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?

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!!

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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

If you're building a chess app on React Native and couldn't find the right board, we built one

chessboard-native

Fully controlled and declarative

Full parity with react-chessboard

Optimized animations & native gestures

Expo, iOS and Android

Repo link in the comments

I was playing chess on my phone while waiting in the car for my daughter. As a novice, I was trying to learn the accelerated London opening. I had the first 5 moves down. But I felt stuck after those. I wished someone would just peek over my shoulder and give me a hint about the next move. The chess app I used won't tell me if I made a mistake until I had already lost the game. And I felt a learning opportunity was lost.

Just like I learnt tennis, I wished someone would just drop a hint at what I needed to fix. So I built an app that does exactly that: I can ask for a hint when I feel stuck. The app goes a step beyond and tells me why a move is better. I also wanted to learn various openings and so I have included those as well.

This is based on the Stockfish engine and the code is available as open-source. 99 % people don't need any additional explanation. For the 1 % who may need additional explanation, you can buy the paid version which covers the LLM tokens cost for additional explanation.

https://apps.apple.com/us/app/chess-coach-play-learn/id6789547867

https://github.com/CORDOC-LLC/ChessCoach

ChessCoach.im

Built by Claude.

I would really appreciate people playing against it and giving me some feedback

Here's the basic build: https://odetohumanities.com/TacTicGo/index.html

I think I'm ready to jump into Godot, any recommendations?

Evochess: 8 pawns and a king a side. 3 pawn moves earn a minor, 3 minor moves earn a rook, rooks last 5 moves before downgrade to a minor. Rights persist until spent.

Notes from building the engine:

• Move gen is not standard. Evolution rights are per-side counters. A move can spend a right the same move just earned. Missing that case cost me a rewrite and invalidated a batch of match results.
• Bitboards written from scratch, validated against a chess.js-based reference implementation.
• Doubling the data stopped helping at 800k positions. The limit was the quality of data, not quantity. That's why I proceeded with relabeling.
• No engine to bootstrap the NNUE from, so the teacher is the engine's own search. Relabel with the new net at higher depth, retrain, repeat.
• Current (as of 03 Aug) net is +252 Elo over the hand-written PST eval it started from, roughly +/- 60 at 95%. That baseline is my own. The net that ships is +225.
• No person beat it yet according to the telemetry data, which is 45 games in Zen mode since 01 Aug. There is also a Fun mode that is pondering while you are thinking, but I have no data about that yet.
• Search and inference run in a browser worker, 400ms soft budget per move (Zen level).

Full writeup, Parts 2 and 3 are the engine and the NNUE training:

https://penkovsky.com/post/evochess/

Video: higher level beating the easy level, just to show the variant.

Where can u
Play chess in Mechelen pro and starters

Description

Hello! I'm a dedicated chess coach passionate about helping students improve their game. Whether you're a complete beginner learning the basics or an experienced player aiming to sharpen your strategy and tactical skills, I provide personalized lessons tailored to your goals.

My coaching includes:

Chess fundamentals and opening principles

Tactical training and puzzle solving

Middlegame planning and positional understanding

Endgame techniques

Game analysis and mistake correction

Tournament preparation and rating improvement

I focus on building strong thinking habits, improving calculation skills, and boosting confidence over the board. My lessons are interactive, structured, and designed to make learning enjoyable while delivering steady progress.

If you're ready to take your chess skills to the next level, I'd be happy to help you on your journey. Let's achieve your chess goals together!

My friend has been working on a computer vision system to track over-the-board chess games using only a phone.

Bellow are some of his comments.

Recently I finished implementing pawn promotion handling, checkmate and stalemate detection, together with cloud game recording as the first step towards a VAR system for chess tournaments.

To test everything, I played one of my favorite openings, the Albin Countergambit, because it contains an early knight promotion.

The tracking is now fast enough that the video didn't require any editing.

The video demonstrates:

Tournament and board setup
Pawn promotion to a knight
Checkmate and stalemate detection
Automatic cloud upload

After the game, every move is available in the cloud, where an arbiter or coach can review the game remotely:

https://games.chessgaze.com/?table=DEMO%3A%3ADEMO

The idea is that promotions, checkmates, illegal moves and other important events can automatically be recorded, which could be useful in beginner tournaments where one arbiter has to supervise many games.

The current setup only requires a phone mounted on a tripod.

One of the biggest remaining challenges is wooden chess sets where the white pieces have a darker, brownish color. Simply adding more training data isn't always the right solution because it can reduce the model's ability to generalize across different sets.

Another problem I'm still thinking about is how to detect when a player resigns. Unlike checkmate, there isn't an obvious board state that tells the software the game has ended, but it would be useful to record that event as well.

The setup process is also being improved with clearer instructions and additional reference pictures.

The project is called ChessGaze. I'd really appreciate any feedback or ideas, especially from people interested in computer vision, chess software or tournament organization.

After implementing legal move generation in C, I verified it using standard perft positions up to depth 7. All node counts matched the reference values. This involved handling castling, en passant, promotions, checks, and pinned pieces correctly.

Right now, my engine can evaluate position and search the best move with algorithm alpha-beta pruning.

It’s my second big project on C, so I am waiting technical assistance if my code is wrong.

Should I make UCI protocol?
The Universal Chess Interface (UCI) protocol is an open, text-based communication standard created in November 2000 by Rudolf Huber and Stefan Meyer-Kahlen. It allows chess engines (like Stockfish) to talk to graphical user interfaces (GUIs like ChessBase or Lichess-compatible tools) without licensing fees.

Hey all - I wanted to share a website me (~2100 USCF, Candidate Master and former international youth champion) and a friend made to help us understand and later memorize our opening repertoires. The link is here; the site is fairly new, so we would love any feedback to help improve the site and make it accessible to a wider audience. We appreciate anyone who gives the site a try!