We have a set of following facts (correct me if I am wrong on any of these):

1. Only 210 games out of 1072 that were allegedly played by AlphaZero against Stockfish 8 are published.

2. Not a single well-known chess player has ever seen or used AlphaZero. Lots of grandmasters were dreaming of getting an opportunity to "play around" with this piece of software but no one got the chance. Why?

3. The only people who has seen this program outside Google are the authors of the book about AlphaZero https://www.amazon.com/Game-Changer-AlphaZeros-Groundbreaking-Strategies/dp/9056918184

4. The source code of AlphaZero was never released despite the fact that this product is abandoned. If Google doesn't want to release the code for free, fine, then sell it as a commercial product. But Google did neither.

For me there is simply not enough evidence to back up the claim that this piece of software ever existed. Why everybody believes otherwise?

I built Autochess NN, a browser-playable neural chess engine that started as a personal experiment in understanding AlphaZero-style systems by actually building one end to end.

This project was unapologetically vibecoded - but not in the “thin wrapper around an API” sense. I used AI heavily as a research/coding assistant in a Karpathy-inspired autoresearch workflow: read papers, inspect ideas, prototype, ablate, optimize, repeat. The interesting part for me was seeing how far that loop could go on home hardware (just ordinary gaming RTX 4090).

Current public V3:

• residual CNN + transformer

• learned thought tokens

• ~16M parameters

• 19-plane 8x8 input

• 4672-move policy head + value head

• trained on 100M+ positions

• pipeline: 2200+ Lichess supervised pretraining -> Syzygy endgame fine-tuning -> self-play RL with search distillation

• CPU inference + shallow 1-ply lookahead / quiescence (below 2ms)

I also wrapped it in a browser app so the model is inspectable, not just benchmarked: play vs AI, board editor, PGN import/replay, puzzles, and move analysis showing top-move probabilities and how the “thinking” step shifts them.

What surprised me is that, after a lot of optimization, this may have ended up being unusually compute-efficient for its strength - possibly one of the more efficient hobbyist neural chess engines above 2500 Elo. I’m saying that as a hypothesis to pressure-test, not as a marketing claim, and I’d genuinely welcome criticism on evaluation methodology.

I’m now working on V4 with a different architecture:

• CNN + Transformer + Thought Tokens + DAB (Dynamic Attention Bias) @ 50M parameters

For V5, I want to test something more speculative that I’m calling Temporal Look-Ahead: the network internally represents future moves and propagates that information backward through attention to inform the current decision.

Demo: https://games.jesion.pl

Project details: https://games.jesion.pl/about

Price: free browser demo. Nickname/email are only needed if you want to appear on the public leaderboard.

1. The feedback I’d value most:

2. Best ablation setup for thought tokens / DAB

3. Better methodology for measuring Elo-vs-compute efficiency on home hardware

4. Whether the Temporal Look-Ahead framing sounds genuinely useful or just fancy rebranding of something already known

5. Ideas for stronger evaluation against classical engines without overclaiming

Cheers, Adam

There are so many interesting chess projects built with Ai. Could we group them all here?

In what face of the project are you? What are the challenges? And what problem are you trying to solve?

Hey everyone! I'm Anna, a Woman FIDE Master and product builder. A while back I teamed up with an AI engineer and we've been building something I genuinely believe in, an AI chess coach.

Here's what it can do so far:

• Teach openings

• Analyze positions and answer your follow-up questions

• Give you puzzles

• Play with you (Maia)

• Explain chess concepts

We're looking for players of all levels to try it out. Would love to hear honest feedback: what's working, what's not and whether it actually adds value.

Link in comments ♟️

I’ve been working on some chess tools and recently put together an Opening Trainer.

You move on the board, Stockfish (19 Lite) shows the evaluation and main line, and there’s an Auto WikiBook that pops up with a short explanation of the opening (ideas, plans, why the moves are played). In the screenshot it’s the Sicilian after 1.e4 c5.

You can mark the variations you want, save them for the session, and hit Save & Train to practice them. No account needed — everything stays in the browser tab.

I made it mostly because I kept switching between the board, an engine, and some wiki or repertoire notes while studying openings. It’s pretty basic and not trying to replace Chessable or anything more complete, but it’s been useful for me to actually understand the lines instead of just repeating moves.

Link:

https://www.caissa-chess.org/opening-trainer

Curious what you think of the flow (analyze → save → practice) or if anything feels off. Still iterating on it.

Hey everyone

For the past few months, I’ve been building Rookify, an AI-powered chess coach that breaks down your play into measurable skills — like opening development, tactical awareness, positional understanding, and endgame technique.

These last two weeks were all about data validation. In my earlier tests, only 1 out of 60 skills showed a meaningful correlation with player ELO (not great 😅).

After refactoring the system and switching from the Chess.com API to the Lichess PGN database (which actually lets me filter games by rating), I re-ran the analysis — and the results were much better:

→ 16 strong correlations
→ 13 moderate correlations
→ 31 weak correlations

The big takeaway I've learned is that skill growth in chess isn’t purely linear.

Some abilities (like blunder rate or development speed) improve steadily with practice, while others (like positional play or endgame precision) evolve through breakthrough moments.

Next, I’m experimenting with hybrid correlation models — combining Pearson, Spearman, and segmented fits — to capture both steady and non-linear patterns of improvement.

If you’re into chess, AI, or data science, I’d love to hear your thoughts — especially around modelling non-linear learning curves.

You can read the full write-up here → https://open.substack.com/pub/vibecodingrookify/p/rookifys-skill-tree-finding-its-first?r=2ldx7j&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Or try Rookify’s Explore Mode (100 tester spots) → https://rookify.io/app/explore

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’m doing some research on how language models explain chess. I'm looking to get some feedback from good chess players :)

You’ll see a position and choose between two short explanations. One set is 20 positions and takes about 6 minutes.
Each completed set earns one raffle entry for one of three $25 gift cards, up to five entries.

https://explaining-chess.vercel.app

Thanks for helping, and if you want to share the link with your friends, I would also be very appreciative!

I had a question in my mind for a long time in my mind...

Can Chess Computers Really Beat Humans?

Now, when I recently researched it and went through many writings, I finally wrote something that might amaze you!
#happy_chessing!

I've been coaching chess for 15 years. The same problem shows up with almost every student: they know the tactics, but they can’t calculate. They see the first moves and stop. Puzzle apps train pattern recognition. Very few train the actual process of calculating.

So I built StreakChess. It only does one thing: calculation training, in short daily sessions with streaks, like Duolingo.

Where it stands today:

• +300 users, several in Premium

• Free to use. There’s a one-time Founder Lifetime plan ($99) for the next weeks.

• Levels from roughly 800 to 2500

Honestly, my biggest challenge is distribution, not the product. I automated Instagram reels and a YouTube channel, but growth is slow.

I’d love feedback on two things:

1. Does the “calculation only” positioning make sense to you, or is it too niche?

2. What would make you come back every day?

streakchess.com

Happy to answer anything about chess, coaching, or the build.

Hi everyone! I’m one of the developers of Chess Folk. Since this community is interested in playing against computers as well as analysing with them, I thought our approach might be relevant here.

We’re trying to make computer chess enjoyable beyond choosing an engine strength and starting another game. Chess Folk has more than 300 fictional opponents with names, biographies, ratings and different playing styles. The idea is to find suitable rivals, return for rematches and play through tournaments with them.

Those are character profiles, not 300 independently developed engines. Our opponent system uses Maia and Stockfish with custom move-selection rules. Making weaker opponents behave consistently is still one of the harder parts: a believable mistake is more interesting than an otherwise strong opponent randomly giving away a piece.

A few things that might interest this community:

• Offline tournaments: Swiss, round-robin and knockout formats, with configurable fields and time controls. You can also watch computer opponents play each other.

• Guided practice: Susu, our panda coach, offers hints and opportunities to explore alternatives and retry decisions.

• Game review and analysis: Revisit mistakes and missed opportunities, explore continuations, and play on from an interesting position.

• PGN import and export: Bring games in for exploration or take them elsewhere for further analysis.

• Local co-op: Two people can share a side against the computer, either alternating moves or playing Hand & Brain.

The opponents’ ratings represent progress within Chess Folk; they aren’t official federation ratings. We also don’t claim that the opponents perfectly imitate people or automatically adapt to stay just above your strength.

I’d particularly welcome feedback on how the opponents lose. Do their mistakes make sense at the level you selected? Do they seem to carry an idea across several moves? Those details matter a lot to the experience we’re trying to build.

💻 Chess Folk on Steam
📱 Free Android download — includes ads and an optional premium upgrade.

For transparency, we use AI tools in development and artwork. Happy to discuss the approach and hear what you miss from older computer-chess programs.

Es la dirección de mi canal de Youtube. Encontrarás problemas de este tipo, problemas de ingenio y también trato de la dieta cetogénica vista por la ciencia.

https://youtu.be/SsGqfgJoppc?is=vnKxRQGj35glqt05

Built a network that predicts human moves instead of good moves. 27M parameter transformer, 64 square tokens, trained from scratch on 338M positions from public Lichess games. No search, single forward pass per move. It has a second head that predicts how long the player would take on the move, and it spends that time on its clock. So it manages a clock like a human, gets low on time, and loses about 3% of its 5+0 games on time.

On held out positions it predicts the move the player actually made 53.8% of the time. Maia-2 gets 49.5% on the same positions. I score mine with an anonymous player vector so it has the same information Maia has. Self play puts it around 1600, but that is self play, so it is on Lichess now to get a real rating. https://lichess.org/@/maulbot, blitz or rapid, rated or casual. New bot accounts start at 3000 so it will take a while to settle. Interested in what people think of the style. So far it squeezes you positionally then misses something tactical.

Looking for a strong player to help label chess blunders for a training dataset

Hi everyone — I’m working on a chess training project and I’m looking to pay a strong player, ideally around 2000+ strength, to help create a small labeled dataset of blunders.

The task would be to review positions where one side has just made a clear mistake/blunder, usually around a 100+ centipawn loss or more, and add human explanations for why the move is bad.

For each position, I’m looking for:

• the blunder type, such as hanging a piece, missing a tactic, weakening king safety, losing a defender, bad pawn move, positional concession, endgame mistake, etc.

• a short explanation of why the move is a blunder

• the key continuation or punishment

• for more positional examples, a deeper explanation of what changed in the position and why it matters

I’m aiming for 300+ positions total, but I’m open to someone doing a smaller paid sample first so we can make sure the format is good.

This is paid work, and the amount would depend on the number and quality of positions completed. Please comment or message me if you’re interested, especially if you have experience coaching, annotating games, or explaining mistakes clearly to improving players.

Thanks!

Hello everyone ,
I have built a free chess tool to show the best/beautiful five moments from your recent games on chess.com and lichess.

I fetch around 400 to 500 games in rated rapid and blitz only. and they are biased toward recent games .

I did it in a way to try to present beautifully the moves in your history and I tried as best as I can to show the best beautiful five moves according to each player's rating.

I really appreciate if you can try it and give me your honest opinion :)

Please find the tool here : mychesslegacy.com

Here is one of my moments in my account on chess dot com (My elo is 1800 )

Note: if you are a new player , you might not see t a lot of games to show the top moments.

Thanks ❤️

So, if you have played on chess.com you might have used game review, and it shows the following moves:
Best, Blunder, Book, Brilliant, Excellent, Good, Great, Inaccuracy, Miss, Mistake.
I have read many articles, but none mentioned how to program that stuff. An article showed they use the expected point model to calculate the move calculation, another article showed that you can't calculate brilliancy by only analyzing data, you have to take some account factors. Apart from that I am making a website, that analyzes your chess game for free. So, I use stockfish.js and stockfish.wasm web workers in frontend, and it gives data like
info depth 22 seldepth 29 multipv 1 score cp 37 nodes 3976016 nps 496071 hashfull 913 time 8015 pv d2d4 d7d5 c2c4 d5c4 e2e4 e7e5 g1f3 e5d4 f1c4 f8b4 b1d2 b8c6 a2a3 b4d6 e1g1 g8e7 f1e1 c8g4 e4e5

info depth 22 seldepth 30 multipv 2 score cp 35 nodes 3976016 nps 496071 hashfull 913 time 8015 pv e2e4 e7e5 g1f3 b8c6 f1b5 a7a6 b5a4 g8f6 e1g1 b7b5 a4b3 f6e4 f1e1 d7d5 b1c3 e4c3 d2c3 c8e6 f3e5 c6e5 e1e5 f8d6 e5d5 e6d5 b3d5 e8g8 d5a8 d8a8

info depth 22 seldepth 28 multipv 3 score cp 26 nodes 3976016 nps 496071 hashfull 913 time 8015 pv g1f3 d7d5 d2d4 g8f6 c2c4 c7c6 e2e3 c8f5 b1c3 e7e6 f3h4 f5e4 f2f3 e4g6 d1b3 d8c7 c1d2 b8d7 h4g6 h7g6 c4d5 f6d5 c3d5 e6d5 e1c1

This is for starting position fen.

Can I get those CP (centipawn) and classify moves? The naive idea is you make a move, calculate the delta of that CP, take the abs value, and classify it. And for miss, if you have a mate in the data and you move a trash move, then it's a miss. Please help me out.

Hi!

I am so tired (and I am sure most of you will relate to this) of Chess.com game review

I have basically rebuilt it from the ground up, and it also includes puzzles, an ai coach, and a full on anticheat that allows you to review your opponents games and decide if they were cheating or not.

You can try it out at chess.sidastuff.com and please keep in mind that it is still in Beta.

I would love if you would give me any suggestions or bug reports in the comments down below. I am working on a very large ui update, so most ui bugs etc have already been reported and will be fixed soon.

Enjoy!

Hey chess enthusiasts!

I'm currently working on creating a next-gen chess bot powered by machine learning. Unlike traditional engines that just find the best moves, this bot aims to explain why certain moves are strong or weak in simple, human terms. The idea is to bridge the gap between beginners and advanced players by turning raw calculations into digestible insights.

To do this, I need feedback on how effective these explanations are. If you’d like to help shape the future of AI chess tools, I’ve set up a quick survey (takes less than 10 minutes): https://forms.gle/EDDbF6pR5qEAmwyJ8

Your input would mean the world—whether you’re a casual player or a seasoned expert! Thanks in advance, and happy playing!

I'm a senior software developer and a frustrated club player, so I built the chess trainer I always wanted to have.

Instead of giving generic puzzles, it works directly from your own games.

Import games from Chess.com or Lichess, run Stockfish to identify mistakes, and automatically turn those exact positions into spaced-repetition exercises. The same idea is used for opening repertoire training and endgames (evaluated using Syzygy tablebases).

It also includes a full analysis board with engine variations and statistics derived from your own games.

The project is free and open source (MIT). Windows binaries are available, so no Python installation is required:

https://github.com/gaelazzo/python_chess/releases/latest

This is the first public release, so I'd really appreciate feedback from players and developers alike. If you find bugs or have suggestions, I'll do my best to address them quickly.

My friend and I are building ChessHint, a free game review tool using Stockfish-backed analysis. It adds move gradings, explanations, alternatives on the board, and a breakdown of missed tactics and strategic opportunities.

The part we’re putting a lot of work into is getting the explanation to pick out the important idea in a position. We recently fixed a case where it described pressure on a defended pawn but missed a more important capture threat that would also attack a rook.

We’d appreciate difficult positions or completed games where review explanations tend to get the point wrong: poisoned captures, intermediate moves, or threats that matter more than the obvious attack. If you try it and find a bad explanation, sharing the PGN and move number would help us reproduce it.

You can import Chess.com or Lichess games, or paste a PGN. The screenshot shows the Tactics tab.

Try it: https://chesshint.com/analysis

Feedback here is welcome, or in our Discord: https://discord.gg/vyrAq2V9c

Chess when assuming the players are imperfect is not purely theoretical, because the opponent cannot completely forsee what position they will end up in. This is why high-level chess agents like stockfish assign probabilities to generalizations like "checkmate" instead of aiming for a specific position to reach their goals. Player's have personalities that can be learned and exploited, to slightly favor your chance of winning in games against them, and these personalities can be categorized to work against a broad variety of chess players. It is important for a chess player to understand whether they are weak and strong in as well as their opponent, because we know that humans and even AI have biases that gives us disproportionate experience across chess positions. For example, you may exploit a 400 elo player by bringing your queen and bishop out early for a quick checkmate, or against higher level players you may want to focus on yourself and play positions you know you're good at. This can be done at a very cheap mental price, such as a grandmaster avoiding sharp positions or leaning into them, and in an engine, a position classifier is already baked within their Neural Nets if its trained through reinforcement learning, or could use an additional position classifier if it wasn't.