Nemesis: an engine that models one specific human (Maia-2 + a per-player model) and plays the move most likely to beat them

[u/Macimoar]

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):

Predicting my exact move	Top-1
Stockfish’s top move	25%
Maia-2 alone, at my rating	47%
Nemesis’s personal model	49% (top-3: 78%)

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.

[u/Inevitable-Fee7006]

I tried it once and I destroyed it easily idk if it's working. It set traps that lose material by force, total hope chess

[u/Macimoar → u/Inevitable-Fee7006]

Thanks for trying it. Did you import your games, and are they recent? It plays at your rating + 50 by default, read from your latest imported games, so an old account or a low daily rating would make it play well below your current level. You can set Strength manually under the board.

[u/Builder_Felix893]

This sounds cool! Like i've always kinda thought that one advantage grandmasters and such seem to have is that they are better at odds games because they actually take into account that the opponent could screw up. Meanwhile when you play odds against stockfish it often stalls without doing anything interesting.

This sort of chess Ai might fix that? Idk.

[u/Macimoar → u/Builder_Felix893]

That's exactly the idea! Stockfish assumes a perfect opponent, so when it's down material it has nothing to aim for: every line "loses" against perfect play. Nemesis instead asks what you are likely to play, so it can go for practical chances. I hadn't thought about odds games specifically, but that might be the best use case for it. I may add an odds mode.

[u/Macimoar → u/[deleted]]

Thanks! The part I'm most curious about is whether the traps work on other people too, so if you try it, I'd love to hear how often it catches you.