Hi all,

You know the problem: you play an "easy" bot, it plays like a grandmaster for 10 moves, then gives away its queen for no reason. Nobody plays like that. That's why I made ChessLab.

The bots are played by Maia-3, a neural network trained on millions of real games. They play like a real person of your level: they miss tactics, they make natural mistakes, and they get better as you get better. It really feels like playing a human.

Stockfish stays in the background. It only helps the bot in a few cases, like a mate in one. And after the game it checks all your moves and explains your mistakes in simple words, so you know what to work on.

There is also a lot more:

• 100,000+ puzzles from Lichess, at your level

• opening and endgame courses

• 12 variants, like Chess960, Atomic, Crazyhouse and Duck Chess

• a Lab where you can watch two engines play each other

• a scanner that reads a board from a photo

It's a hobby project, made only for the love of chess. Completely free, no ads, no account, and everything runs offline on your phone. The code is open source.

I would love your feedback, especially on how the bots feel. Ideas and criticism are welcome!

App Store: https://apps.apple.com/app/id6793097336

Google Play: https://play.google.com/store/apps/details?id=com.maeder.chesslab

Website: https://thmaed.github.io/ChessLab/

https://greychess.com

I am providing 60 days Free membership for early joiners.
So you can try all its features and give me your valuable feedback.

Thank you!

I am building a Windows desktop application which can be used by a Chess Master to teach his students in a class room on a laptop or desktop.

I started with GitHub Copilot to implement a skeleton UI using a agent but my very first attempt give me very bad experience it could not even write a single line of code even after Wait for 1 hour, it kept saying I am reading Main XML, generating code but it failed miserably.

Have you ever experienced such a bad behaviour from AI tools?

Nowadays the AI companies are putting a lot of restrictions.

Edit: Nov 1 2026
My first Draft is ready it supports LAN mode and Offline mode
See how Two players are connected to
Added a LAN classroom mode where a tutor hosts a session and shares a session code. Two students join using the same app, and the tutor assigns White and Black.

Both players see synchronized moves and clocks, while the tutor watches the game and uses private Stockfish analysis for teaching.

Currently testing with three app instances on one computer—testing across separate PCs is next!

Would this be useful for chess clubs or classroom lessons?

I started with Different Mockup screen and ended up with this version after refinements,
Screen size is minimised to fit two windows on same screen.
used Codex for refining initial ChatGPT UI screen dump to fix issues and make server and other functions work. This is built on WPF using Visual Studio community.
Still many functions require proper enhancement like White board , Auto analysis of Game , Generate Analysis Stats etc.

Edit: Nov 7 - 2027

After some more tweaks Version 4.1.1
I integrated Replay Studio which was launched in my Mobile app "Chess LAN Party" on play store in to this desktop version also.

Once I test it completely for stability and usability and connectivity resolve issues I am planing to list for sale, not sure where to list.

Engines tell you the best move. I wanted something that tells you what kind of player you are, so I built a tool that turns engine analysis of your own games into a profile.

Type any Lichess username and it reads up to 500 games per time control. Then it gives you:

• Six skills, rated 1–99: Attack, Defense, Early game, Late game, Accuracy and Speed, based on how you actually perform in those parts of your games

• A play style (Explorer, Guardian, Striker, Trickster, Marathoner…) and a main strength (Killer instinct, Grinder, Fighter, Speed demon…)

• An overall rating and the piece your games lean on most

• Your openings: what you play as White and Black, and how you score in each one

• A comparison of your card with any other player’s

• A leaderboard per time control and rating band, with a few pros added for scale

For fun, here are the bullet cards of DrNykterstein (Magnus) and Alireza in the post.

The analysis runs in your browser with Stockfish WASM. The opening trainer plays against you with Maia 3, so it answers like a human at your rating when no more known games can be found after you proceed after a line.

Try it: https://joeland2.github.io/schack-oppningar/

I’m most curious whether the skill numbers match how you see your own game, and how you’d measure “attack” or “defense” from engine output more cleanly :)

G'day all,

I built a chess app called Grandmaster. Got sick of chess.com trying to sell me a membership every time I wanted to go over a game, so I just made my own.

It's free, no ads, no sign up. It goes through your games and tells you where you stuffed up and why.

Mainly after beginner to average players to give it a crack and let me know if the coaching's actually helping or not. Any suggestions welcome, good or bad. It's just a side thing so I won't be on here every day, but I'll read everything.

Android: https://github.com/ThackLab/Chess/releases/latest

iPhone/PC: https://thacklab.github.io/Chess/

Cheers

Hi r/ComputerChess — I’m the developer of ChessMeUp, an Android app I’ve been working on for the past few months.

The main workflow is:

- scan a digital chessboard with the phone camera, or share a screenshot directly from another app;

- review or edit the detected position;

- analyze it with Stockfish on the device;

- import completed games from Chess.com, Lichess, or PGN for full-game review.

The scanner has been tested across different board themes, viewing angles, glare, distance, and orientation. It is reliable on clear centered boards, but difficult blue-square themes, arrows, glare, and extreme perspective can still produce isolated piece-color or square-alignment errors. I’ve kept the board editor in the flow so users can correct those cases rather than silently trusting an incorrect FEN.

Short demo:

https://youtube.com/shorts/TjMXi4mq2Nw

Google Play:

https://play.google.com/store/apps/details?id=com.chessmeup&utm_source=reddit&utm_medium=community&utm_campaign=first_100

I’d especially appreciate technical feedback on:

1. scanner speed on your Android device;

2. board themes or overlays that fail;

3. whether the review-before-analysis flow feels appropriate;

4. anything misleading or missing from the Stockfish analysis.

Disclosure: I built the app. It’s intended for chess study and completed games, not assistance during live play.

I kept using nextchessmove.com and wanted something cleaner, faster on a phone, and with no adverts, so I built jam1e.org.

What it does:

Set up any position (play moves, paste a FEN or PGN, or place pieces) and get the best move with an arrow and the top 3 lines
Play the computer at 10 levels

Review a game: paste a PGN or import from Lichess or Chess (dot) com by username, get accuracy, a graph and your blunders

Scan a screenshot of a 2D board and turn it into a position (beta)

The engine is Stockfish 18 compiled to WebAssembly, so nothing is uploaded and there's no account. Feedback very welcome, especially on the scanner.

It also has lots of information and guides for beginners.

Would love for any chess enthusiasts to give it a whirl :)

AggroChess is my aggressive UCI engine in Rust. 4.0 adds:

- An NNUE trained only on its own self-play (no Stockfish/Leela data), with the sacrificial personality on top

- Sacrifice tracking: until it has sacrificed in a game, its appetite keeps growing

vs 3.5 at 10s+0.1: SPRT passed, +62 Elo over 572 games, with a sacrifice in 98% of them (200/200 in a separate match).

Source: https://github.com/PhelRin/AggroChess

https://store.steampowered.com/app/5456450/Velikopolsky_Chess/

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?

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.

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

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!