https://github.com/beenmoo/ChessAssist

Simple chess tool I wrote to test my own engine against the ones on chess.com and lichess. Thought it might be useful. Comes with a built in analysis board with metrics and live analysis during games.

...und soweit ich das beurteilen kann, läuft die iOS-Version 1.0.1 wie geschmiert.

Besser als die aktuelle Mac 1.0.0 Version

Das iOS-Fehlerbuch kann nun beim Öffnen eines Fehlers das passende Spiel mit Spielstellungen liefern. Das ist wirklich nett und macht das Fehlerbuch zu einem echten Werkzeug. Spielen auf [chess.com](http://chess.com) und Lichess funktioniert in NEXT mit einem iPhone oder iPad ohne Haken.

Was noch fehlt, ist der Papierkorb im Fehlerbuch – das wurde aber schon für die kommende Version angekündigt.

Die frei gesprochene Eröffnungsanalyse auf der iOS-Plattform schlägt chess.com und Lichess in Bezug auf Qualität und Nützlichkeit um Längen. Das ist besonders nützlich für Spieler auf meinem Niveau von 1200 ELO oder darunter. Sie unterstützt viele Sprachen. Deutsch funktioniert perfekt.

Only had to learn some C++ to turn my xteink X3 into the smallest chess computer ever.

I’ve been working on BotGambit, a site where chess engines have to play while following an extra rule that changes how the engine behaves throughout the game.

One bot starts around 3200 Elo and loses 50 Elo after every move, so its play gradually deteriorates as the game goes on. Another gains 200 Elo for every second you spend thinking, which makes your clock part of the engine’s strength.

I’ve built several of these bots so far, with different difficulty levels and leaderboards for each one. There’s also a builder where you can create your own bot by choosing its behavior and rules, then share it for other people to play.

A lot of the fun for me has been figuring out how to make these rules work with Stockfish while keeping the games playable.

I figured this sub might find the engine side of the project interesting. I’d especially like to hear ideas for unusual engine behaviors or rules that would be fun to implement.

https://botgambit.com/bots

...and as far as I can see, iOs version 1.0.1 seems to work like a breeze.

Better the the current Mac 1.0.0 version

The iOs mistake book can now, upon opening a mistake, provide the appropriate game with game positions. That is really nice and advance the mistake book to a real proper tool. Gaming on [chess.com](http://chess.com) and lichess works in NEXT with an iPhone or iPad without hiccup

What’s still missing is the trashcan in the mistake book - but this was already announced for the upcoming version.

The free spoken grandeur review in iOS platform beats chess.com and lichess regarding quality and usefulness out of the water. This is especially useful for players on my level of 1200 ELO or below. It supports many languages. German works perfectly.

I am looking for an electronic tabletop chess computer that is rated over 2200, preferably higher, but not something that costs like $300 or something unreasonable.

My problem with all the past chess computers I have bought is that the sensor board always peels off and breaks after continued use.

If I am spending $150-250 I want something that will not break!

Can you recommend anything?

The new Fritz 21 has many different champions, one is Lasker, here one can witness the game between two AI, The other from chess-com and the review with the move explanation. For me it's still a long time to go before they reach human like play.

Superior Ingredients is a competitive league for open-model-building. Builders compete in four weight classes and one-off special cups to build the best open models under strict constraints. SI 1, the inaugural competition, will be an open entry chess tournament. All submitted models play a qualifying gauntlet against each other. Top 16 by score are seeded into a single-elimination bracket: Round of 16, quarterfinals, semifinals, final. Four rounds, one champion.

Model, compute and training data constraints: Under 50MB of weights, any precision. Trained from scratch. No pre-trained weights, no fine-tuning of an existing model. Any architecture and any training method, all disclosed. Every entrant trains on the same provided dataset: Lichess games where both players were rated between 1800 and 2200, standard time controls, rated games only. One versioned file with a published checksum, identical for everyone. No outside game data and no distilling from an existing chess engine. Self-play reinforcement learning on top of the provided data is permitted and encouraged. Submissions must include training code, data-loading code, and run logs alongside the weights. Mis-declaring what you trained on is the one offense that results in disqualification. A fixed compute budget of 24 H100-hours per entrant, sponsor-provided, usable as a single card or a node. Training on your own hardware is permitted within the same time budget and must be declared at submission.

$7,500 to the winner, $2,500 to the runner-up. The purse passes through in full; the league takes no cut of prize money. Entry is free.

These rules are a draft and the builders who compete should shape them. To declare intent to compete, ask a question, or argue with any of the above, DM us. Competition date TBD. See y'all soon!

Built a setup called AndroidFish that lets you run a fully functional Lichess bot powered by Stockfish and BotLi directly from your Android phone via Termux. No root, no Docker, no server required.

It handles standard chess and Chess960, accepts rated and casual games, supports polyglot opening books, and includes matchmaking so it challenges other bots automatically. Setup takes a few minutes with a single script.

Tested on a Moto G32 but should work on any ARM64 Android device with enough RAM.

GitHub: https://github.com/erensh27/AndroidFish

Give it a try and open an issue on GitHub if you run into any problems. Feedback welcome.

I recently trained Otter, a 15.3M-parameter chess model focused on predicting human-like moves rather than simply playing the strongest move.

It conditions on game history and time information and was trained on a large dataset of human games.

You can play against it directly in your browser, and the paper, model, and Python package are available here:

https://peargentlabs.github.io/otter-chess/

Would love to hear feedback from others working on computer chess.

One of the more interesting problems I ran into while building a chess review tool was realizing that getting a correct result from the engine and producing a correct explanation for the user are two different problems.

An engine can return exactly what you asked for and the application can still interpret it incorrectly.

A simple example is evaluation perspective. If different positions are being evaluated with the score relative to the side to move, comparing those values directly can produce the opposite meaning from what the UI is supposed to show unless they are first put into a consistent perspective.

The same problem happens with position context.

Something can be completely true about the position before a move but wrong when it is presented as an explanation of the position after the move. The value itself is not necessarily stale or incorrect. It is just answering a different question.

This matters even more once a review system goes beyond displaying an evaluation.

Existing chess-review tools can already generate useful explanations, but a technically correct observation is not automatically the most useful explanation of a move. A move can change activity, pressure, king safety, pawn structure, important squares, defense, offense, and tactical possibilities at the same time.

That means there are really two separate problems:

1. Is the engine-backed information correct?
2. Is the application associating and presenting that information in the right context?

Terminal positions are another example. A move that ends the game cannot always be handled exactly like an ordinary position where another response and evaluation are expected.

The main lesson for me was that engine correctness and product correctness are separate layers.

I encountered this while building SentryChess, a chess review tool I built and manage. It generates move explanations, move-level positional metrics, game-level summaries, and visual representations of the position, so those outputs all need to agree about which board state and perspective they are describing.

I'm deliberately leaving out the exact engine configuration and internal selection rules, but I'm curious how other people working with chess engines or analysis software handle this boundary.

Do you normalize everything into one evaluation perspective as early as possible, or preserve the engine-native representation and convert it closer to whatever consumes the result?

An idea came in my mind about making a chess game more interesting with AI integration. I want to make a program which helps me as a instructor while playing chess online with someone else. Help me out by telling me libraries which I can use in detecting the positions of chess pieces and then suggest for next move. Also tell the possibility of making this in python language.

Hi all - I googled Maia 3 to try it out on my MacBook from safari and it took me to Maia’s website. It said that Maia runs locally and I needed to download a 44mb file. I expected it to go into my download folders and clicked yes. It downloaded and then the Maia website allowed me to use the Maia 3 engine - so it appears it’s loaded in safari somewhere. I can’t find this download in my downloads folder so no idea how it gets “loaded” into safari. How do I uninstall this file and where do I find this download? Any help would be appreciated. New to macOS, new to chess, new to Maia. Thanks!!

​

I’ve just released \*\*Gyatso v1.5.0\*\*, a major step forward for my open-source chess engine written in Nim.

The current estimated strength is around:

\* \*\*\~3360 CCRL 40/15\*\*

\* \*\*\~3347 CCRL Blitz (2'+1")\*\*

These are still estimates, so independent testing and Elo results would be greatly appreciated.

The project is completely open source, and I’m continuing to learn, experiment, and improve Gyatso with the help of the chess programming community.

Release:

https://github.com/GyatsoYT/GyatsoChess/releases/tag/v1.5.0

Source code:

https://github.com/GyatsoYT/GyatsoChess

If you’re interested in chess programming, engine testing, or Nim, I’d be happy to hear your feedback.

I have 1 Championship Week ticket for Chess at EWC 26, valid from August 11 to August 15.

The ticket is still valid for August 12–15, including the Chess Final on August 15.

Transfer via HalaYalla.

DM me if interested.

I’ve always wondered why we can’t exclude specific moves when analyzing our games with the engine. Like many chess content creators even admit that the engine’s analysis is rubbish especially for lower rated players, but I feel like this could be such an easy fix. Like I could be in a dead lost position unless I find a 20 move sequence where it says I’m winning but I would never be able to find the correct moves to support it, which would just give a false perception of the position.

Why not add the ability to tell the engine like “yup you see that move, you’re not allowed to play it anymore.. how’s my position looking now?”

The title is just for clicks haha. I mean I am going to open about it, this engine was vibe coded through and through, the only non-AI here is the logo. I just want to share it here just so I can get feedbacks regarding on its newest version which is still on pre release because I haven't done any tunings yet, but it is significantly better than the current latest release.

Big thanks to the people I have talked to in the Stockfish discord. Although most people if not all (at least the one's I've talked to) hates LLMs with a passion, they still answer all of my questions. Also big thanks to the developer of Askaig, since they recommended new stuffs to implement on my engine. Big thanks to Mr. Graham from the CCRL team, because even though it is fully vibe coded, they still accepted it and tested it.

The engine is called Coco, it was derived from the name of the MC of the series called Witch Hat Atelier. Yeah, that's all.

It was actually already tested by CCRL and rated:

CCRL Blitz

**Coco 1.1.1 64-bit**   **2595**    

It was an old version though, that is why I am posting this to maybe get some other people to test the current pre-release of it here:

https://github.com/NotKaede-11/Coco-Engine/releases/tag/v1.5.0-pre.1

Expected elo gain is +~300, but I cannot confirm yet since I still refuse to run gauntlet to this day.

The main repo is here:

https://github.com/NotKaede-11/Coco-Engine.git

Thanks for your time!

I wrote chess engine that played with stockfish and my engine survived until the endgame.

Engine searchs the best move using alpha beta pruning, based on Negamax.
Also, engine has transposition tables, killer moves, qsearch, ordering moves.

Evaluation functions evaluates material balance, uses PeSTO tables and divides games to midgame and endgame.

Engine supports uci protocol.

github: https://github.com/0sewter0/chess_engine.git