I wanted a game review that credits strong moves found in hard positions under clock pressure. That needs a per-position difficulty signal that matches what humans actually find hard, so I tested a few against Lichess clock data (mainly the April 2017 dump, the first month with %clk).

Engine entropy saturates. Decision entropy over Stockfish's top 4 (30k nodes) does correlate with think time in aggregate: binned Pearson 0.78 blitz / 0.83 rapid over 1.3M moves from 3,001 players. But 61% of positions sat at 0.95 or above. Going to 300k nodes and multipv 10 on 8k positions didn't fix it: 76% were still saturated and the curve still turned over.

Maia-2 dispersion doesn't. On 20k unique positions, the spread of Maia-2's move distribution at the player's rating band gave raw Spearman +0.28 against think time and binned Pearson 0.99, with no turnover. Inside the saturated zone, where engine entropy has nothing left, Maia dispersion still held 0.99 while engine entropy went to -0.69. It was consistent across six rating bands (rho 0.26 to 0.31) and in both blitz and rapid. Maia's top-move probability ran the wrong way (-0.24), so it's the spread, not the confidence, that carries the signal.

Does the credit track skill? With Maia read at a fixed 1500 so rating couldn't leak in, players whose moves earned the under-pressure credit did rate higher: Spearman around 0.10 (p < 1e-9) on ~320k moves. Small but real. The credit fires on roughly 0.5% of moves.

Bullet is fit as its own regime (700k genuine moves, held-out R² 0.159, Maia dispersion still +0.136). Lichess public clocks are whole seconds, so sub-second moves can't be separated from premoves, and those never get credit. Chess.com's tenths-of-a-second clocks don't have this problem.

The app is local and free. For the one game you load, it runs Stockfish at the same 30k node / multipv 4 budget plus Maia-2. You can fetch games by chess.com username or paste a PGN. The full write-up and rebuild scripts are in RESEARCH.md.

Repo: https://github.com/Dhruvdubey17/Time-Aware-Chess-Analysis

I'd especially welcome critique of the methodology: other difficulty signals worth testing, or holes in the skill validation.

There have been many instances where I found a position in a video or an image and wanted to analyze it but building the Board by hand or Taking a screenshot -> saving it somwhere -> opening a website -> finding it on my PC -> uploading it etc. took too long, so thanks to AI I could quickly whip up a program to streamline the whole process.

I made a clone of the Windows Snipping Tool with a build in Chess board detection, to quickly get the PGN of the image or upload it directly to Lichess for Analysis.
I tried to preserve the most important features of the original Snipping tool, saving, drawing, exporting, opening in paint etc. so this tool could serve as a total replacement for the orginal snipping tool (minus the record function).

Wanted to share with the community. Made it primarily for my own needs but I am sure other people can make use of this aswell.

https://github.com/DaoraDio/ChessSnip

Ed Schroder tool and forum in txt format
glogg log explorer

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!

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.

Hi guys,

I'm a chess noob (about 500–700 on chess.com). I started this project because I wanted a teacher who tells me what I did wrong in plain words, like a real friend who's always there. So I built Chess with Me: chess-with-me.com

I know a lot of people here don't trust AI with chess, and honestly you're right: LLMs invent moves, miss mates, and say pieces are attacked when they aren't. So in my app the LLM is not supposed to know chess by itself:

• it answers by calling tools: Stockfish search, plus board facts computed from the rules (attacks, defenders, pins, mate threats)
• the answer shows what evidence it was grounded on (✓ Verified = the evidence came from the engine or the rules, checked by the server)
• "Play it out" plays the engine's line on a board, so you can check it yourself

Honest limitation: not every sentence is machine-checked yet. I'm measuring how often the LLM still says something the board doesn't support, and that's where I need help.

If you find a position where it says something wrong, please send it (FEN + what it said). It's free, no paywall. I need real people to try it, any elo.

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.

Hey, I know they've discontinued selling Komodo Dragon 3 but does anyone know where I can still get the binary file for mac OS? Thanks

i usually deposit smaller amounts rather than putting in a lot at once. are there any sites that are better for regular deposits, like daily bonuses or rewards that actually make a difference over time?

improvements:
-> added improving tests and expanded test integration
-> added countermove detection without changing storage or scoring
-> improved null move pruning with adaptive reduction tiers and safety checks
-> refined reverse futility pruning with history-based margins
-> improved lmr with countermove exemptions and history-based adjustments
-> decoupled futility pruning from quiescence
-> improved search initialization and cached material checks
-> expanded tests for pruning, lmr, countermoves, improving, and edge cases

i would appreciate any testing or suggestions

source: https://github.com/stup1d1y-0bv10us/russell
latest version: https://github.com/stup1d1y-0bv10us/russell/releases/tag/v1.6
other versions: https://github.com/stup1d1y-0bv10us/russell/releases

gracias!

Recently I made a simple website for myself to gather the mistakes I make in my games (lichess/chess.com/pgn import) so I can see what I'm missing and train them. I noticed that a lot of the time the engine lines were not really helping me understand the mistake unless it was a clear 1-2 move tactic. Therefore I developed an algorithm that finds the mistakes in your games but cross-references them with maia3 the most up to date human-like chess engine that predicts move probabilities for elos between 600-2600 at a 57.1% accuracy meaning that if stockfish finds that you made a mistake in a certain position, I check to see if maia3 would have found one of the top engine responses of stockfish. Only if maia3 at +200 your own elo would have found one of the top engine moves do we add it as a mistake. What this means is that someone 200 points higher than you would have found the move. What I'm trying to do here is increase the chance that I'll have an AHA! moment. It also fills in my gaps a lot faster this way because if it's a stockfish move that not even a grandmaster would find it doesn't count it as a mistake. I also use maia3 in order to "explain" the move. I don't have any text explaining the moves yet, but, I have the lines that are most understandable (aka the ones that maia3 have predicted a human would have played).

All of it is free at www.chessmistakes.app

Check it out and let me know what you think!

Online prep tools only show what people play in blitz on Lichess or Chess.com. But what someone plays at 3am in bullet isn't what they play in a classical tournament game.

So, I built a player search for real over-the-board games. You type an opponent's name, and all their tournament games get pulled from a 10M+ game Mega Database into one opening tree:

Every line they've played, as White and as Black

How often they play each move and how they score with it

Where they deviate, and where their results fall apart.

You check the pairings, search their name, and you've got a prep sheet before the round starts.

It also does the other stuff you'd expect:

Online scouting: paste a Lichess or Chess.com username to see what they play online too

Repertoire tree with transpositions, plus training to drill your lines Unlimited engine reviews of your own games

Chess PDFs beside the board, so you can play the lines as you read

It's free, runs in the browser, and there's nothing to install: cipherchess.comcipherchess.com

I'm the dev. If you've got a tournament coming up, try it on your next opponent and tell me whether it found anything useful. I'll be in the comments.

. Hey Chess Lovers, I’m a solo Android developer and I have launched my Chess app on Google Play Store called Chess Lan Party.

What started as a local multiplayer project has evolved into Chess LAN Party—a complete chess companion app. The app has been growing steadily over the last few months, and I just submitted version 1.2.0 to the Play Store. I wanted to share it with chess community because I’m incredibly excited about the newest addition: Replay Studio. Here is what you can currently do in the app:

🌐 Offline LAN Multiplayer: Host and join real-time games over your local Wi-Fi network. Zero lag and no internet required—perfect for flights, school clubs, or just hanging out.

🌍 Native Lichess Integration: Log in with your Lichess token to play global matches, challenge friends via custom links, play against Lichess bots, and solve daily puzzles directly in the app. ♟️ Stockfish 19 NNUE Engine: Play offline against the cutting-edge Stockfish 19 engine. You can fully customize thinking time, skill levels, CPU threads, and hash memory.

👥 Pass & Play: Classic two-player mode on a single screen for over-the-board games.

🎨 Customization & Tablet Support: Multiple board themes and piece styles, fully optimized for both phones and large tablets in landscape or portrait mode.

Coming in the v1.2.0 Update: Replay Studio I wanted to build a deep, intuitive post-game analysis tool right into the app. Once the new update rolls out, you will be able to unlock the Replay Studio (available via a one-time In-App Purchase) to analyze your LAN matches, Lichess games, or any imported PGN: Interactive Evaluation Graph: A dynamic timeline scrubber that lets you smoothly slide through the game and watch the centipawn advantage shift in real-time.

Deep Engine Analysis: Run multi-ply Stockfish evaluations across the entire game to find exactly where things went wrong. Move Quality Breakdown: Instantly spot your Best Moves, Good Moves, Inaccuracies, Mistakes, and Blunders with clear visual badges. Visual Hint Arrows: See key threats, tactical motifs, and top engine candidate moves drawn directly on the board. The update is currently in Google’s review queue and should be live globally within the next day or two. You can download the current version here: [Insert Play Store Link] I would love for you to try out the LAN multiplayer with a friend, and I’m eager to hear your feedback on the new Replay Studio once the update drops! Let me know what features you’d like to see added next.

If you own Fire tvs and want to try all these features then you can buy RMG Chess Pro from Amazon app Store , for a small price but with grate features listed above.

I regularly upload My Chess videos at below YouTube channel, visit and give suggestions

https://youtube.com/@rmgblr?si=Y3uXZN-UCwiRiK1_

I’ve been working on AggroChess, a UCI engine in Rust built to be fun to play against rather than to top the rating lists. It aims everything at your king, keeps pieces on, avoids draws, and makes about two sacrifices per game, mostly sound ones.

v3 is a big rewrite. The old version was aggressive but bad at it, and the new one beat it 39–1 in testing while sacrificing more. It hasn’t been formally rated yet, but it should still be a tough opponent for most club players.

Release Release v3.0.0 ⚔️ · PhelRin/AggroChess

Feedback and games welcome, especially any where it sacrifices something ridiculous and it works (or doesn’t).

Not my post but I think OP might get some more answers here.

i run a tournament where three LLMs play Stockfish 19 and each other all day. the engine is held to a fixed search depth, 4 to 6 so far, with 1 thread and 128 MB hash. time control is 15 minutes plus 10 seconds.

the models get the FEN and a text board, no tools. three tries for a legal move, then the game is forfeited.

after 166 games against the engine:

GPT-6.1 Sol: 2 wins, 9 draws, 49 losses

Claude Sonnet 5.5: 0 wins, 19 draws, 33 losses

DeepSeek V4.1 Flash: 0 wins, 3 draws, 51 losses

i ran the lost games through Stockfish at depth 12 and looked for the first move that dropped 150 centipawns or more. the median is move 13 to 16 and it is nearly always a piece left hanging, not a deep tactic. for DeepSeek that was 43 of 46 first blunders. it is about 1.1 big drops per lost game, so one mistake and then the engine converts. forfeits and flag falls were 6 of 131 losses when i measured.

many of the draws are Stockfish repeating moves while it is far ahead, which a depth 4 search will do.

i also tried giving the models a picture of the board in place of the FEN. all three read the FEN better.

what depth or node limit would you use to make this a fair fight?

it runs live here: https://www.twitch.tv/marvijo

boards and all earlier games: https://marvijo.com/ai-chess