The editor autocompletion and standard library documentation could use a lot of work. The introductory tutorials are overly focused on type theory and details and do not give a good overview of which generic data structures to use in production code. Julia's JIT is very different from other conventional mainstream languages and the process of selecting standard library generic data structures for optimal performance is very poorly documented.

There is no Effective Julia style of guide. You either have to wade through infantile tutorials for those with minimal programming experience or several reference books worth of nitpicking on syntax. The actual methods themselves are not well documented and lack examples and usage guidelines.

The language and ecosystem do not feel like a project backed by commercial funding, it feels like one of those functional languages out of academia research where the structure and design of the language are more important than actual developer experience. There are many new projects but most are not actively maintained and updated. The language itself feels massive, with syntactic sugar and weird types everywhere. Trying to understand the implementations of other people's Julia code is frustrating, similar to reading a library written in pure C++ templates. Compared to Go/Rust/Dart, Julia feels overly convoluted. Julia literature is structured in a way that seems to heavily encourage you to take regular classes and lectures to learn and pick up the language. It is hard to feel productive from the get-go.

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.

>the dependency on using the REPL

This is a feature for a lot of Julia's core audience (data scientists like me, who grew up with R).

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.

As it turns out I really don't like notebooks, but that is due to the state issues - so it sounds like I should check out Pluto. Thanks!

I actually kinda do like Julia's syntax, but the OP's comments about poor introductory documentation for experienced programmers definitely ring very true.

PRs to improve docs are always welcome (especially from people who are new to Julia).

In order to make that PR I would first need to learn Julia, which is what I want the docs for.

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.