# What lessons could Julia's autodiff ecosystem learn from Stan's TinyGrad?

**URL:** <https://discourse.julialang.org/t/what-lessons-could-julias-autodiff-ecosystem-learn-from-stans-tinygrad/103070>\
**Category:** Machine Learning\
**Created:** [August 22, 2023, 2:50pm UTC](https://discourse.julialang.org/t/what-lessons-could-julias-autodiff-ecosystem-learn-from-stans-tinygrad/103070 "2023-08-22T14:50:56Z")\
**Posts on this page:** 1\
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**Author:** ![aplavin](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/aplavin/32/222056_2.png) [@aplavin](https://discourse.julialang.org/u/aplavin)\
**Post date:** [August 21, 2023, 9:36pm UTC](https://discourse.julialang.org/t/what-lessons-could-julias-autodiff-ecosystem-learn-from-stans-tinygrad/103070/2 "2023-08-21T21:36:47Z")

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> [@What steps should the Julia community take to bring Julia to the next level of popularity?](https://discourse.julialang.org/t/what-steps-should-the-julia-community-take-to-bring-julia-to-the-next-level-of-popularity/101389/395):
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> Stan built a world-class autodiff system with a team of two people in about a year, and TinyGrad is now mostly usable after 2-3 years of work by one programmer. Why have 5 years of research, including several paid full-time engineers and PhDs, failed to do the same for Julia?

As for autodiff, I want to really appreciate how ForwardDiff works in Julia! It felt so nice after Python _(a few years ago, maybe they have this now with some magic, idk)_ to be able to just differentiate any function you throw at it.

Yes, it’s forward mode and isn’t suited for large numbers of inputs — but for other usecases it’s straightforward, painless, and totally general.

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