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FUn little experiment with Typesafe AI Jev Model playing chess against stockfish :)
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A local chess workbench for measuring Jev as a move chooser: the code enumerates legal moves with chess.js, Jev picks from them, and Stockfish grades every pick without its evaluations reaching the model.
User repository on GitHub
FUn little experiment with Typesafe AI Jev Model playing chess against stockfish :)
Repo stats from the GitHub API, cached Oct 2026.
A local app for testing TypeSafe Jev (a System One model) as a chess decision-maker. Jev never generates a move: the code lists the legal moves, Jev picks from them, and Stockfish grades every pick. The point is to find out how well Jev chooses, so nothing here is tuned to make it look good or bad. chess.js owns the rules. Jev only chooses. Stockfish only grades. Its evaluations never reach Jev's state, with one deliberate exception: the opt-in lesson levels, which feed Jev's own graded mistakes back to it.
Quoted from the project's README. Read the full README.
Watch out
The app is explicit that Jev never generates a move, so this measures choice quality inside a small legal-move set rather than chess strength. Answers vary between identical requests — the Ask again button exists to show it — so one position is not a stable measurement and the intended unit is a run. Estimated Elo comes from a Stockfish-versus-Stockfish calibration run stored in bench/elo-calibration.json, not FIDE or lichess, and settings the calibration does not cover are shown as unrated and excluded from performance Elo. The lessons levels are the single place Stockfish output reaches Jev, a deliberate exception to the separation. Re-calibrating takes about an hour on 10 workers, and the full bench run is 5,000–6,000 decisions. Copy reviewed against the repository README on 2026-10-04, not from running it.
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