Nice! I’ve dabbled with something similar on my own lately (originally wrote/vibed to explain some concepts that came up when discussing D&D) at diceplots.com - different approach, keeping the distributions exactly analytical at every step, never sampling.

I started this about 9 years ago and never finished it. The idea comes from a course in my telecom degree called "Señales Aleatorias y Ruido" (Random Signals and Noise), I spent so many evenings writing probability by hand, and every time I wanted to check a result with a computer it was a ton of boilerplate.

The engine is Rust, the JIT is built on Cranelift, there is also a WASM backend so everything runs in the browser too.

Full disclosure, I could only finish it now because of AI agents. In my experience they are amazing at the runtime and the numerical code, but pretty bad at language design, so I kept that part for myself.

It's a toy language. Ask me anything!

It might be worth looking into probabilistic programming languages. I'm out of date, but I remember webppl, stan, anglican, pymc (a python library).

Seems worth an investigation and maybe mention on the article.

My system is blocking that site as it is on the HaGeZi blocklist. I don't have any further information, and I'm not expressing an opinion on the site. An alternative might be https://noiselang.com, which is not on the blocklist.

Reminds me of Haskell’s monad-bayes: https://monad-bayes.netlify.app/

This reminds me of https://mc-stan.org