Yes - in multiple companies. It works perfectly fine in production, and is especially useful if mathematical optimisation is part of your app somewhere or you need to write any custom, performant algorithms. It's pretty flexible - you can use it for dynamic code like python scripts, you can write optimised data pipelines, it can function as pretty decent server (the async stuff is working well these days). Not sure on adoption, but it defintely works.

Been using it daily for years now, even at work. Nowadays you can just compile as a library that you can share with other languages.

As a language it’s very pluggable (both directions). I think it’s one the best languages in terms of interop out there at the moment.

Heavy avoid. I've seen companies sink so much time and money into it. What should be an engineered released product become an expensive academic consortium of never ending work to improve the language and the ecosystem. Some people make careers out of getting hired to fix Julia at various companies...

Yes, in the aerospace industry. We love it.

At a new-space startup I've worked at in the past we used it for some satellite (constellation) analysis work. Mechanical, Power, ConOps, that sort of stuff. MATLAB was too expensive and also overkill, as we didn't really need the toolboxes.

I know Microsoft made a geometric optics package for it they apparently used for VR hardware.

All these folks, https://juliahub.com/case-studies

I use daily since years at work for all things bioinformatics/research. I think it's a more fun language than most so it help me keep my sanity.

I saw it used five years ago on a LessWrong post: https://www.lesswrong.com/posts/Y6TtPfcKiHjK9Bmoa/sleeping-j...

That's the only time I've ever even heard it mentioned outside HN. I felt a lot of excitement for Julia's automatic differentiation during the ML era, because you had programmers with weak math backgrounds suddenly needing to do basic calculus (any dev from that era has the chain rule burnt into their brain).

Then the ML community settled on Python as their language, and if performance was an issue you wrote a library in C++, and obviously that was all sucked into the event horizon of LLMs so people "code" in whatever language the LLMs had the best training data for.

There are open references to usage by

ECMWF ASML Pumas JPMorgan Chase

and many other companies.

I use it routinely, but it isn't a company standard where I work.

looking into it more with the release of juliac. i am able to keep algorithms in one place at a high level for simulation/visualization and running things real time in julia, then if I do everything right i can compile it down to a binary for other platforms using juliac

I use it for network science research. It's definitely my language of choice for (scientific) computation.

I used to deliver Julia models wrapped in mex to customers using Matlab, this was during v0.3 if memory serves. It worked pretty well.