Episode Details

What does a boundary model actually eat? This episode serves up the answer: a carefully curated menu of portable features that actually train models with minimal data. Host A and Host B unpack the human-machine collaboration at the heart of their project—Joey sets the questions and validates the labels, while an overnight AI agent session catalogs every signal the pipeline can compute, from whistle detection to scoreboard OCR. A practical deep dive into feature engineering on a sticky note budget. 00:00:00 - Opening: Breaking the curse 00:02:45 - The human-machine division of labor 00:06:30 - The feature menu: Inside the overnight session 00:12:00 - Portability: The secret ingredient 00:16:15 - Closing thoughts --- Sources & further reading: • game-senser-fable repo — docs/podcast/LEARNING-CONTEXT.md §8 (the ep-04 work • session: feature menu, label census, JP pool/semifinal scans, containment/slop • measurement, the generalization bet; commit c5d53d6) • game-senser-fable repo — src/out/jppoolsfbaranatomy.json + • src/out/gstaadcontainmentslop.json (the night's primary measurements) • game-senser-fable repo — src/out/gstaadsample10v2verdicts.json + HUMANINPUT_LOG.md • (the ~20 boundary verdicts; the label-cost ladder) • game-senser-fable repo — data/514945/rallybounds.json + goldparsed_514945.json + • data/468157 manual rows (the label estate census) This podcast episode was fully generated by AI — research, script, voices, and production. Built with Claude, Piper TTS, and automated pipeline tooling.