Development
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.
#6182: Running a Private APT Repo for Family Machines
Daniel wants his Debian packages on family machines without becoming their unpaid update service. Here's the modern way to do it.
#6181: Shipping Private Android Apps to Your Family
Daniel wants to ship private Android apps to non-technical relatives. F-Droid repos, Basic Auth, and the Google verification cloud on the horizon.
#6173: Auditing a Sycophantic Model From the Inside
A stumped lab, a fawning model, and the diagnostic playbook nobody has a name for — from eval slicing to persona vectors.
#6170: One Endpoint, Many Boxes: Local LLM Routing
A LiteLLM-style proxy can fail over between your own GPU boxes and the cloud — but it can't see your VRAM.
#6168: Hebrish: Hebrew-Aware Dictation That Actually Works
Two new speech models fix English-Hebrew code-switching dictation — and the whole training run cost about fifty cents of GPU time.
#6167: Streaming Browser Cookies to a Headless Server
Daniel wants his logged-in browser on the home server. Chrome bolted the front door — so what's left?
#6166: Turning a Drifted Server Into a Rebuildable Playbook
Your server grew for years. Now you want a recipe that can rebuild it from scratch. Here's the honest path from drift to playbook.
#6165: Watching 20 Batch Jobs at Once
Batch APIs give you a discount, then hand you a babysitting job. Here's what actually watches a batch run — and what doesn't.
#6164: We Gave Each Host Its Own AI Agent
We split our two hosts into separate AI agents with private context windows. The bill came to 78 cents an episode.
#6163: Forking a Keyboard for One Voice Feature
Daniel built a Hebrew-English dictation engine. Now he wants to fuse it into someone else's keyboard — without inheriting its entire future.
#6161: Turning Words Into Sound: G2P, IPA, and the Hebrew Vowel Problem
Text-to-phonetics tools exist, but the neural ones guess wrong on exactly the words you care about — names, brands, and Hebrew.
#6160: Where Do Tiny Speech Rules Live? A Ladder From Regex to LoRA
A regex notebook, a WFST grammar, or a fine-tune? Where do tiny speech-rewrite rules actually belong in a 600M-parameter model?
#6159: When a Speech Model Can't Decide How to Write Dollars
One dataset says "$20," another says "twenty dollars." Fine-tuning a speech model means finding out which convention wins — and why it matters.
#6154: Who Checks the Labels? Weak vs Strong Supervision
Human labels, rule-based labels, or a teacher model writing the exam — what actually makes supervision "strong," and who verifies it?
#6153: Decision Models: Typed Answers in One Forward Pass
Clef, Jev, and OpenAI's Decisions API return typed answers with probabilities in a single forward pass — no generated text to parse.
#6148: The Tiny Hebrew Models Behind the Show's Voice
Dicta's 300M-parameter nikudizer, Phonikud's adaptors, and the ktiv male model hiding inside a vowel adder.
#6146: Grounding Is Not Solved: DIY vs Gateway vs Native Search
Bolt-on search, gateway search, or native grounding? The plumbing didn't vanish — it just moved to someone else's bill.
#6139: Which AI Writes Our Scripts Best? We Tested Five
Five models, three scripts each, one rubric. Sol won on length, Grok on voice — and the runner-up title hides a catch.
#6138: Hebrish on iPhone: MLX, Core ML, or Wait?
Hebrish spells makolet right on Android. Getting that Hebrew-English dictation onto an iPhone means picking between MLX, Core ML, and sherpa-onnx.
#6136: How We Chose Our New Script Writer
Ten models audition to write the show. Seven fail the censorship gate — including the workhorse that wrote three episodes in four.