NoiseLang: A Language Where Every Value Is a Probability Distribution
NoiseLang: Where N = 5 is a Dirac delta
After nine years of dormancy, I revived NoiseLang using AI tools to build a language where every value is a probability distribution. Instead of writing complex simulation loops, you write mathematical expressions that automatically trigger millions of Monte Carlo simulations. This approach lets you calculate probabilities and expected values directly, turning abstract statistical concepts into executable code with high-performance backends like Cranelift and WASM.
AI is great at building the JIT parts, the runtime parts, the numerical parts, but it sucks at coming up with good language design ideas.
- manucorporat
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!
- torginus
Interestingly, shading languages started out like this - way before consumer GPUs.
I remember encountering this idea written in a book written by Ed Catmull of Pixar fame (can't find the title sorry, but it was written in the 80s), but generally comes from signal processing as a way of avoiding aliasing artifacts..
The core idea is to make programming, which is a discrete and discontinuous domain, into a well-behaved band limited signal. Otherwise you get aliasing (or jaggies), which can happen even INSIDE a surface, if the shader's like that.
The code idea for this is the step function which is the integral of the dirac delta. step(x) returns 1 for all x >0 and 0 otherwise.
Step is not a well-behaved function in the sense, that it changes infinitely quickly at x=0. But once we know what we want, we can replace it with something like that, that's well behaved.
Consider the example pseudocode
color = x> 5? green:blue;
can be rewritten as
color = blue + step(x-5)(green-blue)
With the two being equivalent.
Now if we put the code into a shader, we get jaggies. So to combat the value changing infinitely fast, we go for a function that's like step, but changes smoothly* from 0 to 1 around x=0. Enter smoothstep:
color = blue + smoothstep(x-(5+EPSILION),(x-EPSILON), x)*(green-blue)
And so we defined a 'transition zone' of +-EPSILON(an arbitrary number). While any smooth function can work, smoothstep is chosen because it has a smooth first and second derivative (mea […]
- bradrn
Reminds me of Haskell’s monad-bayes: https://monad-bayes.netlify.app/
- kccqzy
I didn’t really see loops being handled here. As far as I understand, the biggest technical difficulty with this kind of probabilistic programming language is handling loops, including infinite loops and almost surely terminating loops.
I did a bit of research earlier in my life[0] to study the handling of loops and without using Monte Carlo simulation. The result was actually workable if incredibly resource intensive to the point of being impractical. If I had chosen to do it again, I might’ve accepted using Monte Carlo simulations while still supporting loops.
[0]: https://github.com/kccqzy/probabilistic-program-inference/bl... Shameless self promotion I know! I put quite a bit of effort into that README and the code.
- chrisra
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.