CSSLab

maia-chess

Python2020–2025

CSSLab / maia-chess

maia-chess

Maia is a human-like neural network chess engine trained on millions of human games.

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1.3KGitHub stars

REPOSITORY AT A GLANCE

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What it is

Maia is a human-like neural network chess engine trained on millions of human games.

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Where it is used

No documented product use has been verified yet.

What people are saying

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Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 11:56:56 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year4 dated mentions

Observed mentions: 2020-12-01 19:12:19 UTC to 2025-09-01 20:14:07 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	1	1
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	1	1
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	0	0
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	0	0
2024	2024-01-01 00:00:00 UTC	2025-01-01 00:00:00 UTC	1	1
2025	2025-01-01 00:00:00 UTC	2026-01-01 00:00:00 UTC	1	1

What Hacker News says

4 sources

nilslindemann·Sep 1, 2025

It could be done with e.g. the Maia engine, which was trained on Lichess games and plays like humans on different skill levels – https://www.maiachess.com/ – but I do not know if it supports different ponder depths. Edit: it seems it does not – https://github.com/CSSLab/maia-chess#:~:text=a%20nodes%20lim...

In What Is Complexity in Chess?

mk_stjames·Feb 9, 2024

This was studied with the 'Maia' project. See: https://github.com/CSSLab/maia-chess

In Grandmaster-Level Chess Without Search

woko·Jan 17, 2021

From the README on Github: > Note also, the models are also stronger than the rating they are trained on since they make the average move of a player at that rating. Reference: https://github.com/CSSLab/maia-chess

In Human-like neural network chess engine trained on lichess games

reidmcy·Dec 1, 2020

Hi everyone, We're happy to announce a research project that has been in the works for almost two years! Please meet Maia, a human-like neural network chess engine. Maia is a Leela-style framework that learns from human play instead of self-play, with the goal of making human-like moves instead of optimal moves. Maia predicts the exact moves humans play in real online games over 50% of the time. We intend Maia to power data-driven learning tools and teaching aids, as well as be a fun sparring partner to play against. We trained 9 different versions on 12M Lichess games each, one for each rating level between 1100 and 1900. Each version captures human style at its targeted level, meaning that Maia 1500's play is most similar to 1500-rated players, etc. You can play different versions of Maia yourself on Lichess: Maia 1100 (https://lichess.

Read the rest

org/@/maia1), Maia 1500 (https://lichess.org/@/maia5) and Maia 190 (https://lichess.org/@/maia9) This is an ongoing research project using chess as a model system for understanding how to design machine learning models for better human-AI interaction. For more information about the project, check out http://maiachess.com. We published a research paper (https://arxiv.org/abs/2006.01855) and blog post (http://csslab.cs.toronto.edu/blog/2020/08/24/maia_chess_kdd/) on Maia, and the Microsoft Research blog (https://www.microsoft.com/en-us/research/blog/the-human-side...) covered the project here. All of our code is available on our [GitHub repo](https://github.com/CSSLab/maia-chess). We are super grateful to Lichess.org for making this project possible with their open data policy. In current work, we are developing Maia models that are personalized to individual players. It turns out that personalized Maia can predict a particular player's moves up to 75% of the time. You can read a preprint about this work https://arxiv.org/abs/2008.10086. We'd love to hear your feedback! You can contact us at maiachess@cs.toronto.edu or on our new Twitter account @maiachess.

In Show HN: Maia, a human-like neural network chess engine