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Meet the Hosts
#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.
#6162: Why Dictated Words Rewrite Themselves
Hold-to-talk dictation shows dimmed text that silently changes when you release the key. Here's the architecture behind that rewrite.
#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.
#6158: Naming the ASR Sub-Tasks Nobody Mapped
A 2001 linguistics paper already named Daniel's problem — but it points the other way. So what do you call a fine-tune set that expands "F Y I" to ...
#6157: Where to Sit When You're Fine-Tuning Your Own Voice
Your phone's built-in mic is the point — the setup around it is what ruins the takes. Rooms, remotes, and the app setting that deletes your data.