Maia Chess – Human-like chess AI for playing, learning, and more

Hacker News

We're thrilled to announce that www.maiachess.com is now in open beta, meaning everyone can access it! Maia is the most human-like chess AI, and is an ongoing research project at the University of Toronto developing fun, useful, and novel human-AI collaboration in chess. Please give it a try and let us know what you think. We're still rapidly improving and iterating on it. * Play Maia-2: Play the (updated) most human-like chess engine, tailored to your skill level * Analyze your games: See how you (or the pros!) stack up with both Maia’s human-based predictions and classic Stockfish evaluation * Try Maia-powered puzzles: Tactics puzzles curated and analyzed through Maia’s unique lens * Openings drill: Brand new! Select openings, play through them against Maia, and get instant, personalized feedback * Hand & Brain: Play this fun team variant where you play with Maia as a human-AI team * Bot-or-not: A chess Turing Test: can you spot the bot in a real human-vs-bot game? * Leaderboards: See how you rank in each mode, and challenge yourself to climb higher We’d love your feedback: what works, what doesn’t, what’s missing, or what would make the platform more valuable for you. Join our Discord to chat with us and other users ( https://discord.gg/hHb6gqFpxZ ). If you're interested in our research behind Maia, you can check out these papers: Aligning Superhuman AI with Human Behavior: Chess as a Model System , KDD 2020 Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess , NeurIPS 2021 Learning Models of Individual Behavior in Chess , KDD 2022 Designing Skill-Compatible AI: Methodologies and Frameworks in Chess , ICLR 2024 Maia-2: A Unified Model for Human-AI Alignment in Chess , NeurIPS 2024 Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess , under review

Visit product View on Hacker NewsJul 29, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, efficient, users · Missing: plus, intuitive, reviews

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, users · Missing: mobile apps, ios, entrepreneurs

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

32%32% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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