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Comment by ptero | Hacker News Reader

I think your "ecosystem of python libraries" is the key point. Python got a lot of mileage for a mass adoption from ML. Its libraries provided an "easy ML" for masses at the time ML got popular in science and job market, which quickly brought it into mainstream and built up its network effect.

A similar enabler in a new field could help Julia burst in as a general language. My 2c.

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Autodiff is a place where there is a gulf between Julia and Python, one that I think can't be bridged well: JuliaDiff is astonishingly flexible and performant.

https://www.juliadiff.org/

Urg that website is so incredibly out of date. Julia has amazing things for autodiff. But like not the things listed on that website.

Also python is still doing great with Jax and PyTorch.

I linked to the website (which was updated in May, but its contents could do with more work) because it has examples of how well the suite fits together.

I don't know much about Jax. I've seen competent benchmarks showing an order of magnitude benefit for using ReverseDiff from the AutoDiff suite over Autograd, which is what Pytorch uses for reverse-mode autodiff

It was update in May to fix that the CDN changed. The actual content hasn't been updated in 7 years.

It really should be updated to have basically the content of this thread https://discourse.julialang.org/t/state-of-automatic-differe...

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