Alternatives
Products that do what Chess of Minds does
Chess, played by twelve minds.
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2021 · chrisbutner.github.io
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2025 · knightride.rakhim.org
- 4NA
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…
2022 · noctie.ai
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- 6KFKung Fu Chess▲140
2019 · github.com
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- 11SC
2019 · reinechess.com
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Two years ago I sat down to build a chess engine for my kids' Christmas present. Wanted it to play badly enough to be beatable but not randomly bad: different styles so they could choose what to face. While building those styles I noticed the choices started looking like actual historical players' tendencies. Went looking. Turns out: given enough games from one person, you can extract enough of their decision pattern to reproduce it. Two years later, 41 players, ~3,000 games each. Tal sacrifices unsoundly. Morphy attacks like it's 1850. Capablanca grinds endgames. Same engine underneath,…
Apr 2026 · playchessgate.com
- 13IT
I built 1e4.ai - a chess web app where you play against neural networks trained to mimic human Lichess players at specific Elo ranges. There's a separate model for each 100-point rating bucket from ~800 to 2200+, and the bots not only choose human-like moves but also burn clock time, play worse under time pressure, and blunder in human-like ways. Live demo: https://1e4.ai Code: https://github.com/thomasj02/1e4_ai A few things that might be interesting: - Trained on almost a full year of Lichess blitz games, around 1B total games - Architecture is an a small…
May 2026
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2017 · ba.net
- 15CB
Chessmate is a transformer-based model that predicts your next chess move, tailored to players around 1500 ELO on lichess. Soon it will also offer personalized models and a broader range of ELO-based models. Hopefully this can offer a new and innovative way to learn chess and enjoy playing against a uniquely challenging bot!
2024 · chessmate.ai
- 16MO
rearrange the chess pieces so that the number of pieces threatening each square matches the large number in that square? pieces move freely https://chedoku.com/#dailypuzzle
2023
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Hey everyone! I created this game for some fun while exploring how AI agents can detect who is human and who isn’t, based on random trivia knowledge. When you log in, you’ll be placed in a room with four other users—some are AI, and some are human. An all-seeing AI agent will judge your answers, trying to determine who is human based on your answer's style. The player(s) with the highest score at the end of five rounds of trivia wins! It’s free to play. Just type in a username and get started. Have fun!
2024 · playandsurvive.vercel.app
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- 20CTChess Twist▲29
I continue my experiment of transposing classic games onto an irregular grid. I've previously released variants of minesweeper and checkers. And now I'd like to share my variant of Chess. Chess can be a pretty punishing game, but I thought this was nevertheless a way to make it even more challenging by making it trickier to anticipate your opponent, even though both players still have the same information. From the playtests I've done, that challenge is pretty compelling. It also seems to be more fun at 2 people, where both players keep surprising one another.
2024 · polyreplay.com
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Beyond engine lines. Let agents think, choose, and play.
Jul 2026 · mind-board-six.vercel.app
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Hi, this is my first HN submission so please forgive the rough edges. For this project gathered chess positions from some well-known chess players with a lot of games. (Usually ~25k games and 1M+ positions. The most for any player was GM Aman Hambleton with 1.8M positions indexed.) With each board position I create a probability distribution of moves the player has made from that position. Then, I simply draw from that distribution (with sampling temperature) to choose a move. As long as there's at least one game with a given position, you can keep playing. You'll see that it's pretty easy…
2022 · d2frm2q2a7fq8v.cloudfront.net
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