I've worked quite extensively for Julia as my primary language for quite some time at this point. I've gone from being absolutely in love with the language to extremely disillusioned over the last couple of years.
It is incredibly hard to reason about this language if every developer is not willing to commit to understanding and writing Julia code the right way (e.g. type stable, dispatch via the type system, actually defining interfaces). Given the target demographic of academics, the proportion of people interested in grokking Julia is vastly smaller than the number of academics who hear "walks like Python runs like C" and decide this is their new, free, hip MATLAB/SciPy replacement.
And it's extremely good for that! The SciML ecosystem is top of the class, I would never recommend someone solve a differential equation any other way. If you can build a system that is just a thin wrapper on top of SciML, absolutely go for it. I would recommend anyone wanting to build new systems from scratch to look elsewhere.
But, the tooling is subpar and overfit to the VS Code extension, and even there I found so many false positives with the linter that I just started ignoring all but the most basic of warnings. It's not entirely the fault of the developers, Julia is dynamically typed and there's only so much you can do. But if you have to type things rigidly enough that it's inferrable while developing that Cthulhu.jl or JET.jl can infer the types, why not just work with a static type system where you get that for free?
I have also found the obsession with reducing time to first plot incredibly aggravating. It is finally down to a pretty good level, which is a nice quality of life improvement. However, it has done absolutely nothing for my ability to write code that doesn't crash because someone passed in a type that doesn't obey the implicit interface I expect.
It is incredibly hard to reason about this language if every developer is not willing to commit to understanding and writing Julia code the right way (e.g. type stable, dispatch via the type system, actually defining interfaces). Given the target demographic of academics, the proportion of people interested in grokking Julia is vastly smaller than the number of academics who hear "walks like Python runs like C" and decide this is their new, free, hip MATLAB/SciPy replacement.
And it's extremely good for that! The SciML ecosystem is top of the class, I would never recommend someone solve a differential equation any other way. If you can build a system that is just a thin wrapper on top of SciML, absolutely go for it. I would recommend anyone wanting to build new systems from scratch to look elsewhere.
But, the tooling is subpar and overfit to the VS Code extension, and even there I found so many false positives with the linter that I just started ignoring all but the most basic of warnings. It's not entirely the fault of the developers, Julia is dynamically typed and there's only so much you can do. But if you have to type things rigidly enough that it's inferrable while developing that Cthulhu.jl or JET.jl can infer the types, why not just work with a static type system where you get that for free?
I have also found the obsession with reducing time to first plot incredibly aggravating. It is finally down to a pretty good level, which is a nice quality of life improvement. However, it has done absolutely nothing for my ability to write code that doesn't crash because someone passed in a type that doesn't obey the implicit interface I expect.