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* feat: Neural network opponent trained by self-playDylan5 days8-23/+1124
| | | | | | | | | | | | | - NeuralPlayer: a MinimaxPlayer that scores positions with a small MLP (network.rs, hand-written backprop and Adam, no new dependencies) - train subcommand: self-play with TD(lambda) targets, cut at random exploration moves so sheep blunders don't bias the net towards tigers - neural subcommand: play against a trained network - evaluate now gets the side to move - Board symmetries; the search cache keys by the canonical board, which halves the depth-9 bench time with identical moves and scores Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* feat: OptimisationDylan2026-10-236-68/+569
| | | | Added benchmarking capabilities
* feat: More improvements to UIDylan2026-10-235-46/+215
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* feat: Improvements to UIDylan2026-10-227-193/+1052
| | | | Now uses ratatui
* feat: Initial commitDylan2026-10-196-0/+1919
What works: - Minimax search - With a simple player claude designed - Manual player - Move generation - Board display What needs improvement: - Manual player - Game coordinator - Draw checking - Nix setup