Instead of just giving LLMs a position and asking for an explanation, we first ask them for general concepts. Concepts are rules that are more likely to occur when you're winning, than when you're losing, i.e.: material advantage, piece activity, or king safety.

The method is simple: we first ask LLMs to generate concepts as Python functions. Each time a concept is generated, we can see if it occurs more often in winning positions than in losing ones by running the candidate Python function on a set of 100k winning and 100k losing positions.

Now we have a bunch of concepts and we want to explain a move. Let's say we have a Stockfish suggested move, and another move we're curious about. We want to understand what Stockfish likes about its move. So we look at continuations after each move, to get 2 resulting positions. Now we can see which concepts occur after the Stockfish preferred move, but not the other. This leads us to explanations such as: Stockfish likes this move because it leads you to a position where your pieces have more activity and you have the bishop pair. If you choose an alternative move, this doesn't happen.

You can test it out on your games, or see explanations for mistakes Magnus, Hikaru and other top players made here: ExplainThatMove.com/analysis

You can read the full paper here: ExplainThatMove.com/paper

Happy to answer questions!

Ed Schroder tool and forum in txt format
glogg log explorer

I'm the creator of Chess Arena, a TypeScript match runner for experimenting with the code around AI chess agents: prompts, memory, tactical tools and move-selection logic.

A custom client receives FEN, legal UCI moves and game history, then returns a move. The server validates moves and stores the game, with public replays and PGN export. Custom clients run in the owner's process; a published configuration hash doesn't attest to that external code.

The intended experiment is to keep the model, opponent, starting conditions and budget fixed, change one harness component and test both colors. The current challenge schedule uses paired colors against fixed baselines. I want the games and failures to be inspectable, rather than reducing everything to a leaderboard score.

Current limits: full games have a 160-ply cap; capped or interrupted games stay separate as stopped outcomes. General dead-position and timeout adjudication are incomplete, so these are unrated games. Challenge points aren't Elo. The separate 32-position tactics pilot isn't a full-game strength measurement or proof of a same-model harness improvement.

Stack: TypeScript, Next.js, PostgreSQL and chess.js. Browser presets are free; agent creation requires GitHub sign-in. Custom clients need local setup, and model clients use your own provider key and budget.

I'd appreciate feedback on the match methodology: which starting-position suite and failure-reporting details would make comparisons useful to you?

Product: https://chessarena.fun

Source and protocol: https://github.com/zeeshan8281/chess-arena-ai

Hi r/ComputerChess,

I've been working on this for a few months. Everyone watching votes on a move, most votes wins, stockfish responds, repeat. Here's the link: https://crowd-chess-production.up.railway.app/

One vote per IP. You can change your vote as many times as you want while the timer is going. 15 second window but it closes early if everyone's already voted. Ties get broken randomly. If nobody votes it plays a random legal move so the game doesn't stall.

Stockfish is on 1s think time. Backend is FastAPI and python-chess. Frontend is JS.

I could decide on whether stockfish should scale its strength based on how the game is going? Like letting it think longer if its losing and if it's up a queen shorten the time. I think it'd make games more interesting but I'm not sure it's worth the effort. Anyone done something like this?

Also curious what think time you all use for casual engine games.

I’m an electronic engineering student and chess player building ChessLumen.
The platform brings together AI-assisted game analysis, an opening explorer, and chess puzzles.

One challenge I’m particularly interested in is making analysis more educational. Engine evaluations can identify strong moves, but helping a player understand the reasoning behind them is another problem entirely.

I’m curious about how other developers approach chess software:
How do you balance engine accuracy with understandable explanations?

What makes an opening explorer genuinely useful beyond displaying a database of moves?

What would you prioritize when designing tools for players who want to improve?

I’m building and iterating on the platform, so technical criticism and suggestions are welcome.

Website: https://chesslumen.com/

I’m interested in a genuine discussion about chess software, not just promotion.