Take a Look of the full proyect at Printables

After playing chess for quite some time now, I wanted to get started learning openings. However, I couldn't find a tool that worked how I wanted.

I wanted to visualize them as a branch tree, and learn them through flashcard-style training.

That is why I used codex and after I don't know how many iterations I created an app for that!

Furthermore, I wanted to get started mantaining my first github repo, to see how it is.

And it grew into a much bigger project with generating openings from scratch using Stockfish, analysis of your own games (as chesscom only lets you analyse one game each day), automated puzzle generation and several custom chess game modes.

A few days ago, I made it open-source and free for anybody to use (on Windows) and contribute.

The video shows some of the features I have implemented so far.

I would love to hear any feedback, as I am planning to add new features (like queing opening generation jobs so you can run them overnight). :)

GitHub: https://github.com/AdrianSomething/chess-trainer

Hey Chess people!

I am a solo indie dev and have been working on a little project called Chess Longer

It explores the idea of linking multiple chess boards together and allowing pieces to cross boundaries (up to 99 boards at once).

I also added some experimental modes with 4 armies in a cross pattern or 6 armies around a 3D cube. If you want a free Steam key, just reply and I will send you a DM. Leave a review or some feedback.

https://store.steampowered.com/app/5311800/Chess_Longer/ <-- If you want to support me, buy a copy!

So its taken 4 years to put this together and it has training on for openings, traps, gambits , play through grandmaster games and bots , play online etc. i have had an ok uptake but not to the extent I thought people would join.if anyone is willing to give feedback by using the site i would be grateful.im not sure its correct to put the website on here so message if you want to check it out for me.
ok ive seen others since i posted and it seems ok to share the link its foxychess.com

grandmaster games

traps

openings

play foxy

calculation

play bots

And more etc

Guys if you log in with google or apple you will have the full site to check over and use. Im particularly interested in knowing if the foxy character that you can play that isnt a stockfish bot but just is my engine using all the traps , gambits mating patterns is useful and enjoyable to play as he is supposed to be ( he is supposed to be like a sharp club player , who may make a few mistakes as traps and gambits are not optimal and thats why standard bots like my others and other sites use stockfish etc wont play most of them) and the calculation trainer I have developed to help people train themselves to find their ipponents moves and theirs as a habit -Thanks againfoxychess.com

This is totally FREE FREE to review guys just a log in google or apple . FREE ONLINE AND 3x BOTS PLUS TRAPS AND MORE . THATS FOR AS LONG AS YOU WANT AN ACCOUNT. The full site is available for a small charge but seriously theres enough on it without paying if youre not interested in learning openings , gambits endgames and just want to have fun !

A streamer I know played it recently and beat it. Going through the game, the problem was obvious: it went into a queenless position on move 7 because of one of its own book lines, then kept "attacking" a king that was perfectly safe. A Tal bot with no queens on the board is just a bad endgame player.

So v3.4 is about keeping pieces on:

• Trades cost it twice as much as before, and queens coming off costs it about 1.4 pawns. When it's behind in material it hates trading even more, all the way into the endgame, because a losing swindler wants as many pieces on as possible.

• Book fixes: a few lines (Urusov, Danish, Blackmar-Diemer, Albin and some others) were steering into early queen trades. They now keep the queens on.

• Three new dubious-but-dangerous gambits: the Halloween Gambit (4.Nxe5!? in the Four Knights), the Latvian (2...f5!?) and the Englund (1.d4 e5!?, including the Qc1# trap). Objectively bad, but they set real problems if you don't know them.

Result: 50 games against v3.3 (10s+0.1s, 25 openings from both sides) went 28 wins, 8 draws, 14 losses: 64%, about +100 Elo (error bars +13 to +201). It got more aggressive and stronger, which I didn't expect.

Also, thanks to u/TimothyTaylors for the feedback on my last post. You were right that the sacrifice sound gave the trap away, so it's gone. The site version now also thinks on your time (pondering).

It still finishes with sacrificial mates when it has a forced one, and the swindle mode from v3.3 is still there (UCI option "Swindle").

GitHub (exe in the releases): https://github.com/PhelRin/AggroChess

Feedback welcome, especially if you try the gambits against it.

Hi! I've been working on a janggi engine called sinsan (神算) for a while now.(throwaway account cause my usual interest is very unrelated to this kinda stuff)

It's roughly alphazero-style, using a transformer network and MCTS, though I started with supervised learning from fairy-stockfish rather than pure self-play from scratch.

Janggi is korean chess, pretty closely related to xiangqi. I was a little surprised by how little work there seems to be on modern neural engines for it compared to chess, go, or even shogi, so I figured I'd try making one myself.

The initial training used roughly 50 million positions from 284K full fairy-stockfish NNUE games. I implemented the rules and search in rust, with batched PUCT and gumbel search for self-play. The rules were also tested against pyffish across 845K positions with no mismatches, mostly because janggi has some rather annoying edge cases involving bikjang, passing, and game endings.

I initially tried both a katago-style CNN and a transformer with smolgen-style attention biases, somewhat inspired by lc0. The transformer did considerably better, despite being slower to train. The 34.9M transformer reached 41.2% policy top-1 accuracy versus 37.7% for the 17.4M CNN, and the difference became even more apparent in actual search matches. It also won when compared at equal training time, which I found pretty interesting.

Eventually I scaled it up to a 60.4M transformer with 12 layers and 16 heads.

Then came self-play, which was... less cooperative.

The first RL iteration actually made the engine 217 Elo weaker than its supervised parent. I ended up reworking the training loop to use continuous gumbel self-play, mixing newer positions with the original teacher data, and only promoting checkpoints after they passed head-to-head tests.

That seems to have worked. The supervised model was around 2250 on my internal fairy-stockfish ladder, and later RL checkpoints reached roughly 2500. For reference, the ladder uses FSF NNUE at 1K nodes as its zero point, so these aren't human Elo ratings.

Something else I found interesting was that improvements at lower search visits were often barely noticeable, while the same checkpoints performed significantly better with more visits. I'm still trying to figure out how much of that comes from the network itself versus its interaction with search.

I also wanted something people could actually play without needing a GPU, so I trained a smaller 10.7M transformer using a mixture of teacher data and self-play positions. It gets surprisingly close to the larger models in raw policy accuracy, though obviously not in playing strength.

The smaller network was exported to INT8 ONNX at around 12 MB, with the rust search compiled to WASM for browser play. It manages around 350 visits/s in multithreaded chromium, and I couldn't measure any meaningful strength loss from quantization.

I've also put this smaller model on pychess as a bot, running the pytorch checkpoint with up to 400 search visits per move.

You can challenge it here, no account needed:

https://www.pychess.org/@/sin-san

Select challenge -> janggi.

I'm estimating its human strength at somewhere around amateur 1-dan, but that's mostly a guess until I get enough games against actual players. The pychess bot doesn't receive its own rating, so I'm collecting results against rated opponents instead.

I'm also reviewing those games with the full GPU model to find positions where the smaller network makes particularly bad decisions. Might be useful for another round of training.

The project is open source under VectorSophie/sinsan on github, with the current rebuild merged into master.

At this point I'm mostly curious about the tradeoff between network size, search, and actual playing strength. The transformer seems like the better choice at larger scales, but whether that's still true for much smaller networks is another question. I'm also not entirely convinced throwing more self-play at it is the best way forward.

Would be interested in hearing from anyone who's experimented with similar architectures, compression, or search optimizations, especially for xiangqi or other chess variants.

Ever wondered if an online chess opponent who seemed suspicious was eventually caught cheating?

I built ChessCheaters.com to find out. Enter your Chess.com and/or Lichess username, and it scans your game history for opponents whose accounts were banned for fair-play violations or closed, with dates and links to the games.

100% free. No ads. No sign-up. Runs in your browser. You can easily share your results on social media too.

It's brand new, so feedback on bugs, features, or anything unclear is very welcome.

Thanks!

— François

Hello chess enthusiasts! Chess.com is amazing for providing all the data for all the games happening on the platform, so I had an idea and now need honest feedback on it.

Online fantasy chess - https://sovereign-gambit.com/

Let me know if "How it works" is enough to explain it all, how interesting you find observing the Cup, how easy and fun is to create and play the Leagues, and if Duel is a cool feature for 1v1 against friends.

Hope this is not considered spam or self-promotion; it's completely free, and I would just be happy not playing only bots all the time :)) If some traction - I'll be sure to continue working on it.

Cheers!

I'm developing a chess platform aimed at a relatively small niche: people who love chess variants, logic puzzles, and RPG-style progression.

The foundation is chess, including 2-, 3-, and 4-player games with modifiers like Fog of War, Decay, Diplomacy, and Prisoner Exchange.

One variant, called Intercept, combines cryptography with chess. Players compete to decode a secret transmission represented by chess pieces on a 160-square board. Successful solvers earn places in four-player chess battles, with solving order influencing army selection.

But here's the bigger concept:

Every chess game contributes to an ongoing treasure hunt.

Before each match, players see a partially completed treasure map made of jigsaw pieces. Playing games earns pieces, with greater rewards for more complex variants and better match placements. Players can collect and trade pieces with others.

Complete enough of the map (at least 90%), and a hidden X is revealed, potentially leading to a real-world prize.

Scheduled tournaments would award larger collections of puzzle pieces, and the long-term vision includes RPG-style chess characters and a broader explorable world.

I'm not trying to build another Chess.com. I'm interested in creating something that a smaller community of chess, puzzle, and RPG enthusiasts might genuinely love.

My questions:

• Would this combination actually appeal to you?
• Would collecting and trading treasure-map pieces motivate you to play more chess?
• Does this sound like an interesting evolution of chess variants, or too many unrelated mechanics competing for attention?

I'd especially appreciate critical feedback. I'm trying to figure out whether these ideas genuinely complement each other or whether I'm just combining things I personally enjoy.

AI chess game that gives AI commentary of Trash and Grandmaster commentary!

I’d love if you have any ideas to improvise on this.

I’ve released v3.2 of AggroChess, my aggressive UCI chess engine written in Rust. It’s built to be fun to play against, not to beat Stockfish.

What’s new:

• It now plans sacrifices a move or two ahead, and sacrifices in about 9 out of 10 games
• It hates trading pieces and avoids draws
• When it has a forced mate, it goes for the flashy one (queen sacs into mate)
• New built-in gambit repertoire: King’s Gambit, Evans, Smith-Morra, Blackmar-Diemer, Stafford, Albin, Benko, a three-pawn Scandinavian and more

It’s deliberately weaker than v3.0 against engines because most of its sacrifices are speculative, but it holds up well against people. It beats the 2500 chess.com bots and gets close against the 2800 ones.

Source + Windows exe: https://github.com/PhelRin/AggroChess

Happy to hear feedback, especially your best games against it.

I'm working on a cloud service for analyzing chess positions in bulk. You send a list of positions and choose the engine (Stockfish, Lc0, Fritz) and the settings, and you get evals and lines back without running any hardware yourself. You pay for the engine time you use.

Would something like this be useful to you? I'm curious:

What would you use it for?

Roughly how many positions would you need analyzed?

What would it need to have to replace your current setup?

"No, I'd never need that" helps too. Thanks!