HTML/CSS is Turing complete, so why not compile programs to plain HTML/CSS and runs with zero bytes of JavaScript? zero-js.vercel.app

After one week, there are 5 more small models for specific tasks on the web: 1. gpu-time: natural language to JS date and time by @imarikchakma 2. gpu-query: natural language to structured filters by @cheatyyyy 3. neural-flexbox: model to centre a div (!) by @aaronvanston 4. gpu-cron: natural language to cron expressions by @ManuSchiller 5. tinysarf: arabic morphological analyzers by @Ahmedabdou1996

Got a lot of attention for this (thank you!), but also there're some criticisms and questions: 1) it's a stupid idea Maybe. Again, this is experimental and not meant to be used in production or as a replacement for anything. It is to explore new possibilities. 2) it's unnecessary and sometimes wrong Existing solutions are not always more correct just because they're "algorithmic". For example pic 1 is Highlight.js and 2 is gpu-lexer, lang is JS not something rare. There are counterexamples too. Furthermore, language-agnostic is the real reason for exploring this approach. During training, the neural network learned patterns like "[keyword] variable = value" that are not tied to any specific programming language such as: var ... = ... let ... = ... const ... = ... using ... = ... val ... = ... auto ... = ... type ... = ... def ... = ... And that knowledge can be shared without re-implementing a grammar rule repeatedly. Another advantage is that this approach can potentially generalize to new programming languages or dialects without requiring extensive manual rule creation. If you go to prismjs.com/test, you can't find popular Web framework languages like Vue, Svelte, Astro, and many others. Not to mention that new languages and dialects are constantly emerging, making it impractical to maintain comprehensive grammar rules for all of them. When that's a concern, you either use more comprehensive solutions like Shiki with a cost (carefully bundle, detect and load language definitions on demand, larger size), or use an easier approach for all kinds of source code with another cost (less accurate). That is definitely a good reason to explore this direction.

I trained a small model to do syntax highlighting in the browser with GPU. Meet gpu-lexer from Vercel Labs: Small (27.5KB), fast (runs on WebGPU), and language-agnostic (model guesses the syntax). gpu-lexer.vercel.app It is experimental and built for learning!

Correctness. I’m comparing every span types with Shiki, which is almost the golden standard today. It shows the weighted correctness of top 25 popular languages on GitHub. Shiki uses real AST while others might not, hence the differences.

13 years ago, Apple asked a question that hits me harder today than ever: If everyone is busy making everything, how can anyone perfect anything? Every day, I see a world flooded with effortless AI slops. I’m realizing that to build something truly great today, you need a level of focus the world has never demanded before. It feels like trying to write a quiet, beautiful song in a room full of loud noise.