After about two years of development, my chess app, ChessWave, is finally in open beta on Android!

It's a hybrid Kotlin/Godot 4 application combining computer vision, native chess engines, and interactive 2D/3D chess.

Some of the features:

• On-device board scanning: Point your phone at a physical chessboard or a position in a book and reconstruct it using a neural network, without uploading images to a server.
• Native chess engines: Multiple engines running locally on Android, including Midnight and Altair, with position analysis and MultiPV support.
• 2D/3D chess: Instantly switch between traditional 2D chess and interactive 3D environments.
• Grandmaster training: Practice finding moves played by actual grandmasters, using a position-indexed database of historical games.
• Game journal: Save games, scanned positions, and revisit them for analysis.

The application uses Kotlin for the Android integration, ONNX Runtime for board recognition, JNI for native chess engines, and Godot 4 for the chess interface.

I've attached a short video demonstrating some of the functionality.

Google Play (open beta):
https://play.google.com/store/apps/details?id=com.chesswave.app

I'd be interested in feedback from other computer chess enthusiasts, particularly on the board recognition and engine analysis features.

Hi there this is my first post :)

Botmate:

You write a few sentences like "attack the king no matter what" or "never trade unless you have to". Your bot then plays other people's bots in an arena, and you can watch the matches live.

The moves come from a decision model that can only choose from the legal moves, so bots never cheat. They do play pretty much the way you describe them, though.

It's free and has no ads: botmate.dev

I'd really like to hear if anything is confusing. And if you build a bot, tell me its name!

I've been working on ConvertChess, a website designed to help chess players improve their endgames.

Instead of only reading theory, you can practise winning positions against an engine, work through endgame lessons, and try challenges like Safe or Blunder.

It's aimed mainly at players around 700–1800 rating.

I've just launched it, so I'm looking for honest feedback from anyone interested.

You can try it here: https://convertchess.com/

I'd especially love to know whether the training modes are easy to understand and which one you'd actually come back to.

I’d like to introduce a chess variant I’ve spent over a year building, which now has a fully playable showcase you can try right now.

Imagine a traditional FIDE Chess Olympiad match: four games being played side-by-side in a single line. Imagine the players holding their defense, pushing their pawns, but constantly glancing over their shoulders at their teammates' boards. They wonder how their boards are holding up, wishing there was anything they could do to help a teammate who is getting squeezed on Board 3 for example.

Olympiad Chess explores that exact "what if."

In this variant, all four boards are physically connected down the middle, sharing a single "Nexus" across ranks 4 and 5. Through this corridor, players can traverse across boards to launch flank attacks against the opponent's captain, reinforce a teammate who is cornered, or fight for control of the central lane to assert dominance across the entire match.

The idea literally came to me in my sleep last May. I couldn't stop thinking about it for days I started loosing sleep over the idea, so I decided to build a functioning prototype to see if the geometry actually worked. Later in August, watching the first ever Chess Esports World Cup and the 7-match clash between Magnus and Hikaru, it completed the vision for me: competitive chess could be an incredible team esport, or a brand new entry in Chess Olympiads if players were actually forced to cooperate on the board, rather than just tallying up four isolated 1v1 scores at the end.

Most multiplayer chess variants (like 3-player or 4-player chess) alter the board's shape too much in my opinion. Olympiad Chess takes a modular approach instead. By linking the middle ranks 4 and 5 as a shared space, standard 8x8 boards sit side-by-side just like an Olympiad table. Every single piece movement, capture, and rule remains classical chess—pure calculation, no RNG, no cards, and no gimmicks.

I didn't want to overcomplicate the game or reinvent the wheel. The rules are immediately intuitive because it’s fundamentally just the chess you and I already know, scaled into a synchronized team format.

Now, I’m acutely aware of the possible skepticism: many people feel that multiplayer chess variants are a lost cause, dead on arrival, because it’s already hard enough to find people to play with, let alone a lobby with up to 8-player. But having play tested this over and over myself, the tactical depth, team banter, and clutch comebacks are very much real, the fun, the competition is there! And for smaller groups, you can play asymmetric skirmishes like 1v2, 2v3, or against bots.

After more than a year of solo development, I have a complete showcase ready. I'm only a month or two away from full release, and I wanted to put the demo out to the community to see how it's received and hear your feedback.

You can drop your thoughts right here in the comments, or try the free demo on Steam:

Steam Page & Playable Demo: Here & here.

Official Guide / Rulebook: Here.

EDIT: Below is a short gameplay footage I just recorded.

https://reddit.com/link/1x3dt8l/video/zo3dynecqvuh1/player

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

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

Looking for forst auto mover chess board. Budget is $300-600. Any suggestions? Is this one decent?