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Noctie – A chess AI that predicts your rating

Hacker News

Noctie – A chess AI that predicts your rating

I built over the last two years a human-like neural network chess engine that tries to predict your rating from a single game. It automatically adapts to your play and tries to play like a human at your level would play, giving you a balanced game. At the core I’m using an AlphaZero / Leela Chess Zero style neural network that I have trained on 1 billion human games from the lichess.org open database. Around this network I have built a chess engine in Rust with algorithms that use the outputs from the NN to produce human-like moves at a given rating from beginner to world champion, as well as predicting the level of the opponents play. I want to develop this into kind of an AI coach that you can spar certain positions against and get feedback suited for your level. Happy for any suggestions!

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Actual performance

134points
102comments
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Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, open · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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