Task: Implement Large Lempel-Ziv. As a benchmark, compress all of Project Gutenberg; evaluate the resulting model numerically for autoregressive tokens/second and its ability to compress the Gutenberg corpus (perplexity), and qualitatively for its ability to write prose, technical documentation, code, poems, translations, one-shot prompts, etc. Provide a Nix flake which can be used to reproduce all results.
打开一个AI编程工具，纠结半天该选哪个模型。有人说Claude最强，有人说GPT好用，还有人说免费的DeepSeek足够用了。你花了半小时研究，最后还是随便选了一个。
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The pipeline was very similar to icon-to-image above: ask Opus 4.5 to fulfill a long list of constraints with the addition of Python bindings. But there’s another thing that I wanted to test that would be extremely useful if it worked: WebAssembly (WASM) output with wasm-bindgen. Rust code compiled to WASM allows it to be run in any modern web browser with the speed benefits intact: no dependencies needed, and therefore should be future-proof. However, there’s a problem: I would have to design an interface and I am not a front end person, and I say without hyperbole that for me, designing even a simple HTML/CSS/JS front end for a project is more stressful than training an AI. However, Opus 4.5 is able to take general guidelines and get it into something workable: I first told it to use Pico CSS and vanilla JavaScript and that was enough, but then I had an idea to tell it to use shadcn/ui — a minimalistic design framework normally reserved for Web Components — along with screenshots from that website as examples. That also worked.