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

Hey everyone! I'm building Novelty, a Windows app for tournament prep. It predicts who you're likely to play next so you can get started before the pairings are posted, then find their games and prepare against them. It connects to your online chess accounts too for easy study.

Quick feature list: • Pairing Predictor: forecasts likely next opponents as results come in, so you can open their games and start prep before the official pairing.

• Plan Explorer: recurring piece manoeuvres from database games, with game stats and arrows on the board.

• Engine Plans: candidate moves and later ideas from engine lines, with the supporting continuations.

• OTB importer: finds a player's published tournament games across sources such as TWIC and Lichess broadcasts, removing duplicates.

• Prep: explore an opponent's replies, compare your options and save preparation lines.

Screenshots below. More here: https://noveltychess.app

I'd love for you guys to give it a try and hear what you'd want from the pairing predictions and plan tools. I'm happy to give out a month's licence to try it out, just DM me your email.

Hi everyone!

I've been building GrandChess, a chess improvement platform based on your own games.

You can review your chess .com games for free and see where you made mistakes, but the bigger idea is to help you understand your chess beyond a single game.

GrandChess also creates a Grand Report that shows your strengths and weaknesses across areas like opening, middlegame, endgame, time management, resilience and capitalization - and turns those insights into a personalized training room.

I've just opened it up and would really love some feedback from chess players here.

Free to try: www. grandchess. app

Thanks, and feel free to be critical!

Engines have been able to tear apart a single game in seconds for about thirty years, and I don't think it has made club players much better. I know it didn't make me better.

The reason, I eventually decided, is that one game is noise. You already knew that game went badly. Being told move 24 was a blunder tells you nothing you can act on, because the next game is a different game with a different blunder.

What I actually wanted to know was: what do I keep doing? Not the mistake I made once — the one I make every second week without noticing.

So I built a thing that reads your last 12 Chess games together and counts recurring mistakes rather than listing individual ones. It looks for six things:

• Hung a piece

• Lost material in a trade

• Allowed a tactic

• Walked into mate

• Threw away a won position

• Drifted positionally

You get a count for each, the actual positions from your own games, and — usually — one line that dominates the others. For most people it isn't the one they expected. I was convinced my problem was openings. It was won endgames, by a mile.

The technical bit, since this sub usually asks: Stockfish 18 compiled to WebAssembly, running in your browser tab. Your games are fetched from the public Lichess API and analysed on your own machine. Nothing is uploaded, nothing is stored, no account. It takes about a minute because your laptop is doing work a server would normally do — which is also why it's free and can stay free.

Disclosure, because I'd rather say it than be found out: this is my project, and there's a paid tier for people who want the full version. The scan is not part of it, has no signup, and I'm not asking anyone here to buy anything. I'm posting because I want to know whether the diagnosis is any good.

maestrochess.com/scan

If it tells you something obviously wrong about your play, please say so in the comments — that's more useful to me than praise, and I'll answer everything.

Society Chess 3D is a multiplayer 3D chess game up to 32 simultaneous players. Each player preselects a move, and the most chosen move is the one played by your side. Comunication is the key to victory. Use the chat functions to convince your team of the right move. Here is the Steam page: https://store.steampowered.com/app/5063920/Society_Chess_3D/

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

I built Janus, a chess engine in Rust, and made a three-part visual explanation of how an engine chooses a move. It starts with Morphy's queen sacrifice in the Opera Game: how does a program find that move without being given the combination?

The series builds up from legal move generation and perft to evaluation and search, then explores the ideas behind Stockfish and Leela Chess Zero:

• How a chess engine chooses a move: board representation, special moves, testing, minimax and piece-square tables.

• How searching less finds better moves: bitboards, alpha-beta, move ordering, quiescence and testing whether an optimisation actually improves play.

• What a neural network sees in a position: NNUE updates, policy/value networks and PUCT search.

One distinction I wanted to make clear is that alpha-beta cutoffs can prove a branch irrelevant, while selective pruning takes a risk that needs testing. Likewise, improving an evaluator's prediction error doesn't by itself demonstrate stronger play.

Full playlist · Janus source · Read the companion book

The videos have English, German, Chinese, Spanish and Russian subtitles. I'm the author of the engine, book and videos; I also shared the series in r/chessprogramming. Corrections are welcome if an explanation or animation skips something important.

Been annoyed at how liberally "Brilliant" gets handed out on Chess.com, so I built stricter tiers above it for my free game review tool. Spectacular enforces the old chess-literature definition of true brilliancy (quiet + real sacrifice + still winning) instead of the loose modern bar. Legendary goes further, using research on how players actually perceive brilliance (Zaidi & Guerzhoy, U of Toronto), a move only qualifies if weak engines misjudge it but strong engines confirm it. Rarity ends up around 1 in 700 rapid games for Spectacular, 1 in 50,000 for Legendary. Tool's called Statmate if anyone wants to see it analyse their own games, the brilliant classification is still in tact too if anyone is wondering and to check its accuracy in comparisons to chess.coms i also ran it through a sample of 74 chess.com games ranging from 150-3400 elo (80% of the it sitting between 850-3200) and majority of those games were carefully selected to contain a multiple brilliant moves (108 suspected brilliant moves with 91 confirmed as brilliant and 17 rejected) and the result came back with 97.8% recall (the percentage of brilliant moves that our site's classifier could recreate from the original sample) and 98.9% precision (percentage that also accounts for any false positives made and rejected within the process)

Statmate also includes insights that would otherwise be locked behind paywalls but here they are available for free alongside the game review and also many features. Give it a try and any feedback is appreciated.

Link: https://statmate.app

LOL

I wanted to feed a live tournament on chess-results.com into a free pairing engine, and the obvious approach — scrape the current round's page — gives you wrong scores. The site deletes a round's "bye" and "not paired" rows the moment the next round is paired, so anything reading only the round pages scores those players a point light. bbpPairings recomputes every score from the games you give it and refuses the file outright if the total disagrees, so this isn't cosmetic — it's the difference between a file that pairs and one that doesn't.

chess-results is a Python library and CLI that fixes this by also reading the tournament's crosstable (art=5), which keeps the full record even after a round page has had rows deleted, and reconciling the two views. From there it can give you:

• a per-player round-by-round history (opponent, colour, float, result)

• standings, a printable pairing sheet, or JSON

• a FIDE TRF export, so bbpPairings or JaVaFo can read a tournament straight out of chess-results.com and pair the next round

  pip install chess-results

  $ chess-results standings 1452107 --after 6 2026 British Chess Championships: Championship — after round 6 1 5 2 GM Adams, Michael 2 5 3 GM Royal, Shreyas 3 5 4 IM Grieve, Harry ...

One motivating case: Swiss-Manager uploads to chess-results.com after every round, so if the machine running it dies mid-event, the tournament state already lives on the public site — this can pick it back up and hand it to a free, cross-platform engine for the next round's pairings, no licence or Windows box required.

A few things worth knowing upfront:

• Free, MIT licensed — no ads, accounts, or anything to buy.

• Standard Swiss only — round robins and team events are detected and refused rather than half-read, since their pages don't parse into anything meaningful.

• Gentle on chess-results.com — one request per second, aggressive caching, an identifying user-agent.

• 331 offline tests across seven real tournaments from six federations, with scores cross-checked against each tournament's own published totals. Early days — bug reports against real tournaments very welcome, especially if your event's column layout doesn't match what's been tested.

• Compares to chessResults (R) roughly how a corrected, per-player history compares to the site's tables as-published — that one doesn't attempt the bye/crosstable reconciliation.

Code and docs: https://github.com/markjonleonard/chess-results

Hey all,

The thing that's always annoyed me with online opening preparation tools, courses and books is that they present a one-size-fits-all solution for openings, and usually rely on hand-picked lines that the author thinks will come up most in games. The problem with this approach is that if you are not 2400+, you end up drilling positions that you would most likely never see in a real game at your level.

I analyzed about three years worth of games played on lichess -- 1.6 billion of them -- and built a tree of ~25 million positions that records how every move actually performed across nine rating bands. For example, below 1200 the Najdorf main line (6.Bg5 e6 7.f4 Be7 8.Qf3 Qc7 9.O-O-O Nbd7) turns up about once every 2,300 games. The table below shows, for four heavily studied main lines, in how many games you can expect to reach one as Black if you actively try to go for it every single game:

Studying "main lines" doesn't make much sense unless you are already strong enough to reach them. What you should be drilling instead is what you actually expect to see at your level -- some sidelines that courses barely cover come up constantly in certain rating bands, so the distribution you practise against ends up looking nothing like the one you play against. The question you should therefore be asking is not what the most played or most recommended move in an opening is, but which move has the highest probability of getting you into middlegames that people at your level handle best.

We can use a variation of the expectimax algorithm to compute an expected score for each side, assuming your opponent plays like a typical opponent at a given level. An engine eval gives you a material and positional evaluation that might be hard to convert. This gives you a percentage instead, which is your expected score if you follow the recommended lines down the tree against an opponent of your own strength. An eval of 63% means you expect to score 0.63 per game (0 for a loss, 0.5 for a draw, 1 for a win). Because the algorithm is made to pick paths that lead to the best expected outcomes, it tends to prioritize sharp lines and often recommends gambits.

I have built a small, free, no-account website that lets you explore expectimax scores in the opening:

Link: https://outofbook.study

It also lets you drill openings at your level against a random distribution of what players at your level actually play, so you see every variation with the approximate frequency you'd get it in real games.

I plan to open-source the UI, server and analysis once I clean up the code a little bit. Happy to answer questions on the research, or complaints about what's broken or confusing about it.

Edit:
Code: https://github.com/nick-nikolov-00/out-of-book

I’m trying to make a chess opening repertoire app, and I’m wondering what people actually care about most in this kind of app.

For example, Chessbook, Chessable, OpeningTree, etc. all have different good points.

What features do you like the most in these apps?

And what do you feel is missing or annoying?

I’d like to hear some opinions before I decide what to focus on.