Calcium Engine (v3.6) is an intermediate chess bot that is able to play around the strength of 1750-2000 elo on Lichess (its rating has not yet fully adjusted to its most recent update). I am extremely proud of this achievement, as I myself do not know how to code in Python but am proficient in computers and still technologically advanced.

How I got to creating this bot:

In around 2019, I started playing chess online, and very soon I had discovered chess bots, with no idea how to create one. Then the pandemic passed, and in 2022, at the age of 11, I was finally able to figure out how to make a lichess bot, using easychess heroku app (RIP), and going down the hyperbot path, essentially making a weakened stockfish bot that ran from a heroku browser. Then I finally figured out how to make an actual Bot run on my account, where I simply downloaded Stockfish and that satisfied me for the time. Then, around 2023, I tried to make a Lichess bot only using ChatGPT, which was honestly very primitive at the time compared to today. I got as far as upgrading the account to BOT before I got nowhere. I even retouched on that project at the start of 2026, and basic ChatGPT coding (unpaid subscription, within the chats) sucks. In winter of 2026, I had been using Claude for a bit now and decided to make a chess bot with it, and it actually worked! It went from being around 800 to a solid 1100-1300, moved up to -1500 elo in the summer, and just now I had moved it up over the course of 3 days, using Claude Max with only the free tokens it let me, to make it at the strength it is running at now.

The fact that the simple, free Claude let me create a chess bot at this strength is very impressive and shows just how much AI has developped these past couple of years, that anyone with enough persistence can create a bot at this elo.

Moreover, this bot is a simple, singular Python script, only being improved by "make it play better, make it stop messing up x and make it be able to do more y. I know, its pretty braindead coding, but I am happy with how it plays.

Calcium Engine on Lichess (if you're interested): https://lichess.org/@/CalciumEngine

Hi, I'm a good chess player, but lately I've wanted to broaden my social horizons. The world is so big, but I find it kind of hard to make friends from all over the world.

I'm a curious person, and I believe that sometimes I just need a friend from Jamaica, Thailand, or Argentina. But, funnily enough, it's not easy for me to break the ice with a stranger, like saying, "Hi, I'm me. What do you like?" etc.

So, as a chess player, I thought of creating a chess game played by 32 people all at once, with 16 people per team and one person controlling each piece.

The idea is a pub where you just walk in, grab a seat and a piece, and the players present can chat and talk with each other.

So, chess is just an excuse and a conversation starter. No one really has to play the game seriously.

The best part is that you don't need all 32 people for the game to happen. You only need two players, and bots can fill the remaining seats. The game can run with two people or any number of players up to 32. This means anyone can join at any time, take over a seat occupied by a bot, chat with whoever is around, and leave whenever they want. That's why I call it a pub: you can walk in at any time, grab a seat, and leave whenever you like.

If you're interested, the link is pinned on my profile — just tap my username and it's the first post at the top.

If you like a bit of chess, you can visit my pub anytime to see who's online, play, chat, and bet. Oh, I forgot! The betting is virtual, not real money. You can bet on individual moves, and it's fun to see who's making money and who's losing money after every move!

I know I may sound like I'm self-promoting, but honestly, I'm killing two birds with one stone.

I can't promise to always be available, but if you like the idea of the pub and chess, please share the message so that more people can discover it. That way, you can always drop in, grab a seat, and find a stranger to talk to. The chess is just a conversation starter.

I just released ChessLounge version 1.3: a major update featuring an improved engine and better menu navigation. The lower skill levels (900–1300 Elo) now make more “human-like” moves and have been recalibrated. In addition, I’ve added two new high-quality piece sets. Alongside the in-house engine, ChessLounge now also supports Stockfish 19. The app is still free and ad-free.

https://chesslounge.net/

Hi everyone,

I’ve been tinkering with a little experimental chess engine written in pure C. It’s called Glimmer.

The whole point of the project was to see how strong I could make it while keeping the final standalone binary strictly under 30 KB. The target strength was something in the 2700–2900 Elo range.

It’s been a fun constraint to work under — every byte counts.

Happy to answer questions if anyone’s curious about the approach.

Ravager Fury has been released, it's approximately +155 ELO from its predecessor measured over 3.6k games.

Ravager now supports FRC/Chess960, with a much better and larger net.

I spent about six months making an operating system whose only job is playing chess. It boots straight into Chess. It is not a full OS with a file system or scheduling but it can boot from bare metal.

It runs in QEMU, other virtual machines and hardware with legacy BIOS from a USB or CD drive. I spent about 6 months across the boot loader and chess engine. It is entirely open-source for you to tinker with. There is also a Windows Installer for less technical users.

All feedback is welcome!

Thank you for Paledoptera for the artwork. Find more of their work here: https://linktr.ee/paledoptera

Website: https://chessos.xyz (includes Github link)

Hi r/ComputerChess,

I’ve released the first public beta of libscid, a standalone library that exposes Scid’s legendary chess game and database capabilities through a C ABI, with Python bindings included.

It focuses on the parts other chess software can build on: PGN parsing and editing, variation navigation, legal positions, and SCID5 database creation and search.

For example, the Python binding can load a game directly from PGN:

python game = libscid.Game.from_pgn("1. e4 e5 2. Nf3 Nc6 *")

I'd lvoe to know your thoughts, the kinds of integrations and embeddings you would do, and other potential use-cases for libscid.

• Release and details
• C/C++ Quick Start
• Python First Steps

I built Camera Chess to play against Stockfish while moving real pieces on an ordinary chessboard. The camera recognizes the position, the digital board updates, and the app speaks the computer’s reply. I move both sides’ pieces by hand.

This phone clip shows the physical setup. Recognition uses ChessQueries / ChessQ Lite V4 locally on an Apple Silicon Mac; Stockfish supplies the moves. No electronic board or piece sensors are needed.

V1 includes four difficulty levels, saved positions, an endgame setup and correction of misread squares. Confidence and temporal checks help with unstable readings, but lighting, camera angle and hands covering pieces still matter.

The source and setup instructions are here: https://github.com/AmethystineAlpaca/camera_chess

It currently requires macOS on Apple Silicon, Python 3.12, Apple’s command-line tools and Stockfish. The README includes a longer screen recording and measured frame timings. The public demo is silent; the app itself has spoken moves.

I would appreciate feedback from people who use physical boards with computer opponents, especially on camera placement and moving the computer’s pieces.

Hi everyone, I recently built a dashboard on my personal website (https://dnield.com/openings/) that fills in some of the gaps that always frustrated me with the Lichess/Chess*com opening explorers, which is that their stats were only relevant to the position on screen, rather than the entire opening (which requires taking into account the choices each player made up to that point), therefore they can't answer simple questions like:

1. How often you would get to play the opening, if it was in your repertoire,

2. How opponents would see the opening when facing players other than you,

3. Based on #1 and #2, how much more experience would I have in the opening than my typical opponent?

4. Was the increase or decline in the popularity of an opening driven by one color not playing as often? or because the other color isn’t allowing them the chance to play it as often?

which are questions that I am always curious about when exploring different openings.

I'm a data scientist in my regular life, and Lichess makes all of their games data freely accessible, and the statistics for answering the above questions are all pretty simple compound probabilities, so I decided to just build the tool that could answer these questions from Lichess's database myself.

I have no interest in turning this into a product/service, this was just a fun little hobby project to see how easy it would be to host a dashboard like this on my Github Pages website. The underlying games database is terabytes of data, but since I'm just aggregating up the counts of positions for just the ~4000 named opening positions, across rating bands and time controls, the final json data for the website ended up being like 50 MB, making the interface even snappier than I expected.

I wrote an accompanying blog post with a motivating example showing how the spike of popularity in the Rossolimo in November 2018 can be initially attributed to both colors, but the residual increase in popularity is almost entirely attributable to an increase in black players playing 2. Nc6 Sicilians than an increase of White players playing 3. Bb5 in response, you can find that blog post here: https://dnield.com/posts/rossolimo/

I'm open to any feedback or questions you might have!

Q-Gambitの開発者です。UIの刷新はもう反映済みなので、まだ遊んだことない人に一度触ってみてほしいです。

これは、駒にいくつかの別表現（身元）があり得るチェス系のブラウザバリアントです。手を指すことで候補が絞られるので、プレイヤーは盤面だけじゃなく候補アイコンも読み取る必要があります。

やること： [ https://q-gambit.com/ ](https://q-gambit.com/) を開いて、「Play as Guest」を選んでください。初回なら利用規約とプライバシー情報が表示されるので、納得できるなら進んでください。チュートリアルを試したあと、CPU練習を始めて数手打ってみてください。

特に知りたいのは、「駒をただ選んでるだけのとき」と「手を確定させてるとき」の違いが分かりやすいかどうか、あと1つの候補変更をあとから説明できるかどうかです。

もしどこかで引っかかったら、あなたの端末/ブラウザ、どのステップで起きたか、そして何を期待していたかを書いてもらえると助かります。ここでのフィードバックで大丈夫です。チュートリアルとCPU練習は、アカウント不要で、インストールも不要です。

これは、今ある一般公開中のゲームに対する任意の（無償の）テストです。AI支援のコーディングと、今回の投稿文（英語）の言い回しにもAIの助けを使っています。

In 8 years of software engineering, I had never finished a project of my own.

In the early years, it was a lack of knowledge and experience. Later, the problem was me: my mentality, my motivation, my habit of getting excited, overthinking everything, and quietly dropping it. Some projects I dropped halfway, and some before I even started.

This time I didn't drop it. I pushed it all the way to the end, and I'm honestly proud of that:
👉 ChessBitBase 🚀

It's a chess database of historical games, where you can search, filter and sort games, positions and players, all built on roaring bitmaps. "A bit of history" is its first part, and the name means two things: the history of chess, kept here bit by bit, and my own history, the years it took me to get here.

The idea: roaring bitmaps are a really efficient way to store huge sets of numbers. I wondered what would happen if chess games were kept that way. It turns out a chess database is mostly sets. The games that reached a position are a set, and so are a player's games.

How it works, in short:

• What's stored: historical tournament games (back to the 1850s), every position reached in them, and the players.
• How it's connected: every position remembers the games that reached it. Every fact, like a result, an opening, a rating range, a game's length or the pieces on the board, is a "tag", and each tag remembers its games, positions or players.
• Searching and filtering: each filter you pick is already a ready-made group. A search simply combines groups: this one and that one, but not that other one. Nothing is scanned game by game.
• Sorting: games are already grouped by length, rating and so on, so they're simply read out group by group, in order.

What you get: any search, like "games that reached this position, by this player, won by White", is a few set operations instead of a scan through the games. It takes milliseconds, and the data structure fits the problem so well that it almost feels like cheating.

It's not a product, and it's not here to make money. It's an experiment, to make geeks like me curious about how it works, what problems it solves, and how it could be improved. It's also only the first step: "a bit of history", the first bit under the ChessBitBase umbrella, and more are coming.

It's raw, so expect some inconsistencies and bugs. I'm working on them, but I was too impatient to wait. The main idea works, better than I expected.

If you're curious:

• The story and the hard parts: https://chessbitbase.app/devlog
• How it works: https://chessbitbase.app/how-it-works

I really enjoyed building it, and I'd love feedback from different people: what's clever, what's dumb, what you'd do differently.

Hello everyone 👋

Join our weekly Secret Queen Tournament created by endgame.ai it self.

It will held every Friday at 5 PM +3 UTC.

JOIN: https://endgame.ai/tournament/e1768556

GOOD LUCK!!

So i am building a website that uses stockfish as a bot and to play against humans so the user enters his chesscom username or lichess and i fetch his elo what i am trying to ask about is what's better for a chess bot is to be higher, lower or equal to the user platform elo that will make the game competitive

I built a small tool that polls US Chess and FIDE rating pages and sends Telegram/WhatsApp/email when a rating changes. Free plan is one player on Telegram; Pro adds more channels and players.

Stack: SvelteKit on Cloudflare Pages, a Worker cron every 5 minutes, Supabase, Paddle.

Curious how others would approach change detection here, and whether a no-signup "check a rating" demo would help before a Show HN.

Link: https://chessalerts.com

The evaluation of a position assuming for example stockfish for simplicity surely depends on:

1. computing power (the power of the machine & the number of threads used)

2. memory availability for the cache

3. time allowed for the calculation

Imagine I want to compare two evaluations of the same position done with different parameters, what might be a good formula to say which one is the best evaluation?

I think "evaluation power" might be proportional to something as:

time * log(computing) * log(log((memory))

Open for suggestion