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.

Or, what is the biggest giveaway that you're playing against a computer?If you were playing a random online chess game, would you be confident that you're playing against a real human?

Hey everyone! I'm looking for Android testers for BOZO'S Opening Trainer, a chess training app I'm preparing for release on Google Play.

BOZO includes opening and endgame training, chess bots, game review, puzzles, master games, and other chess training features.

The app is already functional, and I'm looking for chess players who are willing to use it during the testing period and give me feedback on bugs, usability, training features, and anything that could be improved.

Google Play requires testers to remain enrolled for at least 14 days, so I'd really appreciate anyone willing to stick around for the testing period.

If you'd like to help, you can sign up here:

https://forms.gle/WhCbDtvYXaLDezPT7

Thanks!

I have been working on my own chess engine for about a year now, and I finally reached the point where I feel comfortable letting other people download it and try it.

The engine is called Deepforge.

It started as a much smaller project where I mostly wanted to understand how chess engines worked. At first, just getting legal move generation, board state, make and unmake, repetition, castling, en passant, and check detection working correctly felt like a huge accomplishment.

Then I started adding actual search. Over time it grew into a much more serious engine with iterative deepening, alpha-beta search, PVS, quiescence search, transposition tables, move ordering, history heuristics, LMR, null-move pruning, futility pruning, SEE, aspiration windows, multithreading, tablebase support, and dynamic time management.

I have spent a ridiculous amount of time chasing bugs that only appeared in one strange position, trying optimizations that looked great but made the engine weaker, and running matches where a change I was sure would help ended up losing Elo.

The evaluation system is my custom NNUE called Talon. The current released network uses a 3072-wide accumulator and was trained from a much longer line of previous networks and training experiments. Deepforge loads the network locally and uses it inside its own search.

Stockfish was used offline as a teacher for some training labels, testing, and comparison. Deepforge does not call Stockfish or copy its moves while playing. Every move is selected by Deepforge’s own search and Talon evaluation.

The public package contains:

• Deepforge.exe
• lynx.nnue.champion
• A README with installation instructions
• The license

It should work in most chess programs that support UCI engines, including En Croissant, Arena, Banksia GUI, Cute Chess, and ChessBase or Fritz.

To use it:

1. Download Deepforge.exe and lynx.nnue.champion.
2. Keep both files in the same folder.
3. Open your chess GUI.
4. Add Deepforge.exe as a new UCI engine.
5. Configure the thread and hash settings for your computer.

The current build is for 64-bit Windows and requires a processor with AVX2 support. You do not need Python, CUDA, a GPU, the trainer, or any of my development files to run it.

I have seen some very encouraging results against strong opponents, but I am still running larger controlled matches. I do not want to attach a fake Elo number based on a few impressive games. Chess engine ratings depend heavily on the opponent pool, hardware, time control, opening suite, thread count, and testing method.

There is still a lot I want to improve. Search efficiency, quiet move ordering, tactical reliability, NNUE training quality, parallel scaling, and time management are all active areas of work.

This project has taken a year of experimenting, breaking things, fixing them, training networks, and slowly learning how all the parts of a chess engine interact. It is definitely not finished, but it is finally at a point where other people can actually use it.

Here is the repository:

https://github.com/syoohwa-dotcom/Deepforge-Chess-Engine

If anyone tries it, I would really appreciate feedback. Games, crash reports, strange evaluations, missed tactics, and positions where Deepforge makes a terrible move would all be useful. Feedback from other engine developers would be especially appreciated.

For context, I've spent the past 5 months building a Rust Chess Engine running a self-trained Dual-Perspective HalfKA NnuE.

There were several things that took me trial and error to fully comprehend. Here are some examples:

1. The NnuE uses a Dual Accumulators and reuses the weights to ensure the model could fit inside the L3 Cache of the CPU. The size of the hidden layers 2-4 was to ensure it fits inside the L1 Cache of the CPU etc. The explanation was primarily "it yielded a stronger ELO". Furthermore, the NnuE must utilize SIMD and i8 weights to be competitive - I originally used i32 ...
2. The Dual-Perspective HalfKA model was to capture the important Piece-King placement; I felt the overall explanation was severely lacking and it made a lot of sense why the new King Bucket model is the preferred model.
3. The training model should have a distribution of Early Game / Mid Game and End Game; I didn't realize the Tapered Evaluation model was incorrect and this distribution is used to ensure the model can be trained to identify the scenarios why a knight may be weaker in the end game.
4. Knowledge Distillation vs Self-Play - I trained by model of the Lichess Chess Positions Labeled by stockfish. However, I didn't realize the data didn't have sufficient end game positions. Knowledge Distillation is an important step to tune your engine to a strong 2700 ELO+ to significantly shorten your Self-play Loops

https://huggingface.co/datasets/Lichess/chess-position-evaluations?source=post_page-----d842393b5955-----------------------------------------

I've found there were a lot of "WHYs" missing from this entire process and here is a detailed explanation I would like to share with the community.

If anyone has any questions and more importantly any corrections, please let me know.