Someone else commented with more detail, but personally I can't get past the package management and the dependency on using the REPL. Rust gets tooling and packages right.
exactly right. REPL is a key feature for computational scientists. in physics, for example, the value proposition of python was that it provided an open source alternative to matlab (also a REPL based environment) without sacrificing functionality. i believe that the really revolutionary thing with python was that it provided an extremely fertile ground for open source development of numerical methods that far exceeded what was offered on matlab in syntax that resembled pseudo code (much like matlab). julia's value proposition is all of that, plus a much more performant base language with arguably even better syntax
Is that REPL usage still relevant in a world of Jupyter notebooks?
Getting started with Julia always just feels clunky to me - perhaps the other commenter was closer to the mark in blaming the documentation rather than the REPL itself. Either way, despite being a former scientist who has moved into IT (sadly), I get the distinct impression that the language is just not aimed at me. As such, I'm always surprised to see people trying to push it in settings outside its current realm of adoption; feels very much like the language maintainers have no real interest in that.
REPL is pretty similar to jupyter. Julia works well with jupyter, but if you like notebooks, you should definitely look at Pluto. It's a reactive notebook so it automatically tracks cell dependencies and makes it so your notebook is never in an inconsistent state.
I actually kinda do like Julia's syntax, but the OP's comments about poor introductory documentation for experienced programmers definitely ring very true.
I don't dispute that, but for people not noodling around with data who just want to make something immediately useful (ie general purpose programming use-cases), the Julia approach to dependency packaging is sub-par.
This is a feature for a lot of Julia's core audience (data scientists like me, who grew up with R).