They seem to not have a lot of real world experience and/or throw caution to the wind and YOLO through security practices. I'd be weary using any of their products.
Most of my gripes are when trying to use Julia the way a software engineer would use a programming language.
Most "data scientist" code is exploratory (it's a prototype or a script for an one-off exploration) in nature. And my main gripe is that making that code production ready and maintainable over a long period of time is so difficult that I would switch to Rust instead. If I were going to switch to Rust, I might as well start with Python.
I think multiple dispatch (useful as it is) is a little overrated. There's a significant portion of the time where I know I have a closed set of cases to cover, and an enum type with a match-like syntax would have worked better for that. For interfaces, multiple dispatch is good but again I would have preferred a trait based approach with static type checking.
I largely think multiple dispatch works well in Julia, and it enables writing performant code in an elegant manner. I mostly have smaller gripes about subtyping and the patterns it encourages with multiple dispatch in Julia, and larger gripes about the lack of tooling in Julia.
But multiple dispatch is also a hammer where every problem in Julia looks like a nail. And there isn't enough discussion, official or community driven, that expands on this. In my experience the average developer to Julia tends to reach for multiple dispatch without understanding why, mostly because people keep saying it is the best thing since sliced bread.
wrt to hybrid languages, honestly, I think Python/Cython is extremely underrated. Sure you can design an entirely new language like Mojo or Julia, but imo it offers only incremental value over Python/Cython. I would love to peek into another universe where all that money, time and effort for Mojo and Julia went to Cython instead.
And I personally don't think Python/Rust is as bad. With a little discipline (and some tests), you can ensure your boundary is safe, for you and your team. Rust offers so much value that I would take on the pain of going through FFI. PyO3 simplifies this significantly. The development of `polars` is a good case study for how Rust empowers Python.
I think the Julia community could use some reflection on why it hasn't produced the next `polars`. My personal experience with Julia developers (both in-person and online) is that they often believe multiple dispatch is so compelling that any person that "saw the light" would obviously naturally flock to Julia.
Instead, I think the real challenge is meeting users where they are and addressing their needs directly. The fastest way to grow Julia as a language is to tag along Python's success.
Would I prefer a single language that solves all my problems? Yes. But that single language is not Julia, yet, for me.
> Telling what? Did you actually listen to the talk that you linked to, or read the top comment there by Chris Rackauckas?
To clarify exactly where I'm coming from, I'm going to expand on my thoughts here.
What is Julia's central conceit? It aims to solve "the two language" problem, i.e. the problem where prototyping or rapid development is done in a dynamic and interactive language like Python or MATLAB, and then moved for production to a faster and less flexible language like Rust or C++.
This is exactly what the speaker in the talk addresses. They are still using Julia for prototyping, but their production use of Julia was replaced with Rust. I've heard several more anecdotal stories of the exact same thing occurring. Here's another high profile instance of Julia not making it to production:
Julia as a community have to start thinking about what makes a language successful in production.
Quote from the talk:
> "(developers) really love writing Rust ... and I get where they are coming from, especially around the tooling."
Julia's tooling is ... just not good. Try working several hundred thousand line project in Julia and it is painful for so many reasons.
If you don't have a REPL open all the time with the state of your program loaded in the REPL and in your head, Julia becomes painful to work in. The language server crashes all the time, completion is slow, linting has so many false positives, TDD is barebones etc. It's far too easy to write type unstable code. And the worst part is you can write code that you think is type stable, but with a minor refactor your performance can just completely tank. Optimizing for maintaining Julia code over a long period of time with a team just feels futile.
That said, is Python perfect? Absolutely not. There's so many things I wish were different.
But Python was designed (or at the very least evolved) to be a glue language. Being able to write user friendly interfaces to performant C or C++ code was the reason the language took off the way it did.
And the Python language keeps evolving to make it easier to write correct Python code. Type hinting is awesome and Python has much better error messages (static and runtime). I'm far more productive prototyping in Python, even if executing code is slower. When I want to make it fast, it is almost trivial to use PyO3 with Rust to make what I want to run fast. Rust is starting to build up packages used for scientific computing. There's also Numba and Cython, which are pretty awesome and have saved me in a pickle.
As a glue language Python is amazing. And jumping into a million line project still feels practical (Julia's `include` feature alone would prevent this from being tenable). The community is growing still, and projects like `uv` and `ty` are only going to make Python proliferate more.
I do think Julia is ideal for an individual researcher, where one person can keep every line of code in their head and for code that is written to be thrown away. But I'm certainly not betting the near future on this language.
I'm excited to see `--trim` finally make it, but it only works when all code from entrypoints are statically inferrable. In any non-toy Julia program that's not going to be the case. Julia sorely needs a static mode and a static analyzer that can check for correctness. It also needs better sum type support and better error messages (static and runtime).
In 2020, I thought Julia would be _the_ language to use in 2025. Today I think that won't happen until 2030, if even then. The community is growing too slowly, core packages have extremely few maintainers, and Python and Rust are sucking the air out of the room. This talk at JuliaCon was a good summary of how developers using Rust are so much more productive in Rust than in Julia that they switched away from Julia:
I would really like to see more people who have never written C++ before port a Rust program to C++. In my opinion, one can argue it may be easy to port initially but it is an order of magnitude more complex to maintain.
Whereas the other around, porting a C++ program to Rust without knowing Rust is challenging initially (to understand the borrow checker) but orders of magnitude easier to maintain.
Couple that with easily being about to `cargo add` dependencies and good language server features, and the developer experience in Rust blows C++ out of the water.
I will grant that change is hard for people. But when working on a team, Rust is such a productivity enhancer that should be a no-brainer for anyone considering this decision.
afaict, for that project they never went through PCI compliance. See original thread for more information: https://news.ycombinator.com/item?id=40228751
They seem to not have a lot of real world experience and/or throw caution to the wind and YOLO through security practices. I'd be weary using any of their products.