I built an engine with a different objective. Instead of the objectively best move, it plays the move with the best expected result against one specific player, using a model of how that player actually chooses moves.

How it works

• The player model: a conditional-logit model over the legal moves, fitted per player on their imported Lichess or Chess. com games. Its features include Maia-2’s log-probability for the move, Stockfish’s eval loss, and style features: material grabbing, checks, king safety, development, how often the player repeats their own repertoire, and time pressure. The weights are regularised towards a Maia-2-based prior, so a player with few games falls back to “a typical human at your rating”.
• Maia-2: converted to ONNX and run on onnxruntime, so no PyTorch is needed. The rapid and blitz models are downloaded on first use.
• Move choice: Stockfish supplies the candidate moves and evals. For each candidate, the player model predicts the reply distribution, and Nemesis scores the expected outcome against that distribution. A risk dial sets how far it may stray from the objectively best move to set traps.
• Time controls: separate profiles per time control, kept only if they predict that time control’s held-out games better than the main profile.
• Also: it runs as a UCI engine, so it works in any GUI, and there’s a web app with an explain-everything commentary mode.

Results on my own games (I’m about 900 on Chess. com, 93 games, newest quarter held out):

To be clear, Maia-2 does most of the work on top-1; the personal layer adds a couple of points. The more interesting test is whether exploiting the model pays off. Over 16 games against it, Nemesis set 59 traps and I fell for 14 (24%), against an 18% mistake rate (losing a pawn or more) on my other moves. On synthetic players with known habits it also beat plain Stockfish at the same depth: it finished off a “grabber” bot in 16.8 moves on average against Stockfish’s 22.6. That’s a simulation, though, not real people.

Caveats: one real player is a tiny sample, the trap rate comes from only 59 traps, and the synthetic benchmarks only show that it can exploit a known habit.

It’s free and open source (MIT), runs locally, and has downloads for Windows, Mac and Linux: https://github.com/macimoar/nemesis

Built as a one-person project with a lot of help from an AI coding assistant. I’d especially welcome thoughts on the method: better ways to measure whether exploitation works, features worth adding, or prior work I should be comparing against.

Ist das ein Bug oder ein Feature?

Das Fehlerbuch in Chestnuts NEXT 1.0.3 wird nicht über die Cloud synchronisiert. Es wäre schön, wenn das Buch gesehen und bearbeitet werden könnte, egal ob ich ein iPhone, iPad oder Mac benutze – stimmt's?

Was just trying to see what kind of tool I could do with plain effort... and kept asking for stuff.

Not the first scanner, not going to be the last... but hey, maybe it'll of interest to people.

Lichess and Chess.com Account Status Scanner

Unleash the graveyard guardians! We've been having an absolute blast bringing the world of Monster Chess to life. Imagine your classic chess board, but instead of knights and pawns, you're commanding an army of ghoulish beasts, mischievous monsters, and magical creatures. Every piece is animated with that quirky claymation, puppet style we love, making each move a mini-spectacle. It’s a true battle of brains and beasts, testing your tactical skills against an opponent in a truly unique setting. We’ve poured our hearts into making this a fresh, spooky twist on a timeless game. Dive into the tactical fun and see if you can out-scheme the competition!

https://w.run/snappysamurai383/monster-chess?s=N3xFoCg

I have a collection of chess books in PDF format. They contain annotated games, diagrams, variations, and valuable analysis, but everything is locked inside static pages.

I wanted to turn these books into interactive ChessBase databases without manually entering hundreds of games and positions.

The solution combines Python, OCR, a Large Language Model (LLM), Stockfish, and my old ChessBase 15.

A Python program reads the PDF page by page. OCR extracts the text, while the LLM identifies games, moves, variations, and comments. Chess diagrams are converted into FEN positions, and games are reconstructed in PGN format, preserving the original annotations.

Stockfish checks the legality of moves and helps detect errors in the reconstructed positions.

Once the material is processed, a Python bot controls ChessBase 15, imports the PGN files, and organizes everything into a CBH database.

The result is a chess book transformed into an interactive study tool.

Instead of looking at static diagrams, I can replay games, explore variations, analyze positions with modern engines, and compare the author's ideas with current evaluations.

The interesting part is that none of these technologies needs to be reinvented. Python connects the components, AI interprets the content, Stockfish validates the chess positions, and ChessBase provides the study environment.

A chess book written fifty years ago can now be explored using today's technology.

This is a practical example of how AI can give new life to existing knowledge and software.

The name of the app is ChessQuirk. It is available in iOS, Android, and web (ChessQuirk.com)

If anyone here wants to try the premium features, DM me your ChessQuirk username after creating an account and I’ll give you 3 months of Premium for free.

I built a chess app called ChessQuirk that is heavily focused on one thing I always felt chess apps were missing: actually helping you learn and retain opening theory instead of just showing you a database of moves.

The main feature is an Opening Trainer where you can drill opening lines repeatedly until the moves become familiar. There are also opening quizzes, and you can create your own custom opening repertoires if you want to train specific lines you play.
Some of the other features currently in the app:

• Opening drills for a growing library of openings and variations
• Opening quizzes to test whether you actually remember the moves
• Custom opening builder so you can create and train your own repertoire
• Play games starting directly from a specific opening position
• Opening-specific tactics training
• Post-game analysis with Stockfish and an AI chess coach
• Move classifications and explanations of mistakes/blunders
• A “Most Common Mistake” feature to help identify recurring problems in your games
• Saved game history and analysis

I’m still actively developing it, so I’m also very open to feature requests.

Feedback is very welcome, especially criticism. I’m trying to make this genuinely useful for people who spend a lot of time studying openings rather than just turning it into another generic chess app.

Hello everyone! For the past year, I've been working on an online chess research tool that analyzes the opening phase of the game. So far I've been able to get ahold of about 7.5 million games, all played by semi-competitive chess players on lichess (2500+). While that may sound like a large number, I've come to the realization that the ideal amount of games to populate an opening explorer with statistic would be around twice that number.

If anyone has a large amount of chess games played either OTB or online and that's not from lichess or a commercial package like Chessbase, please send me a message!

Ich denke schon länger darüber nach. Die meisten von uns spielen einfach mehr Spiele, lösen ein paar Rätsel und hoffen, besser zu werden. Aber woher weiß man, woran man eigentlich arbeiten sollte?

Ich arbeite an etwas, das versucht, diese Frage anhand eurer eigenen Spiele zu beantworten.

Es heißt Zugzwang: https://4zugzwang.com/en/

Want to improve your chess and win more games? ♟️

Here’s the Blackburne Shilling Trap, narrated by our Dr Strange bot. Spot the idea, try it out, and be ready when it appears in your own games.

We built FoxyChess to help you improve your game through traps, tactics and openings, with a range of bots at different levels to practise against.

Learn it free until 30th November here:

Foxy Chess Blackburn Shilling Gambit

Have you ever caught someone with this trap—or fallen for it yourself?

I've been working on Coliseum, a free Windows app for running engine-vs-engine tournaments. Version 3.6 is out:

- Gauntlet, round robin, Swiss, knockout and double knockout, with proper tiebreaks

- Several games at once, all shown live

- Elo with error bars, and Elo estimation for new engines

- Game review with accuracy and key moments, plus a tactics trainer that turns your games into puzzles

- Works with any UCI engine, fully offline

You can also run up to however many games your computer can handle during tournaments. Meaning you can make a tournament and have 5 games play at the same time or 10. All the way up to 100 games

GitHub: https://github.com/PhelRin/Coliseum

Feedback and bug reports welcome.

The concept: use Stockfish 19 and Maia-3 to make AI coaching less dumb, and have a mobile companion app at your fingertips that remembers your past games from either chesscom or lichess and tracks your progress and profile to help you climb the Elo ladder.

Why I created it: i felt the game review lacked a proper narrative and discovered that pasting the game PGN in Gemini gave a fun analysis of my games that kept me motivated and learning. I created this app to remove that friction, link in engine data, and ensure the advice adapts to me as i progress.

At this stage the app is available on Playstore for Android (iOS coming soon) in closed beta. If you express interest I will reach out to you directly to invite you to join the beta.

Free forever means that beta testers that share genuine feedback will be able to keep using the app forever (despite me shouldering the ongoing infrastructure costs).

Feel free to AMA!

What it does: It runs a player’s last 200 chess.com or Lichess games through Stockfish and finds the mistakes that repeat across games, such as repeat traps, pieces left hanging, missed forks, and winning positions not converted, etc. Patterns are shown with infographic style visualizations and interactive board play.

How it works: Stockfish evaluates every position, Python code turns those evaluations into win-probability swings and then runs detectors over all the games at once. A multi-page report is generated and a chatbot running Claude Haiku helps users explore the findings.

What I'd like critique on: Is depth 11 the right level for meaningful analysis? Is this missing key analysis and functionality? Does the AI chatbot component add positively to the experience?

Some more details:

• Stockfish 19, depth 11 per position, one thread per worker, over the latest 200 games.
• Evaluations go through Lichess's win-probability curve. A blunder is 30 or more points of win probability lost on one move.
• The chat is deliberately dumb, which means the model can't produce a number, it can only call tools that return what the code computed, and a check after each reply blocks any figure that didn't come from a tool.

How do I turn on the best move arrows like lichess has? I'm trying to read a chess book in .pgn format but it is so hard to read and follow. There is a lot of junk and little windows all over in ChessBase too that I wish I could get rid of. And there is no backward or forward buttons, I think we can add those but I don't know how. Any help?

Hi! I’ve been working on CheckLingo, a free and open-source browser extension that adds quality-of-life features to Duolingo Chess.

Current features include:

• highlighting the last completed move;
• separate highlight colors for your moves and your opponent’s moves;
• move history with piece letters or chess symbols;
• board coordinates;
• right-click planning arrows;
• custom chess word replacements;
• prevention of accidental board image dragging;
• configurable colors and light/dark themes;
• Portuguese, English and Spanish interfaces.

Everything is processed locally in the browser. There are no ads, analytics, external servers or required accounts.

Version 1.0.0 supports Firefox and Chromium-based browsers. The Firefox store submission is currently being prepared/reviewed, and manual installation packages are available on the website.

Website: https://lucrazy-fn.github.io/checklingo/
Source code: https://github.com/lucrazy-fn/checklingo

I’d appreciate feedback, especially about move detection, highlighting accuracy and the move history during full matches.

I would like a handheld chess computer that allows me to play without having to buy a digital or sensory board.

In other words, I can use this to work on my blindfold chess, or I could play using a magnetic board or theoretically any board.

The handheld would just give the moves and it is up to me to input them on some board.

Oh, and I want to play over 2400 ELO, or 2200 at the minimum.

Can you recommend anything like this? The Milennium Chess Classics Element MIGHT be what I'm looking for, but I think it needs a sensory board in order to use it.

Thanks for the advice!

Hi! I’m Albert Hamood, a data scientist who runs a data science-focused chess site in my spare time at chessds.com. I build models that predict game results and human moves, enabling analysis like this recent look at rating deflation.

As part of this work, I’ve trained and am releasing six new neural models for human GM move prediction. They independently re-implement and extend Chessformer, the architecture behind Maia-3, with a focus on higher-level play and fine-tuning on classical GM-level chess. On my site, I use them as inputs for full prediction models that also include engine evals. You may use them for whatever you like!

There are six versions available, varying on two dimensions:

• Model size. I re-implemented the same sizes as Maia-3 here at 23M and 79M, and additionally added 150M. These are approximate parameter counts, and so far adding parameters improves performance.
• History. The standard models leverage a bit of the game’s history in terms of recent board positions to improve predictions. I also trained versions that are optimized to run without considering previous positions, and labeled these “FEN” models. They give up a little performance, but not much. This is like running Maia without --use-uci-history, but with models trained specifically to work without previous positions.

Accuracy

On a held-out set of 81,939 classical games with both players rated 2400+, DSC-150M predicts the played move 62.74% of the time, compared with 57.61% for Maia-3 79M on the same positions. DSC was specifically fine-tuned for this population, while Maia-3 targets a broader population of online players. The figure below compares three measures of move prediction accuracy:

• Top-1: The % of the time the move played was the top predicted move by the model.
• Top-3: The % of the time the move played was in the model’s top three choices.
• Log loss: Lower is better here. This measures how much probability the model assigned to the played move, penalizing heavily when this probability is very low.

UCI usage 

Generally I’ve tried to match Maia-3’s implementation, although there are a few added options. The GitHub README is a good resource. The UCI engines I hope will work in any GUI you use, and have the following choices under the “OutputMode” option:

• likelihood-wdl (default): move probabilities plus scores from the model’s auxiliary win/draw/loss predictions, using Maia-3’s output format. These aren’t Stockfish-style evaluations, and they aren't so reliable -- the point of this model is the move probabilities.
• likelihood: move probabilities without evaluation scores, viewable in interfaces such as Nibbler.
• probability-as-score: puts move probabilities in the GUI’s evaluation column—for example, +41.73 means 41.73%. This is useful for viewing predictions in HIARCS and similar interfaces, but the GUI’s evaluation graphs and adjudication will interpret those numbers as evaluations.

Links and license

You can get them at the following links. They are free to use under GPL-3.0-or-later (code) and CC BY 4.0 (weights). Have fun! If you find these useful, a public thanks and a backlink to https://chessds.com would be appreciated.

• UCI engines
• GitHub (code, documentation)
• Hugging Face (model weights)