I can't reply to the guy saying julia is the only one. But there are others.

Ceres uses dual numbers

https://github.com/ceres-solver/ceres-solver/blob/master/inc...

This library from google is used everywhere in robotics, so it's hardly some backwater little side project.

So does c++ autodiff https://github.com/autodiff/autodiff/blob/main/autodiff/forw...

So does Eigen: https://eigen.tuxfamily.org/dox/unsupported/AutoDiffScalar_8...

So does Pytorch: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

it's amazing to me that pointing out a straight up mathematically factual inaccuracy is considered "angry" and "acting a fool".

> there's a reason no one uses dual numbers (non-standard analysis) for anything (neither autodiff nor calculus itself)

wrong

> there are no [dual] numbers anywhere in [...] any other serious autodiff implementation

wrong

> please show me the source line number

did

> i'm wrong and this other guy is right

correct.

> this is why i hate this kind of "TIL, gee whiz" math tidbits

acting a fool. angrily so.

is this like some kind of version of truman show? the full sentence is

>please show me the source line number in pytorch or tensorflow that defines this number

why? because the original comment makes a claim about pytorch. it's all right there in black and white.

Which claim, this one?

> Most autodiff packages (such as Pytorch) use something not much more advanced than this, although there are optimizations to speed it up (e.g. reverse mode diff).

We get it: you're a mechanic who thinks that makes you an automotive engineer.

i haven't a clue what that means but when julia's autodiff is the only one that is implemented using dual numbers i think the claim is pretty obviously false and seemingly everyone has an issue admitting that. without a doubt you'll just say something else sarcastic rather than admit it.