I trained a chess engine to play like humans
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…
I trained a chess engine to play like humans launched on May 10, 2026 with 14 votes, #1,027 of 20,538 launches that month and more than 90% of that year's launches.
In plain words
1e4.ai is a chess web app where players compete against neural networks trained to mimic human Lichess players across specific skill levels from 800 to 2200+ Elo. Built on data from billions of Lichess blitz games, the bots replicate human play patterns including realistic clock management, performance degradation under time pressure, and human-like blunders. Each rating bracket uses separate transformer-based models that consider board state, move history, player rating, and remaining time to generate moves that feel authentically human rather than computationally optimal.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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…
Does a similar job
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Noctie – A chess AI that predicts your rating2022 · noctie.ai · ▲134I 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…
- CAChessCoach – A neural chess engine that comments on each player's moves2021 · chrisbutner.github.io · ▲506
LearnChessJul 2026 · learnchess.ai · ▲29I've been playing chess for a few years with my ELO stuck around 1600 on Lichess. I wanted an app that combined puzzle training, interactive lessons, game analysis, and AI coaching in one place. But all the apps I used over the years either felt overwhelming, had UX that wasn't up to my expectations for a modern app or were lacking some fundamental features that I really wanted. So, I built LearnChess.ai. The technical angle I'm most excited about: it's built local-first using LiveStore (event-sourced, SQLite on-device), which means zero loading delays for user data and full offline support.…
Maia, a human-like neural network chess engine2020 · maiachess.com · ▲10We'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…
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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