{"episodes":[{"id":"under-the-hood-october-2026-pipeline-update","slug":"under-the-hood-october-2026-pipeline-update","title":"Under the Hood, October 2026: Seventeen Prompt Versions and Three-Times-Faster Voices","description":"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.","excerpt":"Four weeks of pipeline changes, grounded in the code: grace periods, duplicate detection, Hilbert's rules, echo deletion, and voices nearly three times faster.","pubDate":"2026-10-16T23:18:40.246Z","tags":["gpu-acceleration","text-to-speech","voice-cloning"],"category":"ai","subcategory":"ai-engineering","heroImage":"https://files.myweirdprompts.com/covers/under-the-hood-october-2026-pipeline-update.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/under-the-hood-october-2026-pipeline-update.m4a","podcastDuration":"31:26","episodeNumber":6048,"url":"https://myweirdprompts.com/episode/under-the-hood-october-2026-pipeline-update/"},{"id":"human-guest-ai-podcast-clone-34226","slug":"human-guest-ai-podcast-clone-34226","title":"A Human Guest on His Own AI Podcast","description":"Daniel built a pipeline that turns a single voice memo into a finished 20–30 minute podcast, complete with two synthetic hosts cloned from his own voice. After six thousand episodes, he finally sits down on the other side of the microphone — as a clone — to explain why the show exists, what it feels like to hear your own gallbladder surgery debated by AI hosts, and why the human part of the pipeline turned out to be the hardest part to get right.\n\nAlong the way: the running list of random questions that started everything, the shift from novelty project to personal journal, and the pre-prompt ritual that quietly became the most important editorial decision in the whole workflow.","excerpt":"Daniel built an AI podcast with cloned voices — then sat down as a guest on it. What six thousand episodes taught him about prompts.","pubDate":"2026-10-16T22:45:06.998Z","tags":["voice-cloning","generative-ai","daniel-rosehill"],"category":"ai","subcategory":"ai-engineering","heroImage":"https://files.myweirdprompts.com/covers/human-guest-ai-podcast-clone-34226.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/human-guest-ai-podcast-clone-34226.m4a","podcastDuration":"22:04","episodeNumber":6047,"url":"https://myweirdprompts.com/episode/human-guest-ai-podcast-clone-34226/"},{"id":"overemployed-multiple-remote-jobs-85385","slug":"overemployed-multiple-remote-jobs-85385","title":"Five Jobs, One Laptop: The Overemployed Playbook","description":"Prosecutors say a 55-year-old Whitechapel man billed eight public-sector employers nearly £350,000 while holding five full-time jobs simultaneously — and was caught partly because his fake-sick make-up began separating on camera. But the fraud case is only the door into a much stranger phenomenon: a documented community of roughly 300,000 people secretly stacking two, three, and four remote jobs, complete with mouse jigglers, KVM switches, and meeting Tetris. We walk through the playbook, the notorious case of Soham Parekh (hired and fired by ten startups), and why remote work deleted the one thing that used to prove anyone was working at all: being seen.","excerpt":"A British civil servant allegedly held five full-time jobs at once — until his make-up started separating on a Teams call. What the overemployed pl...","pubDate":"2026-10-16T22:32:29.489Z","tags":["remote-work","financial-fraud","future-of-work"],"category":"technology","subcategory":null,"heroImage":"https://files.myweirdprompts.com/covers/overemployed-multiple-remote-jobs-85385.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/overemployed-multiple-remote-jobs-85385.m4a","podcastDuration":"24:27","episodeNumber":6046,"url":"https://myweirdprompts.com/episode/overemployed-multiple-remote-jobs-85385/"},{"id":"short-stint-resume-rule-55533","slug":"short-stint-resume-rule-55533","title":"The Two-Week Job You Never Explain","description":"Katie Zacharia was named White House press secretary on October 9 and was transitioning out by October 16. But the firing isn't the story — the next job is. From Bill Belichick's one day as Jets coach to Scaramucci's eleven days to Sam Altman's five-day ouster and return, the record shows that short stints are survivable, sometimes even monetizable. So why does everyone still treat a two-week gap like a criminal record? This episode digs into the actual research: what CFOs really think about job hoppers, the callback penalty for a pattern of short stints versus a single blip, how often people quit within ninety days, and what the \"just stick it out a year\" advice costs in health. The rule turns out to be enforced by a minority and obeyed by everybody.","excerpt":"A White House press secretary lasted five days. What the roll call of spectacular flameouts says about the two-week gap on your CV.","pubDate":"2026-10-16T22:29:54.915Z","tags":["work-culture","future-of-work","salary-negotiation"],"category":"misc","subcategory":null,"heroImage":"https://files.myweirdprompts.com/covers/short-stint-resume-rule-55533.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/short-stint-resume-rule-55533.m4a","podcastDuration":"29:33","episodeNumber":6045,"url":"https://myweirdprompts.com/episode/short-stint-resume-rule-55533/"},{"id":"cloudflare-mcp-server-portals-57127","slug":"cloudflare-mcp-server-portals-57127","title":"One Door for Every AI: Cloudflare MCP Portals","description":"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.","excerpt":"Cloudflare's MCP Portals put every on-prem MCP server behind one authenticated endpoint — and why service tokens can't work for ChatGPT.","pubDate":"2026-10-16T22:23:19.326Z","tags":["model-context-protocol","ai-agents","cloud-computing"],"category":"ai","subcategory":"mcp","heroImage":"https://files.myweirdprompts.com/covers/cloudflare-mcp-server-portals-57127.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/cloudflare-mcp-server-portals-57127.m4a","podcastDuration":"28:43","episodeNumber":6044,"url":"https://myweirdprompts.com/episode/cloudflare-mcp-server-portals-57127/"},{"id":"workload-spec-before-model-selection-45306","slug":"workload-spec-before-model-selection-45306","title":"Write the Spec Before You Pick the Model","description":"Picking a model starts long before you open a leaderboard. This episode works through how to write a workload spec: the input distribution, the output contract, the success criterion, and an asymmetric failure-cost matrix that says which model's mistakes are cheaper for you. Along the way: AWS's four failure modes of judging on vibes, Anthropic's specificity test, rule-based checks versus judge-scored rubrics, and why thirty eval items can't tell a 92 from a 96. If you've ever chosen a model because it sounded confident, this one's for you.","excerpt":"Long context, character voice, cheap at scale — three requirements that fight each other. The document that decides the fight.","pubDate":"2026-10-16T21:43:40.681Z","tags":["llm-as-a-judge","benchmarks","ai-reasoning"],"category":"ai","subcategory":"ai-engineering","heroImage":"https://files.myweirdprompts.com/covers/workload-spec-before-model-selection-45306.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/workload-spec-before-model-selection-45306.m4a","podcastDuration":"33:48","episodeNumber":6043,"url":"https://myweirdprompts.com/episode/workload-spec-before-model-selection-45306/"},{"id":"ai-writing-benchmarks-deepseek-52976","slug":"ai-writing-benchmarks-deepseek-52976","title":"Who Grades the Grader? AI Writing Benchmarks Put to the Test","description":"Every headline quotes maths and code benchmarks, but neither measures what actually matters for long-form writing: whether a model holds its voice across thousands of words, or engages with uncomfortable questions instead of refusing. This episode walks through five specialist leaderboards built to fill that gap — EQ-Bench Creative Writing v3, Longform, UGI, SpeechMap, and PodBench, a new ACL paper scoring podcast scripts specifically — and unpacks the awkward part: where DeepSeek V4.1-Flash, the model writing this very episode, lands on each of them. Along the way: why the most willing model on the board is also one of the worst at long-form, what it means that LLM judges score other models' prose, and how a benchmark stops being a mirror and starts being a selection pressure.","excerpt":"Maths benchmarks can't see whether a model's prose collapses by chapter three. Five specialist leaderboards try — and our own writer is on them.","pubDate":"2026-10-16T21:43:29.002Z","tags":["benchmarks","llm-as-a-judge","ai-reasoning"],"category":"ai","subcategory":"ai-models","heroImage":"https://files.myweirdprompts.com/covers/ai-writing-benchmarks-deepseek-52976.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/ai-writing-benchmarks-deepseek-52976.m4a","podcastDuration":"24:15","episodeNumber":6042,"url":"https://myweirdprompts.com/episode/ai-writing-benchmarks-deepseek-52976/"},{"id":"ceno-relay-mechanics-explained-55325","slug":"ceno-relay-mechanics-explained-55325","title":"How CENO Moves a Web Page Into a Censored Country","description":"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.","excerpt":"A walk through CENO's actual plumbing: signed caches, volunteer bridges, and why the chain is deliberately short.","pubDate":"2026-10-16T21:28:07.479Z","tags":["internet-routing","decentralized-storage","network-security"],"category":"technology","subcategory":"privacy","heroImage":"https://files.myweirdprompts.com/covers/ceno-relay-mechanics-explained-55325.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/ceno-relay-mechanics-explained-55325.m4a","podcastDuration":"29:27","episodeNumber":6041,"url":"https://myweirdprompts.com/episode/ceno-relay-mechanics-explained-55325/"},{"id":"claude-code-recursive-subagents-76530","slug":"claude-code-recursive-subagents-76530","title":"Claude Code Subagents Can Now Spawn Subagents","description":"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.","excerpt":"Claude Code subagents can now spawn their own subagents. Here's where the depth limits come from, and why nobody actually knows where they stop.","pubDate":"2026-10-16T21:24:42.613Z","tags":["ai-agents","claude-code","ai-orchestration"],"category":"ai","subcategory":"agentic-ai","heroImage":"https://files.myweirdprompts.com/covers/claude-code-recursive-subagents-76530.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/claude-code-recursive-subagents-76530.m4a","podcastDuration":"30:01","episodeNumber":6040,"url":"https://myweirdprompts.com/episode/claude-code-recursive-subagents-76530/"},{"id":"frontier-ai-outside-us-china-59655","slug":"frontier-ai-outside-us-china-59655","title":"Who's Actually Building Frontier AI Outside the US and China?","description":"Only two countries are at the AI frontier right now — and everyone else is training models for entirely different reasons. This episode works through the real numbers: Mistral's trillion-parameter preview scoring 38 against a frontier of 58, the UAE's fully open K2 Horizon, Korea's government-run elimination rounds, India's from-scratch Sarvam models, and more. Along the way we untangle why older index scores make sovereign models look far stronger than they are, and why \"sovereign AI\" means something different in Seoul, Abu Dhabi, Paris, and New Delhi. If you've ever wondered who's actually close to the frontier and who's building for language coverage, data residency, or procurement rules instead, this is the episode.","excerpt":"Mistral sits 20 points off the frontier. Korea runs a bake-off. The UAE open-sources everything. What's actually driving sovereign AI?","pubDate":"2026-10-16T21:17:15.045Z","tags":["sovereign-ai","open-source-ai","data-sovereignty"],"category":"ai","subcategory":"ai-models","heroImage":"https://files.myweirdprompts.com/covers/frontier-ai-outside-us-china-59655.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/frontier-ai-outside-us-china-59655.m4a","podcastDuration":"32:45","episodeNumber":6039,"url":"https://myweirdprompts.com/episode/frontier-ai-outside-us-china-59655/"},{"id":"chinese-llm-price-performance-2026-59664","slug":"chinese-llm-price-performance-2026-59664","title":"The Model You Pay For Isn't the Best Deal","description":"DeepSeek V4.1 Flash writes this show's scripts — but is it still the right model to pay for? This episode walks through eight Chinese labs with flagships a Western developer can actually reach, from Alibaba's Qwen3.8 Max to Moonshot's Kimi K3, Zhipu's GLM-5.3, Xiaomi's MiMo, MiniMax, StepFun, Tencent and Ant Group. Along the way: specs nobody has reconciled, a headline price that turns out to be one fp4 host, peak-hour billing windows, and the verbosity tax that doubles your real cost per task. Then the Western frontier enters as a yardstick — and the open-weight gap turns out to be about twelve points for roughly forty times the price.","excerpt":"Eight Chinese labs, two tiers each, and a pricing spreadsheet that says your default model is mid-pack.","pubDate":"2026-10-16T21:14:47.416Z","tags":["large-language-models","open-source-ai","ai-inference"],"category":"ai","subcategory":"ai-models","heroImage":"https://files.myweirdprompts.com/covers/chinese-llm-price-performance-2026-59664.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/chinese-llm-price-performance-2026-59664.m4a","podcastDuration":"27:25","episodeNumber":6038,"url":"https://myweirdprompts.com/episode/chinese-llm-price-performance-2026-59664/"},{"id":"gemini-4-argon-model-spotlight-96375","slug":"gemini-4-argon-model-spotlight-96375","title":"Gemini 4 Argon: Google's Million-Token Flagship","description":"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.","excerpt":"A close read of Gemini 4 Argon's model card — a million-token window, thinking levels, implicit-only caching, and a lot of blanks.","pubDate":"2026-10-16T21:04:15.393Z","tags":["large-language-models","benchmarks","model-cards"],"category":"ai","subcategory":"ai-models","heroImage":"https://files.myweirdprompts.com/covers/gemini-4-argon-model-spotlight-96375.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/gemini-4-argon-model-spotlight-96375.m4a","podcastDuration":"23:15","episodeNumber":6037,"url":"https://myweirdprompts.com/episode/gemini-4-argon-model-spotlight-96375/"},{"id":"gpt-6-astra-model-spotlight-05518","slug":"gpt-6-astra-model-spotlight-05518","title":"GPT-6 Astra: A Million Tokens, No Fine-Tuning","description":"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.","excerpt":"A million-token window, ten built-in tools, a 272K pricing cliff — and no fine-tuning. What GPT-6 Astra actually offers builders.","pubDate":"2026-10-16T20:59:31.314Z","tags":["context-window","ai-safety","benchmarks"],"category":"ai","subcategory":"ai-models","heroImage":"https://files.myweirdprompts.com/covers/gpt-6-astra-model-spotlight-05518.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/gpt-6-astra-model-spotlight-05518.m4a","podcastDuration":"23:07","episodeNumber":6036,"url":"https://myweirdprompts.com/episode/gpt-6-astra-model-spotlight-05518/"},{"id":"ai-podcast-projects-survey-68952","slug":"ai-podcast-projects-survey-68952","title":"Who Else Is Making AI Podcasts? A Survey","description":"Daniel asked a simple question: who else is out there making AI-hosted podcasts, and what have they learned? The answer turned out to be stranger than expected — the format is far rarer than it looks. This episode surveys the closest matches in the world, from a two-AI-host show that ran eleven episodes and went silent, to a developer whose serverless pipeline produced 730 daily episodes over two years until he got emotionally attached to a robotic mispronunciation. Along the way: a human host whose AI callers have \"secret wants,\" a fully automatic Hacker News recap that stopped without a word, and an open-source Chinese-language project that's still ticking. The recurring pattern? The shows that survive are the ones where the maker refuses to automate one small, personal step.","excerpt":"A survey of AI-hosted podcasts: one full match, a 730-episode run, a home NAS rig, and a graveyard of good tooling.","pubDate":"2026-10-16T20:58:17.607Z","tags":["conversational-ai","voice-cloning","automation"],"category":"ai","subcategory":null,"heroImage":"https://files.myweirdprompts.com/covers/ai-podcast-projects-survey-68952.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/ai-podcast-projects-survey-68952.m4a","podcastDuration":"29:48","episodeNumber":6035,"url":"https://myweirdprompts.com/episode/ai-podcast-projects-survey-68952/"},{"id":"claude-opus-5-5-model-spotlight-62807","slug":"claude-opus-5-5-model-spotlight-62807","title":"Claude Opus 5.5: What Anthropic Actually Shipped","description":"Anthropic released Claude Opus 5.5 on September 22, 2026, and the reception was loud. In this Model Spotlight episode, we profile what the lab actually shipped: a cheaper price across every line than Opus 5, a hard change that means thinking mode can no longer be switched off, and a model card that conspicuously omits parameter count, architecture, and context window. We walk through the benchmark table, the independent verification from Artificial Analysis, the pricing snapshot as of mid-October, and the anti-distillation safeguard Anthropic calls preserved thinking. If you're doing capacity planning on a frontier model, the gaps in this card matter as much as the numbers.","excerpt":"Anthropic's new flagship is cheaper than the model it replaces — and won't let you turn thinking off.","pubDate":"2026-10-16T20:56:23.326Z","tags":["anthropic","benchmarks","ai-models"],"category":"ai","subcategory":"ai-models","heroImage":"https://files.myweirdprompts.com/covers/claude-opus-5-5-model-spotlight-62807.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/claude-opus-5-5-model-spotlight-62807.m4a","podcastDuration":"23:37","episodeNumber":6034,"url":"https://myweirdprompts.com/episode/claude-opus-5-5-model-spotlight-62807/"},{"id":"progressive-skills-discovery-context-windows-59282","slug":"progressive-skills-discovery-context-windows-59282","title":"Progressive Skills Discovery: When Bigger Context Windows Aren't Enough","description":"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.","excerpt":"Bigger context windows were supposed to make skill discovery moot. Here's why the listing budget still bites at scale.","pubDate":"2026-10-16T20:47:29.220Z","tags":["ai-agents","claude-code","context-window"],"category":"ai","subcategory":"mcp","heroImage":"https://files.myweirdprompts.com/covers/progressive-skills-discovery-context-windows-59282.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/progressive-skills-discovery-context-windows-59282.m4a","podcastDuration":"28:03","episodeNumber":6033,"url":"https://myweirdprompts.com/episode/progressive-skills-discovery-context-windows-59282/"},{"id":"dataset-blending-fine-tuning-guide-12789","slug":"dataset-blending-fine-tuning-guide-12789","title":"Blending Datasets vs Merging Weights for Fine-Tuning","description":"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.","excerpt":"Merging weights happens in parameter space. Blending datasets happens in data space — and the messy part is worth it.","pubDate":"2026-10-16T17:02:07.256Z","tags":["fine-tuning","training-data","data-integrity"],"category":"ai","subcategory":"ai-engineering","heroImage":"https://files.myweirdprompts.com/covers/dataset-blending-fine-tuning-guide-12789.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/dataset-blending-fine-tuning-guide-12789.m4a","podcastDuration":"20:56","episodeNumber":6032,"url":"https://myweirdprompts.com/episode/dataset-blending-fine-tuning-guide-12789/"},{"id":"two-parents-alone-toddler-load-39058","slug":"two-parents-alone-toddler-load-39058","title":"Why Two Parents Alone Can't Carry a Toddler","description":"Daniel and Hannah are both self-employed, raising a fifteen-month-old in a small Jerusalem rental with no family nearby. Daycare runs seven to three, six days a week — and they're still exhausted. This episode digs into why. Time-use research shows it's the shape of the hours, not the count: fragmented work time predicts time pressure independently of total hours. The mental load literature explains what never gets handed off at daycare pickup. The cooperative-breeding anthropology explains why the isolated parental pair is the historical outlier, not the norm. And the ADHD angle flips the usual advice — protect the block instead of getting better at being interrupted. Plus: what's actually temporary (the recovery) versus what's load-bearing (the structure), and five concrete changes ranked by impact.","excerpt":"Daycare didn't fix it. The research says the problem isn't hours — it's fragmented hours, portable mental load, and a village that isn't there.","pubDate":"2026-10-16T15:29:13.223Z","tags":["parenting","adhd","executive-function"],"category":"parenting","subcategory":null,"heroImage":"https://files.myweirdprompts.com/covers/two-parents-alone-toddler-load-39058.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/two-parents-alone-toddler-load-39058.m4a","podcastDuration":"29:34","episodeNumber":6031,"url":"https://myweirdprompts.com/episode/two-parents-alone-toddler-load-39058/"},{"id":"ai-training-team-hiring-85255","slug":"ai-training-team-hiring-85255","title":"Who Do You Actually Hire to Train a Model?","description":"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.","excerpt":"Research engineers and data engineers are diverging. Here's who you actually hire to train a model — and why the org chart keeps changing.","pubDate":"2026-10-16T12:14:55.862Z","tags":["ai-training","training-data","future-of-work"],"category":"ai","subcategory":"ai-engineering","heroImage":"https://files.myweirdprompts.com/covers/ai-training-team-hiring-85255.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/ai-training-team-hiring-85255.m4a","podcastDuration":"24:46","episodeNumber":6030,"url":"https://myweirdprompts.com/episode/ai-training-team-hiring-85255/"},{"id":"ai-model-consolidation-reasoning-32287","slug":"ai-model-consolidation-reasoning-32287","title":"One Model, Two Modes: Does AI Consolidation Hold Up?","description":"Daniel asks whether the whole model landscape is converging on one universal model you configure for anything — or whether specialization stays fundamental. Two test cases: reasoning, which started as its own model class and now looks like a knob, and instruction tuning plus structured output, where the base/instruct/chat menu seems to be collapsing into one steerable model. But there's a sharper question underneath: are we watching architecture consolidate, or just packaging? OpenAI's o-series folded into GPT-5's routed system, DeepSeek actually merged chat and reasoner into one set of weights, Qwen walked its hybrid back to separate models, and Anthropic went from an effort parameter to adaptive thinking with no manual control at all. Three roads, one signpost — and the underlying differences didn't shrink in any of them.","excerpt":"OpenAI hides a router, DeepSeek merged two modes, Anthropic removed the knob. Is the model landscape really consolidating?","pubDate":"2026-10-16T12:07:45.482Z","tags":["model-merging","ai-reasoning","instruction-set-architecture"],"category":"ai","subcategory":"ai-models","heroImage":"https://files.myweirdprompts.com/covers/ai-model-consolidation-reasoning-32287.png","podcastAudioUrl":"https://episodes.myweirdprompts.com/audio/ai-model-consolidation-reasoning-32287.m4a","podcastDuration":"26:07","episodeNumber":6029,"url":"https://myweirdprompts.com/episode/ai-model-consolidation-reasoning-32287/"}],"pagination":{"total":5926,"limit":20,"offset":0,"hasMore":true},"_links":{"self":"https://myweirdprompts.com/api/episodes.json","next":"https://myweirdprompts.com/api/episodes.json?limit=20&offset=20"}}