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Comment by Q6T46nT668w6i3m | Hacker News Reader

I think the author addresses this. It’s a Catch-22. If you restrict use to a small subset of types you’re undermining one of Julia’s best features.

As someone who has been writing a lot of numerical analysis code recently, I would absolutely love a type system that could describe and enforce numerical stability traits.

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Right. It's important to remember that tools like JAX and PyTorch have total control over the numerical libraries they are differentiating, and have freedom to impose whatever semantics, rules and restrictions are convenient (immutability and referential transparency in JAX, for example). Seemingly small decisions in an existing language and library can have a big impact on the feasibility and practicality of AD.

That's exactly where Dex might improve over Julia, with language level control over mutability and effect handlers and array access safety ... time will tell.

So packages just use those features

Maybe it will hit the right trade off, or maybe Julia will adopt similar language level tools, but adjusted for dynamic semantics. Is that even possible?

> a type system that could describe and enforce numerical stability traits

Wow, that sounds cool! have your reasearched if anyone has done anything in this are? how would you even start to approach the problem?

Do you think it has any change of being done without massive sacrifices to performance?

in rust code, I like using `debug_assert!` to represent numerical expectations/assumptions of the implementation. later if I have a problem, I can turn on debug assertions and I will get a bunch of additional checks. but I can also turn them off and not pay for them all the time.

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