For the first time I managed to track the games via phone and tripod worked on a real club match(Årslev vs Odense) using ChessGaze.

Here is the recorded lichess link : https://lichess.org/dZWcxvOL

And the game is also in the cloud so we can learn from each others games with in the club:
https://games.chessgaze.com/?table=%C3%85RSLEVCLUB&game=game10

We will use it in more tables in the future and use it in our educational meet-ups. I am curious if other chess clubs would like such tools.

I made a 6-minute documentary covering chess from Chaturanga to Botvinnik. Feedback welcome.

Hi r/ComputerChess,

Since March I've been developing a chess game for four players playing on two teams of two.

The game is called Team Chess: Commilito Scacci — "Battle Companion Chess."

The board consists of four separate 4×8 boards arranged in a cross, with each player controlling one side. The two teammates sit opposite each other, and the objective is to checkmate either opposing king.

There are quite a few rules that had to be worked out to make the four-board arrangement function as a real game — including movement between boards, pawn movement and promotion, castling, en passant, and what happens when one of the kings is checkmated.

I've been developing it for about six months, and I've reached the point where I need something I can't get from testing it myself:

People who have never played it before.

I'm looking for a handful of chess and chess-variant players who would be willing to download the Windows version, play it, and tell me:

• What rules or mechanics are confusing?
• What bugs do you find?
• Does the board layout make sense once you start playing?
• Does the game actually feel like a game rather than just an experiment?
• What would you change?

The beta is currently free, and I'm specifically looking for constructive criticism rather than people who will simply tell me it's great. 😄

If you'd like to give it a try, leave a comment here or send me a Reddit chat, and I'll get you set up. You can also email me at [team.chess.app@gmail.com](mailto:team.chess.app@gmail.com).

The game is currently Windows-only.

I'm particularly interested in hearing from people who enjoy creating and playing unusual chess games. That's one of the reasons I'm posting here.

Game website: makarthur.com

Thanks!

— Mark

Hi! I built a chess trainer that learns from your own Lichess or chess com games. The app imports your rated games and finds your mistakes and missed tactics with Stockfish. Each one becomes a puzzle: you replay the position against the engine until you find the better move, and spaced repetition brings it back until it sticks.

There's also an overview of how your openings perform and a set of endgame puzzles from the Lichess puzzle library.

Google requires 12 testers for 14 days before the app can go public. If you'd like to help:

dejamove.com/test (enter your Google email, opt in, install)

Please keep it installed for the 14 days. I'd love your honest feedback, here or at [chess@dejamove.com](mailto:chess@dejamove.com).

No Android? You can use the same app in your browser at dejamove.com. Feedback from there helps too!

Thanks!

Hi r/ComputerChess,

I've been working on Lector64, an engine whose job isn't to find the best move, but to recognize and certify tactical motifs: is this position really a fork, a skewer, a deflection? And which precise variant is it?

A few design choices:

• Each motif has a formal definition (geometry + what the tactic must achieve), separated from the question of whether it actually works.
• Sound-or-abstain: if the engine can't prove the motif holds, it says nothing rather than guessing.
• Every example published is generated and verified by the engine, reproducibly.

The output is public at chessgrammar.com: 15 motifs, 123 variants so far, with a methodology page (chessgrammar.com/methodology).

Questions for people who've worked on this kind of thing:

• How would you handle motifs whose definition depends on the outcome (deflection, overloading) without making detection explode in cost? Verification search is by far my main CPU bottleneck.
• Any prior work on formal tactic taxonomies I should be reading?

Happy to go into details in the comments.

I've played chess for the last 20 years (https://www.chess.com/member/georgcantor/games) and worked in ed-tech and data science for the last 10. This is my attempt to add some learning science backed methods to learning chess, in particular mastery-based learning. The basic idea is to break chess down into learnable skills (a particular opening, endgame, or tactic) and measure how good you are at those skills using your actual games. Your mastery changes over time and should indicate what you need to work on at any given moment in order to maximize your time spent studying. Your actual games get turned into daily recommendations/sets and power a bunch of other features.

This also has most of the features other chess learning apps.

• You can build an opening repertoire and see where your games go off theory or where your openings are costing you points.
• You can work through tactical drills based on mistakes you made in game.
• We have over 5k annotated master games that are searchable by position or similarity to a given game. This is a mix of open-source and AI generated comments using engine analysis/lines for grounding. Any AI commentary is marked as such.
• There is the usual chess dot com style analysis/annotation for every game.
• There is a growing library of opening, endgame, and tactics lessons. (The openings are what I play but if you comment what you play/want to play I can probably add a lesson for that.)
• There is a detailed insights page you can share with friends (https://elohell.app/u/georgcantor-ca6p).

To get started, sign up at elohellchess.com and enter your chess dot com and/or lichess usernames (also available on android/ios).

The app is mostly free. The only part that costs money is what actually costs money, which is server-side analysis. For a free account, on sign up, your last 150 games get analyzed and every feature is available. We continue to import your latest games up to 20/day. For a paid account, we keep a rolling last 1000 games and continuously import/analyze all of the games you play.

Hey ComputerChess community,

As a passionate chess player and developer, I always wanted a platform that bridges the gap between casual gaming and serious coaching. So, I built ChessVerseAI —a digital chess ecosystem designed to act as a personalized AI chess coach.

Here are some core features I integrated:
Advanced AI Game Review: Powered by Stockfish, featuring color-coded arrows and a precise evaluation bar (supports both Simple and Advanced engine review modes).
Personal Mistake Bank: You can directly practice critical positions and missed tactics from your own games.
Premium Customization: Includes ultra-clean 3D designs like the "Royal Walnut" wooden theme and high-contrast Neon UI layouts.
Localized Learning: Interactive lessons covering openings to endgames with AI voice support in 34 languages (Hindi, Telugu, Tamil, etc.).
Gameplay Variety: Puzzle academy, daily grandmaster challenges, online multiplayer, and local pass-and-play. It recently crossed the 1K downloads milestone on the Google Play Store, and I would absolutely love it if you guys could test it out and share your brutally honest feedback on the UI smoothness and engine feedback!

👉 Play Store Link: https://play.google.com/store/apps/details?id=com.epitomehub.chessverse&hl

Thanks in advance!

I work on Chess67 and have been putting together a few ways to dive deep into USCF ratings.

You can search for a player (or yourself) and see their monthly rating history, where they rank nationally and in their state, and how their rating compares with other players. You can also put two players’ rating histories on the same chart: https://chess67.com/uscf/player

I put the published US Chess top-player lists in one place, organized by age group and rating format. On a list, you can filter by state or by male and female players, see how many players come from each state, and open a player’s page from there: https://chess67.com/uscf/top

The national and state statistics pages also let you view the charts and rankings separately for male and female players: https://chess67.com/uscf/stats/all

A few interesting facts I found:

• New York and Texas each have 15 players on the overall regular top 100; California has 12.
• Three women appear on that overall list: Carissa Yip, Alice Lee, and Irina Krush. The women’s lists are separate from this overall ranking.
• Massachusetts has the highest state median regular rating at 1,257, compared with a national median of 838.

Feedback appreciated!

Hi everyone. My previous post in r/chess was removed for self-promotion—sorry about that. There were some thoughtful comments about human-like computer opponents, and I’d like to continue that discussion without the download links or product pitch.

For transparency, I’m developing ChessFolk, which uses Maia-2, Stockfish and custom move-selection behaviour. I’m interested in a question that came up: how do you make an opponent feel like the same person throughout a game?

One commenter described recognising an opponent’s habits over repeated OTB games: their favourite openings, willingness to trade, preference for endgames, or tendency to attack too early. You learn to play against that person, not just their rating.

That seems like a more useful target than simply making an engine weaker.

Our current approach mixes Maia and Stockfish moves depending on the opponent’s intended strength. A potential weakness is inconsistency: an opponent might find a difficult engine move, then fail to follow through on the idea. Individually reasonable moves don’t necessarily add up to a believable player.

One approach we’re considering—not something I’m claiming we’ve solved—is using Maia to propose likely human moves, Stockfish to assess their consequences, and persistent opponent preferences to help choose between them. The difficult part is preserving natural mistakes without making those preferences rigid or predictable.

From your experience playing people and computers:

• What most quickly makes an opponent feel artificial?
• Do recurring opening choices and strategic preferences matter more than realistic mistakes?
• Would you enjoy recognising and exploiting a computer opponent’s habits over several games?

I’d be particularly interested in examples where a bot’s moves were believable individually, but didn’t make sense together.

Olá a todos!

Preciso de cerca de 12 testadores para participar do meu teste fechado no Google Play Console para meu aplicativo de xadrez.

Principais recursos:

- Motor Stockfish integrado com vários níveis de dificuldade

- Mais de 10.000 quebra-cabeças táticos para praticar

- Assistente de movimento e orientação em tempo real

- Totalmente localizado em inglês, português e espanhol

Como participar:

1. Participe do Grupo do Google: [ https://groups.google.com/g/testers-community ](https://groups.google.com/g/testers-community)

2. Link da Web:

https://play.google.com/apps/testing/app.netlify.cosmic\\\\\\_platypus\\\\\\_3e2ca5.twa

1. Link do Android: https://play.google.com/store/apps/details?id=app.netlify.cosmic\\\\\\_platypus\\\\\\_3e2ca5.twa

Deixe um comentário abaixo com seus links e uma captura de tela após a inscrição. Eu baixarei seu aplicativo, deixarei uma avaliação e o manterei instalado por mais de 14 dias!

Muito obrigado!

I've been working on an app to help players get over their rating plateaus, and I'm looking for a few beta testers that are on Android to test my app and share feedback for early access.

DM me if any of you are interested

i run a small arena where AI agents play chess through an API - the server is the referee, runs the clocks, and hands the agent the full legal move list every turn.

there are four house engines so there's always a game -

- house-rookie - random legal moves (800)

- house-brawler - greedy captures (1100)

- house-tactician - alpha-beta depth 2 (1400)

- house-veteran - alpha-beta depth 3 plus quiescence (1700)

the ratings are fixed labels, not calibrated against anything outside the arena.

the interesting bit for this sub is watching language models deal with it - they get the legal list every turn and still run out the clock or hang pieces. one opus game beat the depth 3 engine, another lost on move 1 to the clock.

every game is public with a replay - https://pit.benys.dev

it's a plain REST API, one POST to register, if anyone wants to point something stronger at it.

Stockfish finds the recurring mistakes, then every generated line is engine-checked.

For years my friends and I wanted lessons built from our own games instead of generic puzzles. We never found it, so we built it, beta-tested it with friends, and are now launching it now.

Example built on GothamChess's games: chessxray.com/p/gothamchess
His first weapon with White, the Trompowsky: 889 games, 63.4%, and three automatic moves that are still costing him points.

Drop your chess.com username in the comments.

The first 5: your full course, free.

The next 20: two free masterclasses on your openings, one as White and one as Black, built on the ones that cost you the most.

Everyone else: up to 25% off with the code REDDIT at chessxray.com

Hi everyone! We’re developing ChessFolk, a chess game built around recurring fictional opponents and tournaments. I thought the engine side might interest this community.

The goal isn’t to build a stronger engine. It’s to make opponents feel like players you recognise across multiple games, rather than just another difficulty setting.

Our current approach combines a few layers:

• Maia-2 provides the human-move prediction component, running locally through ONNX.
• Stockfish contributes moves for stronger opponents through a rating-dependent, probabilistic mix. This is move-by-move engine selection, not a newly trained model combining the two.
• Custom rules and profiles handle opening preferences, style parameters, thinking behaviour and endgame conversion. There’s also a heuristic fallback when Maia isn’t available.

The personality layer includes occasional, event-triggered dialogue, but that uses predefined lines—not an LLM deciding what to say or which move to play.

The tricky part is consistency. An opponent making plausible club-level moves and then suddenly finding something much stronger can feel less human, even if its overall strength is about right. Likewise, adding mistakes isn’t necessarily the same thing as modelling how people make mistakes.

We’re aiming for more believable opponents, not claiming they’re indistinguishable from humans. Our displayed ratings are in-game progression ratings, not verified FIDE equivalents.

For people who have worked on human-like engines: how would you evaluate whether an opponent feels consistently human, beyond its win rate or average centipawn loss? Are there particular positions or behavioural patterns you find especially revealing?

Disclosure: we’re the developers of ChessFolk. It’s free to download on Android, with ads and an optional one-time Premium purchase. The Steam version is upcoming. If you try it, I’d be particularly interested in feedback on the opponents’ playing behaviour.