Fine-tune a Kev decision model (open Jev-style System One model) on a user's own workload and serve it with calibrated probabilities. Interviews the user, finds existing Jev/TypeSafe questions or labelled data in their codebase, generates enough synthetic…
Deploy a Kev decision model (the open Jev-style System One model) as the user's own TypeSafe-compatible HTTPS endpoint on Modal with one command, wire it into their code, and take it down. Use when someone wants to host Kev, get a Kev API URL, replace Jev /…
Extremely strict structural review of a Kev branch or PR (code-judo simplifications, spaghetti growth, files past 1k lines, boundaries, duplication of canonical helpers). Use when asked to review a PR, audit a diff, or run a "thermonuclear" or deep code…
Search the Kev knowledge graph (private repo jaredpalmer/kev-knowledge) before re-deriving history. Use when asked what was tried, decided, measured or learned in earlier Kev sessions, why a rule or number exists, what a round or PR did, or when starting a…
Launch, monitor and pull Kev training studies, untrained-base probes, remote benchmarks and new-base smoke checks on Modal (modal_app.py). Use when running trials, delta fine-tunes, base probes, external evals or fit checks for the Kev repo.
Verify a Kev change has no regression and ship it as a reviewed PR. Use when refactoring, editing kev/*.py, scripts, the Space or the playground, and when opening or merging Kev pull requests (stacked branches, squash merges).
Write a Kev pull request title and body that teaches the reader why the change exists and how it works. Use when opening, editing or reviewing a PR on this repo, or when the PR title will become the squash-merge commit message.
Cut AI tells from any writing. Must always apply.