There's no language I'd want to teach (applied) math in more than Julia. Lean is obviously kind for anything involving a proof, but for teaching anything with linear algebra involved the syntax alone is enough of a reason to prefer Julia.
Optimization, specifically the kind that JuMP tackles, is another field where teaching is probably easier in Julia than any other language
Anyone using Julia outside university settings? I tried to get into it around 5 years ago, and it felt good, was conceptually interesting, but haven't seen any adoption or talks anywhere outside the academia
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.
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.
There's no language I'd want to teach (applied) math in more than Julia. Lean is obviously kind for anything involving a proof, but for teaching anything with linear algebra involved the syntax alone is enough of a reason to prefer Julia.
Optimization, specifically the kind that JuMP tackles, is another field where teaching is probably easier in Julia than any other language
I'm just picking up Julia for my DSP course at uni, and came across this nice intro. It's very well-paced for a first introduction to programming!
Anyone using Julia outside university settings? I tried to get into it around 5 years ago, and it felt good, was conceptually interesting, but haven't seen any adoption or talks anywhere outside the academia
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.
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.
I know Microsoft made a geometric optics package for it they apparently used for VR hardware.
All these folks, https://juliahub.com/case-studies
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.
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.