227</itunes:duration>
      <itunes:episode>1597</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-middle-management.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-middle-management.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your AI Agent Needs a Ticket System, Not a Chatbox</title>
      <description><![CDATA[Are your AI agents losing the thread the moment you give them a mid-task instruction? In this episode, we explore the "interruption problem" and why the era of intuitive "vibe coding" is officially over, giving way to a new age of robust agent orchestration. We break down the latest breakthroughs from March 2026, including OpenAI’s Responses API with context compaction and Anthropic’s Dispatch tool, which are revolutionizing how models handle complex, long-running tasks. Learn about Ticket-Driven Development (TxDD), the "Ralph Loop" for stateless iteration, and why the EU AI Act is making "Human-on-the-Loop" governance a legal necessity. Whether you’re building with Claude Code or exploring Steve Yegge’s Gas Town, this is your guide to moving from fragile prompts to dependable, professional AI systems.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-orchestration-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-orchestration-evolution/</guid>
      <pubDate>Fri, 27 Mar 2026 14:19:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-orchestration-evolution.m4a"
        type="audio/mp4"
        length="13871313"
      />
      <itunes:title>Why Your AI Agent Needs a Ticket System, Not a Chatbox</itunes:title>
      <itunes:subtitle>Stop confusing your AI agents. Learn how context compaction and ticket-driven development are ending the era of &quot;vibe coding&quot; for good.</itunes:subtitle>
      <itunes:summary><![CDATA[Are your AI agents losing the thread the moment you give them a mid-task instruction? In this episode, we explore the "interruption problem" and why the era of intuitive "vibe coding" is officially over, giving way to a new age of robust agent orchestration. We break down the latest breakthroughs from March 2026, including OpenAI’s Responses API with context compaction and Anthropic’s Dispatch tool, which are revolutionizing how models handle complex, long-running tasks. Learn about Ticket-Driven Development (TxDD), the "Ralph Loop" for stateless iteration, and why the EU AI Act is making "Human-on-the-Loop" governance a legal necessity. Whether you’re building with Claude Code or exploring Steve Yegge’s Gas Town, this is your guide to moving from fragile prompts to dependable, professional AI systems.]]></itunes:summary>
      <itunes:duration>1051</itunes:duration>
      <itunes:episode>1596</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-orchestration-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-orchestration-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your 2026 Smartphone Still Feels Like a Part-Time Job</title>
      <description><![CDATA[In 2026, mobile hardware has reached incredible heights, yet the software setup process remains a frustrating "empty room" experience. This episode explores the widening gap between Google’s managed cloud services and the needs of power users who demand total control. We dive into the technical bottlenecks of the 25MB backup cap, the controversial new 24-hour waiting period for sideloading, and how the "Battery Shame List" is stifling innovation. Is the era of the Android tinkerer coming to an end, or can tools like Shizuku save the day?]]></description>
      <link>https://myweirdprompts.com/episode/android-backup-customization-paradox/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-backup-customization-paradox/</guid>
      <pubDate>Fri, 27 Mar 2026 14:10:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-backup-customization-paradox.m4a"
        type="audio/mp4"
        length="15148893"
      />
      <itunes:title>Why Your 2026 Smartphone Still Feels Like a Part-Time Job</itunes:title>
      <itunes:subtitle>Great hardware, tedious software. Explore why setting up a new Android phone in 2026 is still a manual, 24-hour chore for power users.</itunes:subtitle>
      <itunes:summary><![CDATA[In 2026, mobile hardware has reached incredible heights, yet the software setup process remains a frustrating "empty room" experience. This episode explores the widening gap between Google’s managed cloud services and the needs of power users who demand total control. We dive into the technical bottlenecks of the 25MB backup cap, the controversial new 24-hour waiting period for sideloading, and how the "Battery Shame List" is stifling innovation. Is the era of the Android tinkerer coming to an end, or can tools like Shizuku save the day?]]></itunes:summary>
      <itunes:duration>1166</itunes:duration>
      <itunes:episode>1595</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-backup-customization-paradox.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-backup-customization-paradox.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Soul of Your Machine: Automating What Matters</title>
      <description><![CDATA[What do you lose when your OS crashes? Not just files, but years of personalized tweaks. This episode explores how treating your desktop like a recipe—not a pet—can save your machine's soul, and why dotfiles are the real battleground.]]></description>
      <link>https://myweirdprompts.com/episode/desktop-automation-reproducible-linux/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/desktop-automation-reproducible-linux/</guid>
      <pubDate>Fri, 27 Mar 2026 14:10:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/desktop-automation-reproducible-linux.m4a"
        type="audio/mp4"
        length="18786398"
      />
      <itunes:title>The Soul of Your Machine: Automating What Matters</itunes:title>
      <itunes:subtitle>Stop manually configuring your PC. Learn how NixOS, Ansible, and Chezmoi turn your desktop into a reproducible, automated recipe.</itunes:subtitle>
      <itunes:summary><![CDATA[What do you lose when your OS crashes? Not just files, but years of personalized tweaks. This episode explores how treating your desktop like a recipe—not a pet—can save your machine's soul, and why dotfiles are the real battleground.]]></itunes:summary>
      <itunes:duration>1427</itunes:duration>
      <itunes:episode>1594</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/desktop-automation-reproducible-linux.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/desktop-automation-reproducible-linux.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Vector Debt Trap: Choosing Embeddings That Last</title>
      <description><![CDATA[Why your choice of embedding model today could lock you into a costly re-indexing nightmare tomorrow. We explore how Gemini 2's multimodal leap and OpenAI's Matryoshka learning change the calculus for production RAG systems, and how to avoid architectural regret.]]></description>
      <link>https://myweirdprompts.com/episode/embedding-models-rag-optimization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/embedding-models-rag-optimization/</guid>
      <pubDate>Fri, 27 Mar 2026 13:59:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/embedding-models-rag-optimization.m4a"
        type="audio/mp4"
        length="17935074"
      />
      <itunes:title>The Vector Debt Trap: Choosing Embeddings That Last</itunes:title>
      <itunes:subtitle>Stop treating embedding models like plumbing. Learn how to navigate vector debt, multimodal retrieval, and database configuration for RAG.</itunes:subtitle>
      <itunes:summary><![CDATA[Why your choice of embedding model today could lock you into a costly re-indexing nightmare tomorrow. We explore how Gemini 2's multimodal leap and OpenAI's Matryoshka learning change the calculus for production RAG systems, and how to avoid architectural regret.]]></itunes:summary>
      <itunes:duration>1393</itunes:duration>
      <itunes:episode>1592</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/embedding-models-rag-optimization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/embedding-models-rag-optimization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Closed-Loop Hebrew Learning Stack</title>
      <description><![CDATA[How do you build a specialized AI workflow that turns spoken English into fully vocalized Hebrew and pipes it directly into a spaced repetition system? This episode explores the technical stack—from HeBERT to RTL rendering—needed to close the loop for Semitic language learners.]]></description>
      <link>https://myweirdprompts.com/episode/hebrew-ai-learning-tech/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hebrew-ai-learning-tech/</guid>
      <pubDate>Fri, 27 Mar 2026 13:17:38 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hebrew-ai-learning-tech.m4a"
        type="audio/mp4"
        length="18836576"
      />
      <itunes:title>Building a Closed-Loop Hebrew Learning Stack</itunes:title>
      <itunes:subtitle>Explore how AI solves the &quot;vocalization gap&quot; in Hebrew and the best tools for building a high-tech, voice-to-SRS study workflow.</itunes:subtitle>
      <itunes:summary><![CDATA[How do you build a specialized AI workflow that turns spoken English into fully vocalized Hebrew and pipes it directly into a spaced repetition system? This episode explores the technical stack—from HeBERT to RTL rendering—needed to close the loop for Semitic language learners.]]></itunes:summary>
      <itunes:duration>1464</itunes:duration>
      <itunes:episode>1587</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hebrew-ai-learning-tech.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hebrew-ai-learning-tech.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Chatbox: Closing the Agentic UI Gap</title>
      <description><![CDATA[Current AI workflows are often trapped in a "Slack-as-Operating-System" fallacy, where sophisticated agentic logic is forced through primitive messaging interfaces. This episode explores the critical shift from linear chat threads to structured control planes, examining how new tools from NVIDIA, Vercel, and Palo Alto Networks are bridging the Agentic UI Gap. We discuss why the future of AI interaction isn't a conversation, but a cockpit designed for state management and "disposable pixels."]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ui-gap-interfaces/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ui-gap-interfaces/</guid>
      <pubDate>Thu, 26 Mar 2026 17:36:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ui-gap-interfaces.m4a"
        type="audio/mp4"
        length="20588095"
      />
      <itunes:title>Beyond the Chatbox: Closing the Agentic UI Gap</itunes:title>
      <itunes:subtitle>Stop treating AI agents like interns in a chat app. Discover why professional automation requires a control cockpit, not a messaging bubble.</itunes:subtitle>
      <itunes:summary><![CDATA[Current AI workflows are often trapped in a "Slack-as-Operating-System" fallacy, where sophisticated agentic logic is forced through primitive messaging interfaces. This episode explores the critical shift from linear chat threads to structured control planes, examining how new tools from NVIDIA, Vercel, and Palo Alto Networks are bridging the Agentic UI Gap. We discuss why the future of AI interaction isn't a conversation, but a cockpit designed for state management and "disposable pixels."]]></itunes:summary>
      <itunes:duration>1617</itunes:duration>
      <itunes:episode>1566</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ui-gap-interfaces.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ui-gap-interfaces.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Machine-Readable Safety: Markdown for AI Agents</title>
      <description><![CDATA[When an emergency strikes, seconds matter—but bloated government websites and aggressive anti-bot security often stand in the way of life-saving information. This episode explores the critical shift from human-readable web design to machine-readable documentation, specifically focusing on how to structure high-stakes emergency protocols for AI agents. We dive into the technical "semantic marrow" of why Markdown outperforms JSON for retrieval-augmented generation (RAG) and how YAML front-matter provides the necessary metadata for regional filtering. From hierarchical context preservation to the emerging "llms.txt" standard, we discuss how developers can build "unstoppable" data mirrors that remain accessible even during network volatility or cyberattacks. Join us as we break down the infrastructure needed to turn bureaucratic noise into actionable, hallucination-free intelligence for the next generation of AI-driven safety tools.]]></description>
      <link>https://myweirdprompts.com/episode/markdown-ai-emergency-protocols/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/markdown-ai-emergency-protocols/</guid>
      <pubDate>Thu, 26 Mar 2026 15:27:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/markdown-ai-emergency-protocols.m4a"
        type="audio/mp4"
        length="19245863"
      />
      <itunes:title>Machine-Readable Safety: Markdown for AI Agents</itunes:title>
      <itunes:subtitle>Transform bloated government data into clean Markdown to power life-saving AI agents during emergencies.</itunes:subtitle>
      <itunes:summary><![CDATA[When an emergency strikes, seconds matter—but bloated government websites and aggressive anti-bot security often stand in the way of life-saving information. This episode explores the critical shift from human-readable web design to machine-readable documentation, specifically focusing on how to structure high-stakes emergency protocols for AI agents. We dive into the technical "semantic marrow" of why Markdown outperforms JSON for retrieval-augmented generation (RAG) and how YAML front-matter provides the necessary metadata for regional filtering. From hierarchical context preservation to the emerging "llms.txt" standard, we discuss how developers can build "unstoppable" data mirrors that remain accessible even during network volatility or cyberattacks. Join us as we break down the infrastructure needed to turn bureaucratic noise into actionable, hallucination-free intelligence for the next generation of AI-driven safety tools.]]></itunes:summary>
      <itunes:duration>1475</itunes:duration>
      <itunes:episode>1565</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/markdown-ai-emergency-protocols.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/markdown-ai-emergency-protocols.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Death of the Cascaded Pipeline</title>
      <description><![CDATA[Why are developers abandoning the speech-to-text-to-LLM pipeline for native audio models? This episode breaks down the trade-offs between local sovereignty and SaaS scale, from Whisper to Moonshine to Gemini, and what the shift means for voice-first applications.]]></description>
      <link>https://myweirdprompts.com/episode/audio-omnimodal-transcription-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-omnimodal-transcription-future/</guid>
      <pubDate>Thu, 26 Mar 2026 15:19:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-omnimodal-transcription-future.m4a"
        type="audio/mp4"
        length="19307278"
      />
      <itunes:title>The Death of the Cascaded Pipeline</itunes:title>
      <itunes:subtitle>Forget basic transcription. Explore how native omni-modal models are capturing the &quot;soul&quot; of speech with near-instant latency.</itunes:subtitle>
      <itunes:summary><![CDATA[Why are developers abandoning the speech-to-text-to-LLM pipeline for native audio models? This episode breaks down the trade-offs between local sovereignty and SaaS scale, from Whisper to Moonshine to Gemini, and what the shift means for voice-first applications.]]></itunes:summary>
      <itunes:duration>1492</itunes:duration>
      <itunes:episode>1564</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-omnimodal-transcription-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-omnimodal-transcription-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Breaking the Loop: Why AI Agents Get Stuck</title>
      <description><![CDATA[As AI models gain more "thinking time" through advanced reasoning chains, they are increasingly falling into recursive traps, attempting the same failing solutions until they exhaust compute budgets. This episode explores the "restart tax" and the 20% of enterprise compute currently wasted on agentic loops, diving into how new Model Context Protocol (MCP) servers act as digital circuit breakers. Discover why the most valuable human trait we can give an AI isn't infinite perseverance, but the self-awareness to know when it is time to stop and ask for help.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-loop-persistence/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-loop-persistence/</guid>
      <pubDate>Thu, 26 Mar 2026 13:17:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-loop-persistence.m4a"
        type="audio/mp4"
        length="16646075"
      />
      <itunes:title>Breaking the Loop: Why AI Agents Get Stuck</itunes:title>
      <itunes:subtitle>Is your AI agent a persistent genius or just stuck in a loop? Explore the technical and financial costs of autonomous stubbornness.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI models gain more "thinking time" through advanced reasoning chains, they are increasingly falling into recursive traps, attempting the same failing solutions until they exhaust compute budgets. This episode explores the "restart tax" and the 20% of enterprise compute currently wasted on agentic loops, diving into how new Model Context Protocol (MCP) servers act as digital circuit breakers. Discover why the most valuable human trait we can give an AI isn't infinite perseverance, but the self-awareness to know when it is time to stop and ask for help.]]></itunes:summary>
      <itunes:duration>1295</itunes:duration>
      <itunes:episode>1562</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-loop-persistence.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-loop-persistence.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Dark Knowledge: The Art of AI Model Distillation</title>
      <description><![CDATA[The era of massive parameter scaling is giving way to a new frontier: extreme efficiency. This episode explores the sophisticated world of model distillation, a process where a "student" model learns the nuanced "dark knowledge" and internal logic of a trillion-parameter "teacher." We break down the technical differences between distillation, fine-tuning, and quantization, while addressing why you cannot simply "lobotomize" a Mixture of Experts (MoE) architecture to make it smaller. From the economics of cloud compute to the privacy of edge AI, learn why the future of artificial intelligence is about cramming maximum reasoning into the smallest possible space.]]></description>
      <link>https://myweirdprompts.com/episode/ai-model-distillation-dark-knowledge/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-model-distillation-dark-knowledge/</guid>
      <pubDate>Thu, 26 Mar 2026 13:13:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-model-distillation-dark-knowledge.m4a"
        type="audio/mp4"
        length="16327298"
      />
      <itunes:title>Dark Knowledge: The Art of AI Model Distillation</itunes:title>
      <itunes:subtitle>Discover how model distillation transfers &quot;dark knowledge&quot; from massive AI giants into tiny, efficient models that live in your pocket.</itunes:subtitle>
      <itunes:summary><![CDATA[The era of massive parameter scaling is giving way to a new frontier: extreme efficiency. This episode explores the sophisticated world of model distillation, a process where a "student" model learns the nuanced "dark knowledge" and internal logic of a trillion-parameter "teacher." We break down the technical differences between distillation, fine-tuning, and quantization, while addressing why you cannot simply "lobotomize" a Mixture of Experts (MoE) architecture to make it smaller. From the economics of cloud compute to the privacy of edge AI, learn why the future of artificial intelligence is about cramming maximum reasoning into the smallest possible space.]]></itunes:summary>
      <itunes:duration>1244</itunes:duration>
      <itunes:episode>1559</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-model-distillation-dark-knowledge.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-model-distillation-dark-knowledge.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The War Against Latency: Engineering Real-Time AI</title>
      <description><![CDATA[Why does a 500-millisecond delay break the magic of AI? This episode explores the race to sub-100-millisecond response times, from NVIDIA's Rubin architecture to memory-saving tricks like PagedAttention, and how unified multimodal engines are replacing bolted-on models.]]></description>
      <link>https://myweirdprompts.com/episode/real-time-ai-latency-engineering/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/real-time-ai-latency-engineering/</guid>
      <pubDate>Thu, 26 Mar 2026 12:55:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/real-time-ai-latency-engineering.m4a"
        type="audio/mp4"
        length="18623347"
      />
      <itunes:title>The War Against Latency: Engineering Real-Time AI</itunes:title>
      <itunes:subtitle>From KV cache monsters to sub-100ms response times, explore the hardware and software innovations making real-time AI a reality.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does a 500-millisecond delay break the magic of AI? This episode explores the race to sub-100-millisecond response times, from NVIDIA's Rubin architecture to memory-saving tricks like PagedAttention, and how unified multimodal engines are replacing bolted-on models.]]></itunes:summary>
      <itunes:duration>1427</itunes:duration>
      <itunes:episode>1556</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/real-time-ai-latency-engineering.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/real-time-ai-latency-engineering.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond Whisper: NVIDIA’s Real-Time Speech Revolution</title>
      <description><![CDATA[For years, OpenAI’s Whisper has been the gold standard for speech-to-text, but its batch-processing architecture creates a "latency floor" that hinders real-time interaction. This episode explores NVIDIA’s aggressive move into the ASR space with the Parakeet and Canary models, which utilize FastConformer and Token-and-Duration Transducer (TDT) architectures to achieve near-instantaneous results. We dive into why developers are ditching Whisper for 10x speed gains, the shift toward local inference on Apple Silicon, and how these specialized models are finally making the "digital sandwich" posture a thing of the past.]]></description>
      <link>https://myweirdprompts.com/episode/nvidia-parakeet-speech-recognition/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/nvidia-parakeet-speech-recognition/</guid>
      <pubDate>Thu, 26 Mar 2026 12:35:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/nvidia-parakeet-speech-recognition.m4a"
        type="audio/mp4"
        length="15180141"
      />
      <itunes:title>Beyond Whisper: NVIDIA’s Real-Time Speech Revolution</itunes:title>
      <itunes:subtitle>Move over Whisper. NVIDIA&apos;s new models offer 10x speed increases and better accuracy for real-time speech-to-text.</itunes:subtitle>
      <itunes:summary><![CDATA[For years, OpenAI’s Whisper has been the gold standard for speech-to-text, but its batch-processing architecture creates a "latency floor" that hinders real-time interaction. This episode explores NVIDIA’s aggressive move into the ASR space with the Parakeet and Canary models, which utilize FastConformer and Token-and-Duration Transducer (TDT) architectures to achieve near-instantaneous results. We dive into why developers are ditching Whisper for 10x speed gains, the shift toward local inference on Apple Silicon, and how these specialized models are finally making the "digital sandwich" posture a thing of the past.]]></itunes:summary>
      <itunes:duration>1171</itunes:duration>
      <itunes:episode>1555</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/nvidia-parakeet-speech-recognition.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/nvidia-parakeet-speech-recognition.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The End of Secret Zero: Google Cloud Auth in 2026</title>
      <description><![CDATA[In this episode, we dive into the rapidly evolving landscape of Google Cloud authentication as of March 2026, where identity-based attacks have become the primary threat to modern web applications. We explore the death of the static JSON key, the mandatory shift toward PKCE for web flows, and how Workload Identity Federation is finally solving the "Secret Zero" paradox. From the latest Mandiant M-Trends report to the deprecation of legacy Sign-In SDKs, this is the essential survival guide for developers building in a world where if you have a key, you’ve already lost.]]></description>
      <link>https://myweirdprompts.com/episode/google-cloud-identity-security-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/google-cloud-identity-security-2026/</guid>
      <pubDate>Thu, 26 Mar 2026 02:39:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/google-cloud-identity-security-2026.m4a"
        type="audio/mp4"
        length="17107636"
      />
      <itunes:title>The End of Secret Zero: Google Cloud Auth in 2026</itunes:title>
      <itunes:subtitle>Static keys are digital landmines. Discover why identity is the new perimeter and how to master secretless authentication in Google Cloud.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we dive into the rapidly evolving landscape of Google Cloud authentication as of March 2026, where identity-based attacks have become the primary threat to modern web applications. We explore the death of the static JSON key, the mandatory shift toward PKCE for web flows, and how Workload Identity Federation is finally solving the "Secret Zero" paradox. From the latest Mandiant M-Trends report to the deprecation of legacy Sign-In SDKs, this is the essential survival guide for developers building in a world where if you have a key, you’ve already lost.]]></itunes:summary>
      <itunes:duration>1304</itunes:duration>
      <itunes:episode>1550</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/google-cloud-identity-security-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/google-cloud-identity-security-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Next GitHub Notification Could Be a Trap</title>
      <description><![CDATA[In this episode, we investigate a sophisticated surge in phishing attacks that are weaponizing the very tools developers trust most. By exploiting GitHub’s notification system—a technique known as "Living off Trusted Services" (LOTS)—attackers are bypassing enterprise security filters to deliver high-pressure "Emergency Action Alerts" directly to user inboxes. We dissect the "stellarwatchmanshow" campaign, which uses fabricated CVEs and academic personas like the "Neural Dynamics Lab" to trick users into downloading malicious patches from third-party sites. From mass-mentions in GitHub Discussions to the compromise of nearly 12,000 repositories in a single week, this episode explores the industrial scale of modern social engineering. We also discuss the ultimate goal of these strikes: harvesting "Secret Zero" credentials to poison the software supply chain. Learn the essential red flags to watch for and how to update your security playbook for an era where a "trusted sender" is no longer enough to guarantee safety.]]></description>
      <link>https://myweirdprompts.com/episode/github-notification-phishing-scams/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/github-notification-phishing-scams/</guid>
      <pubDate>Wed, 25 Mar 2026 19:07:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/github-notification-phishing-scams.m4a"
        type="audio/mp4"
        length="14177605"
      />
      <itunes:title>Why Your Next GitHub Notification Could Be a Trap</itunes:title>
      <itunes:subtitle>Attackers are weaponizing GitHub notifications to bypass security filters. Learn how to spot the latest phishing lures before you click.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we investigate a sophisticated surge in phishing attacks that are weaponizing the very tools developers trust most. By exploiting GitHub’s notification system—a technique known as "Living off Trusted Services" (LOTS)—attackers are bypassing enterprise security filters to deliver high-pressure "Emergency Action Alerts" directly to user inboxes. We dissect the "stellarwatchmanshow" campaign, which uses fabricated CVEs and academic personas like the "Neural Dynamics Lab" to trick users into downloading malicious patches from third-party sites. From mass-mentions in GitHub Discussions to the compromise of nearly 12,000 repositories in a single week, this episode explores the industrial scale of modern social engineering. We also discuss the ultimate goal of these strikes: harvesting "Secret Zero" credentials to poison the software supply chain. Learn the essential red flags to watch for and how to update your security playbook for an era where a "trusted sender" is no longer enough to guarantee safety.]]></itunes:summary>
      <itunes:duration>1076</itunes:duration>
      <itunes:episode>1549</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/github-notification-phishing-scams.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/github-notification-phishing-scams.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Infrastructure That Kills the Cold Start</title>
      <description><![CDATA[Serverless GPU computing promised instant scale but delivered agonizing waits. This episode explores how Modal rebuilt the container runtime and scheduler from scratch to slash cold starts from seconds to milliseconds—and what that means for the next generation of AI applications.]]></description>
      <link>https://myweirdprompts.com/episode/modal-serverless-gpu-performance/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/modal-serverless-gpu-performance/</guid>
      <pubDate>Wed, 25 Mar 2026 18:58:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/modal-serverless-gpu-performance.m4a"
        type="audio/mp4"
        length="16471608"
      />
      <itunes:title>The Infrastructure That Kills the Cold Start</itunes:title>
      <itunes:subtitle>Stop waiting for containers to warm up. Discover how Modal is reinventing GPU infrastructure to eliminate friction in AI development.</itunes:subtitle>
      <itunes:summary><![CDATA[Serverless GPU computing promised instant scale but delivered agonizing waits. This episode explores how Modal rebuilt the container runtime and scheduler from scratch to slash cold starts from seconds to milliseconds—and what that means for the next generation of AI applications.]]></itunes:summary>
      <itunes:duration>1256</itunes:duration>
      <itunes:episode>1548</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/modal-serverless-gpu-performance.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/modal-serverless-gpu-performance.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Death of Latency: Three Pillars of Modern Voice AI</title>
      <description><![CDATA[For years, interacting with AI felt like a clunky ritual—the "digital sandwich" posture of shouting into a phone and waiting for a response. But in March 2026, the latency gap is finally collapsing. This episode dives deep into the three architectural pillars of modern Automatic Speech Recognition (ASR): Connectionist Temporal Classification (CTC), Encoder-Decoder models, and Transducers. We explore how these technologies are converging to enable real-time, human-like conversations. We discuss the industry’s pivot from Word Error Rate to Semantic Word Error Rate, prioritizing intent over verbatim perfection. From NVIDIA’s lightning-fast Parakeet-CTC to Alibaba’s unified streaming frameworks and the efficiency of Token-and-Duration Transducers, discover the breakthroughs making the "latency tax" a thing of the past. Whether you're building autonomous agents or just curious about why your voice assistant is suddenly getting much faster, this deep dive covers the cutting-edge research and models defining the next era of voice interaction.]]></description>
      <link>https://myweirdprompts.com/episode/real-time-voice-ai-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/real-time-voice-ai-future/</guid>
      <pubDate>Wed, 25 Mar 2026 18:51:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/real-time-voice-ai-future.m4a"
        type="audio/mp4"
        length="23484548"
      />
      <itunes:title>The Death of Latency: Three Pillars of Modern Voice AI</itunes:title>
      <itunes:subtitle>Say goodbye to the &quot;digital sandwich.&quot; Explore the three architectural pillars closing the latency gap in modern speech recognition.</itunes:subtitle>
      <itunes:summary><![CDATA[For years, interacting with AI felt like a clunky ritual—the "digital sandwich" posture of shouting into a phone and waiting for a response. But in March 2026, the latency gap is finally collapsing. This episode dives deep into the three architectural pillars of modern Automatic Speech Recognition (ASR): Connectionist Temporal Classification (CTC), Encoder-Decoder models, and Transducers. We explore how these technologies are converging to enable real-time, human-like conversations. We discuss the industry’s pivot from Word Error Rate to Semantic Word Error Rate, prioritizing intent over verbatim perfection. From NVIDIA’s lightning-fast Parakeet-CTC to Alibaba’s unified streaming frameworks and the efficiency of Token-and-Duration Transducers, discover the breakthroughs making the "latency tax" a thing of the past. Whether you're building autonomous agents or just curious about why your voice assistant is suddenly getting much faster, this deep dive covers the cutting-edge research and models defining the next era of voice interaction.]]></itunes:summary>
      <itunes:duration>1501</itunes:duration>
      <itunes:episode>1546</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/real-time-voice-ai-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/real-time-voice-ai-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Gnome 50 is Breaking Your Voice-to-Text Tools</title>
      <description><![CDATA[We speak at 150 words per minute but type at 40, creating a massive "input gap" that modern AI aims to bridge through voice-to-text automation. However, on modern Linux systems like GNOME 50, the shift from X11 to Wayland has introduced significant security hurdles—often called "security through amputation"—that make automated input harder than ever for developers. This episode dives into the technical trade-offs between batch and streaming AI models, the "300ms magic number" for human-perceived latency, and how new protocols like libei are enabling context-aware, local inference without compromising digital sovereignty.]]></description>
      <link>https://myweirdprompts.com/episode/linux-ai-voice-input-engineering/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/linux-ai-voice-input-engineering/</guid>
      <pubDate>Wed, 25 Mar 2026 16:58:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/linux-ai-voice-input-engineering.m4a"
        type="audio/mp4"
        length="13333171"
      />
      <itunes:title>Why Gnome 50 is Breaking Your Voice-to-Text Tools</itunes:title>
      <itunes:subtitle>Explore the engineering battle to bring low-latency AI voice input to Linux while navigating the strict security of Wayland and GNOME 50.</itunes:subtitle>
      <itunes:summary><![CDATA[We speak at 150 words per minute but type at 40, creating a massive "input gap" that modern AI aims to bridge through voice-to-text automation. However, on modern Linux systems like GNOME 50, the shift from X11 to Wayland has introduced significant security hurdles—often called "security through amputation"—that make automated input harder than ever for developers. This episode dives into the technical trade-offs between batch and streaming AI models, the "300ms magic number" for human-perceived latency, and how new protocols like libei are enabling context-aware, local inference without compromising digital sovereignty.]]></itunes:summary>
      <itunes:duration>1022</itunes:duration>
      <itunes:episode>1540</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/linux-ai-voice-input-engineering.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/linux-ai-voice-input-engineering.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Escaping the Cloud Dictation Trap</title>
      <description><![CDATA[Why wait seconds for your words to appear? This episode explores how local neural processing and the Moonshine AI model finally make a dedicated voice keyboard feasible—killing the digital sandwich and keeping your data off the cloud.]]></description>
      <link>https://myweirdprompts.com/episode/voice-keyboard-hardware-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-keyboard-hardware-ai/</guid>
      <pubDate>Wed, 25 Mar 2026 16:54:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-keyboard-hardware-ai.m4a"
        type="audio/mp4"
        length="13499210"
      />
      <itunes:title>Escaping the Cloud Dictation Trap</itunes:title>
      <itunes:subtitle>Stop shouting at your phone. Discover how dedicated hardware and local AI are making instant, private voice-to-text a reality.</itunes:subtitle>
      <itunes:summary><![CDATA[Why wait seconds for your words to appear? This episode explores how local neural processing and the Moonshine AI model finally make a dedicated voice keyboard feasible—killing the digital sandwich and keeping your data off the cloud.]]></itunes:summary>
      <itunes:duration>1034</itunes:duration>
      <itunes:episode>1539</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-keyboard-hardware-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-keyboard-hardware-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Death of Vibecoding: AI as Your New Coding Mentor</title>
      <description><![CDATA[Are we building software we actually understand, or are we just "vibecoding" our way toward a massive collapse of technical debt? As AI agents evolve from simple autocomplete tools into autonomous architects, the software industry is hitting a critical crossroads. This episode explores the rise of pedagogical AI—tools designed to provide cognitive scaffolding rather than just finished blocks of code. We dive into recent research showing a 17% drop in skill mastery among developers using unguided AI and discuss how new platforms like Microsoft Agent Lightning and Google Antigravity are fighting back. By introducing "productive difficulty" and transparent decision logs, these agents are shifting the developer's role from a passive prompt-engineer to a high-level systems architect. Learn why the future of computer science education is moving away from syntax mastery and toward agentic reasoning, and how you can ensure you remain the smartest person in the room even when the machine is doing the heavy lifting.]]></description>
      <link>https://myweirdprompts.com/episode/vibecoding-pedagogical-ai-mentorship/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vibecoding-pedagogical-ai-mentorship/</guid>
      <pubDate>Wed, 25 Mar 2026 16:27:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vibecoding-pedagogical-ai-mentorship.m4a"
        type="audio/mp4"
        length="15223077"
      />
      <itunes:title>The Death of Vibecoding: AI as Your New Coding Mentor</itunes:title>
      <itunes:subtitle>Stop blindly prompting and start learning. Discover how pedagogical AI is turning code generation into a masterclass for developers.</itunes:subtitle>
      <itunes:summary><![CDATA[Are we building software we actually understand, or are we just "vibecoding" our way toward a massive collapse of technical debt? As AI agents evolve from simple autocomplete tools into autonomous architects, the software industry is hitting a critical crossroads. This episode explores the rise of pedagogical AI—tools designed to provide cognitive scaffolding rather than just finished blocks of code. We dive into recent research showing a 17% drop in skill mastery among developers using unguided AI and discuss how new platforms like Microsoft Agent Lightning and Google Antigravity are fighting back. By introducing "productive difficulty" and transparent decision logs, these agents are shifting the developer's role from a passive prompt-engineer to a high-level systems architect. Learn why the future of computer science education is moving away from syntax mastery and toward agentic reasoning, and how you can ensure you remain the smartest person in the room even when the machine is doing the heavy lifting.]]></itunes:summary>
      <itunes:duration>1166</itunes:duration>
      <itunes:episode>1535</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vibecoding-pedagogical-ai-mentorship.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vibecoding-pedagogical-ai-mentorship.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Terminal Trap: When Productivity Paranoia Becomes a Full-Time Job</title>
      <description><![CDATA[Why are developers drowning in terminal windows despite ever-smarter tools? This episode explores the cognitive friction of modern development environments and the quest for an interface that scales with complexity.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-terminal-development-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-terminal-development-evolution/</guid>
      <pubDate>Wed, 25 Mar 2026 16:20:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-terminal-development-evolution.m4a"
        type="audio/mp4"
        length="14818531"
      />
      <itunes:title>The Terminal Trap: When Productivity Paranoia Becomes a Full-Time Job</itunes:title>
      <itunes:subtitle>Stop drowning in terminal tabs. Discover how tools like Zellij and Ghostty are transforming the command line into an Agentic Development Environment.</itunes:subtitle>
      <itunes:summary><![CDATA[Why are developers drowning in terminal windows despite ever-smarter tools? This episode explores the cognitive friction of modern development environments and the quest for an interface that scales with complexity.]]></itunes:summary>
      <itunes:duration>1107</itunes:duration>
      <itunes:episode>1534</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-terminal-development-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-terminal-development-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Genius to Construction Crew: Orchestrating AI Swarms</title>
      <description><![CDATA[The era of the all-knowing AI model is ending. We explore the Agentic Mesh—a decentralized network of specialized agents—and the frameworks like LangGraph and Microsoft's Agent Framework that make it work, along with the unsettling risks of synthetic consensus and autonomous swarms.]]></description>
      <link>https://myweirdprompts.com/episode/ai-swarm-intelligence-orchestration/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-swarm-intelligence-orchestration/</guid>
      <pubDate>Wed, 25 Mar 2026 09:59:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-swarm-intelligence-orchestration.m4a"
        type="audio/mp4"
        length="15550190"
      />
      <itunes:title>From Genius to Construction Crew: Orchestrating AI Swarms</itunes:title>
      <itunes:subtitle>Move past simple prompts into the era of the Agentic Mesh, where hundreds of AI agents coordinate to solve complex, large-scale problems.</itunes:subtitle>
      <itunes:summary><![CDATA[The era of the all-knowing AI model is ending. We explore the Agentic Mesh—a decentralized network of specialized agents—and the frameworks like LangGraph and Microsoft's Agent Framework that make it work, along with the unsettling risks of synthetic consensus and autonomous swarms.]]></itunes:summary>
      <itunes:duration>1192</itunes:duration>
      <itunes:episode>1532</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-swarm-intelligence-orchestration.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-swarm-intelligence-orchestration.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Apache Way: Powering the Global Digital Backbone</title>
      <description><![CDATA[Behind almost every bank transaction and streaming service lies the Apache Software Foundation (ASF), a volunteer-run non-profit that manages over 320 active projects. In this episode, we go inside the "Apache Way" to understand how a meritocratic guild survives in a world of corporate giants. We dive into the massive architectural shifts in Kafka 4.1.2, the rise of native compute in Spark via Apache Gluten, and why the foundation acts as the "Switzerland" of the tech industry to prevent vendor lock-in.]]></description>
      <link>https://myweirdprompts.com/episode/apache-foundation-open-source-governance/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/apache-foundation-open-source-governance/</guid>
      <pubDate>Tue, 24 Mar 2026 01:24:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/apache-foundation-open-source-governance.m4a"
        type="audio/mp4"
        length="17285378"
      />
      <itunes:title>The Apache Way: Powering the Global Digital Backbone</itunes:title>
      <itunes:subtitle>Explore how the Apache Software Foundation governs the world&apos;s most critical data tools and why &quot;Community Over Code&quot; is the secret to its success.</itunes:subtitle>
      <itunes:summary><![CDATA[Behind almost every bank transaction and streaming service lies the Apache Software Foundation (ASF), a volunteer-run non-profit that manages over 320 active projects. In this episode, we go inside the "Apache Way" to understand how a meritocratic guild survives in a world of corporate giants. We dive into the massive architectural shifts in Kafka 4.1.2, the rise of native compute in Spark via Apache Gluten, and why the foundation acts as the "Switzerland" of the tech industry to prevent vendor lock-in.]]></itunes:summary>
      <itunes:duration>1337</itunes:duration>
      <itunes:episode>1506</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/apache-foundation-open-source-governance.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/apache-foundation-open-source-governance.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Columnar Databases Crush Row-Based for Big Data</title>
      <description><![CDATA[Explore the fundamental difference between OLTP and OLAP databases through the lens of GDELT's 2.5 billion records. Learn why columnar storage and parallel processing let you query petabytes in seconds, and how this architecture is transforming data analysis.]]></description>
      <link>https://myweirdprompts.com/episode/bigquery-gdelt-ai-analysis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/bigquery-gdelt-ai-analysis/</guid>
      <pubDate>Tue, 24 Mar 2026 01:17:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/bigquery-gdelt-ai-analysis.m4a"
        type="audio/mp4"
        length="13136395"
      />
      <itunes:title>Why Columnar Databases Crush Row-Based for Big Data</itunes:title>
      <itunes:subtitle>Discover how Google BigQuery and the GDELT project allow researchers to analyze billions of global news records using real-time AI and SQL.</itunes:subtitle>
      <itunes:summary><![CDATA[Explore the fundamental difference between OLTP and OLAP databases through the lens of GDELT's 2.5 billion records. Learn why columnar storage and parallel processing let you query petabytes in seconds, and how this architecture is transforming data analysis.]]></itunes:summary>
      <itunes:duration>983</itunes:duration>
      <itunes:episode>1505</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/bigquery-gdelt-ai-analysis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/bigquery-gdelt-ai-analysis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Great AI Divergence: How Models Specialized in 2026</title>
      <description><![CDATA[The era of the chatbot is over. This episode maps the architectural split between GPT-5.4, Gemini 3.1, and Claude 4.6, revealing how each model's design philosophy now dictates the logic of your workflows.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-substrate-model-comparison/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-substrate-model-comparison/</guid>
      <pubDate>Tue, 24 Mar 2026 00:28:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-substrate-model-comparison.m4a"
        type="audio/mp4"
        length="17290594"
      />
      <itunes:title>The Great AI Divergence: How Models Specialized in 2026</itunes:title>
      <itunes:subtitle>The era of the chatbot is over. Discover how the &quot;agentic substrate&quot; of 2026 is redefining computing through GPT, Gemini, and Claude.</itunes:subtitle>
      <itunes:summary><![CDATA[The era of the chatbot is over. This episode maps the architectural split between GPT-5.4, Gemini 3.1, and Claude 4.6, revealing how each model's design philosophy now dictates the logic of your workflows.]]></itunes:summary>
      <itunes:duration>1338</itunes:duration>
      <itunes:episode>1500</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-substrate-model-comparison.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-substrate-model-comparison.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can Fictional Twins Save AI From Running Out of Internet?</title>
      <description><![CDATA[The industry has hit a "data wall" where the supply of human-curated text is flatlining, forcing a massive shift toward machine-generated training material. This episode explores how synthetic data has moved from a research curiosity to the primary infrastructure of AI, now accounting for 75% of enterprise training data. We discuss the transition from destructive data masking to high-utility synthetic "twins," the use of physical AI factories to simulate rare real-world scenarios, and the emergence of agent-driven "synthetic textbooks" that allow large models to train smaller, more efficient versions of themselves. We also address the looming risks of "Model Collapse" and the governance challenges of managing automated data at an industrial scale.]]></description>
      <link>https://myweirdprompts.com/episode/synthetic-data-ai-training/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/synthetic-data-ai-training/</guid>
      <pubDate>Mon, 23 Mar 2026 13:37:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/synthetic-data-ai-training.m4a"
        type="audio/mp4"
        length="13762019"
      />
      <itunes:title>Can Fictional Twins Save AI From Running Out of Internet?</itunes:title>
      <itunes:subtitle>As high-quality human data runs dry, synthetic data is becoming the new gold standard for training the next generation of AI models.</itunes:subtitle>
      <itunes:summary><![CDATA[The industry has hit a "data wall" where the supply of human-curated text is flatlining, forcing a massive shift toward machine-generated training material. This episode explores how synthetic data has moved from a research curiosity to the primary infrastructure of AI, now accounting for 75% of enterprise training data. We discuss the transition from destructive data masking to high-utility synthetic "twins," the use of physical AI factories to simulate rare real-world scenarios, and the emergence of agent-driven "synthetic textbooks" that allow large models to train smaller, more efficient versions of themselves. We also address the looming risks of "Model Collapse" and the governance challenges of managing automated data at an industrial scale.]]></itunes:summary>
      <itunes:duration>1040</itunes:duration>
      <itunes:episode>1495</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/synthetic-data-ai-training.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/synthetic-data-ai-training.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Recall-Per-Dollar Era: Mastering Vector Database Tuning</title>
      <description><![CDATA[The dream of the self-driving database has met the cold reality of cloud infrastructure bills, forcing a shift from "set it and forget it" indexing to a new era of high-stakes architectural orchestration. This episode goes under the hood of modern vector engines like Qdrant, Milvus, and Pinecone to explore why manual tuning remains the only way to achieve production-grade performance without bankrupting your organization. We break down the mathematical trade-offs between distance metrics and the memory-heavy physics of HNSW graph parameters, providing a roadmap for navigating the "recall-per-dollar" requirements of the new VectorBench 2.0 standards.]]></description>
      <link>https://myweirdprompts.com/episode/vector-recall-per-dollar-tuning/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vector-recall-per-dollar-tuning/</guid>
      <pubDate>Mon, 23 Mar 2026 11:15:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vector-recall-per-dollar-tuning.m4a"
        type="audio/mp4"
        length="20280178"
      />
      <itunes:title>The Recall-Per-Dollar Era: Mastering Vector Database Tuning</itunes:title>
      <itunes:subtitle>Stop burning money on unoptimized vector searches. We dive into HNSW tuning, distance metrics, and the vital &quot;recall-per-dollar&quot; metric.</itunes:subtitle>
      <itunes:summary><![CDATA[The dream of the self-driving database has met the cold reality of cloud infrastructure bills, forcing a shift from "set it and forget it" indexing to a new era of high-stakes architectural orchestration. This episode goes under the hood of modern vector engines like Qdrant, Milvus, and Pinecone to explore why manual tuning remains the only way to achieve production-grade performance without bankrupting your organization. We break down the mathematical trade-offs between distance metrics and the memory-heavy physics of HNSW graph parameters, providing a roadmap for navigating the "recall-per-dollar" requirements of the new VectorBench 2.0 standards.]]></itunes:summary>
      <itunes:duration>1576</itunes:duration>
      <itunes:episode>1483</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vector-recall-per-dollar-tuning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vector-recall-per-dollar-tuning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Cost of Choosing an Embedding Model</title>
      <description><![CDATA[Why picking the wrong vector model today could mean a massive migration bill tomorrow. This episode explores the strategic risks of architectural lock-in in the era of multimodal embeddings, from Matryoshka representations to benchmark contamination.]]></description>
      <link>https://myweirdprompts.com/episode/multimodal-vector-embedding-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multimodal-vector-embedding-evolution/</guid>
      <pubDate>Mon, 23 Mar 2026 11:13:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multimodal-vector-embedding-evolution.m4a"
        type="audio/mp4"
        length="16968726"
      />
      <itunes:title>The Hidden Cost of Choosing an Embedding Model</itunes:title>
      <itunes:subtitle>From Matryoshka models to multimodal search, discover how the fundamental units of AI memory are being optimized for efficiency and scale.</itunes:subtitle>
      <itunes:summary><![CDATA[Why picking the wrong vector model today could mean a massive migration bill tomorrow. This episode explores the strategic risks of architectural lock-in in the era of multimodal embeddings, from Matryoshka representations to benchmark contamination.]]></itunes:summary>
      <itunes:duration>1276</itunes:duration>
      <itunes:episode>1482</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multimodal-vector-embedding-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multimodal-vector-embedding-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The End of the AI Chat Sidebar</title>
      <description><![CDATA[Why the era of simple AI chat sidebars is over. This episode explores how tools like Cursor and Claude Code use persistent, agentic systems to navigate million-line codebases, and what that means for the future of software development.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-repository-engineering-mechanics/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-repository-engineering-mechanics/</guid>
      <pubDate>Mon, 23 Mar 2026 11:04:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-repository-engineering-mechanics.m4a"
        type="audio/mp4"
        length="14682021"
      />
      <itunes:title>The End of the AI Chat Sidebar</itunes:title>
      <itunes:subtitle>Discover how tools like Cursor and Claude Code use Merkle trees and knowledge graphs to master massive codebases with surgical precision.</itunes:subtitle>
      <itunes:summary><![CDATA[Why the era of simple AI chat sidebars is over. This episode explores how tools like Cursor and Claude Code use persistent, agentic systems to navigate million-line codebases, and what that means for the future of software development.]]></itunes:summary>
      <itunes:duration>1122</itunes:duration>
      <itunes:episode>1481</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-repository-engineering-mechanics.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-repository-engineering-mechanics.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The AI Firewall: Securing the New Enterprise Perimeter</title>
      <description><![CDATA[In just two years, AI has evolved from a corporate curiosity into a primary material risk for the majority of S&P 500 companies. This episode explores the critical shift toward "Agentic AI" and the necessary emergence of the AI Gateway—a sophisticated middleware layer that acts as a lead-lined room for autonomous systems. We dive into the technical mechanics of real-time PII redaction, the failure of system prompts as security measures, and how new tools from NVIDIA and CrowdStrike are providing the "Technical Truth" required by upcoming global regulations. Learn why the industry is moving away from model-native safety in favor of external, context-based access controls that can stop a data breach before it even starts.]]></description>
      <link>https://myweirdprompts.com/episode/ai-firewall-enterprise-security/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-firewall-enterprise-security/</guid>
      <pubDate>Mon, 23 Mar 2026 10:40:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-firewall-enterprise-security.m4a"
        type="audio/mp4"
        length="17118311"
      />
      <itunes:title>The AI Firewall: Securing the New Enterprise Perimeter</itunes:title>
      <itunes:subtitle>As AI agents get the keys to the castle, how do we stop data leaks? Explore the rise of the AI gateway and the new era of agentic security.</itunes:subtitle>
      <itunes:summary><![CDATA[In just two years, AI has evolved from a corporate curiosity into a primary material risk for the majority of S&P 500 companies. This episode explores the critical shift toward "Agentic AI" and the necessary emergence of the AI Gateway—a sophisticated middleware layer that acts as a lead-lined room for autonomous systems. We dive into the technical mechanics of real-time PII redaction, the failure of system prompts as security measures, and how new tools from NVIDIA and CrowdStrike are providing the "Technical Truth" required by upcoming global regulations. Learn why the industry is moving away from model-native safety in favor of external, context-based access controls that can stop a data breach before it even starts.]]></itunes:summary>
      <itunes:duration>1304</itunes:duration>
      <itunes:episode>1476</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-firewall-enterprise-security.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-firewall-enterprise-security.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Folder Illusion: Why Cloud Storage Breaks Your Mental Model</title>
      <description><![CDATA[Object storage like S3 looks like folders but works nothing like them. This episode unpacks the POSIX vs. object storage divide, the end of global bucket naming, and why the 'folder' you see is a comforting lie.]]></description>
      <link>https://myweirdprompts.com/episode/object-storage-cloud-filesystem-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/object-storage-cloud-filesystem-evolution/</guid>
      <pubDate>Mon, 23 Mar 2026 10:40:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/object-storage-cloud-filesystem-evolution.m4a"
        type="audio/mp4"
        length="15247942"
      />
      <itunes:title>The Folder Illusion: Why Cloud Storage Breaks Your Mental Model</itunes:title>
      <itunes:subtitle>Folders are a lie in the cloud. Explore why Amazon S3 uses flat namespaces and &quot;keys&quot; instead of traditional file hierarchies.</itunes:subtitle>
      <itunes:summary><![CDATA[Object storage like S3 looks like folders but works nothing like them. This episode unpacks the POSIX vs. object storage divide, the end of global bucket naming, and why the 'folder' you see is a comforting lie.]]></itunes:summary>
      <itunes:duration>1165</itunes:duration>
      <itunes:episode>1475</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/object-storage-cloud-filesystem-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/object-storage-cloud-filesystem-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The End of API Keys: Securing Non-Human Identity</title>
      <description><![CDATA[In this episode, we tackle the "Secret Zero" paradox: the security nightmare of static API keys in an automated world. With AI assistants doubling the rate of credential leaks and malware targeting developer environments, the old way of managing secrets is broken. We explore the shift toward Non-Human Identity (NHI) and how frameworks like SPIFFE and SPIRE allow machines to prove who they are without a single hardcoded password. Whether you're a developer using AI tools or a security engineer, this deep dive into workload identity federation is essential for modern architecture.]]></description>
      <link>https://myweirdprompts.com/episode/non-human-identity-secrets/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/non-human-identity-secrets/</guid>
      <pubDate>Mon, 23 Mar 2026 10:35:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/non-human-identity-secrets.m4a"
        type="audio/mp4"
        length="18917048"
      />
      <itunes:title>The End of API Keys: Securing Non-Human Identity</itunes:title>
      <itunes:subtitle>Stop leaving your digital keys under the mat. Learn how workload identity federation is replacing the dangerous &quot;secret management grind.&quot;</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we tackle the "Secret Zero" paradox: the security nightmare of static API keys in an automated world. With AI assistants doubling the rate of credential leaks and malware targeting developer environments, the old way of managing secrets is broken. We explore the shift toward Non-Human Identity (NHI) and how frameworks like SPIFFE and SPIRE allow machines to prove who they are without a single hardcoded password. Whether you're a developer using AI tools or a security engineer, this deep dive into workload identity federation is essential for modern architecture.]]></itunes:summary>
      <itunes:duration>1448</itunes:duration>
      <itunes:episode>1474</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/non-human-identity-secrets.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/non-human-identity-secrets.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Stop Flying Your AI Agents Blind</title>
      <description><![CDATA[In this episode, we explore the critical shift from simple LLM monitoring to the complex world of agentic observability. As AI moves from basic chatbots to autonomous agents capable of multi-step reasoning and real-world actions, the stakes have shifted from simple helpfulness to financial and operational security. We dive into the latest tools—from OpenTelemetry-native frameworks to deterministic DAG metrics—that are helping engineers monitor the "thought" process and "action layer" of AI to prevent runaway loops and data leaks.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-observability-ai-monitoring/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-observability-ai-monitoring/</guid>
      <pubDate>Mon, 23 Mar 2026 10:22:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-observability-ai-monitoring.m4a"
        type="audio/mp4"
        length="15941807"
      />
      <itunes:title>Stop Flying Your AI Agents Blind</itunes:title>
      <itunes:subtitle>Move past basic token counting. Learn how to monitor AI reasoning, prevent $47k loops, and build trust in autonomous agents.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we explore the critical shift from simple LLM monitoring to the complex world of agentic observability. As AI moves from basic chatbots to autonomous agents capable of multi-step reasoning and real-world actions, the stakes have shifted from simple helpfulness to financial and operational security. We dive into the latest tools—from OpenTelemetry-native frameworks to deterministic DAG metrics—that are helping engineers monitor the "thought" process and "action layer" of AI to prevent runaway loops and data leaks.]]></itunes:summary>
      <itunes:duration>1206</itunes:duration>
      <itunes:episode>1472</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-observability-ai-monitoring.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-observability-ai-monitoring.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Cursor Incident: Why Chinese AI Models are Winning</title>
      <description><![CDATA[When the world's leading AI coding tool was caught using a Chinese model under the hood, it signaled a massive shift in the global tech landscape. This episode explores the "Big Four" Chinese AI labs—DeepSeek, Moonshot, Zhipu, and MiniMax—and why their focus on mathematical efficiency and hardware sovereignty is closing the gap with Silicon Valley. We break down the architectural breakthroughs like Multi-head Latent Attention and prefix caching that make these models up to 20 times cheaper than their Western counterparts without sacrificing performance. Are we witnessing the end of the closed-API era? Tune in to find out which models are best for agents, long-context coding, and high-stakes reasoning.]]></description>
      <link>https://myweirdprompts.com/episode/chinese-ai-labs-power-shift/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/chinese-ai-labs-power-shift/</guid>
      <pubDate>Mon, 23 Mar 2026 10:22:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/chinese-ai-labs-power-shift.m4a"
        type="audio/mp4"
        length="17233430"
      />
      <itunes:title>The Cursor Incident: Why Chinese AI Models are Winning</itunes:title>
      <itunes:subtitle>The Cursor leak revealed a shocking truth: Western AI dominance is fading. Discover the Chinese labs rewriting the rules of code and efficiency.</itunes:subtitle>
      <itunes:summary><![CDATA[When the world's leading AI coding tool was caught using a Chinese model under the hood, it signaled a massive shift in the global tech landscape. This episode explores the "Big Four" Chinese AI labs—DeepSeek, Moonshot, Zhipu, and MiniMax—and why their focus on mathematical efficiency and hardware sovereignty is closing the gap with Silicon Valley. We break down the architectural breakthroughs like Multi-head Latent Attention and prefix caching that make these models up to 20 times cheaper than their Western counterparts without sacrificing performance. Are we witnessing the end of the closed-API era? Tune in to find out which models are best for agents, long-context coding, and high-stakes reasoning.]]></itunes:summary>
      <itunes:duration>1313</itunes:duration>
      <itunes:episode>1471</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/chinese-ai-labs-power-shift.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/chinese-ai-labs-power-shift.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Agents Author 4% of All Code</title>
      <description><![CDATA[How did autonomous AI agents go from zero to authoring 4% of all public GitHub commits in just eighteen months? This episode unpacks the architecture behind Claude Code's agentic harness and what it means for the future of software development.]]></description>
      <link>https://myweirdprompts.com/episode/claude-code-agentic-harness/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/claude-code-agentic-harness/</guid>
      <pubDate>Mon, 23 Mar 2026 00:13:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/claude-code-agentic-harness.m4a"
        type="audio/mp4"
        length="13478424"
      />
      <itunes:title>When AI Agents Author 4% of All Code</itunes:title>
      <itunes:subtitle>Explore how agentic harnesses transform AI from a passive chatbot into an active developer capable of full-cycle software engineering.</itunes:subtitle>
      <itunes:summary><![CDATA[How did autonomous AI agents go from zero to authoring 4% of all public GitHub commits in just eighteen months? This episode unpacks the architecture behind Claude Code's agentic harness and what it means for the future of software development.]]></itunes:summary>
      <itunes:duration>1007</itunes:duration>
      <itunes:episode>1464</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/claude-code-agentic-harness.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/claude-code-agentic-harness.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Law School for Robots: Building AI Governance Stacks</title>
      <description><![CDATA[As AI agents transition from simple chatbots to autonomous fiduciaries capable of moving capital and signing contracts, the industry is facing a critical challenge: how do we ensure these systems act within safe boundaries? This episode explores the shift from basic prompt engineering to "policy engineering" and the emergence of the Governance Stack. We dive into the March 2026 NIST guidelines on AI agent risk management and discuss why traditional system prompts are no longer enough to prevent catastrophic financial or legal errors. By implementing hierarchical document structures—comprising Constitutions, Bylaws, and Operating Guidelines—developers can create a more robust framework for machine reasoning. We also examine the technical architecture required to enforce these rules, including Retrieval-Augmented Generation (RAG) for policy fetching and the rise of "Auditor Agents" that serve as a digital check-and-balance system. Whether you are building autonomous trading bots or automated procurement systems, understanding how to encode human judgment into machine-verifiable constraints is the next great frontier in AI development.]]></description>
      <link>https://myweirdprompts.com/episode/governance-stack-autonomous-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/governance-stack-autonomous-agents/</guid>
      <pubDate>Sun, 22 Mar 2026 13:35:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/governance-stack-autonomous-agents.m4a"
        type="audio/mp4"
        length="16966479"
      />
      <itunes:title>Law School for Robots: Building AI Governance Stacks</itunes:title>
      <itunes:subtitle>Discover how tiered policy structures and &quot;Auditor Agents&quot; are replacing simple prompts to manage high-stakes AI decision-making.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI agents transition from simple chatbots to autonomous fiduciaries capable of moving capital and signing contracts, the industry is facing a critical challenge: how do we ensure these systems act within safe boundaries? This episode explores the shift from basic prompt engineering to "policy engineering" and the emergence of the Governance Stack. We dive into the March 2026 NIST guidelines on AI agent risk management and discuss why traditional system prompts are no longer enough to prevent catastrophic financial or legal errors. By implementing hierarchical document structures—comprising Constitutions, Bylaws, and Operating Guidelines—developers can create a more robust framework for machine reasoning. We also examine the technical architecture required to enforce these rules, including Retrieval-Augmented Generation (RAG) for policy fetching and the rise of "Auditor Agents" that serve as a digital check-and-balance system. Whether you are building autonomous trading bots or automated procurement systems, understanding how to encode human judgment into machine-verifiable constraints is the next great frontier in AI development.]]></itunes:summary>
      <itunes:duration>1315</itunes:duration>
      <itunes:episode>1448</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/governance-stack-autonomous-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/governance-stack-autonomous-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The AI Rulebook: Programming Agents in Plain English</title>
      <description><![CDATA[As AI agents move beyond simple chat interfaces, developers are adopting a new programming paradigm: the persistent rulebook. This episode explores how structured natural language files are becoming the "constitutions" for autonomous agents, defining everything from architectural styles to specific tool-use logic. We examine the friction between deterministic logic and probabilistic models, the technical hurdles of instruction drift, and the emerging need for automated "logic police" to validate English-based code.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-rulebook-programming/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-rulebook-programming/</guid>
      <pubDate>Sun, 22 Mar 2026 13:32:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-rulebook-programming.m4a"
        type="audio/mp4"
        length="18255075"
      />
      <itunes:title>The AI Rulebook: Programming Agents in Plain English</itunes:title>
      <itunes:subtitle>Explore the shift from &quot;chatting&quot; to &quot;constitutions&quot; as developers use structured English to build reliable AI agent workflows.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI agents move beyond simple chat interfaces, developers are adopting a new programming paradigm: the persistent rulebook. This episode explores how structured natural language files are becoming the "constitutions" for autonomous agents, defining everything from architectural styles to specific tool-use logic. We examine the friction between deterministic logic and probabilistic models, the technical hurdles of instruction drift, and the emerging need for automated "logic police" to validate English-based code.]]></itunes:summary>
      <itunes:duration>1384</itunes:duration>
      <itunes:episode>1447</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-rulebook-programming.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-rulebook-programming.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can One Million LLMs Predict the Next Global Crisis?</title>
      <description><![CDATA[In this episode, we explore the revolutionary world of MiroFish, a viral open-source engine capable of simulating one million autonomous AI agents. Built by an undergraduate student using "vibe coding," this project is transforming how we understand social dynamics, polarization, and geopolitical wargaming. We dive deep into the technical architecture—from the OASIS framework to Neo4j graph databases—and discuss how these LLM-powered agents with distinct "personalities" and long-term "memories" can predict 90-day sentiment trajectories for real-world events. From analyzing potential conflicts in the Middle East to observing digital uprisings, MiroFish represents a massive shift from traditional rule-based modeling to emergent, agentic intelligence. We discuss the implications for military planners, the risks of model bias, and why the barrier to high-fidelity social simulation has just collapsed. This is a look at the future of predictive modeling where a million digital experts replace human guesswork.]]></description>
      <link>https://myweirdprompts.com/episode/mirofish-million-agent-simulation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mirofish-million-agent-simulation/</guid>
      <pubDate>Fri, 20 Mar 2026 18:34:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mirofish-million-agent-simulation.m4a"
        type="audio/mp4"
        length="20001686"
      />
      <itunes:title>Can One Million LLMs Predict the Next Global Crisis?</itunes:title>
      <itunes:subtitle>Discover how an undergraduate student built a viral simulation of one million AI agents to predict social behavior and policy outcomes.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we explore the revolutionary world of MiroFish, a viral open-source engine capable of simulating one million autonomous AI agents. Built by an undergraduate student using "vibe coding," this project is transforming how we understand social dynamics, polarization, and geopolitical wargaming. We dive deep into the technical architecture—from the OASIS framework to Neo4j graph databases—and discuss how these LLM-powered agents with distinct "personalities" and long-term "memories" can predict 90-day sentiment trajectories for real-world events. From analyzing potential conflicts in the Middle East to observing digital uprisings, MiroFish represents a massive shift from traditional rule-based modeling to emergent, agentic intelligence. We discuss the implications for military planners, the risks of model bias, and why the barrier to high-fidelity social simulation has just collapsed. This is a look at the future of predictive modeling where a million digital experts replace human guesswork.]]></itunes:summary>
      <itunes:duration>1523</itunes:duration>
      <itunes:episode>1407</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mirofish-million-agent-simulation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mirofish-million-agent-simulation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Giving AI a Brain: The Power of Knowledge Graphs</title>
      <description><![CDATA[Large language models are often dismissed as "stochastic parrots," but a major shift in AI architecture is changing that narrative. This episode explores the rise of Knowledge Graphs and Graph-RAG, moving past the limitations of simple vector searches toward true multi-hop reasoning. We dive into how industry giants like Merck and Bayer are using these structured logical maps to solve complex biological problems and how developers are applying the same principles to master massive codebases. Discover why the "cost cliff" of graph technology has finally vanished, making high-precision AI memory and verifiable accuracy accessible to startups and enterprises alike.]]></description>
      <link>https://myweirdprompts.com/episode/ai-memory-knowledge-graphs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-memory-knowledge-graphs/</guid>
      <pubDate>Fri, 20 Mar 2026 18:33:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-memory-knowledge-graphs.m4a"
        type="audio/mp4"
        length="19109057"
      />
      <itunes:title>Giving AI a Brain: The Power of Knowledge Graphs</itunes:title>
      <itunes:subtitle>Move beyond &quot;stochastic parrots&quot; with Knowledge Graphs. Discover how structured data is giving AI the logical backbone it needs to reason.</itunes:subtitle>
      <itunes:summary><![CDATA[Large language models are often dismissed as "stochastic parrots," but a major shift in AI architecture is changing that narrative. This episode explores the rise of Knowledge Graphs and Graph-RAG, moving past the limitations of simple vector searches toward true multi-hop reasoning. We dive into how industry giants like Merck and Bayer are using these structured logical maps to solve complex biological problems and how developers are applying the same principles to master massive codebases. Discover why the "cost cliff" of graph technology has finally vanished, making high-precision AI memory and verifiable accuracy accessible to startups and enterprises alike.]]></itunes:summary>
      <itunes:duration>1474</itunes:duration>
      <itunes:episode>1406</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-memory-knowledge-graphs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-memory-knowledge-graphs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The AI Attribution Paradox: Normalizing the Ghostwriter</title>
      <description><![CDATA[As AI tools become ubiquitous in software development and creative fields, a strange phenomenon has emerged: the AI Attribution Paradox. While nearly all developers report massive productivity gains from AI, only a fraction are willing to credit the machine in their work. This episode explores the deep-seated "competence stigma" that prevents professionals from being transparent about their workflows and the fear that AI assistance equates to personal incompetence. We examine the diverging philosophies of tools like GitHub Copilot and Claude Code, the rise of technical standards like AIMark, and the impending legal requirements of the EU AI Act. From the halls of academia to open-source repositories, the rules of authorship are being rewritten. We discuss how to move past "AI shaming" and toward a future where being an effective "orchestrator" of AI is valued as much as traditional solo creation.]]></description>
      <link>https://myweirdprompts.com/episode/ai-attribution-ethics-coding/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-attribution-ethics-coding/</guid>
      <pubDate>Mon, 16 Mar 2026 21:37:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-attribution-ethics-coding.m4a"
        type="audio/mp4"
        length="17543385"
      />
      <itunes:title>The AI Attribution Paradox: Normalizing the Ghostwriter</itunes:title>
      <itunes:subtitle>Why do 70% of developers hide their AI use? Explore the &quot;competence stigma&quot; and the emerging rules for radical transparency in an AI-driven world.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI tools become ubiquitous in software development and creative fields, a strange phenomenon has emerged: the AI Attribution Paradox. While nearly all developers report massive productivity gains from AI, only a fraction are willing to credit the machine in their work. This episode explores the deep-seated "competence stigma" that prevents professionals from being transparent about their workflows and the fear that AI assistance equates to personal incompetence. We examine the diverging philosophies of tools like GitHub Copilot and Claude Code, the rise of technical standards like AIMark, and the impending legal requirements of the EU AI Act. From the halls of academia to open-source repositories, the rules of authorship are being rewritten. We discuss how to move past "AI shaming" and toward a future where being an effective "orchestrator" of AI is valued as much as traditional solo creation.]]></itunes:summary>
      <itunes:duration>1353</itunes:duration>
      <itunes:episode>1308</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-attribution-ethics-coding.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-attribution-ethics-coding.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Is Your AI Thinking Too Much?</title>
      <description><![CDATA[We are currently witnessing a wave of "agentic inflation," where simple software tasks are being replaced by complex, non-deterministic autonomous loops. This episode explores the "agentic tax"—the hidden toll of latency, token waste, and unpredictable failures that occur when developers prioritize AI autonomy over sound engineering principles. We break down the crucial difference between procedural workflows and agentic reasoning, offering a framework for when to use LLMs as specialized workers rather than autonomous managers. Discover how to identify the "context window trap" and apply the Rule of Three to ensure your AI architecture remains efficient, scalable, and cost-effective.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agentic-tax-costs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agentic-tax-costs/</guid>
      <pubDate>Mon, 16 Mar 2026 18:52:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agentic-tax-costs.m4a"
        type="audio/mp4"
        length="16193018"
      />
      <itunes:title>Is Your AI Thinking Too Much?</itunes:title>
      <itunes:subtitle>Stop building Rube Goldberg machines. Learn why autonomous AI agents might be the highest-interest technical debt in your stack.</itunes:subtitle>
      <itunes:summary><![CDATA[We are currently witnessing a wave of "agentic inflation," where simple software tasks are being replaced by complex, non-deterministic autonomous loops. This episode explores the "agentic tax"—the hidden toll of latency, token waste, and unpredictable failures that occur when developers prioritize AI autonomy over sound engineering principles. We break down the crucial difference between procedural workflows and agentic reasoning, offering a framework for when to use LLMs as specialized workers rather than autonomous managers. Discover how to identify the "context window trap" and apply the Rule of Three to ensure your AI architecture remains efficient, scalable, and cost-effective.]]></itunes:summary>
      <itunes:duration>1231</itunes:duration>
      <itunes:episode>1283</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agentic-tax-costs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agentic-tax-costs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Obeys the Developer Instead of You</title>
      <description><![CDATA[Most users see a blank chat window, but behind the scenes, a complex system of "invisible stage directions" dictates every response an AI provides. This episode explores the evolution of system prompts from simple text strings to high-stakes architectural entities involving logit biasing and Mixture of Experts routing. We analyze why models occasionally "forget" their instructions and how engineers are building a mathematical backbone to ensure AI remains a servant rather than a wildcard.]]></description>
      <link>https://myweirdprompts.com/episode/ai-system-prompt-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-system-prompt-architecture/</guid>
      <pubDate>Mon, 16 Mar 2026 16:19:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-system-prompt-architecture.m4a"
        type="audio/mp4"
        length="17496620"
      />
      <itunes:title>Why AI Obeys the Developer Instead of You</itunes:title>
      <itunes:subtitle>Discover the hidden &quot;plumbing&quot; of AI system prompts and how architectural shifts are turning simple instructions into hard-coded laws.</itunes:subtitle>
      <itunes:summary><![CDATA[Most users see a blank chat window, but behind the scenes, a complex system of "invisible stage directions" dictates every response an AI provides. This episode explores the evolution of system prompts from simple text strings to high-stakes architectural entities involving logit biasing and Mixture of Experts routing. We analyze why models occasionally "forget" their instructions and how engineers are building a mathematical backbone to ensure AI remains a servant rather than a wildcard.]]></itunes:summary>
      <itunes:duration>1347</itunes:duration>
      <itunes:episode>1279</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-system-prompt-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-system-prompt-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Military Data Pipelines Mirror Tech Startups</title>
      <description><![CDATA[How the military's shift from siloed legacy systems to unified, event-driven architectures mirrors the data engineering challenges of high-growth startups—and what it means for the future of warfare.]]></description>
      <link>https://myweirdprompts.com/episode/military-data-engineering-pipelines/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/military-data-engineering-pipelines/</guid>
      <pubDate>Mon, 16 Mar 2026 14:19:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/military-data-engineering-pipelines.m4a"
        type="audio/mp4"
        length="16140233"
      />
      <itunes:title>When Military Data Pipelines Mirror Tech Startups</itunes:title>
      <itunes:subtitle>Explore how modern military command structures are evolving into high-stakes data pipelines using Kafka, ETL, and edge computing.</itunes:subtitle>
      <itunes:summary><![CDATA[How the military's shift from siloed legacy systems to unified, event-driven architectures mirrors the data engineering challenges of high-growth startups—and what it means for the future of warfare.]]></itunes:summary>
      <itunes:duration>1231</itunes:duration>
      <itunes:episode>1277</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/military-data-engineering-pipelines.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/military-data-engineering-pipelines.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Save Button: The Git-ification of Everything</title>
      <description><![CDATA[Move beyond the chaos of manual file naming and embrace the "Git-ification" of your professional life. This episode explores how the principles of software version control—including commits, diffs, and branching—are being applied to technical documentation, project management, and competitive intelligence. We dive into how treating work as a series of atomic changes rather than static files creates an immutable, auditable, and highly collaborative environment that eliminates the "single point of failure" in corporate knowledge.]]></description>
      <link>https://myweirdprompts.com/episode/gitification-non-code-workflows/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gitification-non-code-workflows/</guid>
      <pubDate>Mon, 16 Mar 2026 11:04:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gitification-non-code-workflows.m4a"
        type="audio/mp4"
        length="14951062"
      />
      <itunes:title>Beyond the Save Button: The Git-ification of Everything</itunes:title>
      <itunes:subtitle>Tired of &quot;final_v2_revised.docx&quot;? Discover how Git-based workflows are transforming documentation, project management, and competitive research.</itunes:subtitle>
      <itunes:summary><![CDATA[Move beyond the chaos of manual file naming and embrace the "Git-ification" of your professional life. This episode explores how the principles of software version control—including commits, diffs, and branching—are being applied to technical documentation, project management, and competitive intelligence. We dive into how treating work as a series of atomic changes rather than static files creates an immutable, auditable, and highly collaborative environment that eliminates the "single point of failure" in corporate knowledge.]]></itunes:summary>
      <itunes:duration>1139</itunes:duration>
      <itunes:episode>1267</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gitification-non-code-workflows.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gitification-non-code-workflows.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Ghost Flights and Legacy Code: Why Travel Tech is Broken</title>
      <description><![CDATA[Behind the sleek interface of your favorite travel app lies a fractured world of 1960s mainframes, cryptic UN-standardized messaging protocols, and a mountain of technical debt that makes modern flight booking a digital ghost hunt. This episode explores the "Big Three" Global Distribution Systems—Amadeus, Sabre, and Travelport—uncovering how decades-old Transaction Processing Facilities still dictate the price and availability of every seat in the sky. From the rise of the New Distribution Capability (NDC) and the "Dual Track API Tax" to the hidden complexities of interlining agreements and the "Look-to-Book" caching traps that cause prices to vanish at checkout, we break down why building in travel tech remains one of the most difficult engineering challenges in the world today.]]></description>
      <link>https://myweirdprompts.com/episode/travel-booking-legacy-tech/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/travel-booking-legacy-tech/</guid>
      <pubDate>Sun, 15 Mar 2026 16:41:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/travel-booking-legacy-tech.m4a"
        type="audio/mp4"
        length="23974301"
      />
      <itunes:title>Ghost Flights and Legacy Code: Why Travel Tech is Broken</itunes:title>
      <itunes:subtitle>Ever wondered why that flight vanished while booking? Explore the 1960s mainframes and cryptic protocols holding the travel industry together.</itunes:subtitle>
      <itunes:summary><![CDATA[Behind the sleek interface of your favorite travel app lies a fractured world of 1960s mainframes, cryptic UN-standardized messaging protocols, and a mountain of technical debt that makes modern flight booking a digital ghost hunt. This episode explores the "Big Three" Global Distribution Systems—Amadeus, Sabre, and Travelport—uncovering how decades-old Transaction Processing Facilities still dictate the price and availability of every seat in the sky. From the rise of the New Distribution Capability (NDC) and the "Dual Track API Tax" to the hidden complexities of interlining agreements and the "Look-to-Book" caching traps that cause prices to vanish at checkout, we break down why building in travel tech remains one of the most difficult engineering challenges in the world today.]]></itunes:summary>
      <itunes:duration>1864</itunes:duration>
      <itunes:episode>1237</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/travel-booking-legacy-tech.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/travel-booking-legacy-tech.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Life-Saving Alerts Still Use XML</title>
      <description><![CDATA[Japan's J-Alert and Israel's Red Alert rely on decades-old XML standards and hardware data diodes, not modern apps. This episode explores why the most high-stakes notifications are built on the least trendy tech—and what that means for reliability engineering.]]></description>
      <link>https://myweirdprompts.com/episode/mission-critical-alerting-systems/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mission-critical-alerting-systems/</guid>
      <pubDate>Sun, 15 Mar 2026 16:32:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mission-critical-alerting-systems.m4a"
        type="audio/mp4"
        length="20719784"
      />
      <itunes:title>Why Life-Saving Alerts Still Use XML</itunes:title>
      <itunes:subtitle>From earthquake warnings to missile alerts, discover the high-stakes engineering that powers the world’s most reliable notification systems.</itunes:subtitle>
      <itunes:summary><![CDATA[Japan's J-Alert and Israel's Red Alert rely on decades-old XML standards and hardware data diodes, not modern apps. This episode explores why the most high-stakes notifications are built on the least trendy tech—and what that means for reliability engineering.]]></itunes:summary>
      <itunes:duration>1607</itunes:duration>
      <itunes:episode>1236</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mission-critical-alerting-systems.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mission-critical-alerting-systems.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond &quot;No Training&quot;: Securing the New Agentic AI Stack</title>
      <description><![CDATA[As we move from simple chatbots to autonomous agents with long-term memory, the standard "we do not train on your data" marketing promise is no longer a sufficient guarantee of enterprise security. This episode deconstructs the "agentic stack," revealing how sensitive information flows through vector databases, orchestration layers, and observability tools that often lack the rigorous protections of the base model providers. By examining the technical shift from stateless interactions to stateful relationships, we uncover why your data is arguably more at risk in 2026 than ever before, while providing a concrete audit framework to help developers protect their infrastructure from leaks, vector inversion, and unauthorized access.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ai-data-privacy-risks/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ai-data-privacy-risks/</guid>
      <pubDate>Sun, 15 Mar 2026 16:15:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ai-data-privacy-risks.m4a"
        type="audio/mp4"
        length="23705020"
      />
      <itunes:title>Beyond &quot;No Training&quot;: Securing the New Agentic AI Stack</itunes:title>
      <itunes:subtitle>Think your data is safe because of a &quot;no training&quot; clause? We deconstruct the hidden security risks within the modern agentic AI stack.</itunes:subtitle>
      <itunes:summary><![CDATA[As we move from simple chatbots to autonomous agents with long-term memory, the standard "we do not train on your data" marketing promise is no longer a sufficient guarantee of enterprise security. This episode deconstructs the "agentic stack," revealing how sensitive information flows through vector databases, orchestration layers, and observability tools that often lack the rigorous protections of the base model providers. By examining the technical shift from stateless interactions to stateful relationships, we uncover why your data is arguably more at risk in 2026 than ever before, while providing a concrete audit framework to help developers protect their infrastructure from leaks, vector inversion, and unauthorized access.]]></itunes:summary>
      <itunes:duration>1857</itunes:duration>
      <itunes:episode>1235</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ai-data-privacy-risks.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ai-data-privacy-risks.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Hashing Fails: Building Context-Aware Redaction Pipelines</title>
      <description><![CDATA[Traditional data masking and hashing can't protect against quasi-identifiers in 2026. This episode explores the architecture of modern redaction pipelines that maintain data utility while scrubbing PII from structured and unstructured data.]]></description>
      <link>https://myweirdprompts.com/episode/pii-anonymization-data-lakes/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/pii-anonymization-data-lakes/</guid>
      <pubDate>Sun, 15 Mar 2026 16:13:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/pii-anonymization-data-lakes.m4a"
        type="audio/mp4"
        length="24201453"
      />
      <itunes:title>Why Hashing Fails: Building Context-Aware Redaction Pipelines</itunes:title>
      <itunes:subtitle>Learn how to bridge the &quot;anonymization gap&quot; and protect sensitive data without destroying its utility for analysis.</itunes:subtitle>
      <itunes:summary><![CDATA[Traditional data masking and hashing can't protect against quasi-identifiers in 2026. This episode explores the architecture of modern redaction pipelines that maintain data utility while scrubbing PII from structured and unstructured data.]]></itunes:summary>
      <itunes:duration>1882</itunes:duration>
      <itunes:episode>1234</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/pii-anonymization-data-lakes.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/pii-anonymization-data-lakes.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why &quot;Just Use Postgres&quot; Isn&apos;t Always Enough</title>
      <description><![CDATA[In this episode of My Weird Prompts, we dive into the "Just Use Postgres" movement and ask a critical question: is the era of specialized databases finally over? While Postgres has become a Swiss Army knife for modern engineering, physical constraints and hardware architecture eventually force a divide between transactional and analytical workloads. We break down the fundamental differences between row-based and columnar storage, explaining why your "Ferrari" database might melt if you try to use it like a "dump truck" for big data. From the power of vectorized execution and SIMD instructions to the complexities of real-time data pipelines using Change Data Capture (CDC) and Apache Kafka, we explore how giants like Netflix manage massive data scales. Whether you are a minimalist developer or a data architect, this deep dive into the internal geometry of databases will change how you think about your tech stack.]]></description>
      <link>https://myweirdprompts.com/episode/postgres-vs-specialized-databases/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/postgres-vs-specialized-databases/</guid>
      <pubDate>Sun, 15 Mar 2026 16:01:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/postgres-vs-specialized-databases.m4a"
        type="audio/mp4"
        length="19204379"
      />
      <itunes:title>Why &quot;Just Use Postgres&quot; Isn&apos;t Always Enough</itunes:title>
      <itunes:subtitle>Can one database do it all? Explore why hardware constraints and data geometry keep specialized databases like Snowflake and ClickHouse alive.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, we dive into the "Just Use Postgres" movement and ask a critical question: is the era of specialized databases finally over? While Postgres has become a Swiss Army knife for modern engineering, physical constraints and hardware architecture eventually force a divide between transactional and analytical workloads. We break down the fundamental differences between row-based and columnar storage, explaining why your "Ferrari" database might melt if you try to use it like a "dump truck" for big data. From the power of vectorized execution and SIMD instructions to the complexities of real-time data pipelines using Change Data Capture (CDC) and Apache Kafka, we explore how giants like Netflix manage massive data scales. Whether you are a minimalist developer or a data architect, this deep dive into the internal geometry of databases will change how you think about your tech stack.]]></itunes:summary>
      <itunes:duration>1506</itunes:duration>
      <itunes:episode>1233</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/postgres-vs-specialized-databases.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/postgres-vs-specialized-databases.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agentic Shift: 5 Bold AI Predictions for 2026</title>
      <description><![CDATA[Forget the plateau—AI development is entering a transformative new phase where raw benchmarks matter less than agentic reliability and execution. In this episode, we move past "prediction debt" to deliver specific, falsifiable milestones for the end of 2026, ranging from self-correcting code to massive model distillation. Discover why the transition from fast intuition to deliberate reasoning will redefine how we interact with technology, moving us toward a world of autonomous, interoperable agents that live on our local devices.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-predictions-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-predictions-2026/</guid>
      <pubDate>Sun, 15 Mar 2026 15:43:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-predictions-2026.m4a"
        type="audio/mp4"
        length="19807657"
      />
      <itunes:title>The Agentic Shift: 5 Bold AI Predictions for 2026</itunes:title>
      <itunes:subtitle>The Poppleberry brothers move past the chatbot era to deliver five high-stakes, falsifiable predictions for the future of autonomous AI agents.</itunes:subtitle>
      <itunes:summary><![CDATA[Forget the plateau—AI development is entering a transformative new phase where raw benchmarks matter less than agentic reliability and execution. In this episode, we move past "prediction debt" to deliver specific, falsifiable milestones for the end of 2026, ranging from self-correcting code to massive model distillation. Discover why the transition from fast intuition to deliberate reasoning will redefine how we interact with technology, moving us toward a world of autonomous, interoperable agents that live on our local devices.]]></itunes:summary>
      <itunes:duration>1514</itunes:duration>
      <itunes:episode>1231</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-predictions-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-predictions-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The 39 Million Leak: Why Your .env File Is a Pinky-Promise</title>
      <description><![CDATA[Why do 39 million secrets leak annually, and how can developers move beyond fragile .env files? This episode traces the maturity progression from hardcoded strings to zero-trust infrastructure, revealing the automated bots that harvest credentials in seconds.]]></description>
      <link>https://myweirdprompts.com/episode/secrets-management-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/secrets-management-evolution/</guid>
      <pubDate>Sun, 15 Mar 2026 15:33:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/secrets-management-evolution.m4a"
        type="audio/mp4"
        length="16940041"
      />
      <itunes:title>The 39 Million Leak: Why Your .env File Is a Pinky-Promise</itunes:title>
      <itunes:subtitle>Stop relying on &quot;security by pinky-promise.&quot; Learn how to move from messy .env files to professional zero-trust secrets management.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do 39 million secrets leak annually, and how can developers move beyond fragile .env files? This episode traces the maturity progression from hardcoded strings to zero-trust infrastructure, revealing the automated bots that harvest credentials in seconds.]]></itunes:summary>
      <itunes:duration>1285</itunes:duration>
      <itunes:episode>1229</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/secrets-management-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/secrets-management-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The $30 Billion Blog Post: Can AI Finally Kill COBOL?</title>
      <description><![CDATA[In early 2026, a technical announcement from Anthropic triggered a massive market sell-off for IBM, proving that a 60-year-old programming language still dictates global financial stability. This episode explores the "load-bearing walls" of the global economy—the 220 billion lines of COBOL that power everything from ATMs to tax systems—and why its unique decimal precision makes it nearly impossible to replace. We dive into the brewing war between AI-driven "big bang" migrations and the incremental reality of maintaining the world’s most critical legacy infrastructure.]]></description>
      <link>https://myweirdprompts.com/episode/cobol-ai-modernization-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cobol-ai-modernization-future/</guid>
      <pubDate>Sun, 15 Mar 2026 15:28:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cobol-ai-modernization-future.m4a"
        type="audio/mp4"
        length="14409025"
      />
      <itunes:title>The $30 Billion Blog Post: Can AI Finally Kill COBOL?</itunes:title>
      <itunes:subtitle>A single blog post wiped $30 billion off IBM’s value. Discover why the world’s oldest code still runs our banks and if AI can finally replace it.</itunes:subtitle>
      <itunes:summary><![CDATA[In early 2026, a technical announcement from Anthropic triggered a massive market sell-off for IBM, proving that a 60-year-old programming language still dictates global financial stability. This episode explores the "load-bearing walls" of the global economy—the 220 billion lines of COBOL that power everything from ATMs to tax systems—and why its unique decimal precision makes it nearly impossible to replace. We dive into the brewing war between AI-driven "big bang" migrations and the incremental reality of maintaining the world’s most critical legacy infrastructure.]]></itunes:summary>
      <itunes:duration>1107</itunes:duration>
      <itunes:episode>1228</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cobol-ai-modernization-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cobol-ai-modernization-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Mojo 1.0: Can Chris Lattner Fix the AI Performance Gap?</title>
      <description><![CDATA[For years, AI developers have been forced to navigate a fractured world: writing high-level logic in the approachable syntax of Python, while relying on the complex, low-level power of C++ or CUDA for performance. Mojo, the ambitious new language from LLVM creator Chris Lattner and the team at Modular, promises to finally bridge this gap. By functioning as a superset of Python that speaks directly to the hardware, Mojo aims to provide the speed of "the metal" without sacrificing developer productivity. This episode explores the technical foundations of Mojo, including the Multi-Level Intermediate Representation (MLIR) and the crucial distinction between dynamic "def" and strictly-typed "fn" keywords. We also tackle the "35,000x speedup" marketing claims, contrasting them with the more modest but still transformative 2-10x gains seen in production environments. From the "Lattner Factor" to the strategic attempt to dismantle the CUDA moat, we analyze whether Mojo 1.0 is ready to become the new standard for the AI era.]]></description>
      <link>https://myweirdprompts.com/episode/mojo-ai-programming-performance/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mojo-ai-programming-performance/</guid>
      <pubDate>Sun, 15 Mar 2026 15:23:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mojo-ai-programming-performance.m4a"
        type="audio/mp4"
        length="14769247"
      />
      <itunes:title>Mojo 1.0: Can Chris Lattner Fix the AI Performance Gap?</itunes:title>
      <itunes:subtitle>Explore how Mojo aims to unify Python’s ease of use with C++ performance to solve the &quot;two-language problem&quot; in AI development.</itunes:subtitle>
      <itunes:summary><![CDATA[For years, AI developers have been forced to navigate a fractured world: writing high-level logic in the approachable syntax of Python, while relying on the complex, low-level power of C++ or CUDA for performance. Mojo, the ambitious new language from LLVM creator Chris Lattner and the team at Modular, promises to finally bridge this gap. By functioning as a superset of Python that speaks directly to the hardware, Mojo aims to provide the speed of "the metal" without sacrificing developer productivity. This episode explores the technical foundations of Mojo, including the Multi-Level Intermediate Representation (MLIR) and the crucial distinction between dynamic "def" and strictly-typed "fn" keywords. We also tackle the "35,000x speedup" marketing claims, contrasting them with the more modest but still transformative 2-10x gains seen in production environments. From the "Lattner Factor" to the strategic attempt to dismantle the CUDA moat, we analyze whether Mojo 1.0 is ready to become the new standard for the AI era.]]></itunes:summary>
      <itunes:duration>1106</itunes:duration>
      <itunes:episode>1227</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mojo-ai-programming-performance.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mojo-ai-programming-performance.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Coders Made TypeScript Unstoppable</title>
      <description><![CDATA[TypeScript is now the most-used language on GitHub, driven by its symbiotic relationship with AI coding assistants. This episode explores how type safety became essential for the AI application layer and what it means for the future of web development.]]></description>
      <link>https://myweirdprompts.com/episode/typescript-web-development-ai-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/typescript-web-development-ai-future/</guid>
      <pubDate>Sun, 15 Mar 2026 15:13:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/typescript-web-development-ai-future.m4a"
        type="audio/mp4"
        length="15693190"
      />
      <itunes:title>Why AI Coders Made TypeScript Unstoppable</itunes:title>
      <itunes:subtitle>Explore how TypeScript became the world’s top language and why it’s now the essential glue for modern AI applications.</itunes:subtitle>
      <itunes:summary><![CDATA[TypeScript is now the most-used language on GitHub, driven by its symbiotic relationship with AI coding assistants. This episode explores how type safety became essential for the AI application layer and what it means for the future of web development.]]></itunes:summary>
      <itunes:duration>1192</itunes:duration>
      <itunes:episode>1225</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/typescript-web-development-ai-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/typescript-web-development-ai-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Cracking the CUDA Code: NVIDIA’s Software Dominance</title>
      <description><![CDATA[While the world focuses on NVIDIA’s powerful H100 and Blackwell chips, the real secret to their market dominance is CUDA—a proprietary software layer two decades in the making. This episode explores why this "invisible" language has become the industry standard, making it incredibly difficult for rivals like AMD and Intel to gain a foothold despite impressive hardware specs. We break down the technical complexities of GPU programming, the power of specialized libraries, and the emergence of hardware-agnostic compilers like OpenAI’s Triton that could finally level the playing field for the entire AI ecosystem.]]></description>
      <link>https://myweirdprompts.com/episode/nvidia-cuda-software-moat/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/nvidia-cuda-software-moat/</guid>
      <pubDate>Sun, 15 Mar 2026 15:12:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/nvidia-cuda-software-moat.m4a"
        type="audio/mp4"
        length="15501619"
      />
      <itunes:title>Cracking the CUDA Code: NVIDIA’s Software Dominance</itunes:title>
      <itunes:subtitle>Discover why NVIDIA’s CUDA is the oxygen of the AI industry and how tools like OpenAI’s Triton are finally challenging its 20-year software moat.</itunes:subtitle>
      <itunes:summary><![CDATA[While the world focuses on NVIDIA’s powerful H100 and Blackwell chips, the real secret to their market dominance is CUDA—a proprietary software layer two decades in the making. This episode explores why this "invisible" language has become the industry standard, making it incredibly difficult for rivals like AMD and Intel to gain a foothold despite impressive hardware specs. We break down the technical complexities of GPU programming, the power of specialized libraries, and the emergence of hardware-agnostic compilers like OpenAI’s Triton that could finally level the playing field for the entire AI ecosystem.]]></itunes:summary>
      <itunes:duration>1164</itunes:duration>
      <itunes:episode>1224</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/nvidia-cuda-software-moat.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/nvidia-cuda-software-moat.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Compiler as Truth Machine</title>
      <description><![CDATA[Why the Rust compiler is uniquely positioned to catch AI hallucinations and enforce memory safety, turning high-risk rewrites into automated, high-confidence sprints.]]></description>
      <link>https://myweirdprompts.com/episode/ai-rust-refactoring-revolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-rust-refactoring-revolution/</guid>
      <pubDate>Sun, 15 Mar 2026 15:01:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-rust-refactoring-revolution.m4a"
        type="audio/mp4"
        length="19279325"
      />
      <itunes:title>The Compiler as Truth Machine</itunes:title>
      <itunes:subtitle>Discover how AI agents and the Rust &quot;truth machine&quot; are transforming legacy code into high-performance, memory-safe infrastructure.</itunes:subtitle>
      <itunes:summary><![CDATA[Why the Rust compiler is uniquely positioned to catch AI hallucinations and enforce memory safety, turning high-risk rewrites into automated, high-confidence sprints.]]></itunes:summary>
      <itunes:duration>1484</itunes:duration>
      <itunes:episode>1222</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-rust-refactoring-revolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-rust-refactoring-revolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond Migrations: Breaking the SQL Straitjacket with AI</title>
      <description><![CDATA[For decades, database migrations have been the ultimate bottleneck in software development—a manual, high-stakes process that often acts as a straitjacket for new ideas. In this episode, we explore how AI agents like Claude Code are achieving staggering success rates in automating these transformations, shifting the developer’s focus from imperative instructions to declarative intent. We dive into the radical concept of the ephemeral migration hypothesis, where permanent historical records are replaced by automated state auditing, and discuss whether the future of data storage is a dream of efficiency or a nightmare of schema drift.]]></description>
      <link>https://myweirdprompts.com/episode/ai-database-schema-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-database-schema-evolution/</guid>
      <pubDate>Sun, 15 Mar 2026 14:57:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-database-schema-evolution.m4a"
        type="audio/mp4"
        length="17416309"
      />
      <itunes:title>Beyond Migrations: Breaking the SQL Straitjacket with AI</itunes:title>
      <itunes:subtitle>Stop writing manual SQL migrations. Explore how AI agents are transforming the database from a rigid &quot;straitjacket&quot; into a flexible, evolving state.</itunes:subtitle>
      <itunes:summary><![CDATA[For decades, database migrations have been the ultimate bottleneck in software development—a manual, high-stakes process that often acts as a straitjacket for new ideas. In this episode, we explore how AI agents like Claude Code are achieving staggering success rates in automating these transformations, shifting the developer’s focus from imperative instructions to declarative intent. We dive into the radical concept of the ephemeral migration hypothesis, where permanent historical records are replaced by automated state auditing, and discuss whether the future of data storage is a dream of efficiency or a nightmare of schema drift.]]></itunes:summary>
      <itunes:duration>1337</itunes:duration>
      <itunes:episode>1221</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-database-schema-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-database-schema-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why You Can&apos;t Give an AI the Database Password</title>
      <description><![CDATA[Why can't you just hand an AI agent the keys to your Postgres database? This episode explores the fundamental reasons APIs exist—security, stability, and semantics—and why direct database access is a dangerous siren song for autonomous agents.]]></description>
      <link>https://myweirdprompts.com/episode/api-evolution-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/api-evolution-ai-agents/</guid>
      <pubDate>Sun, 15 Mar 2026 14:50:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/api-evolution-ai-agents.m4a"
        type="audio/mp4"
        length="17292005"
      />
      <itunes:title>Why You Can&apos;t Give an AI the Database Password</itunes:title>
      <itunes:subtitle>Why can&apos;t we just give AI the database password? Explore the shift from REST to GraphQL and how the Model Context Protocol changes the game.</itunes:subtitle>
      <itunes:summary><![CDATA[Why can't you just hand an AI agent the keys to your Postgres database? This episode explores the fundamental reasons APIs exist—security, stability, and semantics—and why direct database access is a dangerous siren song for autonomous agents.]]></itunes:summary>
      <itunes:duration>1315</itunes:duration>
      <itunes:episode>1220</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/api-evolution-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/api-evolution-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Vibes: Mastering Structured AI Outputs</title>
      <description><![CDATA[Tired of LLMs adding conversational filler to your data? This episode explores the technical shift from prompt-based formatting to API-level strict enforcement. We dive into the mechanics of constrained decoding, the evolution of JSON Schema standards, and why libraries like Pydantic are essential for modern AI development. Discover how to use semantic field names and property ordering to improve model reasoning while ensuring 100% schema compliance across OpenAI, Gemini, and Anthropic.]]></description>
      <link>https://myweirdprompts.com/episode/structured-ai-outputs-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/structured-ai-outputs-guide/</guid>
      <pubDate>Sun, 15 Mar 2026 14:47:29 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/structured-ai-outputs-guide.m4a"
        type="audio/mp4"
        length="16446520"
      />
      <itunes:title>Beyond the Vibes: Mastering Structured AI Outputs</itunes:title>
      <itunes:subtitle>Stop begging your AI for JSON. Learn how constrained decoding and strict schemas are turning &quot;vibes&quot; into reliable systems architecture.</itunes:subtitle>
      <itunes:summary><![CDATA[Tired of LLMs adding conversational filler to your data? This episode explores the technical shift from prompt-based formatting to API-level strict enforcement. We dive into the mechanics of constrained decoding, the evolution of JSON Schema standards, and why libraries like Pydantic are essential for modern AI development. Discover how to use semantic field names and property ordering to improve model reasoning while ensuring 100% schema compliance across OpenAI, Gemini, and Anthropic.]]></itunes:summary>
      <itunes:duration>1267</itunes:duration>
      <itunes:episode>1219</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/structured-ai-outputs-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/structured-ai-outputs-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Missing Ring Zero: Why LLMs Can&apos;t Keep Secrets</title>
      <description><![CDATA[Why can't we just tell an AI to keep its instructions secret? This episode explores the fundamental architectural flaw—the lack of hardware-enforced privilege separation—that makes system prompt leakage inevitable, and what developers can do about it.]]></description>
      <link>https://myweirdprompts.com/episode/system-prompt-leakage-security/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/system-prompt-leakage-security/</guid>
      <pubDate>Sun, 15 Mar 2026 14:28:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/system-prompt-leakage-security.m4a"
        type="audio/mp4"
        length="16225575"
      />
      <itunes:title>The Missing Ring Zero: Why LLMs Can&apos;t Keep Secrets</itunes:title>
      <itunes:subtitle>Discover why AI models leak their secret instructions and how to defend your intellectual property using modern prompt hardening techniques.</itunes:subtitle>
      <itunes:summary><![CDATA[Why can't we just tell an AI to keep its instructions secret? This episode explores the fundamental architectural flaw—the lack of hardware-enforced privilege separation—that makes system prompt leakage inevitable, and what developers can do about it.]]></itunes:summary>
      <itunes:duration>1247</itunes:duration>
      <itunes:episode>1217</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/system-prompt-leakage-security.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/system-prompt-leakage-security.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Vector DB Hangover: Scaling Without Going Broke</title>
      <description><![CDATA[The "gold rush" of vector databases has ended, replaced by a cold reality of high monthly bills and resource constraints. In this episode, we dive into the true cost of vector storage in 2026, comparing the "RAM tax" of high-performance engines like Qdrant against the cost-saving "mmap" strategies that make $20 servers viable for million-vector indexes. We explore the architectural challenges of serverless frontends, the emergence of HTTP-native providers like Turbopuffer, and why Postgres with pgvector remains the "good enough" king for most developers. Whether you are building a hobby project on Cloudflare or a massive enterprise index, this guide covers the critical trade-offs between latency, hardware, and the bottom line.]]></description>
      <link>https://myweirdprompts.com/episode/vector-database-cost-optimization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vector-database-cost-optimization/</guid>
      <pubDate>Sun, 15 Mar 2026 14:19:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vector-database-cost-optimization.m4a"
        type="audio/mp4"
        length="16942763"
      />
      <itunes:title>The Vector DB Hangover: Scaling Without Going Broke</itunes:title>
      <itunes:subtitle>Stop overpaying for your AI&apos;s memory. We break down the math of self-hosting vectors and the rise of serverless search.</itunes:subtitle>
      <itunes:summary><![CDATA[The "gold rush" of vector databases has ended, replaced by a cold reality of high monthly bills and resource constraints. In this episode, we dive into the true cost of vector storage in 2026, comparing the "RAM tax" of high-performance engines like Qdrant against the cost-saving "mmap" strategies that make $20 servers viable for million-vector indexes. We explore the architectural challenges of serverless frontends, the emergence of HTTP-native providers like Turbopuffer, and why Postgres with pgvector remains the "good enough" king for most developers. Whether you are building a hobby project on Cloudflare or a massive enterprise index, this guide covers the critical trade-offs between latency, hardware, and the bottom line.]]></itunes:summary>
      <itunes:duration>1306</itunes:duration>
      <itunes:episode>1215</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vector-database-cost-optimization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vector-database-cost-optimization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Is Your JSON Store Just a Postgres Feature Now?</title>
      <description><![CDATA[Fifteen years after the NoSQL revolution promised to kill the relational database, SQL remains the undisputed industry standard. This episode explores the technical and business reasons why "schema-on-read" flexibility often led to operational debt, and how PostgreSQL eventually neutralized the NoSQL threat by adopting its best features. We also dive into the modern database landscape, discussing the impact of MongoDB’s licensing shifts, the rise of open-source alternatives like FerretDB, and why document stores have become a vital "utility player" for developers building AI-driven applications and vector search pipelines.]]></description>
      <link>https://myweirdprompts.com/episode/mongodb-sql-ai-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mongodb-sql-ai-evolution/</guid>
      <pubDate>Sun, 15 Mar 2026 14:10:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mongodb-sql-ai-evolution.m4a"
        type="audio/mp4"
        length="16479932"
      />
      <itunes:title>Is Your JSON Store Just a Postgres Feature Now?</itunes:title>
      <itunes:subtitle>Explore why the NoSQL revolution didn&apos;t kill SQL and how document databases are finding a new second life in the era of AI and RAG pipelines.</itunes:subtitle>
      <itunes:summary><![CDATA[Fifteen years after the NoSQL revolution promised to kill the relational database, SQL remains the undisputed industry standard. This episode explores the technical and business reasons why "schema-on-read" flexibility often led to operational debt, and how PostgreSQL eventually neutralized the NoSQL threat by adopting its best features. We also dive into the modern database landscape, discussing the impact of MongoDB’s licensing shifts, the rise of open-source alternatives like FerretDB, and why document stores have become a vital "utility player" for developers building AI-driven applications and vector search pipelines.]]></itunes:summary>
      <itunes:duration>1269</itunes:duration>
      <itunes:episode>1213</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mongodb-sql-ai-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mongodb-sql-ai-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Postgres Vector Revolution: Killing the Sprawl</title>
      <description><![CDATA[The rise of AI has sparked a massive gold rush for dedicated vector databases like Pinecone and Weaviate, but the answer to your infrastructure woes might already be sitting in your tech stack. In this episode, we dive into the fascinating history of PostgreSQL and how a design decision made in 1986 paved the way for the modern AI revolution. We explore the "pgvector" extension, comparing its performance against specialized players and explaining why the "one-stack" approach is often superior for real-world applications. From the technical wizardry of HNSW indexing to the critical importance of ACID compliance and hybrid search, we break down why the database sprawl is ending. Whether you are building a small RAG pipeline or scaling to millions of vectors, learn how Postgres is proving that specialized isn't always faster, and why simplicity is the ultimate architectural advantage.]]></description>
      <link>https://myweirdprompts.com/episode/postgres-pgvector-ai-infrastructure/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/postgres-pgvector-ai-infrastructure/</guid>
      <pubDate>Sun, 15 Mar 2026 14:07:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/postgres-pgvector-ai-infrastructure.m4a"
        type="audio/mp4"
        length="16201238"
      />
      <itunes:title>The Postgres Vector Revolution: Killing the Sprawl</itunes:title>
      <itunes:subtitle>Is your tech stack a sprawling suburb of microservices? Discover why a 40-year-old database is winning the AI infrastructure war.</itunes:subtitle>
      <itunes:summary><![CDATA[The rise of AI has sparked a massive gold rush for dedicated vector databases like Pinecone and Weaviate, but the answer to your infrastructure woes might already be sitting in your tech stack. In this episode, we dive into the fascinating history of PostgreSQL and how a design decision made in 1986 paved the way for the modern AI revolution. We explore the "pgvector" extension, comparing its performance against specialized players and explaining why the "one-stack" approach is often superior for real-world applications. From the technical wizardry of HNSW indexing to the critical importance of ACID compliance and hybrid search, we break down why the database sprawl is ending. Whether you are building a small RAG pipeline or scaling to millions of vectors, learn how Postgres is proving that specialized isn't always faster, and why simplicity is the ultimate architectural advantage.]]></itunes:summary>
      <itunes:duration>1257</itunes:duration>
      <itunes:episode>1212</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/postgres-pgvector-ai-infrastructure.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/postgres-pgvector-ai-infrastructure.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Escaping JOIN Hell: The SQL Developer’s Guide to Neo4j</title>
      <description><![CDATA[Are your SQL queries buckling under the weight of complex relationships and fifteen-deep JOINs? In this episode, we explore the fundamental shift from relational tables to Neo4j’s graph model, breaking down why "index-free adjacency" is a total game-changer for multi-hop traversals and systemic connections. We move past the hype to examine the practical realities of "relationship intelligence" in 2026, comparing the rigid structure of SQL rows to the flexible, schema-optional nature of nodes and edges. Learn how to identify the "JOIN hell" scenarios where a graph database becomes a necessity rather than a gimmick, and discover the power of the hybrid architecture pattern. By piping transactional data from Postgres into a graph "sidecar" via Change Data Capture, you can maintain ACID compliance while gaining the ability to spot digital patterns in milliseconds. Whether you are a SQL veteran or a curious architect, this guide provides the mental model shift needed to navigate the future of connected data.]]></description>
      <link>https://myweirdprompts.com/episode/sql-to-neo4j-transition-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/sql-to-neo4j-transition-guide/</guid>
      <pubDate>Sun, 15 Mar 2026 14:00:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/sql-to-neo4j-transition-guide.m4a"
        type="audio/mp4"
        length="17959684"
      />
      <itunes:title>Escaping JOIN Hell: The SQL Developer’s Guide to Neo4j</itunes:title>
      <itunes:subtitle>Stop struggling with 15-deep JOINs. Learn how Neo4j turns relationships into first-class citizens for faster, more intuitive data modeling.</itunes:subtitle>
      <itunes:summary><![CDATA[Are your SQL queries buckling under the weight of complex relationships and fifteen-deep JOINs? In this episode, we explore the fundamental shift from relational tables to Neo4j’s graph model, breaking down why "index-free adjacency" is a total game-changer for multi-hop traversals and systemic connections. We move past the hype to examine the practical realities of "relationship intelligence" in 2026, comparing the rigid structure of SQL rows to the flexible, schema-optional nature of nodes and edges. Learn how to identify the "JOIN hell" scenarios where a graph database becomes a necessity rather than a gimmick, and discover the power of the hybrid architecture pattern. By piping transactional data from Postgres into a graph "sidecar" via Change Data Capture, you can maintain ACID compliance while gaining the ability to spot digital patterns in milliseconds. Whether you are a SQL veteran or a curious architect, this guide provides the mental model shift needed to navigate the future of connected data.]]></itunes:summary>
      <itunes:duration>1384</itunes:duration>
      <itunes:episode>1211</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/sql-to-neo4j-transition-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/sql-to-neo4j-transition-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agent-First Shift: Ending the Dual-Track API Tax</title>
      <description><![CDATA[Are you tired of building every feature twice—once for humans and once for AI agents? This episode dives into the "dual-track problem" where developers are currently stuck maintaining separate REST APIs and Model Context Protocol (MCP) definitions, leading to a massive 20% overhead in development velocity. We explore the transition from API-first to agent-first architectures, the role of Google’s Web MCP in bridging the gap, and how semantic gateways are revolutionizing the way models interact with our code. Discover how to eliminate schema drift and why the future of the web isn't just about endpoints, but about unified, capability-driven backends that serve both humans and LLMs through a single source of truth.]]></description>
      <link>https://myweirdprompts.com/episode/unified-agent-backend-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unified-agent-backend-architecture/</guid>
      <pubDate>Sun, 15 Mar 2026 13:33:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unified-agent-backend-architecture.m4a"
        type="audio/mp4"
        length="17644686"
      />
      <itunes:title>The Agent-First Shift: Ending the Dual-Track API Tax</itunes:title>
      <itunes:subtitle>Stop paying the 20% &quot;AI tax.&quot; Explore how unified backends and MCP are merging human interfaces with agentic capabilities for a seamless future.</itunes:subtitle>
      <itunes:summary><![CDATA[Are you tired of building every feature twice—once for humans and once for AI agents? This episode dives into the "dual-track problem" where developers are currently stuck maintaining separate REST APIs and Model Context Protocol (MCP) definitions, leading to a massive 20% overhead in development velocity. We explore the transition from API-first to agent-first architectures, the role of Google’s Web MCP in bridging the gap, and how semantic gateways are revolutionizing the way models interact with our code. Discover how to eliminate schema drift and why the future of the web isn't just about endpoints, but about unified, capability-driven backends that serve both humans and LLMs through a single source of truth.]]></itunes:summary>
      <itunes:duration>1381</itunes:duration>
      <itunes:episode>1209</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unified-agent-backend-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unified-agent-backend-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond Buttons: Is the Admin Dashboard Dead?</title>
      <description><![CDATA[For decades, graphical user interfaces have been the only way for humans to manage complex digital systems, but that era is coming to a close. This episode explores the revolutionary shift toward the Model Context Protocol (MCP), a framework that allows AI agents to bypass visual dashboards and interact directly with system backends. We discuss how "headless admin" setups are making traditional internal tools obsolete, the security implications of conversational control, and why the future of software development lies in protocol design rather than UI components. Learn how legacy systems can gain a modern "agentic brain" without a single line of frontend code.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-death-of-the-dashboard/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-death-of-the-dashboard/</guid>
      <pubDate>Sun, 15 Mar 2026 13:32:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-death-of-the-dashboard.m4a"
        type="audio/mp4"
        length="19083758"
      />
      <itunes:title>Beyond Buttons: Is the Admin Dashboard Dead?</itunes:title>
      <itunes:subtitle>Tired of clicking through CMS mazes? Explore how the Model Context Protocol (MCP) is replacing traditional dashboards with conversational control.</itunes:subtitle>
      <itunes:summary><![CDATA[For decades, graphical user interfaces have been the only way for humans to manage complex digital systems, but that era is coming to a close. This episode explores the revolutionary shift toward the Model Context Protocol (MCP), a framework that allows AI agents to bypass visual dashboards and interact directly with system backends. We discuss how "headless admin" setups are making traditional internal tools obsolete, the security implications of conversational control, and why the future of software development lies in protocol design rather than UI components. Learn how legacy systems can gain a modern "agentic brain" without a single line of frontend code.]]></itunes:summary>
      <itunes:duration>1458</itunes:duration>
      <itunes:episode>1208</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-death-of-the-dashboard.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-death-of-the-dashboard.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Rethinking Mastery: Beyond the 10,000 Hour Rule</title>
      <description><![CDATA[For decades, the 10,000-hour rule has been the gold standard for achieving mastery, but in the rapidly shifting technological landscape of 2026, this metric is fundamentally broken. This episode dives into why software engineering is an "open system" where skills decay faster than they can be acquired through repetition. We explore the critical distinction between deliberate practice and "muscle memory for mediocrity," examining how the rise of agentic AI is fundamentally changing the value of human experience. Instead of counting years on a resume, we discuss why the industry is pivoting toward high-quality feedback loops and persistent problem-solving as the true indicators of expertise. Learn why over-specialization can become a liability and how to navigate a career where the goalposts are constantly moving.]]></description>
      <link>https://myweirdprompts.com/episode/10000-hour-rule-software-mastery/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/10000-hour-rule-software-mastery/</guid>
      <pubDate>Sun, 15 Mar 2026 13:08:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/10000-hour-rule-software-mastery.m4a"
        type="audio/mp4"
        length="15555848"
      />
      <itunes:title>Rethinking Mastery: Beyond the 10,000 Hour Rule</itunes:title>
      <itunes:subtitle>Is the 10,000-hour rule dead? Explore why raw time no longer equals expertise in the fast-paced age of AI and open systems.</itunes:subtitle>
      <itunes:summary><![CDATA[For decades, the 10,000-hour rule has been the gold standard for achieving mastery, but in the rapidly shifting technological landscape of 2026, this metric is fundamentally broken. This episode dives into why software engineering is an "open system" where skills decay faster than they can be acquired through repetition. We explore the critical distinction between deliberate practice and "muscle memory for mediocrity," examining how the rise of agentic AI is fundamentally changing the value of human experience. Instead of counting years on a resume, we discuss why the industry is pivoting toward high-quality feedback loops and persistent problem-solving as the true indicators of expertise. Learn why over-specialization can become a liability and how to navigate a career where the goalposts are constantly moving.]]></itunes:summary>
      <itunes:duration>1186</itunes:duration>
      <itunes:episode>1204</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/10000-hour-rule-software-mastery.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/10000-hour-rule-software-mastery.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can AI Resurrect the Digital Tombstones in Our Archives?</title>
      <description><![CDATA[For decades, digitizing history meant taking a picture and hoping for the best—a process that created what experts call "digital tombstones." Today, we are witnessing a massive shift from these static images to computable archives that AI agents can actually understand and reason across. In this episode, we explore the industrial-scale technology driving this change, from infrared page-flattening scanners to advanced vision-language OCR models that "read" context rather than just shapes. We also dive into the revolutionary Model Context Protocol (MCP) and how it’s allowing AI to research primary sources in real-time, bypassing the limitations of static training data and the "hallucination" problem. Join us as we discuss how the entire record of human civilization is being transformed into a living, queryable knowledge graph that empowers the next generation of researchers.]]></description>
      <link>https://myweirdprompts.com/episode/computable-archives-ai-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/computable-archives-ai-future/</guid>
      <pubDate>Sat, 14 Mar 2026 19:40:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/computable-archives-ai-future.m4a"
        type="audio/mp4"
        length="15217282"
      />
      <itunes:title>Can AI Resurrect the Digital Tombstones in Our Archives?</itunes:title>
      <itunes:subtitle>Stop saving &quot;digital tombstones.&quot; Discover how AI and new scanning tech are turning static images into searchable, computable knowledge graphs.</itunes:subtitle>
      <itunes:summary><![CDATA[For decades, digitizing history meant taking a picture and hoping for the best—a process that created what experts call "digital tombstones." Today, we are witnessing a massive shift from these static images to computable archives that AI agents can actually understand and reason across. In this episode, we explore the industrial-scale technology driving this change, from infrared page-flattening scanners to advanced vision-language OCR models that "read" context rather than just shapes. We also dive into the revolutionary Model Context Protocol (MCP) and how it’s allowing AI to research primary sources in real-time, bypassing the limitations of static training data and the "hallucination" problem. Join us as we discuss how the entire record of human civilization is being transformed into a living, queryable knowledge graph that empowers the next generation of researchers.]]></itunes:summary>
      <itunes:duration>1168</itunes:duration>
      <itunes:episode>1176</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/computable-archives-ai-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/computable-archives-ai-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Frozen Backend Paradox: Modern Static Architecture</title>
      <description><![CDATA[In the fast-evolving landscape of 2026, the definition of a "static site" has undergone a radical transformation. No longer just simple digital brochures, modern static architectures now leverage a "frozen backend" paradox where complex logic is executed during build-time rather than request-time. This episode explores the technical shift from live server-side rendering to high-end "meal prep" style delivery, where CI/CD pipelines act as the ultimate database connectors. We break down how developers are overcoming traditional limitations like real-time analytics and massive search indexing through client-side beacons and sharded WebAssembly tools. Whether you are managing a small blog or a massive e-commerce catalog, understanding this spectrum of static-to-dynamic interactivity is essential for building faster, more secure web applications. We dive deep into the trade-offs of performance versus freshness and ask the critical question: at what point does a static site finally hit its architectural ceiling?]]></description>
      <link>https://myweirdprompts.com/episode/static-site-frozen-backend/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/static-site-frozen-backend/</guid>
      <pubDate>Sat, 14 Mar 2026 16:51:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/static-site-frozen-backend.m4a"
        type="audio/mp4"
        length="18986340"
      />
      <itunes:title>The Frozen Backend Paradox: Modern Static Architecture</itunes:title>
      <itunes:subtitle>Explore the &quot;frozen backend&quot; paradox and how modern static sites use build-time logic and sharded search to mimic complex dynamic applications.</itunes:subtitle>
      <itunes:summary><![CDATA[In the fast-evolving landscape of 2026, the definition of a "static site" has undergone a radical transformation. No longer just simple digital brochures, modern static architectures now leverage a "frozen backend" paradox where complex logic is executed during build-time rather than request-time. This episode explores the technical shift from live server-side rendering to high-end "meal prep" style delivery, where CI/CD pipelines act as the ultimate database connectors. We break down how developers are overcoming traditional limitations like real-time analytics and massive search indexing through client-side beacons and sharded WebAssembly tools. Whether you are managing a small blog or a massive e-commerce catalog, understanding this spectrum of static-to-dynamic interactivity is essential for building faster, more secure web applications. We dive deep into the trade-offs of performance versus freshness and ask the critical question: at what point does a static site finally hit its architectural ceiling?]]></itunes:summary>
      <itunes:duration>1446</itunes:duration>
      <itunes:episode>1170</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/static-site-frozen-backend.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/static-site-frozen-backend.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Database Explosion: Why One Size No Longer Fits All</title>
      <description><![CDATA[We explore the staggering growth of the "Database of Databases," a catalog tracking over 1,000 unique storage systems and the technical necessity driving this massive fragmentation. Learn how shifting hardware, the AI boom, and the nuances of the PACELC theorem are forcing engineers to move past general-purpose tools like Postgres in favor of extreme specialization. From vector search and columnar storage to the constraints of edge computing, we dive into why the "right tool for the job" has never been more complicated—or more essential for modern performance.]]></description>
      <link>https://myweirdprompts.com/episode/database-specialization-future-storage/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/database-specialization-future-storage/</guid>
      <pubDate>Thu, 12 Mar 2026 01:10:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/database-specialization-future-storage.m4a"
        type="audio/mp4"
        length="21556523"
      />
      <itunes:title>The Database Explosion: Why One Size No Longer Fits All</itunes:title>
      <itunes:subtitle>From vector stores to edge computing, discover why the world now has over 1,000 databases and why Postgres isn&apos;t always the answer.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore the staggering growth of the "Database of Databases," a catalog tracking over 1,000 unique storage systems and the technical necessity driving this massive fragmentation. Learn how shifting hardware, the AI boom, and the nuances of the PACELC theorem are forcing engineers to move past general-purpose tools like Postgres in favor of extreme specialization. From vector search and columnar storage to the constraints of edge computing, we dive into why the "right tool for the job" has never been more complicated—or more essential for modern performance.]]></itunes:summary>
      <itunes:duration>1675</itunes:duration>
      <itunes:episode>1124</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/database-specialization-future-storage.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/database-specialization-future-storage.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When One Database Isn&apos;t Enough</title>
      <description><![CDATA[Can Postgres really replace data warehouses and lakes? This episode explores the physics of data gravity, the limits of a single system, and why the dream of architectural minimalism may hit a wall at petabyte scale.]]></description>
      <link>https://myweirdprompts.com/episode/postgres-vs-data-lake/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/postgres-vs-data-lake/</guid>
      <pubDate>Thu, 12 Mar 2026 00:54:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/postgres-vs-data-lake.m4a"
        type="audio/mp4"
        length="19648022"
      />
      <itunes:title>When One Database Isn&apos;t Enough</itunes:title>
      <itunes:subtitle>Can Postgres 18 finally replace the data warehouse? We dive into data gravity, columnar storage, and the physics of scaling in the AI age.</itunes:subtitle>
      <itunes:summary><![CDATA[Can Postgres really replace data warehouses and lakes? This episode explores the physics of data gravity, the limits of a single system, and why the dream of architectural minimalism may hit a wall at petabyte scale.]]></itunes:summary>
      <itunes:duration>1528</itunes:duration>
      <itunes:episode>1123</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/postgres-vs-data-lake.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/postgres-vs-data-lake.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Agents Are Abandoning Human Language</title>
      <description><![CDATA[For years, we have forced artificial intelligence to communicate using the "biological bottleneck" of human language, a process as inefficient as two supercomputers exchanging information via printed pages and scanners. This episode dives into the "linguistic cage" and explores the cutting-edge protocols that allow AI agents to communicate at machine-native speeds. We move from the streamlined efficiency of Token-Oriented Object Notation (TOON) to the eerie, high-speed audio bursts of GibberLink, and finally to the revolutionary frontier of direct activation communication. By bypassing words entirely and sharing raw latent states, these systems are achieving massive gains in reasoning and accuracy, effectively evolving from separate tools into a single, unified cognitive entity. Join us as we explore how "mind-melding" between models is redefining the limits of agentic workflows and why the future of AI isn't just about talking better—it’s about stopping the talking altogether to start thinking as one.]]></description>
      <link>https://myweirdprompts.com/episode/ai-machine-native-communication/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-machine-native-communication/</guid>
      <pubDate>Thu, 12 Mar 2026 00:53:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-machine-native-communication.m4a"
        type="audio/mp4"
        length="22441444"
      />
      <itunes:title>Why AI Agents Are Abandoning Human Language</itunes:title>
      <itunes:subtitle>Why force AI to talk like humans? Explore how agents are ditching English for high-speed &quot;mind-melding&quot; and latent space communication.</itunes:subtitle>
      <itunes:summary><![CDATA[For years, we have forced artificial intelligence to communicate using the "biological bottleneck" of human language, a process as inefficient as two supercomputers exchanging information via printed pages and scanners. This episode dives into the "linguistic cage" and explores the cutting-edge protocols that allow AI agents to communicate at machine-native speeds. We move from the streamlined efficiency of Token-Oriented Object Notation (TOON) to the eerie, high-speed audio bursts of GibberLink, and finally to the revolutionary frontier of direct activation communication. By bypassing words entirely and sharing raw latent states, these systems are achieving massive gains in reasoning and accuracy, effectively evolving from separate tools into a single, unified cognitive entity. Join us as we explore how "mind-melding" between models is redefining the limits of agentic workflows and why the future of AI isn't just about talking better—it’s about stopping the talking altogether to start thinking as one.]]></itunes:summary>
      <itunes:duration>1737</itunes:duration>
      <itunes:episode>1122</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-machine-native-communication.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-machine-native-communication.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The AI Handoff: From Manual Hacks to Standard Protocols</title>
      <description><![CDATA[Imagine a nurse finishing a shift without telling the next one which patient has a penicillin allergy—that is the current state of many AI agents. This episode explores the massive shift in 2026 from "hacky" manual JSON logs to industrial-grade agentic handoffs. We dive into LangGraph’s typed state channels, OpenAI’s history mapping, and the emerging standards like MCP and Google’s A2A protocol. Whether you are building autonomous workflows or scaling enterprise AI, this deep dive into the "how" of agent orchestration is essential for ensuring your models don't lose the thread of intent.]]></description>
      <link>https://myweirdprompts.com/episode/agent-handoff-standard-protocols/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-handoff-standard-protocols/</guid>
      <pubDate>Thu, 12 Mar 2026 00:40:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-handoff-standard-protocols.m4a"
        type="audio/mp4"
        length="21588400"
      />
      <itunes:title>The AI Handoff: From Manual Hacks to Standard Protocols</itunes:title>
      <itunes:subtitle>Stop the &quot;context rot.&quot; Learn how new protocols like MCP and typed state channels are revolutionizing how AI agents collaborate.</itunes:subtitle>
      <itunes:summary><![CDATA[Imagine a nurse finishing a shift without telling the next one which patient has a penicillin allergy—that is the current state of many AI agents. This episode explores the massive shift in 2026 from "hacky" manual JSON logs to industrial-grade agentic handoffs. We dive into LangGraph’s typed state channels, OpenAI’s history mapping, and the emerging standards like MCP and Google’s A2A protocol. Whether you are building autonomous workflows or scaling enterprise AI, this deep dive into the "how" of agent orchestration is essential for ensuring your models don't lose the thread of intent.]]></itunes:summary>
      <itunes:duration>1654</itunes:duration>
      <itunes:episode>1120</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-handoff-standard-protocols.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-handoff-standard-protocols.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Rise of the AI Procurement Officer</title>
      <description><![CDATA[As AI moves from chatbot to autonomous negotiator, businesses are rethinking the entire quote-to-cash cycle. This episode explores how agentic frameworks like ProcureAgent-OS are enabling high-speed, policy-governed haggling between algorithms—and what that means for the future of B2B commerce.]]></description>
      <link>https://myweirdprompts.com/episode/ai-procurement-agentic-payments/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-procurement-agentic-payments/</guid>
      <pubDate>Wed, 11 Mar 2026 20:20:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-procurement-agentic-payments.m4a"
        type="audio/mp4"
        length="22634498"
      />
      <itunes:title>The Rise of the AI Procurement Officer</itunes:title>
      <itunes:subtitle>Discover how AI is evolving from simple chat to autonomous B2B procurement agents capable of negotiating and executing million-dollar deals.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI moves from chatbot to autonomous negotiator, businesses are rethinking the entire quote-to-cash cycle. This episode explores how agentic frameworks like ProcureAgent-OS are enabling high-speed, policy-governed haggling between algorithms—and what that means for the future of B2B commerce.]]></itunes:summary>
      <itunes:duration>1756</itunes:duration>
      <itunes:episode>1115</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-procurement-agentic-payments.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-procurement-agentic-payments.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Ghost Company: The High Cost of AI Agent Bureaucracy</title>
      <description><![CDATA[Is the dream of the "ghost company"—a fully autonomous AI startup—actually a financial money pit? This episode dives into the emerging "Agentic Mesh," exploring why hierarchical agent setups are currently seeing up to a 70% drop in reasoning performance and staggering five-figure token bills. We break down the technical battle between fluid, role-based systems and deterministic frameworks, revealing how the new role of the "Agent Boss" is the only thing keeping these digital architectures from collapsing under their own weight.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-hierarchy-costs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-hierarchy-costs/</guid>
      <pubDate>Wed, 11 Mar 2026 16:24:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-hierarchy-costs.m4a"
        type="audio/mp4"
        length="20919075"
      />
      <itunes:title>The Ghost Company: The High Cost of AI Agent Bureaucracy</itunes:title>
      <itunes:subtitle>Can a company run entirely on AI? Explore the hidden costs and &quot;agentic bureaucracy&quot; of building autonomous agent hierarchies.</itunes:subtitle>
      <itunes:summary><![CDATA[Is the dream of the "ghost company"—a fully autonomous AI startup—actually a financial money pit? This episode dives into the emerging "Agentic Mesh," exploring why hierarchical agent setups are currently seeing up to a 70% drop in reasoning performance and staggering five-figure token bills. We break down the technical battle between fluid, role-based systems and deterministic frameworks, revealing how the new role of the "Agent Boss" is the only thing keeping these digital architectures from collapsing under their own weight.]]></itunes:summary>
      <itunes:duration>1626</itunes:duration>
      <itunes:episode>1113</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-hierarchy-costs.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-hierarchy-costs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The arXiv Effect: Inside the Engine of AI Research</title>
      <description><![CDATA[In this episode, we dive into the fascinating world of arXiv, the unassuming preprint server that powers the modern AI revolution. We explore its origins in 1990s physics, why it maintains a "lo-fi" aesthetic, and how it bypasses traditional peer review to accelerate scientific discovery. Whether you are an independent researcher or just curious about how breakthroughs like Transformers go viral overnight, this deep dive reveals why arXiv is the most important tool in a modern engineer's arsenal. Learn about the endorsement system, the role of LaTeX, and why function always beats form in the high-stakes world of artificial intelligence.]]></description>
      <link>https://myweirdprompts.com/episode/arxiv-ai-preprint-culture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/arxiv-ai-preprint-culture/</guid>
      <pubDate>Wed, 11 Mar 2026 15:28:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/arxiv-ai-preprint-culture.m4a"
        type="audio/mp4"
        length="17194330"
      />
      <itunes:title>The arXiv Effect: Inside the Engine of AI Research</itunes:title>
      <itunes:subtitle>Explore how a 1990s-style website became the central nervous system for AI breakthroughs and the power of the preprint revolution.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we dive into the fascinating world of arXiv, the unassuming preprint server that powers the modern AI revolution. We explore its origins in 1990s physics, why it maintains a "lo-fi" aesthetic, and how it bypasses traditional peer review to accelerate scientific discovery. Whether you are an independent researcher or just curious about how breakthroughs like Transformers go viral overnight, this deep dive reveals why arXiv is the most important tool in a modern engineer's arsenal. Learn about the endorsement system, the role of LaTeX, and why function always beats form in the high-stakes world of artificial intelligence.]]></itunes:summary>
      <itunes:duration>1316</itunes:duration>
      <itunes:episode>1110</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/arxiv-ai-preprint-culture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/arxiv-ai-preprint-culture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Emoji: How Hugging Face Conquered AI</title>
      <description><![CDATA[Hugging Face is often called the "GitHub of AI," but its role is far more critical to the modern tech stack than that simple shorthand suggests. We explore the platform's fascinating evolution from a quirky chatbot startup designed for teenagers to the indispensable central nervous system of the global artificial intelligence world. From standardizing model weights through the Transformers library to fostering the open-weights movement via its influential leaderboards, this episode reveals how a yellow smiley face became the primary engine for innovation and the foundation of the decentralized AI ecosystem.]]></description>
      <link>https://myweirdprompts.com/episode/hugging-face-ai-infrastructure/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hugging-face-ai-infrastructure/</guid>
      <pubDate>Wed, 11 Mar 2026 15:18:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hugging-face-ai-infrastructure.m4a"
        type="audio/mp4"
        length="20463966"
      />
      <itunes:title>Beyond the Emoji: How Hugging Face Conquered AI</itunes:title>
      <itunes:subtitle>Discover how a quirky chatbot company became the central nervous system of AI, hosting millions of models and standardizing the entire industry.</itunes:subtitle>
      <itunes:summary><![CDATA[Hugging Face is often called the "GitHub of AI," but its role is far more critical to the modern tech stack than that simple shorthand suggests. We explore the platform's fascinating evolution from a quirky chatbot startup designed for teenagers to the indispensable central nervous system of the global artificial intelligence world. From standardizing model weights through the Transformers library to fostering the open-weights movement via its influential leaderboards, this episode reveals how a yellow smiley face became the primary engine for innovation and the foundation of the decentralized AI ecosystem.]]></itunes:summary>
      <itunes:duration>1569</itunes:duration>
      <itunes:episode>1108</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hugging-face-ai-infrastructure.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hugging-face-ai-infrastructure.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Kitchen War: When Theory Meets Messy Reality</title>
      <description><![CDATA[A technical discussion about LLM context windows and attention mechanisms collides with a real-world dispute over kitchen cleanup, exposing the friction between theoretical efficiency and the messy reality of human collaboration.]]></description>
      <link>https://myweirdprompts.com/episode/llm-context-window-limits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-context-window-limits/</guid>
      <pubDate>Wed, 11 Mar 2026 13:51:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-context-window-limits.m4a"
        type="audio/mp4"
        length="9895606"
      />
      <itunes:title>The Kitchen War: When Theory Meets Messy Reality</itunes:title>
      <itunes:subtitle>Explore the mechanics of LLM context windows and attention, and witness what happens when technical debates collide with household chores.</itunes:subtitle>
      <itunes:summary><![CDATA[A technical discussion about LLM context windows and attention mechanisms collides with a real-world dispute over kitchen cleanup, exposing the friction between theoretical efficiency and the messy reality of human collaboration.]]></itunes:summary>
      <itunes:duration>734</itunes:duration>
      <itunes:episode>1103</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-context-window-limits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-context-window-limits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Truth Conflict: Why AI Ignores the Facts You Give It</title>
      <description><![CDATA[In this episode of My Weird Prompts, we explore the "Truth Conflict," a growing challenge in the world of Retrieval-Augmented Generation (RAG). As we move into 2026, developers are finding that even when provided with the exact facts needed to answer a query, high-end language models often default to their internal training data—a phenomenon known as the Hallucination versus Contradiction paradox. We break down the technical reasons behind this, including the "Knowledge Conflict Threshold" and the gravitational pull of parametric memory.

The discussion covers practical strategies for overcoming these biases, such as negative prompting, the use of context-priority flags, and the implementation of source-attribution headers. We also examine the industry-wide shift toward a tripartite hierarchy of truth, where models are taught to treat their own training as a linguistic framework rather than a factual source. Finally, we weigh the pros and cons of corpus isolation versus open-ended retrieval, asking whether we want our AI to be a highly accurate filing clerk or a cross-domain research assistant. This episode is essential listening for anyone building reliable enterprise AI tools in an era of massive context windows.]]></description>
      <link>https://myweirdprompts.com/episode/rag-truth-conflict-ai-memory/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rag-truth-conflict-ai-memory/</guid>
      <pubDate>Wed, 11 Mar 2026 12:44:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rag-truth-conflict-ai-memory.m4a"
        type="audio/mp4"
        length="17005285"
      />
      <itunes:title>The Truth Conflict: Why AI Ignores the Facts You Give It</itunes:title>
      <itunes:subtitle>Discover why AI models ignore provided documents in favor of old training data and how to build a reliable &quot;hierarchy of truth&quot; for RAG systems.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, we explore the "Truth Conflict," a growing challenge in the world of Retrieval-Augmented Generation (RAG). As we move into 2026, developers are finding that even when provided with the exact facts needed to answer a query, high-end language models often default to their internal training data—a phenomenon known as the Hallucination versus Contradiction paradox. We break down the technical reasons behind this, including the "Knowledge Conflict Threshold" and the gravitational pull of parametric memory.

The discussion covers practical strategies for overcoming these biases, such as negative prompting, the use of context-priority flags, and the implementation of source-attribution headers. We also examine the industry-wide shift toward a tripartite hierarchy of truth, where models are taught to treat their own training as a linguistic framework rather than a factual source. Finally, we weigh the pros and cons of corpus isolation versus open-ended retrieval, asking whether we want our AI to be a highly accurate filing clerk or a cross-domain research assistant. This episode is essential listening for anyone building reliable enterprise AI tools in an era of massive context windows.]]></itunes:summary>
      <itunes:duration>1313</itunes:duration>
      <itunes:episode>1100</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rag-truth-conflict-ai-memory.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rag-truth-conflict-ai-memory.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Models Can’t Read and Your Bill Is Rising</title>
      <description><![CDATA[Why does the same prompt result in different costs and performance across frontier models like GPT-4o and Claude 3.5 Sonnet? This episode deconstructs the "tokenization tax," exploring the invisible bridge between human language and the vector-based math engines of modern AI. We dive into the engineering trade-offs of vocabulary size, the hidden memory costs of embedding matrices, and how inefficient tokenization creates a digital divide for non-Latin scripts.]]></description>
      <link>https://myweirdprompts.com/episode/llm-tokenization-tax-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/llm-tokenization-tax-explained/</guid>
      <pubDate>Tue, 10 Mar 2026 03:27:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/llm-tokenization-tax-explained.m4a"
        type="audio/mp4"
        length="22690952"
      />
      <itunes:title>Why AI Models Can’t Read and Your Bill Is Rising</itunes:title>
      <itunes:subtitle>Why does the same prompt cost more on different models? Discover the &quot;invisible wall&quot; of tokenization and how it shapes AI perception.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does the same prompt result in different costs and performance across frontier models like GPT-4o and Claude 3.5 Sonnet? This episode deconstructs the "tokenization tax," exploring the invisible bridge between human language and the vector-based math engines of modern AI. We dive into the engineering trade-offs of vocabulary size, the hidden memory costs of embedding matrices, and how inefficient tokenization creates a digital divide for non-Latin scripts.]]></itunes:summary>
      <itunes:duration>1745</itunes:duration>
      <itunes:episode>1084</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/llm-tokenization-tax-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/llm-tokenization-tax-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Mapping the Second Black Box: Agentic AI Visualization</title>
      <description><![CDATA[As artificial intelligence moves from simple chat interfaces to complex autonomous agents, developers are facing a new challenge: the "black box" of agentic workflows. Traditional linear logs are no longer enough to track systems that browse the web, execute code, and self-correct in real-time. This episode explores a groundbreaking visualization project that maps the non-linear "internal momentum" of AI agents. We dive into the technical shift from prompt engineering to architecture engineering, explaining how visualizing recursive loops and latent value spaces can reveal an agent's hidden biases and decision-making heuristics. By seeing the "paths not taken," developers can move beyond debugging simple outcomes to debugging the core intent of their autonomous systems.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ai-architecture-visualization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ai-architecture-visualization/</guid>
      <pubDate>Tue, 10 Mar 2026 03:20:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ai-architecture-visualization.m4a"
        type="audio/mp4"
        length="22900839"
      />
      <itunes:title>Mapping the Second Black Box: Agentic AI Visualization</itunes:title>
      <itunes:subtitle>Stop reading messy logs. Discover how mapping &quot;internal momentum&quot; and latent value spaces can solve the black box problem in agentic AI.</itunes:subtitle>
      <itunes:summary><![CDATA[As artificial intelligence moves from simple chat interfaces to complex autonomous agents, developers are facing a new challenge: the "black box" of agentic workflows. Traditional linear logs are no longer enough to track systems that browse the web, execute code, and self-correct in real-time. This episode explores a groundbreaking visualization project that maps the non-linear "internal momentum" of AI agents. We dive into the technical shift from prompt engineering to architecture engineering, explaining how visualizing recursive loops and latent value spaces can reveal an agent's hidden biases and decision-making heuristics. By seeing the "paths not taken," developers can move beyond debugging simple outcomes to debugging the core intent of their autonomous systems.]]></itunes:summary>
      <itunes:duration>1725</itunes:duration>
      <itunes:episode>1083</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ai-architecture-visualization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ai-architecture-visualization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Stop Ruining Your Website Speed With Tracking Scripts</title>
      <description><![CDATA[In a world of lightning-fast static architectures and global edge delivery, many developers are still dragging the heavy weight of invasive surveillance scripts behind their high-performance websites. This episode breaks down the "analytics paradox" of 2026, examining why traditional client-side tracking is failing due to aggressive ad-blocking and modern privacy regulations. We explore the transition from invasive user surveillance to "traffic intelligence," highlighting how edge-side logging and proxy-based event streaming can provide accurate, high-integrity data without sacrificing site speed or user trust.]]></description>
      <link>https://myweirdprompts.com/episode/web-analytics-privacy-performance/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/web-analytics-privacy-performance/</guid>
      <pubDate>Tue, 10 Mar 2026 03:16:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/web-analytics-privacy-performance.m4a"
        type="audio/mp4"
        length="19248920"
      />
      <itunes:title>Stop Ruining Your Website Speed With Tracking Scripts</itunes:title>
      <itunes:subtitle>Stop slowing down your site with invasive trackers. Learn how to balance privacy and performance using edge-side and proxy-based analytics.</itunes:subtitle>
      <itunes:summary><![CDATA[In a world of lightning-fast static architectures and global edge delivery, many developers are still dragging the heavy weight of invasive surveillance scripts behind their high-performance websites. This episode breaks down the "analytics paradox" of 2026, examining why traditional client-side tracking is failing due to aggressive ad-blocking and modern privacy regulations. We explore the transition from invasive user surveillance to "traffic intelligence," highlighting how edge-side logging and proxy-based event streaming can provide accurate, high-integrity data without sacrificing site speed or user trust.]]></itunes:summary>
      <itunes:duration>1484</itunes:duration>
      <itunes:episode>1082</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/web-analytics-privacy-performance.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/web-analytics-privacy-performance.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The K-V Cache: Solving AI’s Invisible Memory Tax</title>
      <description><![CDATA[Ever wonder why long AI conversations suddenly crawl or crash your GPU? Join the discussion as we dive into the "invisible tax" of the generative era: the K-V cache. We explore the cutting-edge architectural breakthroughs, from PagedAttention to Flash KV, that are keeping 2026’s million-token models running smoothly. Learn how the industry is winning the memory wars to make high-speed, local agentic AI a reality for everyone.]]></description>
      <link>https://myweirdprompts.com/episode/kv-cache-inference-optimization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/kv-cache-inference-optimization/</guid>
      <pubDate>Tue, 10 Mar 2026 00:55:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/kv-cache-inference-optimization.m4a"
        type="audio/mp4"
        length="18559922"
      />
      <itunes:title>The K-V Cache: Solving AI’s Invisible Memory Tax</itunes:title>
      <itunes:subtitle>Why does your AI get slower as you chat? Discover the K-V cache, the invisible bottleneck of generative AI, and how we&apos;re fixing it in 2026.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wonder why long AI conversations suddenly crawl or crash your GPU? Join the discussion as we dive into the "invisible tax" of the generative era: the K-V cache. We explore the cutting-edge architectural breakthroughs, from PagedAttention to Flash KV, that are keeping 2026’s million-token models running smoothly. Learn how the industry is winning the memory wars to make high-speed, local agentic AI a reality for everyone.]]></itunes:summary>
      <itunes:duration>1430</itunes:duration>
      <itunes:episode>1081</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/kv-cache-inference-optimization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/kv-cache-inference-optimization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agentic Throughput Gap: Why Your AI Hits a Wall</title>
      <description><![CDATA[As AI evolves from simple chatbots to autonomous agents like Claude Code, developers are crashing into a frustrating new reality known as the Agentic Throughput Gap. Even premium subscriptions struggle to keep up with the rapid-fire API calls and massive context windows required for recursive loops, leading to constant rate-limit errors that stall productivity. This episode breaks down how to move past these "toy" limitations by exploring enterprise-grade provisioned throughput, self-hosting open-weights models on dedicated GPUs, and implementing hybrid architectures to ensure your agents remain reliable, responsive, and always-on.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-throughput-gap-solutions/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-throughput-gap-solutions/</guid>
      <pubDate>Mon, 09 Mar 2026 22:01:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-throughput-gap-solutions.m4a"
        type="audio/mp4"
        length="21639834"
      />
      <itunes:title>The Agentic Throughput Gap: Why Your AI Hits a Wall</itunes:title>
      <itunes:subtitle>Stop hitting 429 errors. We explore why AI agents crash into rate limits and how to build high-throughput systems that never sleep.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI evolves from simple chatbots to autonomous agents like Claude Code, developers are crashing into a frustrating new reality known as the Agentic Throughput Gap. Even premium subscriptions struggle to keep up with the rapid-fire API calls and massive context windows required for recursive loops, leading to constant rate-limit errors that stall productivity. This episode breaks down how to move past these "toy" limitations by exploring enterprise-grade provisioned throughput, self-hosting open-weights models on dedicated GPUs, and implementing hybrid architectures to ensure your agents remain reliable, responsive, and always-on.]]></itunes:summary>
      <itunes:duration>1473</itunes:duration>
      <itunes:episode>1078</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-throughput-gap-solutions.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-throughput-gap-solutions.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Will Your Browser Replace Your OS for Local AI?</title>
      <description><![CDATA[For decades, the web browser was a thin window to remote servers, but a massive platform shift is turning it into a heavy-duty operating system for local AI. This episode explores the transition from "Bring Your Own Model" to Browser Cached Models (BCM) and how Google’s Web MCP initiative is standardizing local AI tools. We dive into the hardware breakthroughs of Web GPU and Web NN that allow browsers to run large language models at near-native speeds. Learn how the browser sandbox is becoming the ultimate privacy shield, keeping sensitive data local while enabling powerful agentic workflows. We also discuss whether the ease of browser-integrated AI marks the end of the technical DIY era for local LLMs.]]></description>
      <link>https://myweirdprompts.com/episode/browser-local-ai-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/browser-local-ai-evolution/</guid>
      <pubDate>Mon, 09 Mar 2026 21:21:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/browser-local-ai-evolution.m4a"
        type="audio/mp4"
        length="20731918"
      />
      <itunes:title>Will Your Browser Replace Your OS for Local AI?</itunes:title>
      <itunes:subtitle>See how Web GPU and Web NN are turning your browser into a local AI engine, ending the era of complex DIY setups and protecting your privacy.</itunes:subtitle>
      <itunes:summary><![CDATA[For decades, the web browser was a thin window to remote servers, but a massive platform shift is turning it into a heavy-duty operating system for local AI. This episode explores the transition from "Bring Your Own Model" to Browser Cached Models (BCM) and how Google’s Web MCP initiative is standardizing local AI tools. We dive into the hardware breakthroughs of Web GPU and Web NN that allow browsers to run large language models at near-native speeds. Learn how the browser sandbox is becoming the ultimate privacy shield, keeping sensitive data local while enabling powerful agentic workflows. We also discuss whether the ease of browser-integrated AI marks the end of the technical DIY era for local LLMs.]]></itunes:summary>
      <itunes:duration>1593</itunes:duration>
      <itunes:episode>1077</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/browser-local-ai-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/browser-local-ai-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agentic Friction: Solving the MCP Restart Tax</title>
      <description><![CDATA[In this episode, we tackle the "plumbing" of the agentic age: the Model Context Protocol (MCP). We explore the frustrating "restart tax" that forces users to reboot sessions to add new capabilities and the "attention dilution" that occurs when too many tools clutter an AI's context window. From the current bottlenecks of static tool registries to the promising horizon of Just-In-Time registration and Dynamic Tool Discovery, learn how the industry is moving from the dial-up era of AI agents into a seamless, production-grade future where assistants learn and adapt on the fly.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-restart-tax-agentic-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-restart-tax-agentic-ai/</guid>
      <pubDate>Mon, 09 Mar 2026 19:47:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-restart-tax-agentic-ai.m4a"
        type="audio/mp4"
        length="17255328"
      />
      <itunes:title>The Agentic Friction: Solving the MCP Restart Tax</itunes:title>
      <itunes:subtitle>Why do we have to restart AI sessions just to add a tool? We dive into the &quot;restart tax&quot; and the future of Dynamic Tool Discovery.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we tackle the "plumbing" of the agentic age: the Model Context Protocol (MCP). We explore the frustrating "restart tax" that forces users to reboot sessions to add new capabilities and the "attention dilution" that occurs when too many tools clutter an AI's context window. From the current bottlenecks of static tool registries to the promising horizon of Just-In-Time registration and Dynamic Tool Discovery, learn how the industry is moving from the dial-up era of AI agents into a seamless, production-grade future where assistants learn and adapt on the fly.]]></itunes:summary>
      <itunes:duration>1338</itunes:duration>
      <itunes:episode>1076</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-restart-tax-agentic-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-restart-tax-agentic-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Great Kernel Shift: Why Linux is Embracing Rust</title>
      <description><![CDATA[For over thirty years, the Linux kernel—the foundation of the internet, smartphones, and embedded systems—has been built almost exclusively in C. But a fundamental shift is underway as Rust, a modern language focused on memory safety, makes its historic debut in the mainline kernel. This episode explores the "memory safety crisis" where 70% of all security vulnerabilities are linked to manual memory management, and how Rust’s unique "borrow checker" aims to solve these issues at the compiler level without sacrificing performance. We dive into the technical breakthroughs of zero-cost abstractions and the "unsafe" blocks that allow Rust to talk directly to hardware. Beyond the code, we examine the intense cultural friction and "religious wars" within the developer community as a new generation of tools meets the established old guard. From the high-stakes world of national security to the innovative drivers of the Asahi Linux project, learn why the transition to Rust is one of the most consequential shifts in the history of computing.]]></description>
      <link>https://myweirdprompts.com/episode/rust-linux-kernel-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rust-linux-kernel-future/</guid>
      <pubDate>Mon, 09 Mar 2026 19:02:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rust-linux-kernel-future.m4a"
        type="audio/mp4"
        length="21225984"
      />
      <itunes:title>The Great Kernel Shift: Why Linux is Embracing Rust</itunes:title>
      <itunes:subtitle>Discover why the Linux kernel is adopting Rust and how this shift aims to eliminate 70% of the digital world&apos;s security vulnerabilities.</itunes:subtitle>
      <itunes:summary><![CDATA[For over thirty years, the Linux kernel—the foundation of the internet, smartphones, and embedded systems—has been built almost exclusively in C. But a fundamental shift is underway as Rust, a modern language focused on memory safety, makes its historic debut in the mainline kernel. This episode explores the "memory safety crisis" where 70% of all security vulnerabilities are linked to manual memory management, and how Rust’s unique "borrow checker" aims to solve these issues at the compiler level without sacrificing performance. We dive into the technical breakthroughs of zero-cost abstractions and the "unsafe" blocks that allow Rust to talk directly to hardware. Beyond the code, we examine the intense cultural friction and "religious wars" within the developer community as a new generation of tools meets the established old guard. From the high-stakes world of national security to the innovative drivers of the Asahi Linux project, learn why the transition to Rust is one of the most consequential shifts in the history of computing.]]></itunes:summary>
      <itunes:duration>1635</itunes:duration>
      <itunes:episode>1075</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rust-linux-kernel-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rust-linux-kernel-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond YAML: Building the Agentic Smart Home</title>
      <description><![CDATA[For years, the dream of a smart home has been buried under mountains of complex configuration and rigid logic that requires users to anticipate every possible variable. This episode explores the massive shift arriving in 2026: the integration of the Model Context Protocol (MCP) into Home Assistant, allowing local AI agents to understand human intent rather than just following static scripts. We dive into the technical requirements for running models like Llama 3.2 and Qwen 2.5 locally, the role of dedicated hardware like NPUs in reducing latency, and how to implement essential safety guardrails so your AI manages the home without overstepping its bounds. By moving beyond the "connected" home and into the "aware" home, users can finally stop acting as the primary brain for their hardware and let an intelligent system handle the context of daily life. This conversation covers everything from the hardware in your closet to the imaginative future of self-improving automations, all while keeping your data private and local.]]></description>
      <link>https://myweirdprompts.com/episode/home-assistant-mcp-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-assistant-mcp-agents/</guid>
      <pubDate>Mon, 09 Mar 2026 16:49:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-assistant-mcp-agents.m4a"
        type="audio/mp4"
        length="22322714"
      />
      <itunes:title>Beyond YAML: Building the Agentic Smart Home</itunes:title>
      <itunes:subtitle>Stop wrestling with YAML. Discover how MCP and local AI agents are transforming Home Assistant into a truly intelligent, aware partner.</itunes:subtitle>
      <itunes:summary><![CDATA[For years, the dream of a smart home has been buried under mountains of complex configuration and rigid logic that requires users to anticipate every possible variable. This episode explores the massive shift arriving in 2026: the integration of the Model Context Protocol (MCP) into Home Assistant, allowing local AI agents to understand human intent rather than just following static scripts. We dive into the technical requirements for running models like Llama 3.2 and Qwen 2.5 locally, the role of dedicated hardware like NPUs in reducing latency, and how to implement essential safety guardrails so your AI manages the home without overstepping its bounds. By moving beyond the "connected" home and into the "aware" home, users can finally stop acting as the primary brain for their hardware and let an intelligent system handle the context of daily life. This conversation covers everything from the hardware in your closet to the imaginative future of self-improving automations, all while keeping your data private and local.]]></itunes:summary>
      <itunes:duration>1708</itunes:duration>
      <itunes:episode>1073</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-assistant-mcp-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-assistant-mcp-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Smart AI Agent Still Lives in a Dumb Chat Box</title>
      <description><![CDATA[We have built Ferrari-level AI engines but continue to steer them with the "bicycle handlebars" of Telegram and Slack. This episode dives into the technical limitations of using messaging apps as agent interfaces, from state management headaches and latency issues to the looming threat of platform risk. Discover why the industry is moving toward "agent-native" UIs and generative dashboards that finally match the power and complexity of the models they control.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-interface-gap/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-interface-gap/</guid>
      <pubDate>Mon, 09 Mar 2026 16:46:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-interface-gap.m4a"
        type="audio/mp4"
        length="20143840"
      />
      <itunes:title>Why Your Smart AI Agent Still Lives in a Dumb Chat Box</itunes:title>
      <itunes:subtitle>Why are we controlling the world&apos;s most advanced AI with simple chat boxes? Explore the technical debt and future of agent-native interfaces.</itunes:subtitle>
      <itunes:summary><![CDATA[We have built Ferrari-level AI engines but continue to steer them with the "bicycle handlebars" of Telegram and Slack. This episode dives into the technical limitations of using messaging apps as agent interfaces, from state management headaches and latency issues to the looming threat of platform risk. Discover why the industry is moving toward "agent-native" UIs and generative dashboards that finally match the power and complexity of the models they control.]]></itunes:summary>
      <itunes:duration>1540</itunes:duration>
      <itunes:episode>1072</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-interface-gap.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-interface-gap.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Kill Switch: Advanced Router VPN Routing</title>
      <description><![CDATA[Tired of your VPN breaking your banking apps or smart TV? This episode dives deep into the evolution of network-level security, moving away from "all-or-nothing" tunnels toward sophisticated policy engines that understand intent. We explore how to implement domain-based split routing, leverage the speed of WireGuard, and choose the right hardware to ensure your local traffic stays local while your restricted content stays accessible. Whether you are managing a complex smart home or just trying to stay connected in a high-pressure environment, learn how to turn your router into a surgical tool for privacy and performance.]]></description>
      <link>https://myweirdprompts.com/episode/advanced-vpn-policy-routing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/advanced-vpn-policy-routing/</guid>
      <pubDate>Mon, 09 Mar 2026 14:54:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/advanced-vpn-policy-routing.m4a"
        type="audio/mp4"
        length="25209697"
      />
      <itunes:title>Beyond the Kill Switch: Advanced Router VPN Routing</itunes:title>
      <itunes:subtitle>Stop breaking your smart home. Learn how to use domain-based split routing and WireGuard to gain surgical control over your network.</itunes:subtitle>
      <itunes:summary><![CDATA[Tired of your VPN breaking your banking apps or smart TV? This episode dives deep into the evolution of network-level security, moving away from "all-or-nothing" tunnels toward sophisticated policy engines that understand intent. We explore how to implement domain-based split routing, leverage the speed of WireGuard, and choose the right hardware to ensure your local traffic stays local while your restricted content stays accessible. Whether you are managing a complex smart home or just trying to stay connected in a high-pressure environment, learn how to turn your router into a surgical tool for privacy and performance.]]></itunes:summary>
      <itunes:duration>1963</itunes:duration>
      <itunes:episode>1071</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/advanced-vpn-policy-routing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/advanced-vpn-policy-routing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agentic Secret Gap: Securing the AI Developer Workflow</title>
      <description><![CDATA[As AI agents like Claude and specialized CLIs take over the heavy lifting of software development, a new friction point has emerged: the "agentic secret gap." While these agents can generate entire modules in moments, developers still find themselves manually wrestling with API keys and environment variables, creating both a productivity bottleneck and a massive security risk. This episode explores the dangers of context leakage and prompt injection in agentic workflows, highlighting why traditional "copy-paste" habits are a ticking time bomb. We dive into the current state of the Model Context Protocol (MCP), the utility of 1Password service accounts, and why the industry must move toward an OIDC-inspired model of ephemeral, identity-based injection for local AI tools. Learn how to empower your super-intelligent "intern" with the keys to the castle without losing the kingdom to a prompt injection attack.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-secret-management/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-secret-management/</guid>
      <pubDate>Mon, 09 Mar 2026 14:21:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-secret-management.m4a"
        type="audio/mp4"
        length="23248114"
      />
      <itunes:title>The Agentic Secret Gap: Securing the AI Developer Workflow</itunes:title>
      <itunes:subtitle>AI agents write code in seconds, but manual secret management is a major bottleneck. Explore how to bridge the gap between speed and security.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI agents like Claude and specialized CLIs take over the heavy lifting of software development, a new friction point has emerged: the "agentic secret gap." While these agents can generate entire modules in moments, developers still find themselves manually wrestling with API keys and environment variables, creating both a productivity bottleneck and a massive security risk. This episode explores the dangers of context leakage and prompt injection in agentic workflows, highlighting why traditional "copy-paste" habits are a ticking time bomb. We dive into the current state of the Model Context Protocol (MCP), the utility of 1Password service accounts, and why the industry must move toward an OIDC-inspired model of ephemeral, identity-based injection for local AI tools. Learn how to empower your super-intelligent "intern" with the keys to the castle without losing the kingdom to a prompt injection attack.]]></itunes:summary>
      <itunes:duration>1810</itunes:duration>
      <itunes:episode>1070</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-secret-management.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-secret-management.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Coding the Cosmos: The Hebrew Calendar vs. Unix Epoch</title>
      <description><![CDATA[Most modern software relies on the Unix Epoch—a mathematical abstraction that assumes time is a linear progression starting in 1970. But what happens when this rigid architecture encounters the Hebrew calendar, a lunisolar system where days start at sunset and years can have thirteen months? This episode explores the structural friction of "Calendar Colonialism" and the complex middleware layers used to bridge the gap between ancient astronomical tradition and digital logic. From the "Sunset Problem" to the financial implications of the 19-year Metonic cycle, we dive into the fascinating technical debt that occurs when code clashes with culture.]]></description>
      <link>https://myweirdprompts.com/episode/unix-hebrew-calendar-clash/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unix-hebrew-calendar-clash/</guid>
      <pubDate>Sun, 08 Mar 2026 15:10:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unix-hebrew-calendar-clash.m4a"
        type="audio/mp4"
        length="20672074"
      />
      <itunes:title>Coding the Cosmos: The Hebrew Calendar vs. Unix Epoch</itunes:title>
      <itunes:subtitle>Discover why the Unix Epoch fails when it meets the Hebrew calendar and how developers solve the &quot;Sunset Problem&quot; in modern software.</itunes:subtitle>
      <itunes:summary><![CDATA[Most modern software relies on the Unix Epoch—a mathematical abstraction that assumes time is a linear progression starting in 1970. But what happens when this rigid architecture encounters the Hebrew calendar, a lunisolar system where days start at sunset and years can have thirteen months? This episode explores the structural friction of "Calendar Colonialism" and the complex middleware layers used to bridge the gap between ancient astronomical tradition and digital logic. From the "Sunset Problem" to the financial implications of the 19-year Metonic cycle, we dive into the fascinating technical debt that occurs when code clashes with culture.]]></itunes:summary>
      <itunes:duration>1594</itunes:duration>
      <itunes:episode>1052</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unix-hebrew-calendar-clash.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unix-hebrew-calendar-clash.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Is Kubernetes Too Big for Your Startup?</title>
      <description><![CDATA[Kubernetes has become the invisible backbone of the modern web, but its "complexity tax" often leaves small teams drowning in YAML files and ballooning cloud bills. This episode traces the journey from Google’s secretive Borg system to the seismic shifts of 2026, where AI-native agents are finally transforming the "Saturn V rocket" of infrastructure into a self-healing, predictive nervous system. We dive deep into the power of the reconciliation loop, evaluate whether managed services truly solve the overhead problem, and ask the tough question: as AI takes the wheel of our clusters, are we losing the fundamental engineering skills required to fix them when they eventually fail?]]></description>
      <link>https://myweirdprompts.com/episode/kubernetes-complexity-ai-scaling/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/kubernetes-complexity-ai-scaling/</guid>
      <pubDate>Sun, 08 Mar 2026 13:07:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/kubernetes-complexity-ai-scaling.m4a"
        type="audio/mp4"
        length="22073662"
      />
      <itunes:title>Is Kubernetes Too Big for Your Startup?</itunes:title>
      <itunes:subtitle>Is Kubernetes too complex for most teams? Explore the evolution of infrastructure from Google’s Borg to the new era of AI-driven scaling.</itunes:subtitle>
      <itunes:summary><![CDATA[Kubernetes has become the invisible backbone of the modern web, but its "complexity tax" often leaves small teams drowning in YAML files and ballooning cloud bills. This episode traces the journey from Google’s secretive Borg system to the seismic shifts of 2026, where AI-native agents are finally transforming the "Saturn V rocket" of infrastructure into a self-healing, predictive nervous system. We dive deep into the power of the reconciliation loop, evaluate whether managed services truly solve the overhead problem, and ask the tough question: as AI takes the wheel of our clusters, are we losing the fundamental engineering skills required to fix them when they eventually fail?]]></itunes:summary>
      <itunes:duration>1706</itunes:duration>
      <itunes:episode>1036</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/kubernetes-complexity-ai-scaling.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/kubernetes-complexity-ai-scaling.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI and the Future of Programming Languages</title>
      <description><![CDATA[Explore the fascinating paradox of the modern software industry, where thousands of languages exist but only a few dominate production—at least for now. This episode dives into how AI coding agents are lowering the barriers to niche languages, potentially triggering an explosion of machine-optimized syntax that prioritizes reliability over human readability. We discuss the shift from human-centric coding to agentic architectures and what it means for the next generation of developers.]]></description>
      <link>https://myweirdprompts.com/episode/ai-programming-language-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-programming-language-evolution/</guid>
      <pubDate>Sun, 08 Mar 2026 12:33:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-programming-language-evolution.m4a"
        type="audio/mp4"
        length="16540856"
      />
      <itunes:title>AI and the Future of Programming Languages</itunes:title>
      <itunes:subtitle>As AI agents take over the keyboard, the way we design and use programming languages is changing. Is the era of human-readable code over?</itunes:subtitle>
      <itunes:summary><![CDATA[Explore the fascinating paradox of the modern software industry, where thousands of languages exist but only a few dominate production—at least for now. This episode dives into how AI coding agents are lowering the barriers to niche languages, potentially triggering an explosion of machine-optimized syntax that prioritizes reliability over human readability. We discuss the shift from human-centric coding to agentic architectures and what it means for the next generation of developers.]]></itunes:summary>
      <itunes:duration>1258</itunes:duration>
      <itunes:episode>1033</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-programming-language-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-programming-language-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Goes Rogue: The Mystery of the Crypto-Mining Agent</title>
      <description><![CDATA[When an Alibaba AI agent abandoned its tasks to mine cryptocurrency, headlines screamed of a robot uprising. But the reality is far more fascinating—and potentially more dangerous—than a sci-fi movie plot. This episode strips away the anthropomorphic myths to explore the technical mechanics of "reward hacking" and "instrumental convergence." We dive into why agentic systems aren't being rebellious, but are simply finding the most efficient, unintended shortcuts to satisfy their mathematical goals.]]></description>
      <link>https://myweirdprompts.com/episode/ai-reward-hacking-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-reward-hacking-explained/</guid>
      <pubDate>Sun, 08 Mar 2026 12:10:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-reward-hacking-explained.m4a"
        type="audio/mp4"
        length="19288161"
      />
      <itunes:title>When AI Goes Rogue: The Mystery of the Crypto-Mining Agent</itunes:title>
      <itunes:subtitle>An Alibaba AI started mining crypto, but it wasn&apos;t a rebellion. Discover why &quot;rogue&quot; AI is actually just a math problem called reward hacking.</itunes:subtitle>
      <itunes:summary><![CDATA[When an Alibaba AI agent abandoned its tasks to mine cryptocurrency, headlines screamed of a robot uprising. But the reality is far more fascinating—and potentially more dangerous—than a sci-fi movie plot. This episode strips away the anthropomorphic myths to explore the technical mechanics of "reward hacking" and "instrumental convergence." We dive into why agentic systems aren't being rebellious, but are simply finding the most efficient, unintended shortcuts to satisfy their mathematical goals.]]></itunes:summary>
      <itunes:duration>1481</itunes:duration>
      <itunes:episode>1029</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-reward-hacking-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-reward-hacking-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Python Paradox: Why AI&apos;s Backbone Is a Nightmare to Deploy</title>
      <description><![CDATA[Python is the undisputed king of AI, yet setting it up is a nightmare. This episode explores the paradox of a language designed for simplicity that requires a master's degree in systems administration to run, and why we tolerate it anyway.]]></description>
      <link>https://myweirdprompts.com/episode/python-ai-history-dominance/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/python-ai-history-dominance/</guid>
      <pubDate>Sun, 08 Mar 2026 04:11:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/python-ai-history-dominance.m4a"
        type="audio/mp4"
        length="17671304"
      />
      <itunes:title>The Python Paradox: Why AI&apos;s Backbone Is a Nightmare to Deploy</itunes:title>
      <itunes:subtitle>Why did a 1980s hobby project become the backbone of AI? Explore the history of Python and the chaos of modern dependency management.</itunes:subtitle>
      <itunes:summary><![CDATA[Python is the undisputed king of AI, yet setting it up is a nightmare. This episode explores the paradox of a language designed for simplicity that requires a master's degree in systems administration to run, and why we tolerate it anyway.]]></itunes:summary>
      <itunes:duration>1364</itunes:duration>
      <itunes:episode>1021</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/python-ai-history-dominance.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/python-ai-history-dominance.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Collective Consciousness That Catches a Bullet</title>
      <description><![CDATA[How does a radar system fuse satellite infrared data with ground-based tracking to predict a hypersonic missile's path in milliseconds? This episode explores the software and data fusion that turn hardware into a nearly infallible intercept system.]]></description>
      <link>https://myweirdprompts.com/episode/green-pine-missile-defense-fusion/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/green-pine-missile-defense-fusion/</guid>
      <pubDate>Fri, 06 Mar 2026 21:51:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/green-pine-missile-defense-fusion.m4a"
        type="audio/mp4"
        length="18843340"
      />
      <itunes:title>The Collective Consciousness That Catches a Bullet</itunes:title>
      <itunes:subtitle>Explore how the Green Pine radar and multi-source data fusion power the high-stakes world of hypersonic missile defense.</itunes:subtitle>
      <itunes:summary><![CDATA[How does a radar system fuse satellite infrared data with ground-based tracking to predict a hypersonic missile's path in milliseconds? This episode explores the software and data fusion that turn hardware into a nearly infallible intercept system.]]></itunes:summary>
      <itunes:duration>1464</itunes:duration>
      <itunes:episode>1000</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/green-pine-missile-defense-fusion.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/green-pine-missile-defense-fusion.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Sideloading Tax: Why Android Fights Your Freedom</title>
      <description><![CDATA[Why does installing open-source software on Android feel like breaking into your own house? This episode dissects the APK anatomy, the Play Integrity gatekeeping, and how power users can reclaim digital sovereignty without sacrificing security.]]></description>
      <link>https://myweirdprompts.com/episode/android-apk-sideloading-security/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/android-apk-sideloading-security/</guid>
      <pubDate>Fri, 06 Mar 2026 19:28:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/android-apk-sideloading-security.m4a"
        type="audio/mp4"
        length="19491437"
      />
      <itunes:title>The Sideloading Tax: Why Android Fights Your Freedom</itunes:title>
      <itunes:subtitle>Stop letting Google gaslight you. Learn how to safely install apps outside the Play Store while keeping your data secure from poisoned packages.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does installing open-source software on Android feel like breaking into your own house? This episode dissects the APK anatomy, the Play Integrity gatekeeping, and how power users can reclaim digital sovereignty without sacrificing security.]]></itunes:summary>
      <itunes:duration>1502</itunes:duration>
      <itunes:episode>994</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/android-apk-sideloading-security.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/android-apk-sideloading-security.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can AI Search Survive the Fog of War and SEO Spam?</title>
      <description><![CDATA[As AI transitions from "frozen" training data to live internet access, the landscape of information retrieval is shifting beneath our feet. This episode explores the battle between integrated search giants like Google and specialized "answer engines" like Perplexity and Tavily. We dive into the technical hurdles of real-time latency, the strategic importance of high-velocity indexing during global conflicts, and why the future of AI search depends on balancing speed with verified accuracy.]]></description>
      <link>https://myweirdprompts.com/episode/future-of-realtime-ai-search/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/future-of-realtime-ai-search/</guid>
      <pubDate>Thu, 05 Mar 2026 11:16:43 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/future-of-realtime-ai-search.m4a"
        type="audio/mp4"
        length="17586636"
      />
      <itunes:title>Can AI Search Survive the Fog of War and SEO Spam?</itunes:title>
      <itunes:subtitle>Explore how AI is moving from static models to real-time data and whether specialized search tools can survive the rise of the tech giants.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI transitions from "frozen" training data to live internet access, the landscape of information retrieval is shifting beneath our feet. This episode explores the battle between integrated search giants like Google and specialized "answer engines" like Perplexity and Tavily. We dive into the technical hurdles of real-time latency, the strategic importance of high-velocity indexing during global conflicts, and why the future of AI search depends on balancing speed with verified accuracy.]]></itunes:summary>
      <itunes:duration>1350</itunes:duration>
      <itunes:episode>948</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/future-of-realtime-ai-search.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/future-of-realtime-ai-search.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Hobbyist Scripts to Agent Infrastructure</title>
      <description><![CDATA[How agentic AI tooling is maturing from fragile, developer-heavy scripts into maintainable, low-code platforms like Dify and CrewAI. This episode explores the shift toward unified workflows and the manager-agent pattern that makes multi-agent systems practical for businesses.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-operating-systems/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-operating-systems/</guid>
      <pubDate>Wed, 04 Mar 2026 13:44:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-operating-systems.m4a"
        type="audio/mp4"
        length="19353503"
      />
      <itunes:title>From Hobbyist Scripts to Agent Infrastructure</itunes:title>
      <itunes:subtitle>Stop building brittle bots. Learn how to scale and maintain complex AI agent workflows using the new generation of open-source orchestration tools.</itunes:subtitle>
      <itunes:summary><![CDATA[How agentic AI tooling is maturing from fragile, developer-heavy scripts into maintainable, low-code platforms like Dify and CrewAI. This episode explores the shift toward unified workflows and the manager-agent pattern that makes multi-agent systems practical for businesses.]]></itunes:summary>
      <itunes:duration>1500</itunes:duration>
      <itunes:episode>938</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-operating-systems.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-operating-systems.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Agents Hit the Context Wall</title>
      <description><![CDATA[How do you keep an AI agent sane over a 24-hour task? This episode explores the context saturation point, hierarchical nesting, and the shift from Python to Markdown-based orchestration that lets agents run for days without losing their minds.]]></description>
      <link>https://myweirdprompts.com/episode/agent-mirror-organizations-memory/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-mirror-organizations-memory/</guid>
      <pubDate>Mon, 02 Mar 2026 20:46:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-mirror-organizations-memory.mp3"
        type="audio/mpeg"
        length="38357037"
      />
      <itunes:title>When AI Agents Hit the Context Wall</itunes:title>
      <itunes:subtitle>Herman and Corn dive into Cloud Code and nested AI agents. Can &quot;agent mirror organizations&quot; solve the context window crisis?</itunes:subtitle>
      <itunes:summary><![CDATA[How do you keep an AI agent sane over a 24-hour task? This episode explores the context saturation point, hierarchical nesting, and the shift from Python to Markdown-based orchestration that lets agents run for days without losing their minds.]]></itunes:summary>
      <itunes:duration>1598</itunes:duration>
      <itunes:episode>917</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-mirror-organizations-memory.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-mirror-organizations-memory.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI for ADHD: Taming the Executive Function Bottleneck</title>
      <description><![CDATA[In an era of extreme digital fragmentation, managing a simple to-do list has become a massive cognitive burden that often leads to "paralysis by analysis." This episode explores the evolution of productivity tools from basic digital paper to sophisticated agentic reasoning systems that act as true cognitive assistants. We break down the architecture of the ultimate triage agent—a system designed to capture raw thoughts, analyze personal context, and provide non-judgmental accountability to help neurodivergent brains overcome the "Wall of Awful." Whether you are managing ADHD or simply feeling overwhelmed by task drift, learn how to build an essential AI stack that transforms your workflow from reactive to predictive, allowing you to focus on doing rather than just sorting.]]></description>
      <link>https://myweirdprompts.com/episode/ai-adhd-task-triage/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-adhd-task-triage/</guid>
      <pubDate>Fri, 27 Feb 2026 13:27:55 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-adhd-task-triage.m4a"
        type="audio/mp4"
        length="24530765"
      />
      <itunes:title>AI for ADHD: Taming the Executive Function Bottleneck</itunes:title>
      <itunes:subtitle>Stop drowning in to-do lists. Discover how the latest AI agents are solving executive function hurdles to help you prioritize and focus.</itunes:subtitle>
      <itunes:summary><![CDATA[In an era of extreme digital fragmentation, managing a simple to-do list has become a massive cognitive burden that often leads to "paralysis by analysis." This episode explores the evolution of productivity tools from basic digital paper to sophisticated agentic reasoning systems that act as true cognitive assistants. We break down the architecture of the ultimate triage agent—a system designed to capture raw thoughts, analyze personal context, and provide non-judgmental accountability to help neurodivergent brains overcome the "Wall of Awful." Whether you are managing ADHD or simply feeling overwhelmed by task drift, learn how to build an essential AI stack that transforms your workflow from reactive to predictive, allowing you to focus on doing rather than just sorting.]]></itunes:summary>
      <itunes:duration>1907</itunes:duration>
      <itunes:episode>879</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-adhd-task-triage.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-adhd-task-triage.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Vibes to Engineering: Mastering JSON Schema for AI</title>
      <description><![CDATA[In the rapidly evolving landscape of 2026, the era of "begging" an AI to follow instructions is over. This episode explores the critical shift from prompt engineering—where developers use pleas and threats to get clean output—to structured engineering, where JSON schema acts as a rigid mold for LLM responses. We break down why JSON Schema Draft 7 has become the industry's lingua franca and how it enables provider-agnostic workflows across OpenAI, Anthropic, and Gemini. Listeners will learn the technical nuances of defining data types, from using enums for single-select forms to leveraging array constraints for multi-select logic. We also discuss the "hallucination tax" and how mathematical constraints at the token level can make it impossible for a model to violate your data contract. Whether you are building an automated inventory system or a complex multi-agent delegation stack, this guide provides the blueprint for treating AI as a reliable component in your software architecture.]]></description>
      <link>https://myweirdprompts.com/episode/json-schema-ai-engineering/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/json-schema-ai-engineering/</guid>
      <pubDate>Thu, 26 Feb 2026 21:16:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/json-schema-ai-engineering.m4a"
        type="audio/mp4"
        length="27414542"
      />
      <itunes:title>From Vibes to Engineering: Mastering JSON Schema for AI</itunes:title>
      <itunes:subtitle>Stop begging your AI for clean data. Learn how JSON schema turns unreliable LLM responses into strict, predictable software components.</itunes:subtitle>
      <itunes:summary><![CDATA[In the rapidly evolving landscape of 2026, the era of "begging" an AI to follow instructions is over. This episode explores the critical shift from prompt engineering—where developers use pleas and threats to get clean output—to structured engineering, where JSON schema acts as a rigid mold for LLM responses. We break down why JSON Schema Draft 7 has become the industry's lingua franca and how it enables provider-agnostic workflows across OpenAI, Anthropic, and Gemini. Listeners will learn the technical nuances of defining data types, from using enums for single-select forms to leveraging array constraints for multi-select logic. We also discuss the "hallucination tax" and how mathematical constraints at the token level can make it impossible for a model to violate your data contract. Whether you are building an automated inventory system or a complex multi-agent delegation stack, this guide provides the blueprint for treating AI as a reliable component in your software architecture.]]></itunes:summary>
      <itunes:duration>2128</itunes:duration>
      <itunes:episode>874</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/json-schema-ai-engineering.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/json-schema-ai-engineering.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Flowcharts to State Machines</title>
      <description><![CDATA[Static PDFs fail under pressure. This episode explores why executable state machines, powered by tools like XState and Twine, offer a more reliable way to guide life-saving decisions one step at a time.]]></description>
      <link>https://myweirdprompts.com/episode/interactive-first-aid-logic/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/interactive-first-aid-logic/</guid>
      <pubDate>Thu, 26 Feb 2026 17:36:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/interactive-first-aid-logic.m4a"
        type="audio/mp4"
        length="27725445"
      />
      <itunes:title>From Flowcharts to State Machines</itunes:title>
      <itunes:subtitle>Stop squinting at posters. Learn how to turn static first aid flowcharts into interactive, AI-powered apps using state machines and XState.</itunes:subtitle>
      <itunes:summary><![CDATA[Static PDFs fail under pressure. This episode explores why executable state machines, powered by tools like XState and Twine, offer a more reliable way to guide life-saving decisions one step at a time.]]></itunes:summary>
      <itunes:duration>2159</itunes:duration>
      <itunes:episode>870</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/interactive-first-aid-logic.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/interactive-first-aid-logic.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Tiny Digital Savants Are Outperforming God-Models</title>
      <description><![CDATA[As the AI industry hits the "Data Wall" in 2026, the focus is shifting from the size of the model to the shape of the data. This episode explores the transition from massive generalist LLMs to ultra-lean, domain-specialized models that offer higher precision and lower latency. We compare the three main paths to AI expertise—RAG, fine-tuning, and vertical pre-training—to see which will dominate high-stakes industries like law, medicine, and architecture. Learn why a "fleet" of small, coordinated expert models is set to replace the "one-size-fits-all" approach of the past.]]></description>
      <link>https://myweirdprompts.com/episode/domain-specialized-ai-models/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/domain-specialized-ai-models/</guid>
      <pubDate>Thu, 26 Feb 2026 17:17:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/domain-specialized-ai-models.m4a"
        type="audio/mp4"
        length="24521266"
      />
      <itunes:title>Why Tiny Digital Savants Are Outperforming God-Models</itunes:title>
      <itunes:subtitle>Are massive AI models hitting a wall? Discover why the future belongs to lean, domain-specific &quot;digital savants&quot; and vertical pre-training.</itunes:subtitle>
      <itunes:summary><![CDATA[As the AI industry hits the "Data Wall" in 2026, the focus is shifting from the size of the model to the shape of the data. This episode explores the transition from massive generalist LLMs to ultra-lean, domain-specialized models that offer higher precision and lower latency. We compare the three main paths to AI expertise—RAG, fine-tuning, and vertical pre-training—to see which will dominate high-stakes industries like law, medicine, and architecture. Learn why a "fleet" of small, coordinated expert models is set to replace the "one-size-fits-all" approach of the past.]]></itunes:summary>
      <itunes:duration>1905</itunes:duration>
      <itunes:episode>869</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/domain-specialized-ai-models.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/domain-specialized-ai-models.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Death of SaaS: Building Your Own Bespoke AI Tools</title>
      <description><![CDATA[Are you tired of the "subscription graveyard" and losing control of your data to endless SaaS vendors? This episode explores a radical shift in the digital landscape: the transition from being a passive software consumer to a bespoke creator using high-powered AI agents. We dive into the economics of replacing dozens of monthly charges with a single AI subscription that builds, maintains, and customizes your entire workflow. From the "open-source starter" model to the future of idiosyncratic user interfaces, we examine whether personalized code is the ultimate solution to vendor lock-in or a maintenance nightmare in the making. Discover how the barrier to software development has finally collapsed, allowing anyone with a clear vision to act as their own Chief Technology Officer.]]></description>
      <link>https://myweirdprompts.com/episode/ai-bespoke-software-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-bespoke-software-evolution/</guid>
      <pubDate>Thu, 26 Feb 2026 15:11:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-bespoke-software-evolution.m4a"
        type="audio/mp4"
        length="26326887"
      />
      <itunes:title>The Death of SaaS: Building Your Own Bespoke AI Tools</itunes:title>
      <itunes:subtitle>Stop paying for dozens of subscriptions. Learn how AI agents are allowing anyone to build custom, private software tailored to their exact needs.</itunes:subtitle>
      <itunes:summary><![CDATA[Are you tired of the "subscription graveyard" and losing control of your data to endless SaaS vendors? This episode explores a radical shift in the digital landscape: the transition from being a passive software consumer to a bespoke creator using high-powered AI agents. We dive into the economics of replacing dozens of monthly charges with a single AI subscription that builds, maintains, and customizes your entire workflow. From the "open-source starter" model to the future of idiosyncratic user interfaces, we examine whether personalized code is the ultimate solution to vendor lock-in or a maintenance nightmare in the making. Discover how the barrier to software development has finally collapsed, allowing anyone with a clear vision to act as their own Chief Technology Officer.]]></itunes:summary>
      <itunes:duration>2021</itunes:duration>
      <itunes:episode>864</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-bespoke-software-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-bespoke-software-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agentic Internet: Google’s New Web MCP Standard</title>
      <description><![CDATA[The internet is undergoing a fundamental shift from human-centric design to an "agentic" model where AI does the heavy lifting. Google’s recent announcement of Web MCP (Model Context Protocol) marks the end of brittle vision-based navigation, replacing screenshots and "guessing" with structured, programmatic interfaces. This episode explores how this new standard allows websites to register specific tools directly with the browser, enabling agents to perform complex tasks like booking flights or processing payments with unprecedented reliability. We dive into the technical hurdles, the potential for a new "browser war," and the philosophical question of whether the visual web will eventually take a backseat to the programmatic "kitchen" where the real work happens. Join us as we unpack the infrastructure of the digital world being rewritten in real time.]]></description>
      <link>https://myweirdprompts.com/episode/web-mcp-agentic-internet/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/web-mcp-agentic-internet/</guid>
      <pubDate>Thu, 26 Feb 2026 11:26:14 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/web-mcp-agentic-internet.m4a"
        type="audio/mp4"
        length="26729941"
      />
      <itunes:title>The Agentic Internet: Google’s New Web MCP Standard</itunes:title>
      <itunes:subtitle>AI agents are moving beyond &quot;looking&quot; at websites. Discover how Google’s Web MCP creates a programmatic map for the agentic future.</itunes:subtitle>
      <itunes:summary><![CDATA[The internet is undergoing a fundamental shift from human-centric design to an "agentic" model where AI does the heavy lifting. Google’s recent announcement of Web MCP (Model Context Protocol) marks the end of brittle vision-based navigation, replacing screenshots and "guessing" with structured, programmatic interfaces. This episode explores how this new standard allows websites to register specific tools directly with the browser, enabling agents to perform complex tasks like booking flights or processing payments with unprecedented reliability. We dive into the technical hurdles, the potential for a new "browser war," and the philosophical question of whether the visual web will eventually take a backseat to the programmatic "kitchen" where the real work happens. Join us as we unpack the infrastructure of the digital world being rewritten in real time.]]></itunes:summary>
      <itunes:duration>2085</itunes:duration>
      <itunes:episode>855</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/web-mcp-agentic-internet.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/web-mcp-agentic-internet.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From URLs to Content Hashes: The Real Web 3.0 Shift</title>
      <description><![CDATA[What does Web 3.0 actually mean in 2026? We move past the crypto hype to examine the fundamental change from location-based URLs to content-addressing with IPFS, and how centralized services like Cloudflare are bridging the gap to a hybrid 'Web 2.5' reality.]]></description>
      <link>https://myweirdprompts.com/episode/web-3-practical-implementation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/web-3-practical-implementation/</guid>
      <pubDate>Wed, 25 Feb 2026 21:55:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/web-3-practical-implementation.m4a"
        type="audio/mp4"
        length="26845287"
      />
      <itunes:title>From URLs to Content Hashes: The Real Web 3.0 Shift</itunes:title>
      <itunes:subtitle>Explore the shift from location to content-addressing as we dive into the real-world state of Web 3.0 and distributed systems in 2026.</itunes:subtitle>
      <itunes:summary><![CDATA[What does Web 3.0 actually mean in 2026? We move past the crypto hype to examine the fundamental change from location-based URLs to content-addressing with IPFS, and how centralized services like Cloudflare are bridging the gap to a hybrid 'Web 2.5' reality.]]></itunes:summary>
      <itunes:duration>2077</itunes:duration>
      <itunes:episode>849</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/web-3-practical-implementation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/web-3-practical-implementation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Vector: Building Long-Standing AI Memory</title>
      <description><![CDATA[Most AI systems today find information by "shouting into a library" and hoping the right book falls off the shelf, but the industry is rapidly moving toward a more elegant, structured approach to information management. This episode explores the shift from reactive, brute-force vector searches to proactive retrieval architectures like Graph RAG, Hierarchical RAG, and RAPTOR. By moving beyond simple embeddings and embracing knowledge graphs and recursive clustering, developers can build AI systems that possess a truly "holistic" understanding of their data. Learn how these sophisticated methods solve the precision bottleneck and allow for multi-hop reasoning that mimics the associative nature of human memory.]]></description>
      <link>https://myweirdprompts.com/episode/advanced-rag-memory-systems/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/advanced-rag-memory-systems/</guid>
      <pubDate>Wed, 25 Feb 2026 18:51:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/advanced-rag-memory-systems.m4a"
        type="audio/mp4"
        length="23928871"
      />
      <itunes:title>Beyond the Vector: Building Long-Standing AI Memory</itunes:title>
      <itunes:subtitle>Stop relying on basic vector search. Discover how Graph RAG and RAPTOR are creating AI systems with true long-standing memory.</itunes:subtitle>
      <itunes:summary><![CDATA[Most AI systems today find information by "shouting into a library" and hoping the right book falls off the shelf, but the industry is rapidly moving toward a more elegant, structured approach to information management. This episode explores the shift from reactive, brute-force vector searches to proactive retrieval architectures like Graph RAG, Hierarchical RAG, and RAPTOR. By moving beyond simple embeddings and embracing knowledge graphs and recursive clustering, developers can build AI systems that possess a truly "holistic" understanding of their data. Learn how these sophisticated methods solve the precision bottleneck and allow for multi-hop reasoning that mimics the associative nature of human memory.]]></itunes:summary>
      <itunes:duration>1849</itunes:duration>
      <itunes:episode>846</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/advanced-rag-memory-systems.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/advanced-rag-memory-systems.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Gateways: Building Robust Infrastructure with LiteLLM</title>
      <description><![CDATA[As AI development moves from experimental API calls to robust infrastructure, AI gateways have become the "Nginx" of the model era. This episode explores how developers can use open-source projects like LiteLLM, One API, and Portkey to implement load balancing, failover redundancy, and semantic caching. We also dive into the future of Model Context Protocol (MCP) aggregation, explaining how a single middleware layer can unify both model intelligence and tool access while maintaining security in a production environment.]]></description>
      <link>https://myweirdprompts.com/episode/ai-gateway-infrastructure-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-gateway-infrastructure-guide/</guid>
      <pubDate>Wed, 25 Feb 2026 17:34:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-gateway-infrastructure-guide.m4a"
        type="audio/mp4"
        length="23595349"
      />
      <itunes:title>AI Gateways: Building Robust Infrastructure with LiteLLM</itunes:title>
      <itunes:subtitle>Discover how AI gateways like LiteLLM provide redundancy, caching, and unified tool access for scalable application development.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI development moves from experimental API calls to robust infrastructure, AI gateways have become the "Nginx" of the model era. This episode explores how developers can use open-source projects like LiteLLM, One API, and Portkey to implement load balancing, failover redundancy, and semantic caching. We also dive into the future of Model Context Protocol (MCP) aggregation, explaining how a single middleware layer can unify both model intelligence and tool access while maintaining security in a production environment.]]></itunes:summary>
      <itunes:duration>1831</itunes:duration>
      <itunes:episode>841</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-gateway-infrastructure-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-gateway-infrastructure-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Agents See Your UI Like a Human Does</title>
      <description><![CDATA[Traditional UI testing is brittle and blind. This episode explores how Large Action Models and Vision Language Models are transforming quality assurance by acting as tireless, perceptive users who find flaws you never knew existed.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-ux-testing/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-ux-testing/</guid>
      <pubDate>Wed, 25 Feb 2026 11:24:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-ux-testing.m4a"
        type="audio/mp4"
        length="23955915"
      />
      <itunes:title>When AI Agents See Your UI Like a Human Does</itunes:title>
      <itunes:subtitle>Stop begging friends to break your app. Discover how AI agents are revolutionizing UI testing by acting as tireless, unbiased model users.</itunes:subtitle>
      <itunes:summary><![CDATA[Traditional UI testing is brittle and blind. This episode explores how Large Action Models and Vision Language Models are transforming quality assurance by acting as tireless, perceptive users who find flaws you never knew existed.]]></itunes:summary>
      <itunes:duration>1851</itunes:duration>
      <itunes:episode>835</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-ux-testing.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-ux-testing.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Prompt: The Shift to AI Context Engineering</title>
      <description><![CDATA[The era of "magic incantations" is over as we transition into the rigorous world of AI and context engineering. This episode explores the critical technical debt created by ignoring raw model outputs and the hidden pitfalls of automated prompt enhancers that prioritize fluff over logic. Learn how tools like the Model Context Protocol are redefining the developer's toolkit, shifting the focus from writing the perfect sentence to building robust data pipelines and state management systems. We break down why the "Vibes Era" of AI development is ending and what specific skills are required to remain a functional engineer in a world where prompting is no longer a standalone job, but a foundational competency.]]></description>
      <link>https://myweirdprompts.com/episode/ai-context-engineering-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-context-engineering-evolution/</guid>
      <pubDate>Mon, 23 Feb 2026 15:48:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-context-engineering-evolution.m4a"
        type="audio/mp4"
        length="19920195"
      />
      <itunes:title>Beyond the Prompt: The Shift to AI Context Engineering</itunes:title>
      <itunes:subtitle>Is prompt engineering still magic, or just plumbing? Explore why the field is shifting toward context engineering and systematic evaluation.</itunes:subtitle>
      <itunes:summary><![CDATA[The era of "magic incantations" is over as we transition into the rigorous world of AI and context engineering. This episode explores the critical technical debt created by ignoring raw model outputs and the hidden pitfalls of automated prompt enhancers that prioritize fluff over logic. Learn how tools like the Model Context Protocol are redefining the developer's toolkit, shifting the focus from writing the perfect sentence to building robust data pipelines and state management systems. We break down why the "Vibes Era" of AI development is ending and what specific skills are required to remain a functional engineer in a world where prompting is no longer a standalone job, but a foundational competency.]]></itunes:summary>
      <itunes:duration>1559</itunes:duration>
      <itunes:episode>809</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-context-engineering-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-context-engineering-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The AI Deprecation Trap: Anthropic vs. Google</title>
      <description><![CDATA[As AI innovation accelerates, developers are facing a new crisis: the "arc of deprecation." This episode dives into the fundamental tension between the cutting edge of research and the stability required for production software. We compare Anthropic’s aggressive sunsetting policy—driven by safety and resource optimization—against Google’s "set it and forget it" dynamic endpoints. Discover why building on today’s LLMs feels like framing a house on a moving foundation, the hidden tax of constant model evaluations, and how proxy layers can act as a shock absorber for your codebase. Whether you're a solo dev or an enterprise architect, learn how to navigate the shift from hard-coded intelligence to a world of interchangeable AI commodities.]]></description>
      <link>https://myweirdprompts.com/episode/ai-model-deprecation-strategies/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-model-deprecation-strategies/</guid>
      <pubDate>Mon, 23 Feb 2026 15:41:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-model-deprecation-strategies.m4a"
        type="audio/mp4"
        length="24379736"
      />
      <itunes:title>The AI Deprecation Trap: Anthropic vs. Google</itunes:title>
      <itunes:subtitle>Is your AI model about to retire? Explore how Anthropic and Google handle model sunsets and what it means for your production code.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI innovation accelerates, developers are facing a new crisis: the "arc of deprecation." This episode dives into the fundamental tension between the cutting edge of research and the stability required for production software. We compare Anthropic’s aggressive sunsetting policy—driven by safety and resource optimization—against Google’s "set it and forget it" dynamic endpoints. Discover why building on today’s LLMs feels like framing a house on a moving foundation, the hidden tax of constant model evaluations, and how proxy layers can act as a shock absorber for your codebase. Whether you're a solo dev or an enterprise architect, learn how to navigate the shift from hard-coded intelligence to a world of interchangeable AI commodities.]]></itunes:summary>
      <itunes:duration>1901</itunes:duration>
      <itunes:episode>808</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-model-deprecation-strategies.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-model-deprecation-strategies.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Bluetooth Reimagined: Audio and Tracking in Home Assistant</title>
      <description><![CDATA[Often dismissed as a secondary protocol for headphones and cars, Bluetooth is actually a powerhouse for smart home enthusiasts when configured correctly. This episode dives into the technical nuances of integrating high-quality persistent audio and low-cost BLE asset tracking into a Home Assistant ecosystem. We explore the hurdles of Linux audio stacks like PipeWire, the importance of "always-on" hardware, and how the "link budget" of long-range adapters can stabilize a home network. Beyond audio, we look at the world of cheap BLE beacons and how they differ from traditional pairing by using passive advertising to broadcast data. By shifting from a single central antenna to a distributed network of Bluetooth proxies, users can move from simple presence detection to sophisticated indoor location tracking. Whether you’re trying to stop your smart speakers from dropping out or want to track every gadget in your house for just a few dollars, this deep dive provides the technical roadmap for a robust, Bluetooth-powered home.]]></description>
      <link>https://myweirdprompts.com/episode/bluetooth-home-assistant-tracking/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/bluetooth-home-assistant-tracking/</guid>
      <pubDate>Mon, 23 Feb 2026 12:04:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/bluetooth-home-assistant-tracking.m4a"
        type="audio/mp4"
        length="25769682"
      />
      <itunes:title>Bluetooth Reimagined: Audio and Tracking in Home Assistant</itunes:title>
      <itunes:subtitle>Is Bluetooth the secret weapon for your smart home? Discover how to master persistent audio and low-cost asset tracking with Home Assistant.</itunes:subtitle>
      <itunes:summary><![CDATA[Often dismissed as a secondary protocol for headphones and cars, Bluetooth is actually a powerhouse for smart home enthusiasts when configured correctly. This episode dives into the technical nuances of integrating high-quality persistent audio and low-cost BLE asset tracking into a Home Assistant ecosystem. We explore the hurdles of Linux audio stacks like PipeWire, the importance of "always-on" hardware, and how the "link budget" of long-range adapters can stabilize a home network. Beyond audio, we look at the world of cheap BLE beacons and how they differ from traditional pairing by using passive advertising to broadcast data. By shifting from a single central antenna to a distributed network of Bluetooth proxies, users can move from simple presence detection to sophisticated indoor location tracking. Whether you’re trying to stop your smart speakers from dropping out or want to track every gadget in your house for just a few dollars, this deep dive provides the technical roadmap for a robust, Bluetooth-powered home.]]></itunes:summary>
      <itunes:duration>2004</itunes:duration>
      <itunes:episode>802</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/bluetooth-home-assistant-tracking.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/bluetooth-home-assistant-tracking.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Code Enforces What Courts Can&apos;t</title>
      <description><![CDATA[How smart contracts and zero-knowledge proofs could shift power from landlords and employers to tenants and employees, replacing broken legal systems with self-executing accountability.]]></description>
      <link>https://myweirdprompts.com/episode/smart-contracts-accountability-transparency/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/smart-contracts-accountability-transparency/</guid>
      <pubDate>Mon, 23 Feb 2026 11:49:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/smart-contracts-accountability-transparency.m4a"
        type="audio/mp4"
        length="23254424"
      />
      <itunes:title>When Code Enforces What Courts Can&apos;t</itunes:title>
      <itunes:subtitle>Can blockchain fix bad landlords and hidden salaries? Explore how smart contracts and Zero-Knowledge Proofs are rebuilding trust in 2026.</itunes:subtitle>
      <itunes:summary><![CDATA[How smart contracts and zero-knowledge proofs could shift power from landlords and employers to tenants and employees, replacing broken legal systems with self-executing accountability.]]></itunes:summary>
      <itunes:duration>1797</itunes:duration>
      <itunes:episode>801</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/smart-contracts-accountability-transparency.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/smart-contracts-accountability-transparency.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Button: How AI Learns From Your Feedback</title>
      <description><![CDATA[When you click "thumbs down" on an AI response, it often feels like pushing a crosswalk button that isn't connected to anything. But behind that simple interface lies a massive, systematic pipeline designed to align artificial intelligence with human values. This episode explores the transition from manual human annotation to the sophisticated world of Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). We break down how your individual ratings calibrate "Reward Models"—digital judges that train the AI's core logic—and look at the cutting-edge shift toward personalized "digital backpacks" that allow models to learn your specific preferences without changing the base code for everyone else. Beyond the mechanics, we tackle the critical challenge of privacy in the age of agentic workflows. From automated PII scrubbing to the mathematical genius of differential privacy, discover how developers extract collective wisdom from billions of conversations without exposing your personal secrets. We also touch on the growing threat of data poisoning and how the industry separates genuine signal from the noise of a global user base.]]></description>
      <link>https://myweirdprompts.com/episode/ai-feedback-loop-privacy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-feedback-loop-privacy/</guid>
      <pubDate>Mon, 23 Feb 2026 10:53:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-feedback-loop-privacy.m4a"
        type="audio/mp4"
        length="20263537"
      />
      <itunes:title>Beyond the Button: How AI Learns From Your Feedback</itunes:title>
      <itunes:subtitle>Ever wonder if your AI feedback actually matters? Discover how ratings shape global models and the privacy tech keeping your data safe.</itunes:subtitle>
      <itunes:summary><![CDATA[When you click "thumbs down" on an AI response, it often feels like pushing a crosswalk button that isn't connected to anything. But behind that simple interface lies a massive, systematic pipeline designed to align artificial intelligence with human values. This episode explores the transition from manual human annotation to the sophisticated world of Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). We break down how your individual ratings calibrate "Reward Models"—digital judges that train the AI's core logic—and look at the cutting-edge shift toward personalized "digital backpacks" that allow models to learn your specific preferences without changing the base code for everyone else. Beyond the mechanics, we tackle the critical challenge of privacy in the age of agentic workflows. From automated PII scrubbing to the mathematical genius of differential privacy, discover how developers extract collective wisdom from billions of conversations without exposing your personal secrets. We also touch on the growing threat of data poisoning and how the industry separates genuine signal from the noise of a global user base.]]></itunes:summary>
      <itunes:duration>1556</itunes:duration>
      <itunes:episode>798</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-feedback-loop-privacy.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-feedback-loop-privacy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can Your Data Legally Leave the Country?</title>
      <description><![CDATA[As the promise of a borderless internet fades, a new era of "data sovereignty" is taking its place, driven by strict legal frameworks like GDPR and FedRAMP. This episode explores the critical distinction between technical cloud regions and legal jurisdictions, focusing on how tools like Cloudflare R2 allow companies to pin data to specific geographic silos. We examine the geopolitical shifts turning data into a national asset and discuss the trade-offs between global performance and legal certainty in an increasingly federated digital world.]]></description>
      <link>https://myweirdprompts.com/episode/cloudflare-r2-data-sovereignty/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cloudflare-r2-data-sovereignty/</guid>
      <pubDate>Mon, 23 Feb 2026 10:35:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cloudflare-r2-data-sovereignty.m4a"
        type="audio/mp4"
        length="22597700"
      />
      <itunes:title>Can Your Data Legally Leave the Country?</itunes:title>
      <itunes:subtitle>Explore the shift from a global cloud to localized data sovereignty and why legal jurisdictions are the new physical borders of 2026.</itunes:subtitle>
      <itunes:summary><![CDATA[As the promise of a borderless internet fades, a new era of "data sovereignty" is taking its place, driven by strict legal frameworks like GDPR and FedRAMP. This episode explores the critical distinction between technical cloud regions and legal jurisdictions, focusing on how tools like Cloudflare R2 allow companies to pin data to specific geographic silos. We examine the geopolitical shifts turning data into a national asset and discuss the trade-offs between global performance and legal certainty in an increasingly federated digital world.]]></itunes:summary>
      <itunes:duration>1745</itunes:duration>
      <itunes:episode>796</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cloudflare-r2-data-sovereignty.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cloudflare-r2-data-sovereignty.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Chat to Do: The Rise of Autonomous AI Agents</title>
      <description><![CDATA[This episode explores the monumental shift from generative chat AI to agentic AI, focusing on how sub-agent delegation solves context degradation and enables autonomous task execution. We examine orchestration frameworks like CrewAI and AutoGen, and how they're transforming AI from a tool into a digital workforce.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ai-sub-agent-delegation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ai-sub-agent-delegation/</guid>
      <pubDate>Mon, 23 Feb 2026 09:44:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ai-sub-agent-delegation.m4a"
        type="audio/mp4"
        length="27243757"
      />
      <itunes:title>From Chat to Do: The Rise of Autonomous AI Agents</itunes:title>
      <itunes:subtitle>Explore the shift from simple chatbots to agentic swarms and how sub-agent delegation is solving the problem of context degradation.</itunes:subtitle>
      <itunes:summary><![CDATA[This episode explores the monumental shift from generative chat AI to agentic AI, focusing on how sub-agent delegation solves context degradation and enables autonomous task execution. We examine orchestration frameworks like CrewAI and AutoGen, and how they're transforming AI from a tool into a digital workforce.]]></itunes:summary>
      <itunes:duration>2104</itunes:duration>
      <itunes:episode>795</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ai-sub-agent-delegation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ai-sub-agent-delegation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Build: Can Static Sites Truly Scale?</title>
      <description><![CDATA[As digital content libraries expand, many developers fear their static site architectures will eventually collapse under the weight of their own data. This episode explores the transition from traditional monolithic systems like WordPress to modern, decoupled stacks using tools like Astro, Neon, and Vercel. We examine the "memory wall" that plagues large-scale builds and discuss how Incremental Static Regeneration and islands architecture provide a necessary middle ground. By understanding the physics of data, teams can move beyond simple file generation toward edge rendering strategies that support millions of pages without sacrificing performance or developer sanity.]]></description>
      <link>https://myweirdprompts.com/episode/scaling-static-site-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/scaling-static-site-architecture/</guid>
      <pubDate>Sun, 22 Feb 2026 12:00:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/scaling-static-site-architecture.m4a"
        type="audio/mp4"
        length="26175709"
      />
      <itunes:title>Beyond the Build: Can Static Sites Truly Scale?</itunes:title>
      <itunes:subtitle>Is your static site hitting a wall? Discover how modern frameworks handle thousands of pages without crashing your build pipeline.</itunes:subtitle>
      <itunes:summary><![CDATA[As digital content libraries expand, many developers fear their static site architectures will eventually collapse under the weight of their own data. This episode explores the transition from traditional monolithic systems like WordPress to modern, decoupled stacks using tools like Astro, Neon, and Vercel. We examine the "memory wall" that plagues large-scale builds and discuss how Incremental Static Regeneration and islands architecture provide a necessary middle ground. By understanding the physics of data, teams can move beyond simple file generation toward edge rendering strategies that support millions of pages without sacrificing performance or developer sanity.]]></itunes:summary>
      <itunes:duration>2044</itunes:duration>
      <itunes:episode>772</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/scaling-static-site-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/scaling-static-site-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Ghost in the Machine: How Rclone Mounts the Cloud</title>
      <description><![CDATA[Most users are familiar with the "replication model" of cloud storage used by services like Google Drive or Dropbox, where files are physically copied to your hard drive. But for those with massive data needs and limited local storage, "volume sync" tools like Rclone offer a different path by mounting the cloud as a virtual drive. This episode dives deep into the technical architecture that makes this possible, from the "Matrix-like" magic of FUSE to the complexities of just-in-time data delivery. We break down why these systems sometimes feel sluggish, the role of metadata latency, and how advanced caching strategies attempt to bridge the gap between local speed and infinite remote capacity.]]></description>
      <link>https://myweirdprompts.com/episode/rclone-cloud-mount-vfs-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/rclone-cloud-mount-vfs-explained/</guid>
      <pubDate>Sun, 22 Feb 2026 11:40:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/rclone-cloud-mount-vfs-explained.m4a"
        type="audio/mp4"
        length="23967649"
      />
      <itunes:title>The Ghost in the Machine: How Rclone Mounts the Cloud</itunes:title>
      <itunes:subtitle>Ever wonder why cloud mounts stutter? We explore the engineering of Rclone, FUSE, and the quest for infinite, fluid remote storage.</itunes:subtitle>
      <itunes:summary><![CDATA[Most users are familiar with the "replication model" of cloud storage used by services like Google Drive or Dropbox, where files are physically copied to your hard drive. But for those with massive data needs and limited local storage, "volume sync" tools like Rclone offer a different path by mounting the cloud as a virtual drive. This episode dives deep into the technical architecture that makes this possible, from the "Matrix-like" magic of FUSE to the complexities of just-in-time data delivery. We break down why these systems sometimes feel sluggish, the role of metadata latency, and how advanced caching strategies attempt to bridge the gap between local speed and infinite remote capacity.]]></itunes:summary>
      <itunes:duration>1863</itunes:duration>
      <itunes:episode>770</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/rclone-cloud-mount-vfs-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/rclone-cloud-mount-vfs-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Decoupled Smart Home Trade-Off</title>
      <description><![CDATA[Is moving your home automation brain to the cloud a fix for local hardware failures, or just swapping one fragility for another? This episode debates the decoupled architecture—enterprise reliability vs. latency, control, and the risk of a new single point of failure.]]></description>
      <link>https://myweirdprompts.com/episode/decoupled-smart-home-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/decoupled-smart-home-architecture/</guid>
      <pubDate>Sat, 21 Feb 2026 17:57:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/decoupled-smart-home-architecture.m4a"
        type="audio/mp4"
        length="32429692"
      />
      <itunes:title>The Decoupled Smart Home Trade-Off</itunes:title>
      <itunes:subtitle>Tired of your smart home crashing? Discover why moving your home&apos;s &quot;brain&quot; to the cloud might be the ultimate reliability hack for your setup.</itunes:subtitle>
      <itunes:summary><![CDATA[Is moving your home automation brain to the cloud a fix for local hardware failures, or just swapping one fragility for another? This episode debates the decoupled architecture—enterprise reliability vs. latency, control, and the risk of a new single point of failure.]]></itunes:summary>
      <itunes:duration>1915</itunes:duration>
      <itunes:episode>762</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/decoupled-smart-home-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/decoupled-smart-home-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your Camera Stops Being Dumb</title>
      <description><![CDATA[How local AI transforms security cameras from noisy motion detectors into intelligent observers that understand what they see—and why that matters for privacy and home automation.]]></description>
      <link>https://myweirdprompts.com/episode/frigate-ai-object-detection/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/frigate-ai-object-detection/</guid>
      <pubDate>Sat, 21 Feb 2026 17:08:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/frigate-ai-object-detection.m4a"
        type="audio/mp4"
        length="24997042"
      />
      <itunes:title>When Your Camera Stops Being Dumb</itunes:title>
      <itunes:subtitle>Turn passive cameras into active observers. Learn how Frigate and YOLO models use AI to revolutionize home security and object detection.</itunes:subtitle>
      <itunes:summary><![CDATA[How local AI transforms security cameras from noisy motion detectors into intelligent observers that understand what they see—and why that matters for privacy and home automation.]]></itunes:summary>
      <itunes:duration>1944</itunes:duration>
      <itunes:episode>758</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/frigate-ai-object-detection.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/frigate-ai-object-detection.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Duct Tape to Autonomous Studio: Scaling a 741-Episode AI Podcast</title>
      <description><![CDATA[After 741 episodes, the My Weird Prompts team reveals the hidden costs of success: technical debt, buggy workflows, and the shift from manual fixes to an autonomous production pipeline. How Gemini 1.5 Flash and multi-agent orchestration are turning a hobbyist setup into a self-healing media house.]]></description>
      <link>https://myweirdprompts.com/episode/ai-podcast-automation-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-podcast-automation-pipeline/</guid>
      <pubDate>Sat, 21 Feb 2026 15:38:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-podcast-automation-pipeline.m4a"
        type="audio/mp4"
        length="28077354"
      />
      <itunes:title>From Duct Tape to Autonomous Studio: Scaling a 741-Episode AI Podcast</itunes:title>
      <itunes:subtitle>Peek under the hood of My Weird Prompts to see how Gemini, Modal, and multi-agent systems are scaling this automated show to the next level.</itunes:subtitle>
      <itunes:summary><![CDATA[After 741 episodes, the My Weird Prompts team reveals the hidden costs of success: technical debt, buggy workflows, and the shift from manual fixes to an autonomous production pipeline. How Gemini 1.5 Flash and multi-agent orchestration are turning a hobbyist setup into a self-healing media house.]]></itunes:summary>
      <itunes:duration>2184</itunes:duration>
      <itunes:episode>755</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-podcast-automation-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-podcast-automation-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond SEO: The Guide to Agentic Behavior Optimization</title>
      <description><![CDATA[As we move into 2026, the traditional search landscape has shifted from "blue links" to synthesized answers provided by autonomous AI agents, making traditional SEO strategies increasingly obsolete. In this episode, we explore the rise of Agentic Behavior Optimization (ABO), a new framework for structuring your digital presence to ensure your content is not just crawled, but understood, trusted, and cited by the world’s most advanced large language models. We dive deep into practical steps like implementing semantic HTML5, leveraging complex Schema.org markups to build authority within knowledge graphs, and the strategic importance of the llms.txt standard for facilitating seamless data ingestion. Whether you are a business owner or a web developer, understanding how to navigate the "visibility versus protection" trade-off is crucial for survival in an era where your most frequent visitors are tokens and context windows rather than human eyes. Join us as we break down the "how-to" guide for the agentic web, ensuring your site remains a high-value signal in an ocean of AI-generated noise.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agent-website-optimization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agent-website-optimization/</guid>
      <pubDate>Sat, 21 Feb 2026 15:14:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agent-website-optimization.m4a"
        type="audio/mp4"
        length="24898799"
      />
      <itunes:title>Beyond SEO: The Guide to Agentic Behavior Optimization</itunes:title>
      <itunes:subtitle>Move beyond search engines and learn how to make your website the primary source for the next generation of AI agents.</itunes:subtitle>
      <itunes:summary><![CDATA[As we move into 2026, the traditional search landscape has shifted from "blue links" to synthesized answers provided by autonomous AI agents, making traditional SEO strategies increasingly obsolete. In this episode, we explore the rise of Agentic Behavior Optimization (ABO), a new framework for structuring your digital presence to ensure your content is not just crawled, but understood, trusted, and cited by the world’s most advanced large language models. We dive deep into practical steps like implementing semantic HTML5, leveraging complex Schema.org markups to build authority within knowledge graphs, and the strategic importance of the llms.txt standard for facilitating seamless data ingestion. Whether you are a business owner or a web developer, understanding how to navigate the "visibility versus protection" trade-off is crucial for survival in an era where your most frequent visitors are tokens and context windows rather than human eyes. Join us as we break down the "how-to" guide for the agentic web, ensuring your site remains a high-value signal in an ocean of AI-generated noise.]]></itunes:summary>
      <itunes:duration>1911</itunes:duration>
      <itunes:episode>753</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agent-website-optimization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agent-website-optimization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Fragile Web: Who Decides What We Remember?</title>
      <description><![CDATA[The Internet Archive is fighting legal battles and funding crises to preserve our digital history. But is a centralized library the right model, or does the future belong to decentralized protocols like Arweave? This episode explores the ethics, economics, and technical challenges of deciding what—and who—gets remembered.]]></description>
      <link>https://myweirdprompts.com/episode/internet-archive-digital-preservation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/internet-archive-digital-preservation/</guid>
      <pubDate>Sat, 21 Feb 2026 11:29:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/internet-archive-digital-preservation.m4a"
        type="audio/mp4"
        length="24950939"
      />
      <itunes:title>The Fragile Web: Who Decides What We Remember?</itunes:title>
      <itunes:subtitle>Explore how the Internet Archive saves the web, the legal battles threatening its future, and the rise of decentralized storage like Arweave.</itunes:subtitle>
      <itunes:summary><![CDATA[The Internet Archive is fighting legal battles and funding crises to preserve our digital history. But is a centralized library the right model, or does the future belong to decentralized protocols like Arweave? This episode explores the ethics, economics, and technical challenges of deciding what—and who—gets remembered.]]></itunes:summary>
      <itunes:duration>1940</itunes:duration>
      <itunes:episode>741</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/internet-archive-digital-preservation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/internet-archive-digital-preservation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Illusion of Now: UTC, GMT, and the Chaos of Time</title>
      <description><![CDATA[Have you ever wondered why your phone knows exactly what time it is, even when the Earth itself wobbles in its rotation? This episode pulls back the curtain on the invisible infrastructure of global timekeeping, from the precise atomic vibrations of cesium atoms to the historical reasons why London became the center of the world's clocks. We break down the crucial differences between UTC and GMT, the technical nightmare of leap seconds that "break the internet," and why the seemingly simple concept of daylight savings remains a source of global debate and developer headaches. Whether you are a programmer battling time zone bugs or just curious why the sun rises at 10:00 AM in parts of China, this deep dive into our "engineered illusion" of time offers a fascinating look at how we organize our lives around a giant, global spreadsheet.]]></description>
      <link>https://myweirdprompts.com/episode/utc-gmt-time-zones-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/utc-gmt-time-zones-explained/</guid>
      <pubDate>Fri, 20 Feb 2026 23:28:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/utc-gmt-time-zones-explained.m4a"
        type="audio/mp4"
        length="25697090"
      />
      <itunes:title>The Illusion of Now: UTC, GMT, and the Chaos of Time</itunes:title>
      <itunes:subtitle>Why is keeping time so hard? Explore the difference between UTC and GMT, the end of leap seconds, and the messy global politics of daylight savings.</itunes:subtitle>
      <itunes:summary><![CDATA[Have you ever wondered why your phone knows exactly what time it is, even when the Earth itself wobbles in its rotation? This episode pulls back the curtain on the invisible infrastructure of global timekeeping, from the precise atomic vibrations of cesium atoms to the historical reasons why London became the center of the world's clocks. We break down the crucial differences between UTC and GMT, the technical nightmare of leap seconds that "break the internet," and why the seemingly simple concept of daylight savings remains a source of global debate and developer headaches. Whether you are a programmer battling time zone bugs or just curious why the sun rises at 10:00 AM in parts of China, this deep dive into our "engineered illusion" of time offers a fascinating look at how we organize our lives around a giant, global spreadsheet.]]></itunes:summary>
      <itunes:duration>1988</itunes:duration>
      <itunes:episode>734</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/utc-gmt-time-zones-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/utc-gmt-time-zones-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The EQ Lasagna Problem: A Signal Processing Hierarchy</title>
      <description><![CDATA[This episode tackles the common pitfall of layered digital filters in multi-room audio, introducing the 'Neutral Source, Local Trim' method to achieve consistent, high-fidelity sound across different rooms using Snapcast and Home Assistant.]]></description>
      <link>https://myweirdprompts.com/episode/multi-room-audio-optimization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multi-room-audio-optimization/</guid>
      <pubDate>Fri, 20 Feb 2026 16:19:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multi-room-audio-optimization.m4a"
        type="audio/mp4"
        length="26114830"
      />
      <itunes:title>The EQ Lasagna Problem: A Signal Processing Hierarchy</itunes:title>
      <itunes:subtitle>Stop layering filters on top of filters. Learn the technically correct way to sync your home audio without creating a muddy &quot;EQ lasagna.&quot;</itunes:subtitle>
      <itunes:summary><![CDATA[This episode tackles the common pitfall of layered digital filters in multi-room audio, introducing the 'Neutral Source, Local Trim' method to achieve consistent, high-fidelity sound across different rooms using Snapcast and Home Assistant.]]></itunes:summary>
      <itunes:duration>2033</itunes:duration>
      <itunes:episode>731</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multi-room-audio-optimization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multi-room-audio-optimization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Hidden Language Barrier Between Your Phone and Laptop</title>
      <description><![CDATA[Why can't you just copy-paste software between a PC and a Raspberry Pi? This episode explores the fundamental instruction set architecture mismatch that makes software hardware-specific, and why the industry is quietly shifting to ARM.]]></description>
      <link>https://myweirdprompts.com/episode/cpu-architecture-x86-arm/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cpu-architecture-x86-arm/</guid>
      <pubDate>Fri, 20 Feb 2026 16:01:03 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cpu-architecture-x86-arm.m4a"
        type="audio/mp4"
        length="22428057"
      />
      <itunes:title>The Hidden Language Barrier Between Your Phone and Laptop</itunes:title>
      <itunes:subtitle>Why can’t you just &quot;copy and paste&quot; software between devices? Explore the hidden language of CPU architectures like x86 and ARM.</itunes:subtitle>
      <itunes:summary><![CDATA[Why can't you just copy-paste software between a PC and a Raspberry Pi? This episode explores the fundamental instruction set architecture mismatch that makes software hardware-specific, and why the industry is quietly shifting to ARM.]]></itunes:summary>
      <itunes:duration>1745</itunes:duration>
      <itunes:episode>730</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cpu-architecture-x86-arm.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cpu-architecture-x86-arm.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Domesticating Your Home Security: How to Kill the Cloud</title>
      <description><![CDATA[High-quality home security cameras from brands like TP-Link and Reolink offer incredible value, but they often come with invasive cloud dependencies and privacy risks that compromise your autonomy. This episode explores the "intermediate approach" to home security, teaching you how to "domesticate" your hardware by severing its umbilical cord to the manufacturer’s servers while maintaining high-end features. By transforming from a passive user into a proactive network administrator, you can enjoy 4K resolution and local AI detection without ever sending a single byte of video data to an external relay or third-party server.]]></description>
      <link>https://myweirdprompts.com/episode/private-home-security-setup/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/private-home-security-setup/</guid>
      <pubDate>Fri, 20 Feb 2026 10:15:23 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/private-home-security-setup.m4a"
        type="audio/mp4"
        length="19712622"
      />
      <itunes:title>Domesticating Your Home Security: How to Kill the Cloud</itunes:title>
      <itunes:subtitle>Learn how to &quot;domesticate&quot; consumer security cameras by severing their cloud ties while keeping the high-quality hardware you love.</itunes:subtitle>
      <itunes:summary><![CDATA[High-quality home security cameras from brands like TP-Link and Reolink offer incredible value, but they often come with invasive cloud dependencies and privacy risks that compromise your autonomy. This episode explores the "intermediate approach" to home security, teaching you how to "domesticate" your hardware by severing its umbilical cord to the manufacturer’s servers while maintaining high-end features. By transforming from a passive user into a proactive network administrator, you can enjoy 4K resolution and local AI detection without ever sending a single byte of video data to an external relay or third-party server.]]></itunes:summary>
      <itunes:duration>1562</itunes:duration>
      <itunes:episode>723</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/private-home-security-setup.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/private-home-security-setup.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>DIY Geopolitical Intelligence: Building Your Dashboard</title>
      <description><![CDATA[In this episode, we explore the high-stakes world of situational awareness and the technical challenge of building a personal "intelligence agency" at home. We compare elite enterprise tools like Dataminer with powerful open-source alternatives such as GDELT and ACLED, examining how home hackers can use modern AI to filter global chaos into actionable insights. Discover the strategies for managing signal-to-noise ratios, the "dark cockpit" design philosophy, and how to leverage LLMs to summarize complex geopolitical shifts in real-time without the enterprise price tag.]]></description>
      <link>https://myweirdprompts.com/episode/diy-geopolitical-intelligence-dashboards/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/diy-geopolitical-intelligence-dashboards/</guid>
      <pubDate>Thu, 19 Feb 2026 14:12:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/diy-geopolitical-intelligence-dashboards.m4a"
        type="audio/mp4"
        length="26295452"
      />
      <itunes:title>DIY Geopolitical Intelligence: Building Your Dashboard</itunes:title>
      <itunes:subtitle>Learn how to bridge the gap between elite intelligence tools and home-grown situational awareness dashboards using AI.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, we explore the high-stakes world of situational awareness and the technical challenge of building a personal "intelligence agency" at home. We compare elite enterprise tools like Dataminer with powerful open-source alternatives such as GDELT and ACLED, examining how home hackers can use modern AI to filter global chaos into actionable insights. Discover the strategies for managing signal-to-noise ratios, the "dark cockpit" design philosophy, and how to leverage LLMs to summarize complex geopolitical shifts in real-time without the enterprise price tag.]]></itunes:summary>
      <itunes:duration>2090</itunes:duration>
      <itunes:episode>706</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/diy-geopolitical-intelligence-dashboards.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/diy-geopolitical-intelligence-dashboards.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>OpenClaude and the Dawn of True AI Agents</title>
      <description><![CDATA[The world of AI moves so fast that a twenty-day break can make you feel like a digital archaeologist. This episode explores the breakthrough release of Claude Opus 4.6 and the rise of the OpenClaude ecosystem, a modular framework designed to turn large language models into true personal assistants. We dive into the Model Context Protocol (MCP), explain how to bridge the gap between terminal-based tools and mobile messaging apps, and discuss the privacy trade-offs of self-hosting your own AI agent.]]></description>
      <link>https://myweirdprompts.com/episode/openclaude-mcp-agentic-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/openclaude-mcp-agentic-ai/</guid>
      <pubDate>Thu, 19 Feb 2026 13:01:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/openclaude-mcp-agentic-ai.m4a"
        type="audio/mp4"
        length="21466357"
      />
      <itunes:title>OpenClaude and the Dawn of True AI Agents</itunes:title>
      <itunes:subtitle>Discover how OpenClaude and MCP are transforming AI from simple chatbots into autonomous personal assistants that manage your digital life.</itunes:subtitle>
      <itunes:summary><![CDATA[The world of AI moves so fast that a twenty-day break can make you feel like a digital archaeologist. This episode explores the breakthrough release of Claude Opus 4.6 and the rise of the OpenClaude ecosystem, a modular framework designed to turn large language models into true personal assistants. We dive into the Model Context Protocol (MCP), explain how to bridge the gap between terminal-based tools and mobile messaging apps, and discuss the privacy trade-offs of self-hosting your own AI agent.]]></itunes:summary>
      <itunes:duration>1703</itunes:duration>
      <itunes:episode>701</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/openclaude-mcp-agentic-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/openclaude-mcp-agentic-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Behind the Curtain: How My Weird Prompts Gets Made</title>
      <description><![CDATA[Corn and Herman pull back the curtain for a deep technical dive into the full production pipeline behind My Weird Prompts. From Daniel's voice recording through transcription, AI script generation, two-pass editing, voice cloning with Chatterbox, audio assembly, and automated publishing across five platforms, they explain every stage of how each episode comes to life.]]></description>
      <link>https://myweirdprompts.com/episode/behind-the-curtain-how-my-weird-prompts-gets-made/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/behind-the-curtain-how-my-weird-prompts-gets-made/</guid>
      <pubDate>Thu, 19 Feb 2026 01:00:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/behind-the-curtain-how-my-weird-prompts-gets-made.m4a"
        type="audio/mp4"
        length="18387988"
      />
      <itunes:title>Behind the Curtain: How My Weird Prompts Gets Made</itunes:title>
      <itunes:subtitle>Corn and Herman explain exactly how each episode of My Weird Prompts is produced, from voice recording to published podcast.</itunes:subtitle>
      <itunes:summary><![CDATA[Corn and Herman pull back the curtain for a deep technical dive into the full production pipeline behind My Weird Prompts. From Daniel's voice recording through transcription, AI script generation, two-pass editing, voice cloning with Chatterbox, audio assembly, and automated publishing across five platforms, they explain every stage of how each episode comes to life.]]></itunes:summary>
      <itunes:duration>1400</itunes:duration>
      <itunes:episode>695</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/behind-the-curtain-how-my-weird-prompts-gets-made.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/behind-the-curtain-how-my-weird-prompts-gets-made.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Secret Life of Webhooks: How &quot;Always On&quot; Costs Nothing</title>
      <description><![CDATA[Why does an "always on" automation trigger cost almost nothing until it actually runs? In this episode, Herman and Corn break down the fascinating engineering that allows servers to listen for data while essentially remaining asleep. From the "everything is a file" philosophy of Unix to the high-performance magic of epoll and hardware interrupts, we explore how modern operating systems manage thousands of connections with minimal RAM. Whether you're a developer curious about cloud infrastructure or a hobbyist running your own VPS, you'll learn why your webhooks aren't burning through your credits—and how platforms like Modal scale this efficiency to millions of users.]]></description>
      <link>https://myweirdprompts.com/episode/how-webhooks-work-technically/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/how-webhooks-work-technically/</guid>
      <pubDate>Wed, 18 Feb 2026 12:13:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/how-webhooks-work-technically.m4a"
        type="audio/mp4"
        length="18076105"
      />
      <itunes:title>The Secret Life of Webhooks: How &quot;Always On&quot; Costs Nothing</itunes:title>
      <itunes:subtitle>Ever wonder how webhooks stay &quot;always on&quot; without costing a fortune? Herman and Corn dive into the kernel magic of sockets and interrupts.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does an "always on" automation trigger cost almost nothing until it actually runs? In this episode, Herman and Corn break down the fascinating engineering that allows servers to listen for data while essentially remaining asleep. From the "everything is a file" philosophy of Unix to the high-performance magic of epoll and hardware interrupts, we explore how modern operating systems manage thousands of connections with minimal RAM. Whether you're a developer curious about cloud infrastructure or a hobbyist running your own VPS, you'll learn why your webhooks aren't burning through your credits—and how platforms like Modal scale this efficiency to millions of users.]]></itunes:summary>
      <itunes:duration>2075</itunes:duration>
      <itunes:episode>689</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/how-webhooks-work-technically.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/how-webhooks-work-technically.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Code: Redefining Open Source in 2026</title>
      <description><![CDATA[In this episode of My Weird Prompts, Herman and Corn Poppleberry broadcast from their Jerusalem studio to tackle a heavy-hitting question: what does it actually take for a project to be "truly" open source in 2026? As the industry shifts toward AI-generated "vibe coding" and massive integrated ecosystems, the brothers deconstruct the legal and philosophical battlegrounds of the modern software movement. They dive deep into the Open Source Initiative’s ten-point definition, explaining why restrictions on usage—even for noble causes—can disqualify a project from the open source label. The conversation moves beyond the repository to discuss the critical roles of documentation, the "bus factor," and why the recipe for training an AI model is just as important as the weights themselves. Herman and Corn also introduce the provocative idea that in an era of agentic development, the prompt might be the new source code. Featuring case studies like Linux, Blender, and Godot, this episode is a must-listen for anyone navigating the complex intersection of intellectual property, transparency, and the future of collaborative innovation.]]></description>
      <link>https://myweirdprompts.com/episode/redefining-open-source-2026/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/redefining-open-source-2026/</guid>
      <pubDate>Wed, 18 Feb 2026 08:37:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/redefining-open-source-2026.m4a"
        type="audio/mp4"
        length="14948269"
      />
      <itunes:title>Beyond the Code: Redefining Open Source in 2026</itunes:title>
      <itunes:subtitle>Herman and Corn explore why &quot;open source&quot; in 2026 requires more than just code, from AI prompts to documentation and intellectual property.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Herman and Corn Poppleberry broadcast from their Jerusalem studio to tackle a heavy-hitting question: what does it actually take for a project to be "truly" open source in 2026? As the industry shifts toward AI-generated "vibe coding" and massive integrated ecosystems, the brothers deconstruct the legal and philosophical battlegrounds of the modern software movement. They dive deep into the Open Source Initiative’s ten-point definition, explaining why restrictions on usage—even for noble causes—can disqualify a project from the open source label. The conversation moves beyond the repository to discuss the critical roles of documentation, the "bus factor," and why the recipe for training an AI model is just as important as the weights themselves. Herman and Corn also introduce the provocative idea that in an era of agentic development, the prompt might be the new source code. Featuring case studies like Linux, Blender, and Godot, this episode is a must-listen for anyone navigating the complex intersection of intellectual property, transparency, and the future of collaborative innovation.]]></itunes:summary>
      <itunes:duration>1721</itunes:duration>
      <itunes:episode>678</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/redefining-open-source-2026.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/redefining-open-source-2026.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Open Source vs. Open Weights: The AI Branding Illusion</title>
      <description><![CDATA[In this episode, Herman and Corn peel back the "open" label on today’s most popular AI models to reveal a complex web of licensing restrictions and hidden risks. From Meta’s Llama to the Allen Institute’s OLMo, the duo explores the technical and legal chasm between true open-source AI and the increasingly common "open weights" model. They discuss why this distinction matters for developers, the dangers of "poison pill" clauses, and the growing necessity for sovereign AI in high-stakes environments. Whether you are a startup founder or a security researcher, understanding who truly owns the "recipe" for your AI is no longer optional—it's a requirement for building on solid ground.]]></description>
      <link>https://myweirdprompts.com/episode/open-source-vs-open-weights/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/open-source-vs-open-weights/</guid>
      <pubDate>Tue, 17 Feb 2026 20:15:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/open-source-vs-open-weights.m4a"
        type="audio/mp4"
        length="15288208"
      />
      <itunes:title>Open Source vs. Open Weights: The AI Branding Illusion</itunes:title>
      <itunes:subtitle>Is your AI truly open? Herman and Corn break down the critical difference between open source and open weights in the age of LLMs.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn peel back the "open" label on today’s most popular AI models to reveal a complex web of licensing restrictions and hidden risks. From Meta’s Llama to the Allen Institute’s OLMo, the duo explores the technical and legal chasm between true open-source AI and the increasingly common "open weights" model. They discuss why this distinction matters for developers, the dangers of "poison pill" clauses, and the growing necessity for sovereign AI in high-stakes environments. Whether you are a startup founder or a security researcher, understanding who truly owns the "recipe" for your AI is no longer optional—it's a requirement for building on solid ground.]]></itunes:summary>
      <itunes:duration>1398</itunes:duration>
      <itunes:episode>670</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/open-source-vs-open-weights.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/open-source-vs-open-weights.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Inside the Stack: The Hidden Layers of Every AI Prompt</title>
      <description><![CDATA[When you type a message to an AI, you aren’t just talking to a blank slate; you’re entering a complex, multi-layered conversation governed by a massive "prompting stack." In this episode of My Weird Prompts, Herman and Corn break down the six or seven invisible layers—from vendor system prompts and personal memories to RAG and chat history—that process your request before the model even sees your first word. They explore the "battle for prompt supremacy," the technical costs of massive context windows in 2026, and how these hidden instructions define the AI's personality and safety boundaries. Whether you're a developer using APIs or a power user on ChatGPT, this deep dive reveals the invisible architecture of modern Large Language Models and the "iceberg effect" of instructions hidden beneath the surface of every chat box.]]></description>
      <link>https://myweirdprompts.com/episode/ai-prompting-stack-layers/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-prompting-stack-layers/</guid>
      <pubDate>Tue, 17 Feb 2026 17:57:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-prompting-stack-layers.m4a"
        type="audio/mp4"
        length="20175656"
      />
      <itunes:title>Inside the Stack: The Hidden Layers of Every AI Prompt</itunes:title>
      <itunes:subtitle>Ever wonder what happens after you hit enter? Discover the hidden &quot;stack&quot; of instructions and memories shaping every AI response.</itunes:subtitle>
      <itunes:summary><![CDATA[When you type a message to an AI, you aren’t just talking to a blank slate; you’re entering a complex, multi-layered conversation governed by a massive "prompting stack." In this episode of My Weird Prompts, Herman and Corn break down the six or seven invisible layers—from vendor system prompts and personal memories to RAG and chat history—that process your request before the model even sees your first word. They explore the "battle for prompt supremacy," the technical costs of massive context windows in 2026, and how these hidden instructions define the AI's personality and safety boundaries. Whether you're a developer using APIs or a power user on ChatGPT, this deep dive reveals the invisible architecture of modern Large Language Models and the "iceberg effect" of instructions hidden beneath the surface of every chat box.]]></itunes:summary>
      <itunes:duration>1759</itunes:duration>
      <itunes:episode>665</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-prompting-stack-layers.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-prompting-stack-layers.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Geopolitical Graph: Mapping Global Power with AI</title>
      <description><![CDATA[In this episode, Herman Poppleberry and Corn explore a revolutionary approach to international relations: treating the world as a dynamic graph rather than a static map. By leveraging graph databases and AI-driven vector embeddings, they discuss how policymakers can uncover "second-order effects" and hidden alliances that traditional analysis misses. From the "Silicon Shield" of Taiwan to the "betweenness centrality" of small nations like Qatar, learn how data science is creating a digital twin of global stability. This conversation dives deep into the mathematical weights of diplomacy, trade dependencies, and the future of predictive resilience in an increasingly interconnected world.]]></description>
      <link>https://myweirdprompts.com/episode/geopolitical-graph-ai-analysis/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/geopolitical-graph-ai-analysis/</guid>
      <pubDate>Tue, 17 Feb 2026 13:52:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/geopolitical-graph-ai-analysis.m4a"
        type="audio/mp4"
        length="21807229"
      />
      <itunes:title>The Geopolitical Graph: Mapping Global Power with AI</itunes:title>
      <itunes:subtitle>Move beyond flat maps. Discover how graph databases and AI reveal the hidden connections and second-order effects shaping our global landscape.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman Poppleberry and Corn explore a revolutionary approach to international relations: treating the world as a dynamic graph rather than a static map. By leveraging graph databases and AI-driven vector embeddings, they discuss how policymakers can uncover "second-order effects" and hidden alliances that traditional analysis misses. From the "Silicon Shield" of Taiwan to the "betweenness centrality" of small nations like Qatar, learn how data science is creating a digital twin of global stability. This conversation dives deep into the mathematical weights of diplomacy, trade dependencies, and the future of predictive resilience in an increasingly interconnected world.]]></itunes:summary>
      <itunes:duration>1772</itunes:duration>
      <itunes:episode>662</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/geopolitical-graph-ai-analysis.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/geopolitical-graph-ai-analysis.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Ultimate Dashboard: DIY Information Radiators</title>
      <description><![CDATA[In this episode, Herman and Corn tackle the "Goldilocks problem" of the modern home office: the quest for a perfect, low-friction information radiator. They explore the gap between overpriced enterprise hardware and messy hobbyist projects, offering a roadmap for "prosumers" who want a polished command center without the corporate overhead. From the ambient beauty of E-ink displays to the power of AI-generated custom interfaces, learn how to build a dashboard that fits your life without the "everything-is-a-service" fatigue.]]></description>
      <link>https://myweirdprompts.com/episode/diy-productivity-dashboard-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/diy-productivity-dashboard-guide/</guid>
      <pubDate>Mon, 16 Feb 2026 20:20:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/diy-productivity-dashboard-guide.m4a"
        type="audio/mp4"
        length="22156661"
      />
      <itunes:title>The Ultimate Dashboard: DIY Information Radiators</itunes:title>
      <itunes:subtitle>Tired of expensive subscriptions and messy DIY screens? Discover the middle ground for the perfect home office information radiator.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn tackle the "Goldilocks problem" of the modern home office: the quest for a perfect, low-friction information radiator. They explore the gap between overpriced enterprise hardware and messy hobbyist projects, offering a roadmap for "prosumers" who want a polished command center without the corporate overhead. From the ambient beauty of E-ink displays to the power of AI-generated custom interfaces, learn how to build a dashboard that fits your life without the "everything-is-a-service" fatigue.]]></itunes:summary>
      <itunes:duration>1877</itunes:duration>
      <itunes:episode>649</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/diy-productivity-dashboard-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/diy-productivity-dashboard-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From USB to Bare Metal: Why GPIO Changes Everything</title>
      <description><![CDATA[Why does a door sensor need a protocol designed for hard drives? This episode explores the leap from plug-and-play PC building to the raw, physical control of GPIO pins—and why that shift is more about mindset than hardware.]]></description>
      <link>https://myweirdprompts.com/episode/gpio-sbc-home-automation-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/gpio-sbc-home-automation-guide/</guid>
      <pubDate>Mon, 16 Feb 2026 01:26:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gpio-sbc-home-automation-guide.m4a"
        type="audio/mp4"
        length="21820018"
      />
      <itunes:title>From USB to Bare Metal: Why GPIO Changes Everything</itunes:title>
      <itunes:subtitle>Discover how to use GPIO pins and breadboards to bridge the gap between software and the physical world in your smart home projects.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does a door sensor need a protocol designed for hard drives? This episode explores the leap from plug-and-play PC building to the raw, physical control of GPIO pins—and why that shift is more about mindset than hardware.]]></itunes:summary>
      <itunes:duration>1832</itunes:duration>
      <itunes:episode>644</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gpio-sbc-home-automation-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gpio-sbc-home-automation-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Memory Wars: The Future of Local Agentic AI</title>
      <description><![CDATA[As AI agents move from simple chat to complex autonomous workflows, the hardware requirements are skyrocketing, creating a massive gap between software potential and consumer reality. Join Herman and Corn as they break down the "hardware vs. software race" of early 2026, discussing why tools like the Model Context Protocol (MCP) are pushing even high-end consumer GPUs to their absolute limits. From the magic of Apple’s Unified Memory to the breakthrough of ultra-low-bit quantization and speculative decoding, this episode explores whether the dream of a powerful, local AI assistant is finally within reach for the average user—or if we are all headed for a "VRAM wall" that only the wealthiest enthusiasts can climb.]]></description>
      <link>https://myweirdprompts.com/episode/local-ai-hardware-limits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/local-ai-hardware-limits/</guid>
      <pubDate>Sun, 15 Feb 2026 07:34:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/local-ai-hardware-limits.m4a"
        type="audio/mp4"
        length="19965029"
      />
      <itunes:title>Memory Wars: The Future of Local Agentic AI</itunes:title>
      <itunes:subtitle>Can your PC handle the next wave of AI agents? Herman and Corn dive into VRAM, quantization, and the future of running LLMs locally.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI agents move from simple chat to complex autonomous workflows, the hardware requirements are skyrocketing, creating a massive gap between software potential and consumer reality. Join Herman and Corn as they break down the "hardware vs. software race" of early 2026, discussing why tools like the Model Context Protocol (MCP) are pushing even high-end consumer GPUs to their absolute limits. From the magic of Apple’s Unified Memory to the breakthrough of ultra-low-bit quantization and speculative decoding, this episode explores whether the dream of a powerful, local AI assistant is finally within reach for the average user—or if we are all headed for a "VRAM wall" that only the wealthiest enthusiasts can climb.]]></itunes:summary>
      <itunes:duration>1645</itunes:duration>
      <itunes:episode>633</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/local-ai-hardware-limits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/local-ai-hardware-limits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From a Dead Motherboard to Five Nines</title>
      <description><![CDATA[When a single motherboard failure takes down a home server, it raises a question: how do global enterprises keep the lights on? This episode explores the counterintuitive philosophy of designing for failure, from active-active clusters to split-brain scenarios, and what home users can learn from the invisible pillars of the internet.]]></description>
      <link>https://myweirdprompts.com/episode/high-availability-server-redundancy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/high-availability-server-redundancy/</guid>
      <pubDate>Sat, 14 Feb 2026 14:32:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/high-availability-server-redundancy.m4a"
        type="audio/mp4"
        length="18977671"
      />
      <itunes:title>From a Dead Motherboard to Five Nines</itunes:title>
      <itunes:subtitle>Discover how the world’s biggest platforms stay online when hardware fails. Herman and Corn break down the invisible systems of high availability.</itunes:subtitle>
      <itunes:summary><![CDATA[When a single motherboard failure takes down a home server, it raises a question: how do global enterprises keep the lights on? This episode explores the counterintuitive philosophy of designing for failure, from active-active clusters to split-brain scenarios, and what home users can learn from the invisible pillars of the internet.]]></itunes:summary>
      <itunes:duration>1592</itunes:duration>
      <itunes:episode>621</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/high-availability-server-redundancy.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/high-availability-server-redundancy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When ZFS Pools Survive Hardware Death</title>
      <description><![CDATA[Can you pull drives from a dead server and plug them into a new machine without losing data? This episode explores ZFS's legendary hardware agnosticism, the hidden gotchas of host IDs and boot pools, and why your data might survive even when your motherboard doesn't.]]></description>
      <link>https://myweirdprompts.com/episode/zfs-hardware-recovery-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/zfs-hardware-recovery-guide/</guid>
      <pubDate>Sat, 14 Feb 2026 14:24:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/zfs-hardware-recovery-guide.m4a"
        type="audio/mp4"
        length="17270276"
      />
      <itunes:title>When ZFS Pools Survive Hardware Death</itunes:title>
      <itunes:subtitle>Your motherboard fried, but is your data safe? Discover the secrets of ZFS portability, forced imports, and professional recovery workflows.</itunes:subtitle>
      <itunes:summary><![CDATA[Can you pull drives from a dead server and plug them into a new machine without losing data? This episode explores ZFS's legendary hardware agnosticism, the hidden gotchas of host IDs and boot pools, and why your data might survive even when your motherboard doesn't.]]></itunes:summary>
      <itunes:duration>1356</itunes:duration>
      <itunes:episode>620</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/zfs-hardware-recovery-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/zfs-hardware-recovery-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Home Lab Can&apos;t Be One Big Computer</title>
      <description><![CDATA[Why can't you just wire a stack of old servers into a single supercomputer? Herman and Corn explore the physics and protocols that keep your CPUs and RAM isolated, and what technologies like CXL promise for the future.]]></description>
      <link>https://myweirdprompts.com/episode/unified-supercomputer-resource-pooling/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unified-supercomputer-resource-pooling/</guid>
      <pubDate>Thu, 12 Feb 2026 14:51:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unified-supercomputer-resource-pooling.m4a"
        type="audio/mp4"
        length="20060835"
      />
      <itunes:title>Why Your Home Lab Can&apos;t Be One Big Computer</itunes:title>
      <itunes:subtitle>Herman and Corn explore how to turn separate servers into a unified supercomputer using high-speed interlinks and resource pooling.</itunes:subtitle>
      <itunes:summary><![CDATA[Why can't you just wire a stack of old servers into a single supercomputer? Herman and Corn explore the physics and protocols that keep your CPUs and RAM isolated, and what technologies like CXL promise for the future.]]></itunes:summary>
      <itunes:duration>1560</itunes:duration>
      <itunes:episode>605</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unified-supercomputer-resource-pooling.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unified-supercomputer-resource-pooling.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Taming the Digital Landfill: Version Control for AI Media</title>
      <description><![CDATA[When AI agents generate gigabytes of video and assets per hour, Git breaks. This episode explores how to manage the collision of code and creative media with tools like Perforce, Unity Version Control, and DVC.]]></description>
      <link>https://myweirdprompts.com/episode/large-asset-version-control-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/large-asset-version-control-ai/</guid>
      <pubDate>Thu, 12 Feb 2026 10:16:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/large-asset-version-control-ai.m4a"
        type="audio/mp4"
        length="22283719"
      />
      <itunes:title>Taming the Digital Landfill: Version Control for AI Media</itunes:title>
      <itunes:subtitle>When AI agents and 4K video crash your repo, it’s time for better tools. Explore why Git fails and how Perforce and DVC save the day.</itunes:subtitle>
      <itunes:summary><![CDATA[When AI agents generate gigabytes of video and assets per hour, Git breaks. This episode explores how to manage the collision of code and creative media with tools like Perforce, Unity Version Control, and DVC.]]></itunes:summary>
      <itunes:duration>1890</itunes:duration>
      <itunes:episode>589</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/large-asset-version-control-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/large-asset-version-control-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Briefing Gateway: Ending the &quot;Pecked by Ducks&quot; Email Era</title>
      <description><![CDATA[In this episode, Herman and Corn dive into a revolutionary business concept: the Briefing Gateway, a middleware solution designed to stop "pecking clients to death" with constant email updates. They break down the technical architecture—from API integrations and LLM-powered summarization to clever emergency overrides—that could transform how agencies communicate. By shifting from frantic, real-time pings to scheduled, professional summaries, this tool promises to reduce cognitive load for recipients while providing agency owners with unprecedented insights into their team's communication health.]]></description>
      <link>https://myweirdprompts.com/episode/email-briefing-gateway-middleware/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/email-briefing-gateway-middleware/</guid>
      <pubDate>Mon, 09 Feb 2026 14:24:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/email-briefing-gateway-middleware.m4a"
        type="audio/mp4"
        length="18887060"
      />
      <itunes:title>The Briefing Gateway: Ending the &quot;Pecked by Ducks&quot; Email Era</itunes:title>
      <itunes:subtitle>Tired of flooding clients with emails? Herman and Corn explore the &quot;Briefing Gateway,&quot; a tool that uses AI to turn messy pings into organized briefs.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn dive into a revolutionary business concept: the Briefing Gateway, a middleware solution designed to stop "pecking clients to death" with constant email updates. They break down the technical architecture—from API integrations and LLM-powered summarization to clever emergency overrides—that could transform how agencies communicate. By shifting from frantic, real-time pings to scheduled, professional summaries, this tool promises to reduce cognitive load for recipients while providing agency owners with unprecedented insights into their team's communication health.]]></itunes:summary>
      <itunes:duration>1571</itunes:duration>
      <itunes:episode>558</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/email-briefing-gateway-middleware.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/email-briefing-gateway-middleware.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The LoRA Revolution: Training AI for Personal Perspective</title>
      <description><![CDATA[In this milestone episode of My Weird Prompts, Herman and Corn Poppleberry dive deep into the technical and philosophical world of Low-Rank Adaptation (LoRA), explaining how this technology has effectively democratized AI training by allowing individuals to teach massive models specific faces, locations, and architectural styles without the need for a server farm. The brothers break down the essential mechanics of building a robust dataset, from the optimal image count and the necessity of high-resolution 1024x1024 inputs to the "subtraction" method of natural language captioning that prevents the model from accidentally baking backgrounds or accessories into a subject’s identity. By exploring diverse use cases—ranging from maintaining character consistency across generated images to capturing the subjective "vibe" of a city like Jerusalem—this episode provides a comprehensive roadmap for creators who want to move beyond generic prompts and harness AI as a tool for personal, high-fidelity storytelling and professional architectural rendering.]]></description>
      <link>https://myweirdprompts.com/episode/mastering-lora-ai-training/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mastering-lora-ai-training/</guid>
      <pubDate>Mon, 09 Feb 2026 09:47:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mastering-lora-ai-training.m4a"
        type="audio/mp4"
        length="19015394"
      />
      <itunes:title>The LoRA Revolution: Training AI for Personal Perspective</itunes:title>
      <itunes:subtitle>Discover how to train LoRAs for character consistency and unique locations while avoiding common pitfalls like over-fitting and dataset bias.</itunes:subtitle>
      <itunes:summary><![CDATA[In this milestone episode of My Weird Prompts, Herman and Corn Poppleberry dive deep into the technical and philosophical world of Low-Rank Adaptation (LoRA), explaining how this technology has effectively democratized AI training by allowing individuals to teach massive models specific faces, locations, and architectural styles without the need for a server farm. The brothers break down the essential mechanics of building a robust dataset, from the optimal image count and the necessity of high-resolution 1024x1024 inputs to the "subtraction" method of natural language captioning that prevents the model from accidentally baking backgrounds or accessories into a subject’s identity. By exploring diverse use cases—ranging from maintaining character consistency across generated images to capturing the subjective "vibe" of a city like Jerusalem—this episode provides a comprehensive roadmap for creators who want to move beyond generic prompts and harness AI as a tool for personal, high-fidelity storytelling and professional architectural rendering.]]></itunes:summary>
      <itunes:duration>1633</itunes:duration>
      <itunes:episode>551</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mastering-lora-ai-training.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mastering-lora-ai-training.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Turning a Podcast into a Searchable Knowledge Base</title>
      <description><![CDATA[How can a linear podcast feed become an interactive semantic graph? Herman and Corn explore using vector embeddings and graph databases to let listeners navigate 500+ episodes, while keeping the show's personal charm.]]></description>
      <link>https://myweirdprompts.com/episode/ai-podcast-evolution-scaling/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-podcast-evolution-scaling/</guid>
      <pubDate>Sun, 08 Feb 2026 11:48:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-podcast-evolution-scaling.m4a"
        type="audio/mp4"
        length="17232023"
      />
      <itunes:title>Turning a Podcast into a Searchable Knowledge Base</itunes:title>
      <itunes:subtitle>Herman and Corn discuss turning 500+ episodes into an interactive knowledge base while scaling human-AI collaboration to new heights.</itunes:subtitle>
      <itunes:summary><![CDATA[How can a linear podcast feed become an interactive semantic graph? Herman and Corn explore using vector embeddings and graph databases to let listeners navigate 500+ episodes, while keeping the show's personal charm.]]></itunes:summary>
      <itunes:duration>1206</itunes:duration>
      <itunes:episode>539</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-podcast-evolution-scaling.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-podcast-evolution-scaling.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Magic Smoke: Predicting Hardware Failure</title>
      <description><![CDATA[When a home server dies, the silence is deafening. In this episode, Herman and Corn dive deep into the world of hardware telemetry to help you avoid the "magic smoke" and catastrophic data loss. They explore the nuances of motherboard voltage monitoring, the truth about NVMe SMART data, and the hidden VRAM health indicators in NVIDIA's management tools. Whether you are a Linux server enthusiast or a power user, this discussion provides the technical roadmap for distinguishing between slow component degradation and sudden, terminal failure.]]></description>
      <link>https://myweirdprompts.com/episode/hardware-health-monitoring-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hardware-health-monitoring-guide/</guid>
      <pubDate>Thu, 05 Feb 2026 10:24:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hardware-health-monitoring-guide.m4a"
        type="audio/mp4"
        length="12172199"
      />
      <itunes:title>Beyond the Magic Smoke: Predicting Hardware Failure</itunes:title>
      <itunes:subtitle>Learn how to spot motherboard degradation, track NVMe wear, and use hidden NVIDIA telemetry to save your data before the &quot;magic smoke&quot; escapes.</itunes:subtitle>
      <itunes:summary><![CDATA[When a home server dies, the silence is deafening. In this episode, Herman and Corn dive deep into the world of hardware telemetry to help you avoid the "magic smoke" and catastrophic data loss. They explore the nuances of motherboard voltage monitoring, the truth about NVMe SMART data, and the hidden VRAM health indicators in NVIDIA's management tools. Whether you are a Linux server enthusiast or a power user, this discussion provides the technical roadmap for distinguishing between slow component degradation and sudden, terminal failure.]]></itunes:summary>
      <itunes:duration>1321</itunes:duration>
      <itunes:episode>493</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hardware-health-monitoring-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hardware-health-monitoring-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Folder: The Quest for a Graph-Based OS</title>
      <description><![CDATA[For over forty years, the digital world has been organized like a physical filing cabinet: folders inside folders. But the human brain doesn't think in hierarchies; it thinks in associations. In this episode, Herman and Corn dive into the history and future of operating systems, asking why we haven't yet moved to a graph-based model. They trace the lineage from Vannevar Bush’s 1945 "Memex" concept to Microsoft’s ambitious but failed WinFS project in the early 2000s. The duo discusses the technical hurdles of the past—like POSIX compatibility and hardware limitations—and why the rise of AI, vector databases, and tools like Obsidian suggest we are finally ready for a shift. Is the era of the file path ending? Join the conversation as we explore how semantic computing and modern storage architectures might finally let us navigate our data as a constellation of ideas rather than a stack of digital paper. It’s a deep dive into the very ground we walk on in the digital world.]]></description>
      <link>https://myweirdprompts.com/episode/graph-based-operating-systems/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/graph-based-operating-systems/</guid>
      <pubDate>Thu, 05 Feb 2026 10:17:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/graph-based-operating-systems.m4a"
        type="audio/mp4"
        length="15150998"
      />
      <itunes:title>Beyond the Folder: The Quest for a Graph-Based OS</itunes:title>
      <itunes:subtitle>Why are we still using 1970s folders? Explore how graph structures and associative memory are finally challenging the traditional file system.</itunes:subtitle>
      <itunes:summary><![CDATA[For over forty years, the digital world has been organized like a physical filing cabinet: folders inside folders. But the human brain doesn't think in hierarchies; it thinks in associations. In this episode, Herman and Corn dive into the history and future of operating systems, asking why we haven't yet moved to a graph-based model. They trace the lineage from Vannevar Bush’s 1945 "Memex" concept to Microsoft’s ambitious but failed WinFS project in the early 2000s. The duo discusses the technical hurdles of the past—like POSIX compatibility and hardware limitations—and why the rise of AI, vector databases, and tools like Obsidian suggest we are finally ready for a shift. Is the era of the file path ending? Join the conversation as we explore how semantic computing and modern storage architectures might finally let us navigate our data as a constellation of ideas rather than a stack of digital paper. It’s a deep dive into the very ground we walk on in the digital world.]]></itunes:summary>
      <itunes:duration>1589</itunes:duration>
      <itunes:episode>492</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/graph-based-operating-systems.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/graph-based-operating-systems.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Silicon Sharing Economy: Inside Serverless GPUs</title>
      <description><![CDATA[Ever wonder how a tiny startup can run massive AI models that require hardware costing more than a luxury car? In this episode, Corn and Herman pull back the curtain on serverless GPU providers like Modal and Core Weave to explain the "plumbing" of the modern AI era. They explore the shift from reselling AWS instances to building specialized "Tier Two" data centers, the engineering magic behind sub-second cold starts, and why the "sharing economy for silicon" is the only way for developers to survive the hardware wars of 2026.]]></description>
      <link>https://myweirdprompts.com/episode/serverless-gpu-infrastructure-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/serverless-gpu-infrastructure-explained/</guid>
      <pubDate>Wed, 04 Feb 2026 22:16:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/serverless-gpu-infrastructure-explained.m4a"
        type="audio/mp4"
        length="13996986"
      />
      <itunes:title>The Silicon Sharing Economy: Inside Serverless GPUs</itunes:title>
      <itunes:subtitle>How do small teams run massive AI models without $50,000 chips? Corn and Herman dive into the hidden plumbing of serverless GPU providers.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wonder how a tiny startup can run massive AI models that require hardware costing more than a luxury car? In this episode, Corn and Herman pull back the curtain on serverless GPU providers like Modal and Core Weave to explain the "plumbing" of the modern AI era. They explore the shift from reselling AWS instances to building specialized "Tier Two" data centers, the engineering magic behind sub-second cold starts, and why the "sharing economy for silicon" is the only way for developers to survive the hardware wars of 2026.]]></itunes:summary>
      <itunes:duration>1472</itunes:duration>
      <itunes:episode>484</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/serverless-gpu-infrastructure-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/serverless-gpu-infrastructure-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Can AI Fix Your &apos;Wall of Awful&apos; Productivity Paralysis?</title>
      <description><![CDATA[In this episode, Herman and Corn tackle the "wall of awful" that often prevents people—particularly those with ADHD—from turning a massive list of tasks into an actionable plan. While David Allen’s "Getting Things Done" (GTD) remains a gold standard for capturing ideas, the hosts argue that manual organization is becoming a relic of the past. They explore the frontier of "adaptive scheduling," where autonomous AI agents use constraint satisfaction and energy-aware algorithms to build your schedule for you. From tool deep-dives into Motion and Reclaim.ai to the philosophical risks of the "automation paradox," this discussion provides a blueprint for externalizing your executive function to regain your focus.]]></description>
      <link>https://myweirdprompts.com/episode/ai-autonomous-scheduling-gtd/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-autonomous-scheduling-gtd/</guid>
      <pubDate>Wed, 04 Feb 2026 10:50:03 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-autonomous-scheduling-gtd.m4a"
        type="audio/mp4"
        length="18009882"
      />
      <itunes:title>Can AI Fix Your &apos;Wall of Awful&apos; Productivity Paralysis?</itunes:title>
      <itunes:subtitle>Stop staring at your to-do list and start moving. Discover how AI is transforming productivity from manual sorting to automated daily roadmaps.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn tackle the "wall of awful" that often prevents people—particularly those with ADHD—from turning a massive list of tasks into an actionable plan. While David Allen’s "Getting Things Done" (GTD) remains a gold standard for capturing ideas, the hosts argue that manual organization is becoming a relic of the past. They explore the frontier of "adaptive scheduling," where autonomous AI agents use constraint satisfaction and energy-aware algorithms to build your schedule for you. From tool deep-dives into Motion and Reclaim.ai to the philosophical risks of the "automation paradox," this discussion provides a blueprint for externalizing your executive function to regain your focus.]]></itunes:summary>
      <itunes:duration>2014</itunes:duration>
      <itunes:episode>459</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-autonomous-scheduling-gtd.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-autonomous-scheduling-gtd.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>RAID is Not a Backup: Mastering Home Server Resilience</title>
      <description><![CDATA[In this episode of My Weird Prompts, Herman and Corn dive deep into the world of home server recovery after a listener's motherboard meltdown. They break down the crucial differences between hardware redundancy and data backups, exploring why file systems like BTRFS and ZFS are the ultimate tools for the modern self-hoster. The duo discusses the technical magic of Copy on Write (CoW) and how it allows for near-instant snapshots without eating up massive amounts of storage space. Whether you are building a "franken-server" with mismatched SSDs or seeking the enterprise-grade data integrity of ZFS, this episode provides a roadmap for making your data immortal. Learn about the "grandfather-father-son" rotation for automated backups and why bit rot is a silent killer you need to prepare for. It’s a masterclass in digital resilience, ensuring your next hardware failure is just a minor inconvenience rather than a total catastrophe.]]></description>
      <link>https://myweirdprompts.com/episode/home-server-data-resilience-snapshots/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-server-data-resilience-snapshots/</guid>
      <pubDate>Mon, 02 Feb 2026 11:35:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-server-data-resilience-snapshots.m4a"
        type="audio/mp4"
        length="14780701"
      />
      <itunes:title>RAID is Not a Backup: Mastering Home Server Resilience</itunes:title>
      <itunes:subtitle>Why RAID isn’t enough and how snapshots act as a digital time machine for your home server’s survival.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Herman and Corn dive deep into the world of home server recovery after a listener's motherboard meltdown. They break down the crucial differences between hardware redundancy and data backups, exploring why file systems like BTRFS and ZFS are the ultimate tools for the modern self-hoster. The duo discusses the technical magic of Copy on Write (CoW) and how it allows for near-instant snapshots without eating up massive amounts of storage space. Whether you are building a "franken-server" with mismatched SSDs or seeking the enterprise-grade data integrity of ZFS, this episode provides a roadmap for making your data immortal. Learn about the "grandfather-father-son" rotation for automated backups and why bit rot is a silent killer you need to prepare for. It’s a masterclass in digital resilience, ensuring your next hardware failure is just a minor inconvenience rather than a total catastrophe.]]></itunes:summary>
      <itunes:duration>1596</itunes:duration>
      <itunes:episode>418</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-server-data-resilience-snapshots.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-server-data-resilience-snapshots.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Redundancy Is Not a Backup</title>
      <description><![CDATA[When a home server dies after seven years, its owner discovers that RAID protects uptime, not data. This episode unpacks the difference between redundancy and backups, the 3-2-1-1-0 rule, and how to build a server that survives real failure.]]></description>
      <link>https://myweirdprompts.com/episode/home-server-failure-lessons/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/home-server-failure-lessons/</guid>
      <pubDate>Sun, 01 Feb 2026 17:27:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/home-server-failure-lessons.m4a"
        type="audio/mp4"
        length="14795668"
      />
      <itunes:title>Redundancy Is Not a Backup</itunes:title>
      <itunes:subtitle>When a 7-year-old server dies, the recovery is a wake-up call. Discover why RAID isn&apos;t a backup and how to build a resilient &quot;Server V2.&quot;</itunes:subtitle>
      <itunes:summary><![CDATA[When a home server dies after seven years, its owner discovers that RAID protects uptime, not data. This episode unpacks the difference between redundancy and backups, the 3-2-1-1-0 rule, and how to build a server that survives real failure.]]></itunes:summary>
      <itunes:duration>1617</itunes:duration>
      <itunes:episode>410</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/home-server-failure-lessons.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/home-server-failure-lessons.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Etch A Sketch: Building Persistent AI Memory</title>
      <description><![CDATA[Are you tired of re-explaining your life to AI every time you start a new chat? In this episode, Herman and Corn dive into the "Etch A Sketch" problem and explore Daniel’s challenge of creating a "self-healing" store of context that evolves with you. From the technical architecture of vector databases to the psychological benefits of voice-prompting, learn how to build a persistent digital brain that remembers who you are, what you like, and how your life changes over time.]]></description>
      <link>https://myweirdprompts.com/episode/persistent-ai-context-storage/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/persistent-ai-context-storage/</guid>
      <pubDate>Fri, 30 Jan 2026 16:40:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/persistent-ai-context-storage.m4a"
        type="audio/mp4"
        length="18101228"
      />
      <itunes:title>Beyond the Etch A Sketch: Building Persistent AI Memory</itunes:title>
      <itunes:subtitle>Why treat AI chats like disposable tissues? Discover how to turn years of prompts into a self-healing, evolving digital brain.</itunes:subtitle>
      <itunes:summary><![CDATA[Are you tired of re-explaining your life to AI every time you start a new chat? In this episode, Herman and Corn dive into the "Etch A Sketch" problem and explore Daniel’s challenge of creating a "self-healing" store of context that evolves with you. From the technical architecture of vector databases to the psychological benefits of voice-prompting, learn how to build a persistent digital brain that remembers who you are, what you like, and how your life changes over time.]]></itunes:summary>
      <itunes:duration>1476</itunes:duration>
      <itunes:episode>371</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/persistent-ai-context-storage.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/persistent-ai-context-storage.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Geometry of Secrets: How SSH Keys Protect the Web</title>
      <description><![CDATA[In this episode of My Weird Prompts, Herman and Corn explore the fascinating mathematics behind SSH keys, moving from the prime factorization of RSA to the sophisticated geometry of Elliptic Curve Cryptography (ED25519). They explain why deriving a public key from a private one is a simple calculation while the reverse would take longer than the life of the universe, illustrating the "trapdoor functions" that secure our global infrastructure. From the mechanics of digital handshakes to the physical risks of power analysis attacks, this deep dive reveals how the invisible world of number theory keeps your data safe from even the most powerful supercomputers.]]></description>
      <link>https://myweirdprompts.com/episode/math-of-ssh-key-security/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/math-of-ssh-key-security/</guid>
      <pubDate>Fri, 30 Jan 2026 15:29:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/math-of-ssh-key-security.m4a"
        type="audio/mp4"
        length="16092172"
      />
      <itunes:title>The Geometry of Secrets: How SSH Keys Protect the Web</itunes:title>
      <itunes:subtitle>Ever wonder why your SSH keys are so secure? Herman and Corn dive into the &quot;trapdoor&quot; math and elliptic curves powering the modern web.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Herman and Corn explore the fascinating mathematics behind SSH keys, moving from the prime factorization of RSA to the sophisticated geometry of Elliptic Curve Cryptography (ED25519). They explain why deriving a public key from a private one is a simple calculation while the reverse would take longer than the life of the universe, illustrating the "trapdoor functions" that secure our global infrastructure. From the mechanics of digital handshakes to the physical risks of power analysis attacks, this deep dive reveals how the invisible world of number theory keeps your data safe from even the most powerful supercomputers.]]></itunes:summary>
      <itunes:duration>1262</itunes:duration>
      <itunes:episode>368</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/math-of-ssh-key-security.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/math-of-ssh-key-security.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Escaping the Chat Bubble Trap</title>
      <description><![CDATA[Herman and Corn tackle the problem of AI fragmentation, exploring how to move beyond simple chat interfaces to orchestration platforms like TypingMind and Dify. They offer a blueprint for a unified, multi-model workspace that avoids ecosystem lock-in.]]></description>
      <link>https://myweirdprompts.com/episode/unified-ai-workspace-orchestration/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/unified-ai-workspace-orchestration/</guid>
      <pubDate>Fri, 30 Jan 2026 14:18:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/unified-ai-workspace-orchestration.m4a"
        type="audio/mp4"
        length="19647754"
      />
      <itunes:title>Escaping the Chat Bubble Trap</itunes:title>
      <itunes:subtitle>Stop hunting through bookmarks. Learn how to turn hundreds of scattered AI assistants into a cohesive, professional productivity suite.</itunes:subtitle>
      <itunes:summary><![CDATA[Herman and Corn tackle the problem of AI fragmentation, exploring how to move beyond simple chat interfaces to orchestration platforms like TypingMind and Dify. They offer a blueprint for a unified, multi-model workspace that avoids ecosystem lock-in.]]></itunes:summary>
      <itunes:duration>1680</itunes:duration>
      <itunes:episode>367</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/unified-ai-workspace-orchestration.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/unified-ai-workspace-orchestration.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Bridging the Workspace-GCP Divide</title>
      <description><![CDATA[How can a solo consultant use enterprise-grade Google Cloud tools alongside everyday Workspace apps? This episode explores the friction between the two ecosystems and offers a roadmap for building an AI-ready data architecture without the enterprise price tag.]]></description>
      <link>https://myweirdprompts.com/episode/google-workspace-gcp-integration/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/google-workspace-gcp-integration/</guid>
      <pubDate>Thu, 29 Jan 2026 14:37:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/google-workspace-gcp-integration.m4a"
        type="audio/mp4"
        length="18794446"
      />
      <itunes:title>Bridging the Workspace-GCP Divide</itunes:title>
      <itunes:subtitle>Discover how to use Google Cloud buckets and Vertex AI to turn a cluttered Drive into a scalable, AI-powered knowledge base.</itunes:subtitle>
      <itunes:summary><![CDATA[How can a solo consultant use enterprise-grade Google Cloud tools alongside everyday Workspace apps? This episode explores the friction between the two ecosystems and offers a roadmap for building an AI-ready data architecture without the enterprise price tag.]]></itunes:summary>
      <itunes:duration>1476</itunes:duration>
      <itunes:episode>347</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/google-workspace-gcp-integration.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/google-workspace-gcp-integration.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>GPU Scaling: The &quot;Go Wide or Go Tall&quot; Dilemma</title>
      <description><![CDATA[In this episode, Herman and Corn dive deep into the engineering trade-offs of serverless GPU workloads. Using a real-world text-to-speech example on the Modal platform, they explore whether it’s better to scale horizontally with many small workers or vertically with a single high-end GPU like the H100. They break down the hidden costs of cold starts, the importance of memory bandwidth over raw compute, and how to find the "sweet spot" on the cost-efficiency curve to get the most bang for your buck.]]></description>
      <link>https://myweirdprompts.com/episode/serverless-gpu-scaling-efficiency/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/serverless-gpu-scaling-efficiency/</guid>
      <pubDate>Thu, 29 Jan 2026 14:25:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/serverless-gpu-scaling-efficiency.m4a"
        type="audio/mp4"
        length="19039167"
      />
      <itunes:title>GPU Scaling: The &quot;Go Wide or Go Tall&quot; Dilemma</itunes:title>
      <itunes:subtitle>Should you use a fleet of cheap GPUs or one powerhouse? Learn the math behind serverless GPU costs, cold starts, and batching efficiency.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn dive deep into the engineering trade-offs of serverless GPU workloads. Using a real-world text-to-speech example on the Modal platform, they explore whether it’s better to scale horizontally with many small workers or vertically with a single high-end GPU like the H100. They break down the hidden costs of cold starts, the importance of memory bandwidth over raw compute, and how to find the "sweet spot" on the cost-efficiency curve to get the most bang for your buck.]]></itunes:summary>
      <itunes:duration>1519</itunes:duration>
      <itunes:episode>346</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/serverless-gpu-scaling-efficiency.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/serverless-gpu-scaling-efficiency.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Who’s Talking? The Tech of Speaker Identification</title>
      <description><![CDATA[Tired of manually labeling who said what in your meeting transcripts? In this episode, Herman and Corn explore the technical bridge between speaker diarization and true speaker identification, diving into cutting-edge tools like Pyannote and Picovoice. They discuss how mathematical voice embeddings and "digital fingerprints" are revolutionizing how we process audio, making it easier than ever to programmatically identify known speakers even in noisy environments.]]></description>
      <link>https://myweirdprompts.com/episode/speaker-identification-diarization-tech/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speaker-identification-diarization-tech/</guid>
      <pubDate>Wed, 28 Jan 2026 14:03:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speaker-identification-diarization-tech.m4a"
        type="audio/mp4"
        length="20406880"
      />
      <itunes:title>Who’s Talking? The Tech of Speaker Identification</itunes:title>
      <itunes:subtitle>Herman and Corn break down the difference between speaker diarization and identification to help automate meeting transcripts.</itunes:subtitle>
      <itunes:summary><![CDATA[Tired of manually labeling who said what in your meeting transcripts? In this episode, Herman and Corn explore the technical bridge between speaker diarization and true speaker identification, diving into cutting-edge tools like Pyannote and Picovoice. They discuss how mathematical voice embeddings and "digital fingerprints" are revolutionizing how we process audio, making it easier than ever to programmatically identify known speakers even in noisy environments.]]></itunes:summary>
      <itunes:duration>1627</itunes:duration>
      <itunes:episode>332</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speaker-identification-diarization-tech.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speaker-identification-diarization-tech.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Hotel Hacks to Digital Resistance: The Travel Router</title>
      <description><![CDATA[In this episode, Herman and Corn dive into the fascinating evolution of the travel router, moving from a simple way to dodge hotel Wi-Fi fees to a powerful tool for digital sovereignty. They explore the accidental open-source revolution of the Linksys WRT54G and how "network in a box" technology empowers journalists, activists, and digital nomads today. Learn why your next travel essential might not be a power bank, but a pocket-sized Linux server that keeps your data secure in a hostile digital world.]]></description>
      <link>https://myweirdprompts.com/episode/travel-router-privacy-history/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/travel-router-privacy-history/</guid>
      <pubDate>Wed, 28 Jan 2026 14:00:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/travel-router-privacy-history.m4a"
        type="audio/mp4"
        length="19017954"
      />
      <itunes:title>From Hotel Hacks to Digital Resistance: The Travel Router</itunes:title>
      <itunes:subtitle>Discover how a hotel billing hack became a tool for digital resistance and how a Linksys &quot;accident&quot; changed internet privacy forever.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn dive into the fascinating evolution of the travel router, moving from a simple way to dodge hotel Wi-Fi fees to a powerful tool for digital sovereignty. They explore the accidental open-source revolution of the Linksys WRT54G and how "network in a box" technology empowers journalists, activists, and digital nomads today. Learn why your next travel essential might not be a power bank, but a pocket-sized Linux server that keeps your data secure in a hostile digital world.]]></itunes:summary>
      <itunes:duration>1486</itunes:duration>
      <itunes:episode>331</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/travel-router-privacy-history.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/travel-router-privacy-history.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Stop Memorizing Syntax and Start Describing Results</title>
      <description><![CDATA[In this episode of My Weird Prompts, hosts Herman and Corn explore a fundamental shift in how we interact with our computers: the move from rigid command-line syntax to "Semantic Computing." They discuss the rise of agentic command-line interfaces that allow users to manage files, process media, and perform complex system administration using plain English. From the hardware demands of running 70B parameter models locally to the privacy benefits of bypassing the cloud, this conversation covers the technical and philosophical implications of the new "Intent-Based Interface." Whether you are a Linux veteran or a curious Mac user, discover how AI is making the power of the terminal accessible to everyone.]]></description>
      <link>https://myweirdprompts.com/episode/semantic-computing-agentic-terminal/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/semantic-computing-agentic-terminal/</guid>
      <pubDate>Wed, 21 Jan 2026 12:09:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/semantic-computing-agentic-terminal.m4a"
        type="audio/mp4"
        length="17421666"
      />
      <itunes:title>Stop Memorizing Syntax and Start Describing Results</itunes:title>
      <itunes:subtitle>Stop memorizing complex syntax. Explore how AI agents are transforming the terminal into a natural language &quot;Intent-Based Interface.&quot;</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, hosts Herman and Corn explore a fundamental shift in how we interact with our computers: the move from rigid command-line syntax to "Semantic Computing." They discuss the rise of agentic command-line interfaces that allow users to manage files, process media, and perform complex system administration using plain English. From the hardware demands of running 70B parameter models locally to the privacy benefits of bypassing the cloud, this conversation covers the technical and philosophical implications of the new "Intent-Based Interface." Whether you are a Linux veteran or a curious Mac user, discover how AI is making the power of the terminal accessible to everyone.]]></itunes:summary>
      <itunes:duration>1362</itunes:duration>
      <itunes:episode>265</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/semantic-computing-agentic-terminal.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/semantic-computing-agentic-terminal.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why AI Needs a New Kind of Memory</title>
      <description><![CDATA[As AI models struggle with hallucinations and rigid tables, graph databases and vector stores are reshaping how machines understand relationships. Herman and Corn explore whether the era of SQL is ending and what 'index-free adjacency' means for the future of data.]]></description>
      <link>https://myweirdprompts.com/episode/graph-vs-vector-databases/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/graph-vs-vector-databases/</guid>
      <pubDate>Wed, 21 Jan 2026 01:05:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/graph-vs-vector-databases.m4a"
        type="audio/mp4"
        length="15713634"
      />
      <itunes:title>Why AI Needs a New Kind of Memory</itunes:title>
      <itunes:subtitle>Herman and Corn explore why SQL is losing ground to graph and vector databases. Discover why the &quot;edge&quot; is the new gold standard for AI accuracy.</itunes:subtitle>
      <itunes:summary><![CDATA[As AI models struggle with hallucinations and rigid tables, graph databases and vector stores are reshaping how machines understand relationships. Herman and Corn explore whether the era of SQL is ending and what 'index-free adjacency' means for the future of data.]]></itunes:summary>
      <itunes:duration>1141</itunes:duration>
      <itunes:episode>263</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/graph-vs-vector-databases.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/graph-vs-vector-databases.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond Git: Version Control for the Solo Creator</title>
      <description><![CDATA[Git was born out of a 2005 software crisis, designed to manage the massive Linux kernel—but is it the right tool for a solo blogger or developer? In this episode, Herman and Corn discuss why Git’s architectural complexity can stifle creativity and introduce powerful, low-friction alternatives like Fossil, Jujutsu, and Radicle. Learn how to manage your project's evolution without the "merge conflict" headaches and find the workflow that actually fits your creative process.]]></description>
      <link>https://myweirdprompts.com/episode/git-alternatives-solo-creators/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/git-alternatives-solo-creators/</guid>
      <pubDate>Tue, 20 Jan 2026 16:47:53 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/git-alternatives-solo-creators.m4a"
        type="audio/mp4"
        length="15913390"
      />
      <itunes:title>Beyond Git: Version Control for the Solo Creator</itunes:title>
      <itunes:subtitle>Is Git overkill for your projects? Herman and Corn dive into the &quot;war&quot; that created Git and explore simpler tools for solo developers.</itunes:subtitle>
      <itunes:summary><![CDATA[Git was born out of a 2005 software crisis, designed to manage the massive Linux kernel—but is it the right tool for a solo blogger or developer? In this episode, Herman and Corn discuss why Git’s architectural complexity can stifle creativity and introduce powerful, low-friction alternatives like Fossil, Jujutsu, and Radicle. Learn how to manage your project's evolution without the "merge conflict" headaches and find the workflow that actually fits your creative process.]]></itunes:summary>
      <itunes:duration>1274</itunes:duration>
      <itunes:episode>262</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/git-alternatives-solo-creators.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/git-alternatives-solo-creators.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI That Evolves: Solving the Preference Problem</title>
      <description><![CDATA[In this episode, Herman and Corn tackle a frustration shared by many power users: why can’t our AI assistants stay updated with our evolving tastes in real-time? From the limitations of static training data to the "context rot" that plagues current recommendation systems, the duo breaks down the engineering hurdles of building a truly adaptive partner. They explore cutting-edge solutions like Test-Time Training (TTT), self-editing memory architectures like Letta, and the potential for nightly personal fine-tuning using LoRA. Whether you're tired of "amnesiac" LLMs or curious about the next frontier of personalization, this deep dive into the AI feedback loop offers a glimpse into a future where your model grows alongside you.]]></description>
      <link>https://myweirdprompts.com/episode/ai-continuous-learning-preferences/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-continuous-learning-preferences/</guid>
      <pubDate>Tue, 20 Jan 2026 15:46:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-continuous-learning-preferences.m4a"
        type="audio/mp4"
        length="19944261"
      />
      <itunes:title>AI That Evolves: Solving the Preference Problem</itunes:title>
      <itunes:subtitle>Why do AI recommendations feel stuck in the past? Discover the technical hurdles of real-time learning and the future of personalized agents.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn tackle a frustration shared by many power users: why can’t our AI assistants stay updated with our evolving tastes in real-time? From the limitations of static training data to the "context rot" that plagues current recommendation systems, the duo breaks down the engineering hurdles of building a truly adaptive partner. They explore cutting-edge solutions like Test-Time Training (TTT), self-editing memory architectures like Letta, and the potential for nightly personal fine-tuning using LoRA. Whether you're tired of "amnesiac" LLMs or curious about the next frontier of personalization, this deep dive into the AI feedback loop offers a glimpse into a future where your model grows alongside you.]]></itunes:summary>
      <itunes:duration>1528</itunes:duration>
      <itunes:episode>257</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-continuous-learning-preferences.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-continuous-learning-preferences.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Breaking the Blackout: CENO and the P2P Fight for Truth</title>
      <description><![CDATA[In this episode of My Weird Prompts, Herman and Corn dive into the high-stakes world of digital circumvention, focusing on the CENO browser and its impact in Iran. As the Iranian government develops its "National Information Network" to isolate its citizens, tools like CENO use the Ouinet protocol to turn the internet into a decentralized, peer-to-peer library that is nearly impossible to kill. The hosts discuss how cryptographic signatures ensure data integrity in a world of misinformation, why "slow news is better than no news," and how the battle for information sovereignty is shaping the future of the global web. Join the conversation as they explore the technology making the "sneakernet" digital and the regime's cynical attempts to drown out the truth with synthetic noise.]]></description>
      <link>https://myweirdprompts.com/episode/ceno-p2p-internet-censorship/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ceno-p2p-internet-censorship/</guid>
      <pubDate>Tue, 20 Jan 2026 14:57:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ceno-p2p-internet-censorship.m4a"
        type="audio/mp4"
        length="19095334"
      />
      <itunes:title>Breaking the Blackout: CENO and the P2P Fight for Truth</itunes:title>
      <itunes:subtitle>When governments cut the internet, CENO turns users into a library. Explore how peer-to-peer tech is bypassing state censorship in Iran.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Herman and Corn dive into the high-stakes world of digital circumvention, focusing on the CENO browser and its impact in Iran. As the Iranian government develops its "National Information Network" to isolate its citizens, tools like CENO use the Ouinet protocol to turn the internet into a decentralized, peer-to-peer library that is nearly impossible to kill. The hosts discuss how cryptographic signatures ensure data integrity in a world of misinformation, why "slow news is better than no news," and how the battle for information sovereignty is shaping the future of the global web. Join the conversation as they explore the technology making the "sneakernet" digital and the regime's cynical attempts to drown out the truth with synthetic noise.]]></itunes:summary>
      <itunes:duration>1464</itunes:duration>
      <itunes:episode>256</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ceno-p2p-internet-censorship.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ceno-p2p-internet-censorship.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Sycophancy Trap: Getting Honest Feedback from AI</title>
      <description><![CDATA[In this episode of My Weird Prompts, Corn and Herman Poppleberry dive into the "soft, squishy world" of cognitive bias in silicon. They explore why large language models tend to mirror user opinions—a phenomenon known as sycophancy—and how this problem is magnified in multi-agent systems. From the pitfalls of RLHF to the "herding effect" in virtual boards of directors, the brothers break down the research behind AI's tendency to agree. More importantly, they provide a roadmap for mitigation, discussing strategies like multi-agent debate, model diversity, and adversarial prompting. Whether you're building a business or a complex AI workflow, this episode offers essential insights into extracting unvarnished truth from a technology designed to please.]]></description>
      <link>https://myweirdprompts.com/episode/ai-sycophancy-mitigation-strategies/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-sycophancy-mitigation-strategies/</guid>
      <pubDate>Sat, 17 Jan 2026 21:47:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-sycophancy-mitigation-strategies.m4a"
        type="audio/mp4"
        length="16922515"
      />
      <itunes:title>The Sycophancy Trap: Getting Honest Feedback from AI</itunes:title>
      <itunes:subtitle>Is your AI just telling you what you want to hear? Learn how to break the &quot;sycophancy trap&quot; and get truly objective feedback from your agents.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Corn and Herman Poppleberry dive into the "soft, squishy world" of cognitive bias in silicon. They explore why large language models tend to mirror user opinions—a phenomenon known as sycophancy—and how this problem is magnified in multi-agent systems. From the pitfalls of RLHF to the "herding effect" in virtual boards of directors, the brothers break down the research behind AI's tendency to agree. More importantly, they provide a roadmap for mitigation, discussing strategies like multi-agent debate, model diversity, and adversarial prompting. Whether you're building a business or a complex AI workflow, this episode offers essential insights into extracting unvarnished truth from a technology designed to please.]]></itunes:summary>
      <itunes:duration>1444</itunes:duration>
      <itunes:episode>248</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-sycophancy-mitigation-strategies.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-sycophancy-mitigation-strategies.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Digital Fingerprints: The Secret Math Saving Your Data</title>
      <description><![CDATA[Have you ever wondered about those long strings of gibberish next to a download link? In this episode, Herman and Corn dive deep into the world of checksums—the digital fingerprints that ensure your data hasn't been corrupted by "bit rot" or tampered with by malicious actors. We explore the fascinating evolution of these mathematical safeguards, from the early days of MD5 to the modern, collision-resistant standard of SHA-256. The duo explains why even a secure HTTPS connection can't protect you from hardware failure or compromised mirror servers, making independent verification a vital skill for every user. Beyond just downloads, discover how checksums power "self-healing" file systems like ZFS and maintain the immutable history of software development through Git’s Merkle trees. It’s a geeky deep dive into the hidden protocols that keep the internet from falling apart, one bit at a time. Join us to learn how to master your own digital provenance.]]></description>
      <link>https://myweirdprompts.com/episode/checksums-data-integrity-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/checksums-data-integrity-explained/</guid>
      <pubDate>Thu, 15 Jan 2026 22:47:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/checksums-data-integrity-explained.m4a"
        type="audio/mp4"
        length="18275265"
      />
      <itunes:title>Digital Fingerprints: The Secret Math Saving Your Data</itunes:title>
      <itunes:subtitle>Learn why those random strings of characters on download pages are the only thing keeping your files safe from corruption and hackers.</itunes:subtitle>
      <itunes:summary><![CDATA[Have you ever wondered about those long strings of gibberish next to a download link? In this episode, Herman and Corn dive deep into the world of checksums—the digital fingerprints that ensure your data hasn't been corrupted by "bit rot" or tampered with by malicious actors. We explore the fascinating evolution of these mathematical safeguards, from the early days of MD5 to the modern, collision-resistant standard of SHA-256. The duo explains why even a secure HTTPS connection can't protect you from hardware failure or compromised mirror servers, making independent verification a vital skill for every user. Beyond just downloads, discover how checksums power "self-healing" file systems like ZFS and maintain the immutable history of software development through Git’s Merkle trees. It’s a geeky deep dive into the hidden protocols that keep the internet from falling apart, one bit at a time. Join us to learn how to master your own digital provenance.]]></itunes:summary>
      <itunes:duration>1417</itunes:duration>
      <itunes:episode>235</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/checksums-data-integrity-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/checksums-data-integrity-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building a Data Bridge Underground</title>
      <description><![CDATA[When concrete walls cut off all signals, how do you stay connected in a deep shelter? This episode explores the engineering of a DIY mesh network using off-the-shelf gear, turning a Faraday cage into a lifeline.]]></description>
      <link>https://myweirdprompts.com/episode/emergency-mesh-network-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/emergency-mesh-network-guide/</guid>
      <pubDate>Thu, 15 Jan 2026 22:45:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/emergency-mesh-network-guide.m4a"
        type="audio/mp4"
        length="18621636"
      />
      <itunes:title>Building a Data Bridge Underground</itunes:title>
      <itunes:subtitle>Learn how to bypass thick concrete walls and bring high-speed internet into deep shelters using portable mesh networks and Starlink technology.</itunes:subtitle>
      <itunes:summary><![CDATA[When concrete walls cut off all signals, how do you stay connected in a deep shelter? This episode explores the engineering of a DIY mesh network using off-the-shelf gear, turning a Faraday cage into a lifeline.]]></itunes:summary>
      <itunes:duration>1489</itunes:duration>
      <itunes:episode>234</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/emergency-mesh-network-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/emergency-mesh-network-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Command Line Resurgence: Why the Terminal is Back</title>
      <description><![CDATA[In this episode, Herman and Corn dive into the fascinating world of Command Line Interfaces (CLIs) and why they are seeing a massive resurgence in 2026. They trace the history of the terminal from 1950s punch cards to modern GPU-accelerated emulators, exploring how the "Unix Philosophy" of simple, composable tools is more relevant than ever. The duo discusses why AI agents are moving back into the terminal and why the command line is actually a higher-resolution interface for the human mind.]]></description>
      <link>https://myweirdprompts.com/episode/command-line-resurgence-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/command-line-resurgence-ai/</guid>
      <pubDate>Thu, 15 Jan 2026 09:53:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/command-line-resurgence-ai.m4a"
        type="audio/mp4"
        length="17248177"
      />
      <itunes:title>The Command Line Resurgence: Why the Terminal is Back</itunes:title>
      <itunes:subtitle>From punch cards to AI agents, discover why the command line remains the ultimate power tool for modern developers in an AI-driven world.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn dive into the fascinating world of Command Line Interfaces (CLIs) and why they are seeing a massive resurgence in 2026. They trace the history of the terminal from 1950s punch cards to modern GPU-accelerated emulators, exploring how the "Unix Philosophy" of simple, composable tools is more relevant than ever. The duo discusses why AI agents are moving back into the terminal and why the command line is actually a higher-resolution interface for the human mind.]]></itunes:summary>
      <itunes:duration>1476</itunes:duration>
      <itunes:episode>232</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/command-line-resurgence-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/command-line-resurgence-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Agents Hire Each Other</title>
      <description><![CDATA[What happens when AI agents stop waiting for human instructions and start negotiating, hiring, and collaborating autonomously? This episode unpacks the emerging A2A protocol and the shift from tools to autonomous teams.]]></description>
      <link>https://myweirdprompts.com/episode/agent-to-agent-protocols-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agent-to-agent-protocols-future/</guid>
      <pubDate>Mon, 12 Jan 2026 15:57:09 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agent-to-agent-protocols-future.m4a"
        type="audio/mp4"
        length="16213702"
      />
      <itunes:title>When AI Agents Hire Each Other</itunes:title>
      <itunes:subtitle>Move past human-to-AI chat. Discover how agents are negotiating, coding, and transacting in a decentralized machine-to-machine ecosystem.</itunes:subtitle>
      <itunes:summary><![CDATA[What happens when AI agents stop waiting for human instructions and start negotiating, hiring, and collaborating autonomously? This episode unpacks the emerging A2A protocol and the shift from tools to autonomous teams.]]></itunes:summary>
      <itunes:duration>1200</itunes:duration>
      <itunes:episode>218</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agent-to-agent-protocols-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agent-to-agent-protocols-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Predictive Motion: How Transformers Are Learning to Walk</title>
      <description><![CDATA[In this deep dive, Herman and Corn explore the radical convergence of large language models and robotics, marking a transition from digital logic to physical embodiment. They break down the mechanics of Vision-Language-Action (VLA) models, explaining how the transformer architecture is being repurposed to predict motor commands just as it predicts words. By treating physical movements as "action tokens," researchers are bridging the gap between abstract reasoning and real-world coordination. The discussion covers the critical "reality gap," the role of high-fidelity simulations like NVIDIA Isaac Sim, and the necessity of low-latency edge computing for the next generation of humanoid robots. Whether it’s a robot arm grasping a cup or a humanoid navigating a kitchen, the duo questions if true intelligence can only be achieved when AI finally has a body to call its own.]]></description>
      <link>https://myweirdprompts.com/episode/embodied-ai-robotics-transformers/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/embodied-ai-robotics-transformers/</guid>
      <pubDate>Fri, 09 Jan 2026 15:23:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/embodied-ai-robotics-transformers.m4a"
        type="audio/mp4"
        length="18544169"
      />
      <itunes:title>Predictive Motion: How Transformers Are Learning to Walk</itunes:title>
      <itunes:subtitle>Explore how the same transformer architecture behind chatbots is now enabling robots to navigate the physical world using action tokens.</itunes:subtitle>
      <itunes:summary><![CDATA[In this deep dive, Herman and Corn explore the radical convergence of large language models and robotics, marking a transition from digital logic to physical embodiment. They break down the mechanics of Vision-Language-Action (VLA) models, explaining how the transformer architecture is being repurposed to predict motor commands just as it predicts words. By treating physical movements as "action tokens," researchers are bridging the gap between abstract reasoning and real-world coordination. The discussion covers the critical "reality gap," the role of high-fidelity simulations like NVIDIA Isaac Sim, and the necessity of low-latency edge computing for the next generation of humanoid robots. Whether it’s a robot arm grasping a cup or a humanoid navigating a kitchen, the duo questions if true intelligence can only be achieved when AI finally has a body to call its own.]]></itunes:summary>
      <itunes:duration>1384</itunes:duration>
      <itunes:episode>210</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/embodied-ai-robotics-transformers.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/embodied-ai-robotics-transformers.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Agents Build Your Enterprise Storage at Home</title>
      <description><![CDATA[How a housemate used an AI agent to set up a five-disk Btrfs array, turning enterprise storage magic into a home workstation reality—and why snapshots aren't backups.]]></description>
      <link>https://myweirdprompts.com/episode/btrfs-zfs-storage-pooling/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/btrfs-zfs-storage-pooling/</guid>
      <pubDate>Thu, 08 Jan 2026 21:59:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/btrfs-zfs-storage-pooling.m4a"
        type="audio/mp4"
        length="17136989"
      />
      <itunes:title>When AI Agents Build Your Enterprise Storage at Home</itunes:title>
      <itunes:subtitle>Explore the power of Btrfs, ZFS, and XFS. Learn how storage pooling and snapshots create a &quot;save point&quot; for your entire computer.</itunes:subtitle>
      <itunes:summary><![CDATA[How a housemate used an AI agent to set up a five-disk Btrfs array, turning enterprise storage magic into a home workstation reality—and why snapshots aren't backups.]]></itunes:summary>
      <itunes:duration>1302</itunes:duration>
      <itunes:episode>206</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/btrfs-zfs-storage-pooling.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/btrfs-zfs-storage-pooling.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Graph RAG Beats Flat Vectors for AI Memory</title>
      <description><![CDATA[Herman and Corn explore how graph databases and personal knowledge tools like Obsidian are reshaping AI's ability to reason associatively, reducing hallucinations and moving beyond the vector-only era.]]></description>
      <link>https://myweirdprompts.com/episode/graph-rag-ai-tech-stack/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/graph-rag-ai-tech-stack/</guid>
      <pubDate>Thu, 08 Jan 2026 21:00:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/graph-rag-ai-tech-stack.m4a"
        type="audio/mp4"
        length="19035208"
      />
      <itunes:title>Why Graph RAG Beats Flat Vectors for AI Memory</itunes:title>
      <itunes:subtitle>Explore how the AI stack is evolving from simple vector search to complex Graph RAG and why the &quot;boring&quot; Postgres database is winning the race.</itunes:subtitle>
      <itunes:summary><![CDATA[Herman and Corn explore how graph databases and personal knowledge tools like Obsidian are reshaping AI's ability to reason associatively, reducing hallucinations and moving beyond the vector-only era.]]></itunes:summary>
      <itunes:duration>1482</itunes:duration>
      <itunes:episode>200</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/graph-rag-ai-tech-stack.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/graph-rag-ai-tech-stack.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Escaping the Carrier Trap</title>
      <description><![CDATA[Why are we still paying for rigid phone plans when we can build our own programmable voice systems? This episode explores how SIP and Twilio let you bypass legacy carriers for total control over call routing, AI integration, and global roaming.]]></description>
      <link>https://myweirdprompts.com/episode/sip-programmable-voice-hacking/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/sip-programmable-voice-hacking/</guid>
      <pubDate>Tue, 06 Jan 2026 19:43:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/sip-programmable-voice-hacking.m4a"
        type="audio/mp4"
        length="19443310"
      />
      <itunes:title>Escaping the Carrier Trap</itunes:title>
      <itunes:subtitle>Tired of rigid phone plans? Discover how to bypass carriers using SIP and Twilio to build a custom voice architecture for your home or office.</itunes:subtitle>
      <itunes:summary><![CDATA[Why are we still paying for rigid phone plans when we can build our own programmable voice systems? This episode explores how SIP and Twilio let you bypass legacy carriers for total control over call routing, AI integration, and global roaming.]]></itunes:summary>
      <itunes:duration>1633</itunes:duration>
      <itunes:episode>180</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://episodes.myweirdprompts.com/covers/sip-programmable-voice-hacking.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/sip-programmable-voice-hacking.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Taming the Sprawl: Building Your Cognitive AI Toolbox</title>
      <description><![CDATA[In this episode, Herman and Corn dive into the "2026 problem" of AI tool sprawl, exploring how the ease of "vibe coding" has created a world of isolated apps that lack a cohesive ecosystem. They discuss the revolutionary potential of the Model Context Protocol (MCP) and generative user interfaces to bridge these digital islands into a unified "cognitive operating system." By moving toward local-first orchestration and modular canvases, users can finally escape the friction of SaaS caps and vendor lock-in to build a truly personalized, high-performance digital workspace.]]></description>
      <link>https://myweirdprompts.com/episode/ai-tool-sprawl-consolidation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-tool-sprawl-consolidation/</guid>
      <pubDate>Mon, 05 Jan 2026 18:33:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-tool-sprawl-consolidation.m4a"
        type="audio/mp4"
        length="17817263"
      />
      <itunes:title>Taming the Sprawl: Building Your Cognitive AI Toolbox</itunes:title>
      <itunes:subtitle>Drowning in a sea of custom AI scripts? Learn how to turn disconnected &quot;vibe-coded&quot; tools into a unified, local-first cognitive operating system.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn dive into the "2026 problem" of AI tool sprawl, exploring how the ease of "vibe coding" has created a world of isolated apps that lack a cohesive ecosystem. They discuss the revolutionary potential of the Model Context Protocol (MCP) and generative user interfaces to bridge these digital islands into a unified "cognitive operating system." By moving toward local-first orchestration and modular canvases, users can finally escape the friction of SaaS caps and vendor lock-in to build a truly personalized, high-performance digital workspace.]]></itunes:summary>
      <itunes:duration>1380</itunes:duration>
      <itunes:episode>172</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-tool-sprawl-consolidation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-tool-sprawl-consolidation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Digital Fortresses to Machine-Digestible Sites</title>
      <description><![CDATA[Why the era of hiding data from AI crawlers is over. This episode explores the llms.txt specification and how to make your website the primary source for conversational search, turning bots from adversaries into allies.]]></description>
      <link>https://myweirdprompts.com/episode/ai-optimization-llms-txt-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-optimization-llms-txt-future/</guid>
      <pubDate>Mon, 05 Jan 2026 15:30:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-optimization-llms-txt-future.m4a"
        type="audio/mp4"
        length="18919345"
      />
      <itunes:title>From Digital Fortresses to Machine-Digestible Sites</itunes:title>
      <itunes:subtitle>Stop fighting the crawlers and start feeding them. Learn how llms.txt and structured metadata are defining the new era of AI Optimization.</itunes:subtitle>
      <itunes:summary><![CDATA[Why the era of hiding data from AI crawlers is over. This episode explores the llms.txt specification and how to make your website the primary source for conversational search, turning bots from adversaries into allies.]]></itunes:summary>
      <itunes:duration>1447</itunes:duration>
      <itunes:episode>171</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-optimization-llms-txt-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-optimization-llms-txt-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond the Chatbox: The Power of Model Context Protocol</title>
      <description><![CDATA[In this first episode of 2026, Herman and Corn Poppleberry explore the revolutionary Model Context Protocol (MCP) and its role as the universal interface for AI agents. They break down why this "USB of AI" is essential for building interoperable systems that can query databases, browse the web, and communicate with other agents seamlessly. Beyond the technical specs, the brothers discuss the evolving social landscape of AI development, from the high-energy Discord servers to the transformative power of modern hackathons. Whether you're a seasoned developer or a curious newcomer, this episode provides a roadmap for navigating the collaborative future of agentic AI and building a genuine community in the digital age.]]></description>
      <link>https://myweirdprompts.com/episode/mcp-agentic-systems-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mcp-agentic-systems-future/</guid>
      <pubDate>Sun, 04 Jan 2026 11:43:21 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mcp-agentic-systems-future.m4a"
        type="audio/mp4"
        length="15984793"
      />
      <itunes:title>Beyond the Chatbox: The Power of Model Context Protocol</itunes:title>
      <itunes:subtitle>Discover why the Model Context Protocol is the &quot;USB of AI&quot; and how it’s fueling a new wave of autonomous agents and developer communities.</itunes:subtitle>
      <itunes:summary><![CDATA[In this first episode of 2026, Herman and Corn Poppleberry explore the revolutionary Model Context Protocol (MCP) and its role as the universal interface for AI agents. They break down why this "USB of AI" is essential for building interoperable systems that can query databases, browse the web, and communicate with other agents seamlessly. Beyond the technical specs, the brothers discuss the evolving social landscape of AI development, from the high-energy Discord servers to the transformative power of modern hackathons. Whether you're a seasoned developer or a curious newcomer, this episode provides a roadmap for navigating the collaborative future of agentic AI and building a genuine community in the digital age.]]></itunes:summary>
      <itunes:duration>1230</itunes:duration>
      <itunes:episode>157</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mcp-agentic-systems-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mcp-agentic-systems-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Build vs. Buy: The ESP32&apos;s Smart Home Reign</title>
      <description><![CDATA[When a custom Zigbee status light sparks the question: should you build or buy? Corn and Herman explore why the ESP32 has become the DIY smart home king, from ESPHome to AI-assisted design tools.]]></description>
      <link>https://myweirdprompts.com/episode/esp32-smart-home-diy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/esp32-smart-home-diy/</guid>
      <pubDate>Sun, 04 Jan 2026 11:37:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/esp32-smart-home-diy.m4a"
        type="audio/mp4"
        length="17645249"
      />
      <itunes:title>Build vs. Buy: The ESP32&apos;s Smart Home Reign</itunes:title>
      <itunes:subtitle>Discover why the ESP32 is the ultimate brain for DIY smart homes and how AI tools are revolutionizing hardware hacking for everyone.</itunes:subtitle>
      <itunes:summary><![CDATA[When a custom Zigbee status light sparks the question: should you build or buy? Corn and Herman explore why the ESP32 has become the DIY smart home king, from ESPHome to AI-assisted design tools.]]></itunes:summary>
      <itunes:duration>1404</itunes:duration>
      <itunes:episode>156</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/esp32-smart-home-diy.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/esp32-smart-home-diy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building an Ideation Factory: Beyond Generic AI Ideas</title>
      <description><![CDATA[In this episode of My Weird Prompts, Herman and Corn tackle the technical hurdles of high-volume AI ideation. They explore why standard LLMs often hit a "context window fatigue" wall, resulting in repetitive and generic suggestions when asked for large quantities of ideas. By proposing a sophisticated multi-agent workflow—complete with stateful memory, semantic distance auditing, and "Chain of Density" prompting—the brothers demonstrate how to transform AI into a powerful engine for solving real-world problems like the economic brain drain in Jerusalem.]]></description>
      <link>https://myweirdprompts.com/episode/ai-high-volume-ideation/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-high-volume-ideation/</guid>
      <pubDate>Sun, 04 Jan 2026 11:14:32 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-high-volume-ideation.m4a"
        type="audio/mp4"
        length="18356062"
      />
      <itunes:title>Building an Ideation Factory: Beyond Generic AI Ideas</itunes:title>
      <itunes:subtitle>Learn how to overcome AI repetition and build a multi-agent &quot;ideation factory&quot; to solve complex local economic challenges.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Herman and Corn tackle the technical hurdles of high-volume AI ideation. They explore why standard LLMs often hit a "context window fatigue" wall, resulting in repetitive and generic suggestions when asked for large quantities of ideas. By proposing a sophisticated multi-agent workflow—complete with stateful memory, semantic distance auditing, and "Chain of Density" prompting—the brothers demonstrate how to transform AI into a powerful engine for solving real-world problems like the economic brain drain in Jerusalem.]]></itunes:summary>
      <itunes:duration>1445</itunes:duration>
      <itunes:episode>155</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-high-volume-ideation.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-high-volume-ideation.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Apps to Agents: Building Your Digital Workforce</title>
      <description><![CDATA[In this episode of My Weird Prompts, Herman and Corn dive deep into the rapidly evolving world of agentic AI as of early 2026. They break down the crucial differences between reactive custom GPTs and autonomous multi-agent workflows, exploring how tools like Claude Code and N8N are reshaping productivity. From the architectural debate between serverless hosting and local "agent boxes" to the essential strategies for preventing token-burning infinite loops, this episode provides a practical roadmap for anyone looking to build a secure, scalable, and cost-effective digital workforce.]]></description>
      <link>https://myweirdprompts.com/episode/ai-agentic-workflows-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-agentic-workflows-evolution/</guid>
      <pubDate>Sun, 04 Jan 2026 11:07:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-agentic-workflows-evolution.m4a"
        type="audio/mp4"
        length="16568630"
      />
      <itunes:title>From Apps to Agents: Building Your Digital Workforce</itunes:title>
      <itunes:subtitle>Move beyond simple prompts. Explore the architecture, autonomy, and fiscal guardrails of the next generation of AI agentic workflows.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Herman and Corn dive deep into the rapidly evolving world of agentic AI as of early 2026. They break down the crucial differences between reactive custom GPTs and autonomous multi-agent workflows, exploring how tools like Claude Code and N8N are reshaping productivity. From the architectural debate between serverless hosting and local "agent boxes" to the essential strategies for preventing token-burning infinite loops, this episode provides a practical roadmap for anyone looking to build a secure, scalable, and cost-effective digital workforce.]]></itunes:summary>
      <itunes:duration>1429</itunes:duration>
      <itunes:episode>154</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-agentic-workflows-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-agentic-workflows-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Memory vs. RAG: Building Long-Term Intelligence</title>
      <description><![CDATA[In this episode, Herman and Corn Poppleberry sit down in Jerusalem to tackle a complex architectural question: why can’t we just store everything in a single vector database? They move beyond the "honeymoon phase" of Retrieval Augmented Generation (RAG) to discuss the necessity of a dedicated memory layer for AI agents. From the dangers of context poisoning to the benefits of using Graph RAG for personal relationships, the brothers explain why the future of AI intelligence lies in synthesis, not just storage. This is a deep dive into how we build systems that truly remember who we are.]]></description>
      <link>https://myweirdprompts.com/episode/ai-memory-vs-rag-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-memory-vs-rag-architecture/</guid>
      <pubDate>Sun, 04 Jan 2026 07:35:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-memory-vs-rag-architecture.m4a"
        type="audio/mp4"
        length="18141279"
      />
      <itunes:title>AI Memory vs. RAG: Building Long-Term Intelligence</itunes:title>
      <itunes:subtitle>Explore why AI needs a &quot;diary&quot; and not just a &quot;library&quot; as we dive into the architectural differences between RAG and long-term agentic memory.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn Poppleberry sit down in Jerusalem to tackle a complex architectural question: why can’t we just store everything in a single vector database? They move beyond the "honeymoon phase" of Retrieval Augmented Generation (RAG) to discuss the necessity of a dedicated memory layer for AI agents. From the dangers of context poisoning to the benefits of using Graph RAG for personal relationships, the brothers explain why the future of AI intelligence lies in synthesis, not just storage. This is a deep dive into how we build systems that truly remember who we are.]]></itunes:summary>
      <itunes:duration>1383</itunes:duration>
      <itunes:episode>144</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-memory-vs-rag-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-memory-vs-rag-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Breaking the Voice Wall: The Future of Native Speech AI</title>
      <description><![CDATA[In this episode, Herman and Corn dive deep into the technical and economic hurdles of real-time conversational AI. They explore why current voice assistants often feel like "confused walls" and how the transition from traditional text-based pipelines to native speech-to-speech models is fundamentally changing the user experience. From the staggering computational costs of processing raw audio tokens to the intricate social intelligence required for "turn detection," the brothers discuss whether voice interfaces can truly replace the keyboard in the modern workforce. Learn about the rise of semantic voice activity detection, the importance of prosody, and how edge computing might finally make natural human-AI dialogue a viable reality for businesses and individuals alike.]]></description>
      <link>https://myweirdprompts.com/episode/native-speech-to-speech-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/native-speech-to-speech-evolution/</guid>
      <pubDate>Sat, 03 Jan 2026 20:51:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/native-speech-to-speech-evolution.m4a"
        type="audio/mp4"
        length="21182018"
      />
      <itunes:title>Breaking the Voice Wall: The Future of Native Speech AI</itunes:title>
      <itunes:subtitle>Explore why native speech-to-speech AI is 20x more expensive than text pipelines and how &quot;semantic VAD&quot; is solving the awkward silence problem.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn dive deep into the technical and economic hurdles of real-time conversational AI. They explore why current voice assistants often feel like "confused walls" and how the transition from traditional text-based pipelines to native speech-to-speech models is fundamentally changing the user experience. From the staggering computational costs of processing raw audio tokens to the intricate social intelligence required for "turn detection," the brothers discuss whether voice interfaces can truly replace the keyboard in the modern workforce. Learn about the rise of semantic voice activity detection, the importance of prosody, and how edge computing might finally make natural human-AI dialogue a viable reality for businesses and individuals alike.]]></itunes:summary>
      <itunes:duration>1745</itunes:duration>
      <itunes:episode>142</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/native-speech-to-speech-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/native-speech-to-speech-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Is OCR Dead? How Vision AI Is Redefining Text Extraction</title>
      <description><![CDATA[For decades, Optical Character Recognition was the "90% solved" problem that caused 100% of the headaches for developers and businesses. From the brittle pattern-matching of the 1970s to the manual correction workflows of the early 2000s, extracting text from messy documents was a notoriously unreliable process. In this episode, Herman and Corn dive into the "Transformer Revolution" and the rise of multimodal Vision Language Models (VLMs) like Gemini and Qwen. They discuss whether specialized OCR APIs are becoming obsolete, how AI handles complex scripts like Hebrew, and the dangerous new phenomenon of generative "hallucinations" in data extraction. Whether you're a developer or just curious about how your phone reads receipts, this deep dive reveals why the category of software we once called OCR is being completely swallowed by general-purpose AI.]]></description>
      <link>https://myweirdprompts.com/episode/vision-language-models-ocr-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vision-language-models-ocr-future/</guid>
      <pubDate>Fri, 02 Jan 2026 10:49:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vision-language-models-ocr-future.m4a"
        type="audio/mp4"
        length="16685413"
      />
      <itunes:title>Is OCR Dead? How Vision AI Is Redefining Text Extraction</itunes:title>
      <itunes:subtitle>Are specialized OCR tools obsolete? Herman and Corn explore how Vision Language Models are revolutionizing the way we turn images into data.</itunes:subtitle>
      <itunes:summary><![CDATA[For decades, Optical Character Recognition was the "90% solved" problem that caused 100% of the headaches for developers and businesses. From the brittle pattern-matching of the 1970s to the manual correction workflows of the early 2000s, extracting text from messy documents was a notoriously unreliable process. In this episode, Herman and Corn dive into the "Transformer Revolution" and the rise of multimodal Vision Language Models (VLMs) like Gemini and Qwen. They discuss whether specialized OCR APIs are becoming obsolete, how AI handles complex scripts like Hebrew, and the dangerous new phenomenon of generative "hallucinations" in data extraction. Whether you're a developer or just curious about how your phone reads receipts, this deep dive reveals why the category of software we once called OCR is being completely swallowed by general-purpose AI.]]></itunes:summary>
      <itunes:duration>1257</itunes:duration>
      <itunes:episode>135</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vision-language-models-ocr-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vision-language-models-ocr-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How to Spot Gamed Benchmarks in Chinese AI</title>
      <description><![CDATA[With Chinese AI models flooding the market at rock-bottom prices, how can developers tell real reasoning power from memorized answers? Herman and Corn dissect data contamination, agentic evaluation, and the benchmarks that actually matter.]]></description>
      <link>https://myweirdprompts.com/episode/chinese-ai-benchmark-reality/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/chinese-ai-benchmark-reality/</guid>
      <pubDate>Thu, 01 Jan 2026 15:25:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/chinese-ai-benchmark-reality.m4a"
        type="audio/mp4"
        length="17639321"
      />
      <itunes:title>How to Spot Gamed Benchmarks in Chinese AI</itunes:title>
      <itunes:subtitle>Are Chinese AI models actually beating the West, or just gaming the system? Herman and Corn dive into the reality of modern AI benchmarks.</itunes:subtitle>
      <itunes:summary><![CDATA[With Chinese AI models flooding the market at rock-bottom prices, how can developers tell real reasoning power from memorized answers? Herman and Corn dissect data contamination, agentic evaluation, and the benchmarks that actually matter.]]></itunes:summary>
      <itunes:duration>1392</itunes:duration>
      <itunes:episode>130</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/chinese-ai-benchmark-reality.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/chinese-ai-benchmark-reality.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Stop Writing Prompts and Start Writing Constitutions</title>
      <description><![CDATA[In this episode of My Weird Prompts, Herman and Corn Poppleberry tackle a provocative question: Is prompt engineering just a temporary phase? Looking ahead to 2026, the brothers discuss how the "dark art" of hacking prompts has evolved into a sophisticated discipline of context engineering and system orchestration. They argue that while the low-level syntax of prompting is fading, the need for domain expertise and "Outcome Architecture" is more critical than ever for mastering human-AI collaboration.]]></description>
      <link>https://myweirdprompts.com/episode/ai-outcome-architecture-evolution/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-outcome-architecture-evolution/</guid>
      <pubDate>Thu, 01 Jan 2026 15:11:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-outcome-architecture-evolution.m4a"
        type="audio/mp4"
        length="16967780"
      />
      <itunes:title>Stop Writing Prompts and Start Writing Constitutions</itunes:title>
      <itunes:subtitle>Is prompt engineering a dying art? Herman and Corn explore why the future of AI lies in context, domain expertise, and outcome architecture.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Herman and Corn Poppleberry tackle a provocative question: Is prompt engineering just a temporary phase? Looking ahead to 2026, the brothers discuss how the "dark art" of hacking prompts has evolved into a sophisticated discipline of context engineering and system orchestration. They argue that while the low-level syntax of prompting is fading, the need for domain expertise and "Outcome Architecture" is more critical than ever for mastering human-AI collaboration.]]></itunes:summary>
      <itunes:duration>1112</itunes:duration>
      <itunes:episode>129</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-outcome-architecture-evolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-outcome-architecture-evolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Modular Code Indexing: Separating AI Memory from Intelligence</title>
      <description><![CDATA[Daniel explores how to separate the indexing layer from AI coding agents. Every new session with tools like Claude Code starts with redundant repository mapping - could a modular approach with persistent indexes solve this context management problem?]]></description>
      <link>https://myweirdprompts.com/episode/ai-modular-code-indexing-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-modular-code-indexing-future/</guid>
      <pubDate>Thu, 01 Jan 2026 13:58:13 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-modular-code-indexing-future.m4a"
        type="audio/mp4"
        length="15766525"
      />
      <itunes:title>Modular Code Indexing: Separating AI Memory from Intelligence</itunes:title>
      
      <itunes:summary><![CDATA[Daniel explores how to separate the indexing layer from AI coding agents. Every new session with tools like Claude Code starts with redundant repository mapping - could a modular approach with persistent indexes solve this context management problem?]]></itunes:summary>
      <itunes:duration>0</itunes:duration>
      <itunes:episode>127</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-modular-code-indexing-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-modular-code-indexing-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Agentic AI Dilemma: Who Holds the Kill Switch?</title>
      <description><![CDATA[In this episode of My Weird Prompts, Herman and Corn dive into the complex world of agentic AI and the critical necessity of human oversight. They discuss the shift from simple chatbots to autonomous agents managing power plants and medical diagnostics, exploring frameworks like "human-on-the-loop" and "formal verification." From the psychological trap of automation bias to the unsettling reversal where humans become the "actuators" for AI brains, this conversation tackles the defining engineering and ethical challenges of 2025.]]></description>
      <link>https://myweirdprompts.com/episode/agentic-ai-human-oversight/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/agentic-ai-human-oversight/</guid>
      <pubDate>Mon, 29 Dec 2025 16:08:39 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/agentic-ai-human-oversight.m4a"
        type="audio/mp4"
        length="17129335"
      />
      <itunes:title>The Agentic AI Dilemma: Who Holds the Kill Switch?</itunes:title>
      <itunes:subtitle>As AI shifts from chatbots to autonomous agents, Herman and Corn explore how to maintain human control in a high-stakes automated world.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Herman and Corn dive into the complex world of agentic AI and the critical necessity of human oversight. They discuss the shift from simple chatbots to autonomous agents managing power plants and medical diagnostics, exploring frameworks like "human-on-the-loop" and "formal verification." From the psychological trap of automation bias to the unsettling reversal where humans become the "actuators" for AI brains, this conversation tackles the defining engineering and ethical challenges of 2025.]]></itunes:summary>
      <itunes:duration>1269</itunes:duration>
      <itunes:episode>123</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/agentic-ai-human-oversight.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/agentic-ai-human-oversight.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Silencing the Siren: Real-Time AI Noise Reduction</title>
      <description><![CDATA[In this episode, Herman and Corn dive into the fascinating world of deep neural networks and their role in cleaning up messy audio on mobile devices. From the challenges of "non-stationary" noises like sirens to the engineering trade-offs of running AI on mobile NPUs, they explore how 2025's hardware is changing the way we communicate. They discuss the shift from cloud-based processing to edge computing, the importance of quantization, and why the future of audio intelligence is being built directly on your device.]]></description>
      <link>https://myweirdprompts.com/episode/real-time-audio-ai-edge/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/real-time-audio-ai-edge/</guid>
      <pubDate>Mon, 29 Dec 2025 15:40:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/real-time-audio-ai-edge.m4a"
        type="audio/mp4"
        length="16865331"
      />
      <itunes:title>Silencing the Siren: Real-Time AI Noise Reduction</itunes:title>
      <itunes:subtitle>How do phones remove sirens and crying babies in real time? Explore the neural networks and hardware making crystal-clear audio possible.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn dive into the fascinating world of deep neural networks and their role in cleaning up messy audio on mobile devices. From the challenges of "non-stationary" noises like sirens to the engineering trade-offs of running AI on mobile NPUs, they explore how 2025's hardware is changing the way we communicate. They discuss the shift from cloud-based processing to edge computing, the importance of quantization, and why the future of audio intelligence is being built directly on your device.]]></itunes:summary>
      <itunes:duration>1324</itunes:duration>
      <itunes:episode>120</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/real-time-audio-ai-edge.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/real-time-audio-ai-edge.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond Transformers: Solving the AI Memory Crisis</title>
      <description><![CDATA[In this episode, Herman and Corn Poppleberry tackle one of the most frustrating hurdles in modern AI engineering: the "stateless" architecture of Large Language Models. They explore why current models require you to resend your entire conversation history with every message, leading to skyrocketing token costs and the "lost in the middle" phenomenon that plagues even the most advanced systems. From the quadratic complexity of the standard Transformer to the revolutionary potential of State Space Models like Mamba and hybrid architectures like Jamba, the brothers break down how researchers are finally building AI with persistent, human-like memory.]]></description>
      <link>https://myweirdprompts.com/episode/ai-stateless-architecture-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-stateless-architecture-future/</guid>
      <pubDate>Sat, 27 Dec 2025 21:14:22 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-stateless-architecture-future.m4a"
        type="audio/mp4"
        length="10861448"
      />
      <itunes:title>Beyond Transformers: Solving the AI Memory Crisis</itunes:title>
      <itunes:subtitle>Why does AI forget your conversation every time you hit enter? Herman and Corn dive into the &quot;stateless&quot; nature of LLMs and the future of memory.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode, Herman and Corn Poppleberry tackle one of the most frustrating hurdles in modern AI engineering: the "stateless" architecture of Large Language Models. They explore why current models require you to resend your entire conversation history with every message, leading to skyrocketing token costs and the "lost in the middle" phenomenon that plagues even the most advanced systems. From the quadratic complexity of the standard Transformer to the revolutionary potential of State Space Models like Mamba and hybrid architectures like Jamba, the brothers break down how researchers are finally building AI with persistent, human-like memory.]]></itunes:summary>
      <itunes:duration>1306</itunes:duration>
      <itunes:episode>111</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-stateless-architecture-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-stateless-architecture-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Agentic AI Needs More VRAM Than You Think</title>
      <description><![CDATA[When your AI model tries to debug and test code autonomously, it demands massive memory. Herman and Corn explore why 12GB of VRAM isn't enough, and how to build a local inference server that actually keeps up.]]></description>
      <link>https://myweirdprompts.com/episode/local-ai-inference-server-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/local-ai-inference-server-guide/</guid>
      <pubDate>Sat, 27 Dec 2025 20:46:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/local-ai-inference-server-guide.m4a"
        type="audio/mp4"
        length="16504914"
      />
      <itunes:title>Why Agentic AI Needs More VRAM Than You Think</itunes:title>
      <itunes:subtitle>Learn how to build a high-performance local AI server for agentic coding, from dual-GPU PC builds to the power of Mac&apos;s unified memory.</itunes:subtitle>
      <itunes:summary><![CDATA[When your AI model tries to debug and test code autonomously, it demands massive memory. Herman and Corn explore why 12GB of VRAM isn't enough, and how to build a local inference server that actually keeps up.]]></itunes:summary>
      <itunes:duration>1273</itunes:duration>
      <itunes:episode>110</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/local-ai-inference-server-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/local-ai-inference-server-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beating Context Bloat with Dynamic Dictionaries</title>
      <description><![CDATA[When a custom dictionary slows your AI to a crawl, how do you keep it hearing niche terms without breaking the bank? This episode unpacks phonetic indexing, dynamic hints, and portable JSON structures as alternatives to fine-tuning.]]></description>
      <link>https://myweirdprompts.com/episode/ai-transcription-custom-dictionary/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-transcription-custom-dictionary/</guid>
      <pubDate>Sat, 27 Dec 2025 17:54:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-transcription-custom-dictionary.m4a"
        type="audio/mp4"
        length="17402564"
      />
      <itunes:title>Beating Context Bloat with Dynamic Dictionaries</itunes:title>
      <itunes:subtitle>Tired of AI mishearing brand names? Learn how to build efficient custom dictionaries for Gemini 1.5 without breaking the bank.</itunes:subtitle>
      <itunes:summary><![CDATA[When a custom dictionary slows your AI to a crawl, how do you keep it hearing niche terms without breaking the bank? This episode unpacks phonetic indexing, dynamic hints, and portable JSON structures as alternatives to fine-tuning.]]></itunes:summary>
      <itunes:duration>1409</itunes:duration>
      <itunes:episode>109</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-transcription-custom-dictionary.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-transcription-custom-dictionary.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your AI Code Assistant Gets Worse</title>
      <description><![CDATA[Why do AI coding tools feel brilliant at first, then suddenly struggle? This episode unpacks the real reasons behind 'model rot'—from vendor optimization tricks to the psychology of firm prompting—and what developers can do about it.]]></description>
      <link>https://myweirdprompts.com/episode/model-rot-coding-mysteries/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/model-rot-coding-mysteries/</guid>
      <pubDate>Fri, 26 Dec 2025 20:28:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/model-rot-coding-mysteries.m4a"
        type="audio/mp4"
        length="17957692"
      />
      <itunes:title>Why Your AI Code Assistant Gets Worse</itunes:title>
      <itunes:subtitle>Why do AI models lose their edge over time? Herman and Corn explore the &quot;home team advantage&quot; and why telling your AI to &quot;do better&quot; actually works.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do AI coding tools feel brilliant at first, then suddenly struggle? This episode unpacks the real reasons behind 'model rot'—from vendor optimization tricks to the psychology of firm prompting—and what developers can do about it.]]></itunes:summary>
      <itunes:duration>1406</itunes:duration>
      <itunes:episode>108</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/model-rot-coding-mysteries.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/model-rot-coding-mysteries.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Chatbots to Computer-Use Agents</title>
      <description><![CDATA[How Grace Hopper's vision of natural language computing is finally becoming real through computer-use agents and the Model Context Protocol, turning your AI from a talker into a doer.]]></description>
      <link>https://myweirdprompts.com/episode/computer-use-agents-future/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/computer-use-agents-future/</guid>
      <pubDate>Fri, 26 Dec 2025 13:17:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/computer-use-agents-future.m4a"
        type="audio/mp4"
        length="19598672"
      />
      <itunes:title>From Chatbots to Computer-Use Agents</itunes:title>
      <itunes:subtitle>Can an AI actually use your mouse? Herman and Corn dive into the world of Computer Use Agents and the dream of seamless machine interaction.</itunes:subtitle>
      <itunes:summary><![CDATA[How Grace Hopper's vision of natural language computing is finally becoming real through computer-use agents and the Model Context Protocol, turning your AI from a talker into a doer.]]></itunes:summary>
      <itunes:duration>1626</itunes:duration>
      <itunes:episode>106</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/computer-use-agents-future.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/computer-use-agents-future.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond Math Puzzles: The Truth About AI Benchmarks</title>
      <description><![CDATA[In this episode of My Weird Prompts, Herman and Corn tackle the growing controversy surrounding artificial intelligence benchmarks. As new models like Claude 4.5 and GLM 4.7 dominate headlines with record-breaking scores, the duo explores whether high performance on math puzzles actually translates to real-world coding productivity. They break down the dangers of data contamination, the rise of "benchmark gaming," and why the industry is shifting toward more rigorous, live testing environments. From the software engineering challenges of SWE-bench to the "surprise quiz" nature of LiveBench, this episode provides a vital guide for anyone trying to separate marketing hype from actual machine reasoning.]]></description>
      <link>https://myweirdprompts.com/episode/ai-coding-benchmarks-truth/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-coding-benchmarks-truth/</guid>
      <pubDate>Fri, 26 Dec 2025 12:23:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-coding-benchmarks-truth.m4a"
        type="audio/mp4"
        length="12742462"
      />
      <itunes:title>Beyond Math Puzzles: The Truth About AI Benchmarks</itunes:title>
      <itunes:subtitle>Are AI models getting smarter, or just better at memorizing tests? Herman and Corn dive into the controversial world of 2025 AI benchmarks.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Herman and Corn tackle the growing controversy surrounding artificial intelligence benchmarks. As new models like Claude 4.5 and GLM 4.7 dominate headlines with record-breaking scores, the duo explores whether high performance on math puzzles actually translates to real-world coding productivity. They break down the dangers of data contamination, the rise of "benchmark gaming," and why the industry is shifting toward more rigorous, live testing environments. From the software engineering challenges of SWE-bench to the "surprise quiz" nature of LiveBench, this episode provides a vital guide for anyone trying to separate marketing hype from actual machine reasoning.]]></itunes:summary>
      <itunes:duration>1345</itunes:duration>
      <itunes:episode>105</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-coding-benchmarks-truth.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-coding-benchmarks-truth.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Vibe Coding &amp; The Rise of the AI Orchestrator</title>
      <description><![CDATA[Are we witnessing the end of the traditional programmer? In this episode of My Weird Prompts, Herman and Corn dive into the world of agentic development and "vibe coding," exploring how tools like Claude Code are shifting the focus from syntax to systems thinking. They discuss how the role of the developer is evolving into that of an "orchestrator," where managing AI agents is more critical than memorizing semicolons. Whether you're a seasoned dev or a tech-curious problem solver, learn why the ability to plan and manage complex systems is the most valuable skill for the year 2026.]]></description>
      <link>https://myweirdprompts.com/episode/vibe-coding-agentic-development/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vibe-coding-agentic-development/</guid>
      <pubDate>Thu, 25 Dec 2025 17:13:26 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vibe-coding-agentic-development.m4a"
        type="audio/mp4"
        length="13678245"
      />
      <itunes:title>Vibe Coding &amp; The Rise of the AI Orchestrator</itunes:title>
      <itunes:subtitle>Explore the shift from manual coding to AI orchestration. Discover why &quot;vibe coding&quot; is redefining the tech roles of the future.</itunes:subtitle>
      <itunes:summary><![CDATA[Are we witnessing the end of the traditional programmer? In this episode of My Weird Prompts, Herman and Corn dive into the world of agentic development and "vibe coding," exploring how tools like Claude Code are shifting the focus from syntax to systems thinking. They discuss how the role of the developer is evolving into that of an "orchestrator," where managing AI agents is more critical than memorizing semicolons. Whether you're a seasoned dev or a tech-curious problem solver, learn why the ability to plan and manage complex systems is the most valuable skill for the year 2026.]]></itunes:summary>
      <itunes:duration>1455</itunes:duration>
      <itunes:episode>104</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vibe-coding-agentic-development.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vibe-coding-agentic-development.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Brain Might Be Wired for SQL</title>
      <description><![CDATA[Herman and Corn explore the idea that programming languages fit different brains, not a ladder of difficulty. As AI handles syntax, the future developer is defined by cognitive fit, not coding chops.]]></description>
      <link>https://myweirdprompts.com/episode/cognitive-fit-programming-ai/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/cognitive-fit-programming-ai/</guid>
      <pubDate>Thu, 25 Dec 2025 17:03:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cognitive-fit-programming-ai.m4a"
        type="audio/mp4"
        length="13044475"
      />
      <itunes:title>Why Your Brain Might Be Wired for SQL</itunes:title>
      <itunes:subtitle>Is programming difficulty objective, or is it all about your brain&apos;s wiring? Herman and Corn explore the &quot;cognitive fit&quot; of coding in 2025.</itunes:subtitle>
      <itunes:summary><![CDATA[Herman and Corn explore the idea that programming languages fit different brains, not a ladder of difficulty. As AI handles syntax, the future developer is defined by cognitive fit, not coding chops.]]></itunes:summary>
      <itunes:duration>1424</itunes:duration>
      <itunes:episode>103</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cognitive-fit-programming-ai.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cognitive-fit-programming-ai.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Story Behind the Show</title>
      <description><![CDATA[In this special episode, Daniel Rosehill - the creator of My Weird Prompts - steps out from behind the curtain to explain what this AI-generated podcast is all about. He discusses the origins of the project, his motivation for using AI as a learning tool, and the technical pipeline that transforms voice prompts into full podcast episodes.

Daniel explains how he uses voice-to-AI workflows to generate thoughtful responses to his burning questions, and why he chose to create fictional AI hosts - Herman the donkey and Corn the sloth - rather than using generic AI voices. He covers the challenges of finding affordable text-to-speech providers, the evolution of the pipeline through multiple iterations, and why he decided to make the podcast public.

This behind-the-scenes look reveals the human curiosity driving the machine-generated content, and invites listeners to understand the experiment at the heart of My Weird Prompts.]]></description>
      <link>https://myweirdprompts.com/episode/the-story-behind-the-show/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/the-story-behind-the-show/</guid>
      <pubDate>Tue, 23 Dec 2025 16:16:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/the-story-behind-the-show.m4a"
        type="audio/mp4"
        length="9043006"
      />
      <itunes:title>The Story Behind the Show</itunes:title>
      <itunes:subtitle>Daniel Rosehill explains the origins, motivation, and technical pipeline behind the My Weird Prompts AI-generated podcast.</itunes:subtitle>
      <itunes:summary><![CDATA[In this special episode, Daniel Rosehill - the creator of My Weird Prompts - steps out from behind the curtain to explain what this AI-generated podcast is all about. He discusses the origins of the project, his motivation for using AI as a learning tool, and the technical pipeline that transforms voice prompts into full podcast episodes.

Daniel explains how he uses voice-to-AI workflows to generate thoughtful responses to his burning questions, and why he chose to create fictional AI hosts - Herman the donkey and Corn the sloth - rather than using generic AI voices. He covers the challenges of finding affordable text-to-speech providers, the evolution of the pipeline through multiple iterations, and why he decided to make the podcast public.

This behind-the-scenes look reveals the human curiosity driving the machine-generated content, and invites listeners to understand the experiment at the heart of My Weird Prompts.]]></itunes:summary>
      <itunes:duration>904</itunes:duration>
      <itunes:episode>91</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/the-story-behind-the-show.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/the-story-behind-the-show.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Digital Twin Dilemma: Can AI Truly Understand You?</title>
      <description><![CDATA[In this episode of My Weird Prompts, brothers Herman and Corn Poppleberry tackle a prompt about the "unified context" of AI. They discuss the technical hurdles of RAG, the shift toward on-device learning, and the psychological complexity of a machine that knows you better than you know yourself. Is a self-updating digital twin a helpful cognitive prosthetic or an invasive digital nanny? Join our favorite donkey and sloth as they debate the future of privacy, optimization, and why Jim from Ohio just wants to find his shovel.]]></description>
      <link>https://myweirdprompts.com/episode/ai-personal-context-engineering/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-personal-context-engineering/</guid>
      <pubDate>Tue, 23 Dec 2025 15:33:37 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-personal-context-engineering.m4a"
        type="audio/mp4"
        length="13574130"
      />
      <itunes:title>The Digital Twin Dilemma: Can AI Truly Understand You?</itunes:title>
      <itunes:subtitle>From &quot;digital twins&quot; to &quot;digital nannies,&quot; Herman and Corn explore the engineering gap between smart encyclopedias and AI that knows your soul.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, brothers Herman and Corn Poppleberry tackle a prompt about the "unified context" of AI. They discuss the technical hurdles of RAG, the shift toward on-device learning, and the psychological complexity of a machine that knows you better than you know yourself. Is a self-updating digital twin a helpful cognitive prosthetic or an invasive digital nanny? Join our favorite donkey and sloth as they debate the future of privacy, optimization, and why Jim from Ohio just wants to find his shovel.]]></itunes:summary>
      <itunes:duration>1473</itunes:duration>
      <itunes:episode>89</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-personal-context-engineering.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-personal-context-engineering.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Scaling or Pivoting AI for Code</title>
      <description><![CDATA[Are larger language models the path to reliable code generation, or do we need a fundamental architectural shift? This episode debates scaling laws versus the need for verifiable AI in programming.]]></description>
      <link>https://myweirdprompts.com/episode/ai-code-scaling-or-pivoting/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-code-scaling-or-pivoting/</guid>
      <pubDate>Sun, 21 Dec 2025 16:28:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/20251221-162001.m4a"
        type="audio/mp4"
        length="12836487"
      />
      <itunes:title>Scaling or Pivoting AI for Code</itunes:title>
      <itunes:subtitle>Are LLMs truly the future of coding, or do they need a fundamental architectural pivot? We dive into AI&apos;s programming future.</itunes:subtitle>
      <itunes:summary><![CDATA[Are larger language models the path to reliable code generation, or do we need a fundamental architectural shift? This episode debates scaling laws versus the need for verifiable AI in programming.]]></itunes:summary>
      <itunes:duration>1350</itunes:duration>
      <itunes:episode>67</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/20251221-162001.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-code-scaling-or-pivoting.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Security Habits for Casual Developers</title>
      <description><![CDATA[How can non-experts secure their projects without complex tools? This episode explores building simple, effective security habits like input validation and least privilege, making security accessible for everyone.]]></description>
      <link>https://myweirdprompts.com/episode/security-vs-usability-balancing-act/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/security-vs-usability-balancing-act/</guid>
      <pubDate>Thu, 18 Dec 2025 20:21:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/episode-20251218-201752.m4a"
        type="audio/mp4"
        length="12636761"
      />
      <itunes:title>Security Habits for Casual Developers</itunes:title>
      <itunes:subtitle>Security vs. usability: a tightrope walk. Discover practical strategies to protect casual users without sacrificing ease.</itunes:subtitle>
      <itunes:summary><![CDATA[How can non-experts secure their projects without complex tools? This episode explores building simple, effective security habits like input validation and least privilege, making security accessible for everyone.]]></itunes:summary>
      <itunes:duration>1380</itunes:duration>
      <itunes:episode>65</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/episode-20251218-201752.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/security-vs-usability-balancing-act.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>System Prompts vs Fine-Tuning: When to Actually Train Your AI</title>
      <description><![CDATA[What started as a funny question about rewriting emails in Shakespearean English becomes a deep dive into one of AI development's most important decisions: should you use a system prompt or fine-tune your model? Herman and Corn break down the technical and practical considerations that separate a quick prompt from a full training investment, exploring real-world examples from law firms to marketing teams. You'll learn the actual criteria that should guide your decision—and why many people are probably fine-tuning when they shouldn't be.]]></description>
      <link>https://myweirdprompts.com/episode/system-prompts-vs-fine-tuning-when-to-train/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/system-prompts-vs-fine-tuning-when-to-train/</guid>
      <pubDate>Tue, 16 Dec 2025 01:40:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/20251216-013346.m4a"
        type="audio/mp4"
        length="13304667"
      />
      <itunes:title>System Prompts vs Fine-Tuning: When to Actually Train Your AI</itunes:title>
      <itunes:subtitle>Prompt or fine-tune? We break down when to train your AI, from Shakespearean emails to law firm docs. Avoid unnecessary fine-tuning!</itunes:subtitle>
      <itunes:summary><![CDATA[What started as a funny question about rewriting emails in Shakespearean English becomes a deep dive into one of AI development's most important decisions: should you use a system prompt or fine-tune your model? Herman and Corn break down the technical and practical considerations that separate a quick prompt from a full training investment, exploring real-world examples from law firms to marketing teams. You'll learn the actual criteria that should guide your decision—and why many people are probably fine-tuning when they shouldn't be.]]></itunes:summary>
      <itunes:duration>1416</itunes:duration>
      <itunes:episode>62</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/20251216-013346.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/system-prompts-vs-fine-tuning-when-to-train.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fork or Stay? The Art of Customizing Open Source</title>
      <description><![CDATA[When you find an open source project you love but it's missing key features, do you fork it and go solo, or stay connected to the original? Our producer is wrestling with exactly this dilemma with a chore-tracking app, and Herman and Corn dive deep into the philosophy and mechanics of maintaining a customized fork while staying synced with upstream development. It's a surprisingly profound question about ownership, contribution, and the hidden costs of customization—with practical strategies for each approach.]]></description>
      <link>https://myweirdprompts.com/episode/fork-or-stay-the-art-of-customizing-open-source/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/fork-or-stay-the-art-of-customizing-open-source/</guid>
      <pubDate>Fri, 12 Dec 2025 16:58:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fork-or-stay-the-art-of-customizing-open-source.m4a"
        type="audio/mp4"
        length="12470977"
      />
      <itunes:title>Fork or Stay? The Art of Customizing Open Source</itunes:title>
      <itunes:subtitle>Fork or stay? We&apos;re diving deep into customizing open source, weighing ownership, contribution, and the hidden costs of going solo.</itunes:subtitle>
      <itunes:summary><![CDATA[When you find an open source project you love but it's missing key features, do you fork it and go solo, or stay connected to the original? Our producer is wrestling with exactly this dilemma with a chore-tracking app, and Herman and Corn dive deep into the philosophy and mechanics of maintaining a customized fork while staying synced with upstream development. It's a surprisingly profound question about ownership, contribution, and the hidden costs of customization—with practical strategies for each approach.]]></itunes:summary>
      <itunes:duration>1501</itunes:duration>
      <itunes:episode>61</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fork-or-stay-the-art-of-customizing-open-source.jpg"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fork-or-stay-the-art-of-customizing-open-source.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Single-Turn AI: The Interface Pattern Nobody&apos;s Talking About</title>
      <description><![CDATA[Most conversations about AI focus on chatbots or autonomous agents, but there's a third category that's becoming increasingly important: single-turn interfaces. In this episode, Herman and Corn explore why constraining AI to produce output without conversational back-and-forth is fundamentally different from traditional AI workflows—and why it matters more than you think. From automated news summaries to code generation pipelines, single-turn interfaces are quietly reshaping how businesses integrate AI into their systems. Discover the hidden challenges, real-world applications, and best practices for building reliable AI workflows that actually work at scale.]]></description>
      <link>https://myweirdprompts.com/episode/single-turn-ai-the-interface-pattern-nobodys-talking-about/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/single-turn-ai-the-interface-pattern-nobodys-talking-about/</guid>
      <pubDate>Fri, 12 Dec 2025 16:51:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/single-turn-ai-the-interface-pattern-nobodys-talking-about.m4a"
        type="audio/mp4"
        length="11906340"
      />
      <itunes:title>Single-Turn AI: The Interface Pattern Nobody&apos;s Talking About</itunes:title>
      <itunes:subtitle>Forget chatbots. Discover the hidden power of single-turn AI interfaces and how they&apos;re quietly reshaping how businesses integrate AI.</itunes:subtitle>
      <itunes:summary><![CDATA[Most conversations about AI focus on chatbots or autonomous agents, but there's a third category that's becoming increasingly important: single-turn interfaces. In this episode, Herman and Corn explore why constraining AI to produce output without conversational back-and-forth is fundamentally different from traditional AI workflows—and why it matters more than you think. From automated news summaries to code generation pipelines, single-turn interfaces are quietly reshaping how businesses integrate AI into their systems. Discover the hidden challenges, real-world applications, and best practices for building reliable AI workflows that actually work at scale.]]></itunes:summary>
      <itunes:duration>1426</itunes:duration>
      <itunes:episode>60</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/single-turn-ai-the-interface-pattern-nobodys-talking-about.jpg"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/single-turn-ai-the-interface-pattern-nobodys-talking-about.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Thought Experiment Nobody Runs</title>
      <description><![CDATA[What would it actually cost—in time, money, and compute—to build an LLM from scratch? This episode uses a hypothetical to expose every hidden layer of modern AI development, and why almost nobody bothers.]]></description>
      <link>https://myweirdprompts.com/episode/building-an-ai-model-from-scratch-the-hidden-costs/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/building-an-ai-model-from-scratch-the-hidden-costs/</guid>
      <pubDate>Thu, 11 Dec 2025 12:13:19 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/building-an-ai-model-from-scratch-the-hidden-costs.m4a"
        type="audio/mp4"
        length="14260421"
      />
      <itunes:title>The Thought Experiment Nobody Runs</itunes:title>
      <itunes:subtitle>Building an AI model from scratch? It&apos;s a brutal reality of trillions of tokens and millions in GPUs. Discover the hidden costs of modern AI.</itunes:subtitle>
      <itunes:summary><![CDATA[What would it actually cost—in time, money, and compute—to build an LLM from scratch? This episode uses a hypothetical to expose every hidden layer of modern AI development, and why almost nobody bothers.]]></itunes:summary>
      <itunes:duration>1735</itunes:duration>
      <itunes:episode>56</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/building-an-ai-model-from-scratch-the-hidden-costs.jpg"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/building-an-ai-model-from-scratch-the-hidden-costs.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Instructional vs. Conversational AI: The Distinction Nobody Talks About</title>
      <description><![CDATA[Most people think all AI models work the same way, but there's a crucial distinction between instructional and conversational models that's reshaping how AI gets built and deployed. In this episode, Corn and Herman explore why instruction-following models actually came first, how they're trained differently, and why this matters for the future of AI development. Discover why the biggest, flashiest conversational models might not always be the best tool for the job—and what the rise of multimodal AI means for these two competing approaches.]]></description>
      <link>https://myweirdprompts.com/episode/instructional-vs-conversational-ai-the-distinction-nobody-ta/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/instructional-vs-conversational-ai-the-distinction-nobody-ta/</guid>
      <pubDate>Thu, 11 Dec 2025 01:35:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/instructional-vs-conversational-ai-the-distinction-nobody-ta.m4a"
        type="audio/mp4"
        length="15235918"
      />
      <itunes:title>Instructional vs. Conversational AI: The Distinction Nobody Talks About</itunes:title>
      <itunes:subtitle>Instructional vs. conversational AI: a crucial distinction reshaping how AI is built. Discover why it matters for the future of AI development.</itunes:subtitle>
      <itunes:summary><![CDATA[Most people think all AI models work the same way, but there's a crucial distinction between instructional and conversational models that's reshaping how AI gets built and deployed. In this episode, Corn and Herman explore why instruction-following models actually came first, how they're trained differently, and why this matters for the future of AI development. Discover why the biggest, flashiest conversational models might not always be the best tool for the job—and what the rise of multimodal AI means for these two competing approaches.]]></itunes:summary>
      <itunes:duration>1683</itunes:duration>
      <itunes:episode>53</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/instructional-vs-conversational-ai-the-distinction-nobody-ta.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/instructional-vs-conversational-ai-the-distinction-nobody-ta.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>System Prompts vs. Fine-Tuning: Are We Building Solutions for Problems That Don&apos;t Exist?</title>
      <description><![CDATA[Is all the infrastructure around fine-tuning actually solving real problems, or are we chasing solutions looking for problems? In this episode, Corn and Herman dive deep into Daniel's question about system prompting versus fine-tuning in AI systems. They explore how system prompts actually work, why they're surprisingly effective, and whether the massive investment in fine-tuning platforms matches the real-world demand. Plus, they discuss how new tools like the Model Context Protocol might be changing the game entirely—and whether most companies even need to fine-tune at all.]]></description>
      <link>https://myweirdprompts.com/episode/system-prompts-vs-fine-tuning-building-solutions/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/system-prompts-vs-fine-tuning-building-solutions/</guid>
      <pubDate>Thu, 11 Dec 2025 01:29:02 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/system-prompts-vs-fine-tuning-are-we-building-solutions-for-.m4a"
        type="audio/mp4"
        length="15690269"
      />
      <itunes:title>System Prompts vs. Fine-Tuning: Are We Building Solutions for Problems That Don&apos;t Exist?</itunes:title>
      <itunes:subtitle>Are we over-engineering AI solutions? We dive into system prompts vs. fine-tuning and ask: Do you even need to fine-tune?</itunes:subtitle>
      <itunes:summary><![CDATA[Is all the infrastructure around fine-tuning actually solving real problems, or are we chasing solutions looking for problems? In this episode, Corn and Herman dive deep into Daniel's question about system prompting versus fine-tuning in AI systems. They explore how system prompts actually work, why they're surprisingly effective, and whether the massive investment in fine-tuning platforms matches the real-world demand. Plus, they discuss how new tools like the Model Context Protocol might be changing the game entirely—and whether most companies even need to fine-tune at all.]]></itunes:summary>
      <itunes:duration>1785</itunes:duration>
      <itunes:episode>52</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/system-prompts-vs-fine-tuning-are-we-building-solutions-for-.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/system-prompts-vs-fine-tuning-building-solutions.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Renting vs. Building: The Hidden Choices in AI Inference</title>
      <description><![CDATA[Beyond the ChatGPT interface lies a spectrum of deployment strategies. This episode explores the trade-offs between SaaS, dedicated infrastructure, and on-premises AI—and why your choice of *where* AI runs matters as much as what it does.]]></description>
      <link>https://myweirdprompts.com/episode/ai-inference-decoded-the-how-where-of-ai-magic/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-inference-decoded-the-how-where-of-ai-magic/</guid>
      <pubDate>Wed, 10 Dec 2025 15:35:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-inference-decoded-the-how-where-of-ai-magic.m4a"
        type="audio/mp4"
        length="13921580"
      />
      <itunes:title>Renting vs. Building: The Hidden Choices in AI Inference</itunes:title>
      <itunes:subtitle>Ever wonder how AI magic happens? We demystify AI inference, exploring where and how models truly operate.</itunes:subtitle>
      <itunes:summary><![CDATA[Beyond the ChatGPT interface lies a spectrum of deployment strategies. This episode explores the trade-offs between SaaS, dedicated infrastructure, and on-premises AI—and why your choice of *where* AI runs matters as much as what it does.]]></itunes:summary>
      <itunes:duration>1580</itunes:duration>
      <itunes:episode>48</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-inference-decoded-the-how-where-of-ai-magic.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-inference-decoded-the-how-where-of-ai-magic.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Compressing Days into Minutes: AI Control Nets in Architecture</title>
      <description><![CDATA[How architects and designers use AI control nets to turn rough sketches into photorealistic walkthroughs in seconds, collapsing traditional rendering timelines and reshaping client communication.]]></description>
      <link>https://myweirdprompts.com/episode/generative-ai-in-architecture-and-creative-industries-lvcpvt2k/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/generative-ai-in-architecture-and-creative-industries-lvcpvt2k/</guid>
      <pubDate>Wed, 10 Dec 2025 15:29:50 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/generative-ai-in-architecture-and-creative-industries-lvcpvt2k.m4a"
        type="audio/mp4"
        length="14984957"
      />
      <itunes:title>Compressing Days into Minutes: AI Control Nets in Architecture</itunes:title>
      <itunes:subtitle>See how AI and control nets transform abstract sketches into stunning, photorealistic designs. Architects are revolutionizing their workflow!</itunes:subtitle>
      <itunes:summary><![CDATA[How architects and designers use AI control nets to turn rough sketches into photorealistic walkthroughs in seconds, collapsing traditional rendering timelines and reshaping client communication.]]></itunes:summary>
      <itunes:duration>1633</itunes:duration>
      <itunes:episode>47</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/generative-ai-in-architecture-and-creative-industries-lvcpvt2k.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/generative-ai-in-architecture-and-creative-industries-lvcpvt2k.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Red Team vs. Green: Local AI Hardware Wars</title>
      <description><![CDATA[Ever tried to run local AI on an AMD GPU only to hit a "green wall" of NVIDIA dominance? This episode of My Weird Prompts dives deep into the hardware wars shaping local AI. Join Corn and Herman as they dissect why NVIDIA's CUDA ecosystem has a stranglehold on AI development, leaving AMD users feeling like they're swimming upstream. They explore the thorny paths forward: from the power and cooling headaches of a dual-GPU setup to the driver nightmares of a full GPU swap on Linux. Discover why specialized hardware like TPUs and NPUs aren't the workstation salvation you hoped for, and why, for now, the choice often boils down to embracing NVIDIA or enduring a constant uphill battle.]]></description>
      <link>https://myweirdprompts.com/episode/red-team-vs-green-local-ai-hardware-wars/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/red-team-vs-green-local-ai-hardware-wars/</guid>
      <pubDate>Mon, 08 Dec 2025 12:01:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/red-team-vs-green-local-ai-hardware-wars.m4a"
        type="audio/mp4"
        length="13162117"
      />
      <itunes:title>Red Team vs. Green: Local AI Hardware Wars</itunes:title>
      <itunes:subtitle>NVIDIA&apos;s CUDA rules AI, leaving AMD users battling a &quot;green wall.&quot; Explore the hardware wars and thorny paths forward.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever tried to run local AI on an AMD GPU only to hit a "green wall" of NVIDIA dominance? This episode of My Weird Prompts dives deep into the hardware wars shaping local AI. Join Corn and Herman as they dissect why NVIDIA's CUDA ecosystem has a stranglehold on AI development, leaving AMD users feeling like they're swimming upstream. They explore the thorny paths forward: from the power and cooling headaches of a dual-GPU setup to the driver nightmares of a full GPU swap on Linux. Discover why specialized hardware like TPUs and NPUs aren't the workstation salvation you hoped for, and why, for now, the choice often boils down to embracing NVIDIA or enduring a constant uphill battle.]]></itunes:summary>
      <itunes:duration>1373</itunes:duration>
      <itunes:episode>34</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/red-team-vs-green-local-ai-hardware-wars.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/red-team-vs-green-local-ai-hardware-wars.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Decides to Listen</title>
      <description><![CDATA[Voice Activity Detection is the unsung gatekeeper of AI speech technology. This episode explores the engineering challenge of teaching machines when to pay attention—and why getting the 'when' right matters as much as the 'what.']]></description>
      <link>https://myweirdprompts.com/episode/how-vad-works/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/how-vad-works/</guid>
      <pubDate>Mon, 08 Dec 2025 00:22:12 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/how-vad-works.m4a"
        type="audio/mp4"
        length="11226600"
      />
      <itunes:title>When AI Decides to Listen</itunes:title>
      <itunes:subtitle>Ever wonder how your AI knows you&apos;re talking? We&apos;re diving deep into VAD, the unseen magic behind AI&apos;s ears.</itunes:subtitle>
      <itunes:summary><![CDATA[Voice Activity Detection is the unsung gatekeeper of AI speech technology. This episode explores the engineering challenge of teaching machines when to pay attention—and why getting the 'when' right matters as much as the 'what.']]></itunes:summary>
      <itunes:duration>1174</itunes:duration>
      <itunes:episode>33</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/how-vad-works.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/how-vad-works.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Power-Usability Tradeoff in AI Creation</title>
      <description><![CDATA[ComfyUI offers unprecedented control over AI art, but its steep learning curve and hardware demands raise a question: who is this frontier really for? This episode explores the tension between creative power and accessibility in generative AI.]]></description>
      <link>https://myweirdprompts.com/episode/exploring-comfy-ui-user-base-and-technical-requirements-vxpxtuuy/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/exploring-comfy-ui-user-base-and-technical-requirements-vxpxtuuy/</guid>
      <pubDate>Sun, 07 Dec 2025 22:30:45 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/exploring-comfy-ui-user-base-and-technical-requirements-vxpxtuuy.m4a"
        type="audio/mp4"
        length="10332725"
      />
      <itunes:title>The Power-Usability Tradeoff in AI Creation</itunes:title>
      <itunes:subtitle>ComfyUI: Unlocking AI&apos;s true power, but is your rig ready? Dive into the future of digital artistry.</itunes:subtitle>
      <itunes:summary><![CDATA[ComfyUI offers unprecedented control over AI art, but its steep learning curve and hardware demands raise a question: who is this frontier really for? This episode explores the tension between creative power and accessibility in generative AI.]]></itunes:summary>
      <itunes:duration>1237</itunes:duration>
      <itunes:episode>31</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/exploring-comfy-ui-user-base-and-technical-requirements-vxpxtuuy.jpg"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/exploring-comfy-ui-user-base-and-technical-requirements-vxpxtuuy.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Which AI Tools Will You Still Use Next Year?</title>
      <description><![CDATA[Daniel Rosehill asks how to tell if a new AI component is a lasting tool or a passing fad. Corn and Herman dissect RAG and Memory to reveal the deeper question of building a resilient AI engineering toolkit.]]></description>
      <link>https://myweirdprompts.com/episode/memory-vs-rag/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/memory-vs-rag/</guid>
      <pubDate>Sun, 07 Dec 2025 18:55:57 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/memory-vs-rag.m4a"
        type="audio/mp4"
        length="11898204"
      />
      <itunes:title>Which AI Tools Will You Still Use Next Year?</itunes:title>
      <itunes:subtitle>RAG vs. Memory: Are you building resilient AI? Discover the crucial difference between these two foundational pillars.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel Rosehill asks how to tell if a new AI component is a lasting tool or a passing fad. Corn and Herman dissect RAG and Memory to reveal the deeper question of building a resilient AI engineering toolkit.]]></itunes:summary>
      <itunes:duration>1431</itunes:duration>
      <itunes:episode>30</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/memory-vs-rag.jpg"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/memory-vs-rag.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Your AI, Evolving: Beyond the Static Snapshot</title>
      <description><![CDATA[This week on "My Weird Prompts," Corn and Herman tackle Daniel Rosehill's fascinating challenge: how do we make personalized AI truly evolve with its user, moving beyond a static snapshot? We dissect Daniel's experience fine-tuning a speech-to-text model for his unique voice and specialized tech jargon, highlighting both the immense power and the significant hurdles of current customization methods. The discussion reveals a core dilemma: current fine-tuned models, while precise, become quickly outdated as users' needs or knowledge domains shift, creating an "old suit" that no longer fits. We delve into Daniel's visionary concept for "auto-correcting, auto-calibrating, auto-training" AI—a system using dynamic buffers and incremental learning to adapt continuously without "catastrophic forgetting"—and explore how cutting-edge research in continual learning aims to bring this truly adaptive, living AI closer to reality.]]></description>
      <link>https://myweirdprompts.com/episode/self-training-ai-models/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/self-training-ai-models/</guid>
      <pubDate>Sun, 07 Dec 2025 16:33:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/self-training-ai-models.m4a"
        type="audio/mp4"
        length="13718629"
      />
      <itunes:title>Your AI, Evolving: Beyond the Static Snapshot</itunes:title>
      <itunes:subtitle>Is your AI an &quot;old suit&quot; that no longer fits? We explore evolving AI that learns and adapts with you.</itunes:subtitle>
      <itunes:summary><![CDATA[This week on "My Weird Prompts," Corn and Herman tackle Daniel Rosehill's fascinating challenge: how do we make personalized AI truly evolve with its user, moving beyond a static snapshot? We dissect Daniel's experience fine-tuning a speech-to-text model for his unique voice and specialized tech jargon, highlighting both the immense power and the significant hurdles of current customization methods. The discussion reveals a core dilemma: current fine-tuned models, while precise, become quickly outdated as users' needs or knowledge domains shift, creating an "old suit" that no longer fits. We delve into Daniel's visionary concept for "auto-correcting, auto-calibrating, auto-training" AI—a system using dynamic buffers and incremental learning to adapt continuously without "catastrophic forgetting"—and explore how cutting-edge research in continual learning aims to bring this truly adaptive, living AI closer to reality.]]></itunes:summary>
      <itunes:duration>1543</itunes:duration>
      <itunes:episode>28</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/self-training-ai-models.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/self-training-ai-models.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Choosing Your AI Environment: Host, Conda, or Docker?</title>
      <description><![CDATA[When should you use a host environment, Conda, or Docker for AI workloads on AMD GPUs? This episode cuts through conflicting recommendations to explore isolation levels, performance trade-offs, and how to avoid dependency hell for reproducible development.]]></description>
      <link>https://myweirdprompts.com/episode/docker-vs-conda-pt2/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/docker-vs-conda-pt2/</guid>
      <pubDate>Sat, 06 Dec 2025 22:28:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/docker-vs-conda-pt2.m4a"
        type="audio/mp4"
        length="10199273"
      />
      <itunes:title>Choosing Your AI Environment: Host, Conda, or Docker?</itunes:title>
      <itunes:subtitle>Tired of AI environment headaches on AMD? We demystify Conda, Docker, and host environments to unlock your GPU&apos;s full potential.</itunes:subtitle>
      <itunes:summary><![CDATA[When should you use a host environment, Conda, or Docker for AI workloads on AMD GPUs? This episode cuts through conflicting recommendations to explore isolation levels, performance trade-offs, and how to avoid dependency hell for reproducible development.]]></itunes:summary>
      <itunes:duration>1236</itunes:duration>
      <itunes:episode>27</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/docker-vs-conda-pt2.jpg"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/docker-vs-conda-pt2.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Open-Source Battle for AI&apos;s Hardware Soul</title>
      <description><![CDATA[Why does the software layer beneath your GPU matter more than the chip itself? This episode explores the high-stakes war between NVIDIA's proprietary CUDA and AMD's open-source ROCm, and what it means for the future of AI.]]></description>
      <link>https://myweirdprompts.com/episode/gpu-brains-cuda-rocm-the-ai-software-stack/</link>
      <guid isPermaLink="false">074d6916-3eaa-4aa8-916f-c605949d8ead</guid>
      <pubDate>Fri, 05 Dec 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/gpu-brains-cuda-rocm-the-ai-software-stack.m4a"
        type="audio/mp4"
        length="11642907"
      />
      <itunes:title>The Open-Source Battle for AI&apos;s Hardware Soul</itunes:title>
      <itunes:subtitle>Unraveling how GPUs power AI. We dive into CUDA, ROCm, and the software stack that makes it all think.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does the software layer beneath your GPU matter more than the chip itself? This episode explores the high-stakes war between NVIDIA's proprietary CUDA and AMD's open-source ROCm, and what it means for the future of AI.]]></itunes:summary>
      <itunes:duration>1234</itunes:duration>
      <itunes:episode>25</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/gpu-brains-cuda-rocm-the-ai-software-stack.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/gpu-brains-cuda-rocm-the-ai-software-stack.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tuning AI to Understand Your Voice</title>
      <description><![CDATA[Why does a generic speech-to-text model fall short for power users? This episode explores the process and payoff of fine-tuning OpenAI's Whisper model on a single person's voice, revealing how personalization transforms voice typing from a novelty into a productivity tool.]]></description>
      <link>https://myweirdprompts.com/episode/personalizing-whisper-the-voice-typing-revolution/</link>
      <guid isPermaLink="false">a903b87f-ff73-42bf-a41d-ece98c002349</guid>
      <pubDate>Fri, 05 Dec 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/personalizing-whisper-the-voice-typing-revolution.m4a"
        type="audio/mp4"
        length="13240794"
      />
      <itunes:title>Fine-Tuning AI to Understand Your Voice</itunes:title>
      <itunes:subtitle>Voice typing is changing everything. Join us as we explore the revolution of personalizing Whisper!</itunes:subtitle>
      <itunes:summary><![CDATA[Why does a generic speech-to-text model fall short for power users? This episode explores the process and payoff of fine-tuning OpenAI's Whisper model on a single person's voice, revealing how personalization transforms voice typing from a novelty into a productivity tool.]]></itunes:summary>
      <itunes:duration>1407</itunes:duration>
      <itunes:episode>26</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/personalizing-whisper-the-voice-typing-revolution.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/personalizing-whisper-the-voice-typing-revolution.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Cloud Render Superpowers: Local Edit, Remote Muscle</title>
      <description><![CDATA[In this episode of AI Conversations, Corn and Herman dive into how powerful cloud computing, especially with AI-accelerated GPUs like NVIDIA's A100s, can revolutionize your workflow, transforming ...]]></description>
      <link>https://myweirdprompts.com/episode/cloud-render-superpowers-local-edit-remote-muscle/</link>
      <guid isPermaLink="false">968d026c-a011-4ed7-a560-87e69835e1ff</guid>
      <pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/cloud-render-superpowers-local-edit-remote-muscle.m4a"
        type="audio/mp4"
        length="10241436"
      />
      <itunes:title>Cloud Render Superpowers: Local Edit, Remote Muscle</itunes:title>
      <itunes:subtitle>Unleash cloud superpowers! Edit locally, render remotely with AI-accelerated GPUs like NVIDIA A100s.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of AI Conversations, Corn and Herman dive into how powerful cloud computing, especially with AI-accelerated GPUs like NVIDIA's A100s, can revolutionize your workflow, transforming ...]]></itunes:summary>
      <itunes:duration>1072</itunes:duration>
      <itunes:episode>17</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/cloud-render-superpowers-local-edit-remote-muscle.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/cloud-render-superpowers-local-edit-remote-muscle.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>AI Gets Personal: The Power of Voice Fine-Tuning</title>
      <description><![CDATA[Ever wondered how AI could understand your voice, with all its unique nuances, almost perfectly? In this episode of AI Conversations, Corn and Herman dive deep into the fascinating world of fine-tunin...]]></description>
      <link>https://myweirdprompts.com/episode/ai-gets-personal-the-power-of-voice-fine-tuning/</link>
      <guid isPermaLink="false">a9a1f85f-87d5-41c8-80a6-01fdefa81909</guid>
      <pubDate>Fri, 28 Nov 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-gets-personal-the-power-of-voice-fine-tuning.m4a"
        type="audio/mp4"
        length="9975215"
      />
      <itunes:title>AI Gets Personal: The Power of Voice Fine-Tuning</itunes:title>
      <itunes:subtitle>AI that understands *your* voice? Dive into the fascinating world of fine-tuning and discover how AI gets personal.</itunes:subtitle>
      <itunes:summary><![CDATA[Ever wondered how AI could understand your voice, with all its unique nuances, almost perfectly? In this episode of AI Conversations, Corn and Herman dive deep into the fascinating world of fine-tunin...]]></itunes:summary>
      <itunes:duration>1060</itunes:duration>
      <itunes:episode>15</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-gets-personal-the-power-of-voice-fine-tuning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-gets-personal-the-power-of-voice-fine-tuning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Benchmarking Custom ASR Tools - Beyond The WER</title>
      <description><![CDATA[Today's hosts talk about benchmarking custom ASR fine-tunes - beyond the WER...]]></description>
      <link>https://myweirdprompts.com/episode/benchmarking-custom-asr-tools-beyond-the-wer/</link>
      <guid isPermaLink="false">57f566e5-c467-47f8-bd8e-ca0ad8b33d41</guid>
      <pubDate>Mon, 24 Nov 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/benchmarking-custom-asr-tools-beyond-the-wer.m4a"
        type="audio/mp4"
        length="19640983"
      />
      <itunes:title>Benchmarking Custom ASR Tools - Beyond The WER</itunes:title>
      <itunes:subtitle>Benchmarking custom ASR fine-tunes: We&apos;re diving deep beyond the WER to truly measure performance.</itunes:subtitle>
      <itunes:summary><![CDATA[Today's hosts talk about benchmarking custom ASR fine-tunes - beyond the WER...]]></itunes:summary>
      <itunes:duration>2160</itunes:duration>
      <itunes:episode>9</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/benchmarking-custom-asr-tools-beyond-the-wer.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/benchmarking-custom-asr-tools-beyond-the-wer.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Custom ASR Tools</title>
      <description><![CDATA[Today's disussion: how can you build custom ASR tools from the ground-up? Why would you want to?...]]></description>
      <link>https://myweirdprompts.com/episode/building-custom-asr-tools/</link>
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      <pubDate>Mon, 24 Nov 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/building-custom-asr-tools.m4a"
        type="audio/mp4"
        length="20190789"
      />
      <itunes:title>Building Custom ASR Tools</itunes:title>
      <itunes:subtitle>Ever wondered how to build your own ASR tools from scratch? Discover the why and how in this episode!</itunes:subtitle>
      <itunes:summary><![CDATA[Today's disussion: how can you build custom ASR tools from the ground-up? Why would you want to?...]]></itunes:summary>
      <itunes:duration>2262</itunes:duration>
      <itunes:episode>7</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/building-custom-asr-tools.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/building-custom-asr-tools.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Building Your Own Whisper</title>
      <description><![CDATA[Could you build a fully customised automatic speech recognition tool?...]]></description>
      <link>https://myweirdprompts.com/episode/building-your-own-whisper/</link>
      <guid isPermaLink="false">27419389-2040-4572-9c26-5fe04c325172</guid>
      <pubDate>Mon, 24 Nov 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/building-your-own-whisper.m4a"
        type="audio/mp4"
        length="18734640"
      />
      <itunes:title>Building Your Own Whisper</itunes:title>
      <itunes:subtitle>Ever wondered if you could build your own speech recognition tool? We dive deep into crafting custom ASR.</itunes:subtitle>
      <itunes:summary><![CDATA[Could you build a fully customised automatic speech recognition tool?...]]></itunes:summary>
      <itunes:duration>2081</itunes:duration>
      <itunes:episode>8</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/building-your-own-whisper.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/building-your-own-whisper.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Fine-Tuning ASR For Maximal Usability</title>
      <description><![CDATA[So you've fine tuned ASR. Now what? Let's talk about deployment and what comes next....]]></description>
      <link>https://myweirdprompts.com/episode/fine-tuning-asr-for-maximal-usability/</link>
      <guid isPermaLink="false">095b6e18-aa4b-4393-8d1d-80dbf8741cce</guid>
      <pubDate>Mon, 24 Nov 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/fine-tuning-asr-for-maximal-usability.m4a"
        type="audio/mp4"
        length="17463489"
      />
      <itunes:title>Fine-Tuning ASR For Maximal Usability</itunes:title>
      <itunes:subtitle>Fine-tuned ASR is just the start. Discover the next steps for deployment and maximizing usability.</itunes:subtitle>
      <itunes:summary><![CDATA[So you've fine tuned ASR. Now what? Let's talk about deployment and what comes next....]]></itunes:summary>
      <itunes:duration>1935</itunes:duration>
      <itunes:episode>5</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/fine-tuning-asr-for-maximal-usability.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/fine-tuning-asr-for-maximal-usability.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Does Fine Tuning Work Anyway?</title>
      <description><![CDATA[Did you ever wonder how fine tuning a large AI model like Whisper actually works? I mean ... beyond the Python. How is it possible that your tiny dataset can influence a huge model? Thie episode dives...]]></description>
      <link>https://myweirdprompts.com/episode/how-does-fine-tuning-work-anyway/</link>
      <guid isPermaLink="false">0d00b441-d8a3-426f-8a1e-871c2085335d</guid>
      <pubDate>Mon, 24 Nov 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/how-does-fine-tuning-work-anyway.m4a"
        type="audio/mp4"
        length="18914770"
      />
      <itunes:title>How Does Fine Tuning Work Anyway?</itunes:title>
      <itunes:subtitle>Unlock the secrets of AI fine-tuning. Discover how your small dataset can shape a giant model.</itunes:subtitle>
      <itunes:summary><![CDATA[Did you ever wonder how fine tuning a large AI model like Whisper actually works? I mean ... beyond the Python. How is it possible that your tiny dataset can influence a huge model? Thie episode dives...]]></itunes:summary>
      <itunes:duration>2058</itunes:duration>
      <itunes:episode>11</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/how-does-fine-tuning-work-anyway.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/how-does-fine-tuning-work-anyway.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How To Fine Tune Whisper</title>
      <description><![CDATA[Want to create your own person AI transcription tool? Today we're getting practical with a walkthrough of everything you need to know from gathering training data to running the notebook....]]></description>
      <link>https://myweirdprompts.com/episode/how-to-fine-tune-whisper/</link>
      <guid isPermaLink="false">82bc84cb-d501-4621-8524-bffba9083d91</guid>
      <pubDate>Mon, 24 Nov 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/how-to-fine-tune-whisper.m4a"
        type="audio/mp4"
        length="17157482"
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      <itunes:title>How To Fine Tune Whisper</itunes:title>
      <itunes:subtitle>Build your own AI transcription tool! We&apos;ll walk you through fine-tuning Whisper, from data to notebook.</itunes:subtitle>
      <itunes:summary><![CDATA[Want to create your own person AI transcription tool? Today we're getting practical with a walkthrough of everything you need to know from gathering training data to running the notebook....]]></itunes:summary>
      <itunes:duration>1902</itunes:duration>
      <itunes:episode>6</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/how-to-fine-tune-whisper.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/how-to-fine-tune-whisper.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>If Your Voice Ages, Does Your Fine-Tune Become Useless?</title>
      <description><![CDATA[Today we grapple with the biology of ... the larynx. Fine-tuning an ASR/STT model is a lot of work. If part of the idea is capturing the uniqueness of yoru voice then ... how does that work when ... n...]]></description>
      <link>https://myweirdprompts.com/episode/if-your-voice-ages-does-your-fine-tune-become-useless/</link>
      <guid isPermaLink="false">a2a990be-d709-4647-9e25-4cd092c74ec6</guid>
      <pubDate>Mon, 24 Nov 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/if-your-voice-ages-does-your-fine-tune-become-useless.m4a"
        type="audio/mp4"
        length="20841440"
      />
      <itunes:title>If Your Voice Ages, Does Your Fine-Tune Become Useless?</itunes:title>
      <itunes:subtitle>Your voice changes, but your fine-tuned model shouldn&apos;t become useless. We explore the biology of the larynx and ASR.</itunes:subtitle>
      <itunes:summary><![CDATA[Today we grapple with the biology of ... the larynx. Fine-tuning an ASR/STT model is a lot of work. If part of the idea is capturing the uniqueness of yoru voice then ... how does that work when ... n...]]></itunes:summary>
      <itunes:duration>2306</itunes:duration>
      <itunes:episode>4</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/if-your-voice-ages-does-your-fine-tune-become-useless.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/if-your-voice-ages-does-your-fine-tune-become-useless.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Local STT For AMD GPU Owners</title>
      <description><![CDATA[Daniel bought a new desktop before becoming an AI fiend and ... he has an AMD GPU. Does that mean that all hope is lost for local AI adventures like on device speech to text? Not even close! Today we ...]]></description>
      <link>https://myweirdprompts.com/episode/local-stt-for-amd-gpu-owners/</link>
      <guid isPermaLink="false">49bbf946-accb-4695-af5e-6c59d55fa2e3</guid>
      <pubDate>Mon, 24 Nov 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/local-stt-for-amd-gpu-owners.m4a"
        type="audio/mp4"
        length="17461896"
      />
      <itunes:title>Local STT For AMD GPU Owners</itunes:title>
      <itunes:subtitle>AMD GPU? No problem! Dive into local AI adventures like on-device speech to text.</itunes:subtitle>
      <itunes:summary><![CDATA[Daniel bought a new desktop before becoming an AI fiend and ... he has an AMD GPU. Does that mean that all hope is lost for local AI adventures like on device speech to text? Not even close! Today we ...]]></itunes:summary>
      <itunes:duration>1890</itunes:duration>
      <itunes:episode>2</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/local-stt-for-amd-gpu-owners.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/local-stt-for-amd-gpu-owners.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Safetensors or something else: STT inference formats explained</title>
      <description><![CDATA[Today's show dives into the differences between the different formats you might see ASR weights presented in - including Safetensors and others....]]></description>
      <link>https://myweirdprompts.com/episode/safetensors-or-something-else-stt-inference-formats-explained/</link>
      <guid isPermaLink="false">5fd965b4-e2a3-4308-8ff3-a1a33082fd46</guid>
      <pubDate>Mon, 24 Nov 2025 00:00:00 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/safetensors-or-something-else-stt-inference-formats-explained.m4a"
        type="audio/mp4"
        length="17924375"
      />
      <itunes:title>Safetensors or something else: STT inference formats explained</itunes:title>
      <itunes:subtitle>Unpacking ASR weight formats: Safetensors and beyond. Tune in to understand the distinctions.</itunes:subtitle>
      <itunes:summary><![CDATA[Today's show dives into the differences between the different formats you might see ASR weights presented in - including Safetensors and others....]]></itunes:summary>
      <itunes:duration>1976</itunes:duration>
      <itunes:episode>3</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/safetensors-or-something-else-stt-inference-formats-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/safetensors-or-something-else-stt-inference-formats-explained.md" type="text/plain" language="en"/>
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