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.

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 I'm out of date, but I remember webppl, stan, anglican, pymc (a python library).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.

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!

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