I've also had success with this. One of my hobby horses is a second, independent implementation of the Perchance language for creating random generators [0]. Perchance is genuinely very cool, but it was never designed to be embedded into other things, and I've always wanted a solution for that.

Anyway, I have/had an obscene amount of Claude Code Web credits to burn, so I set it to work on implementing a completely standalone Rust implementation of Perchance using documentation and examples alone, and, well, it exists now [1]. And yes, it was done entirely with CCW [2].

It's deterministic, can be embedded anywhere that Rust compiles to (including WASM), has pretty readable code, is largely pure (all I/O is controlled by the user), and features high-quality diagnostics. As proof of it working, I had it build and set up the deploys for a React frontend [3]. This also features an experimental "trace" feature that Perchance-proper does not have, but it's experimental because it doesn't work properly :p

Now, I can't be certain it's 1-for-1-spec-accurate, as the documentation does not constitute a spec, and we're dealing with randomness, but it's close enough that it's satisfactory for my use cases. I genuinely think this is pretty damn cool: with a few days of automated PRs, I have a second, independent mostly-complete interpreter for a language that has never had one (previous attempts, including my own, have fizzled out early).

[0]: https://perchance.org/welcome [1]: https://github.com/philpax/perchance-interpreter [2]: https://github.com/philpax/perchance-interpreter/pulls?q=is%... [3]: https://philpax.me/experimental/perchance/

Thanks for sharing. I hear people make extraordinary claims about LLMs (not saying that is what you are doing) but it's hard to evaluate exactly what they mean without seeing the results. I've been working on a similar project (a static analysis tool) and I've been using sonnet 4.5 to help me build it. On cursory review it produces acceptable results but closer inspection reveals obvious performance or architectural mistakes. In its current state, one-shotted llm code feels like wood filler: very useful in many cases but I would not trust it to be load bearing.

Fun stuff! I can see also using ICU MFv{1,2} for this, sprinkling in randomization in the skeletons