Hi HN,
I built a chess position recognition system that runs entirely in the browser.
The project walks through the full pipeline: manually collecting data from book scans, comparing Autoencoders vs. SimCLR for representation learning, and deploying with ONNX Runtime and Pyodide to avoid server costs.
The included demo recognizes board diagrams and generates Lichess links for analysis.
Seems like reading the code is now the real work. AI writes PRs instantly but reviewing them still takes time. Everything flipped. Expect more projects to follow - maintainers can just use ai themselves without needing external contributions.
I'm working on interactive explorations of algorithms and machine learning.
Small, visual, hands-on demos that help build intuition by letting people tweak code/parameters and see how things behave in real time.
Great idea, thanks for sharing!
Tried your prompts with ChatGPT and Claude than iterated on it. The ASCII doesn't render perfectly in the web interface but looks good when copy/pasted into a text editor.
Key benefit: I used to iterate on layout by generating HTML+Tailwind directly, which burns tokens fast. This ASCII approach lets you nail the structure first without the token cost. Much better for free tier usage.
Appreciate the writeup!
Nice project! I'm not very strong at note recognition, so I see the “Wrong note” message quite often (which is totally fine), but the frequent blinking feels a bit distracting. You might consider making the feedback a little less visually intense.
I like this book. The probability section is great, especially how they handle the Monty Hall paradox. They use "four step method" that breaks it down perfectly - way clearer than the explanations you get in movies like 21 or numb3rs.
I skimmed the source code (base.js, light.js) to see what he was using.
It appears to be entirely custom, with no graphics libraries like Three.js.
He even implements his own low-level math functions from scratch.
It's impressive to see that kind of discipline.