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Telling what? Did you actually listen to the talk that you linked to, or read the top comment there by Chris Rackauckas? > Given all that, outside of depending

• wolvesechoes 1y ago
  Python has useful and rich ecosystem that grows every day. Julia is mostly pile of broken promises (it neither reads as Python, nor it runs as C, at least not without significant effort required to produce curated benchmarks) and desperate hype generators.
  • tagrun 1y ago
    Since you have a rosy picture of Python, I assume you're young. Python has been mostly a fringe/toy language for 2 decades, until around ~2010, when a Python fad started not too different from the Rust fad of today, and at some point Google started using it seriously and thought they can fix Python but gave up eventually. The fad lived on and kept evolving and somehow found some popularity with SciPy and then ML. I used it in 90s a little, and I found the language bad for anything other than replacing simple bash scripts or simple desktop applications or a desktop calculator, and I still think it is (but sure, there are people who disagree and think it is a good language). It was slow and didn't have type system, you didn't know whether your code would crash or not until you run that line of code, and the correctness of your program depended on invisible characters. "Ecosystem" is not a part of the language, and in any case, the Python ecosystem is not written in Python, because Python is not a suitable language for scientific computing, which is unsurprising because that's not what it was designed for. It is ironic you bring up hype to criticize Julia while praising Python which found popularity thanks to hype rather than technical merit. What promise are you referring to? Who promised you what? It's a programming language.
    • wolvesechoes 1y ago
      > Ecosystem" is not a part of the language, and in any case, the Python ecosystem is not written in Python, because Python is not a suitable language for scientific computing Doesn't matter. Languages do not matter, ecosystems do, for they determine what is practically achievable. And it doesn't matter that Python ecosystem relies on huge amounts of C/C++ code. Python people made the effort to wrap this code, document it and maintain those wrappers. Other people use such code through Python APIs. Yes, every language with FFI can do the same. For some reason none achieved that. Even people using Julia use PythonCall.jl, that's how much Python is unsuitable. > What promise are you referring to? Who promised you what? It's a programming language. Acting dumb is poor rhetorical strategy, and ignores such a nice rhetorical advice as principle of charity - it is quite obvious that I didn't mean that programming language made any promise. Making a promise is something that only people can do. And Julia creators and people promoting it made quite bombastic claims throughout the years that turned out to not have much support in reality. I leave your assumptions about my age or other properties to you.
      • tagrun 1y ago
        If what determines the value of a language libraries (which makes no sense to me at all, but let's play your game), then it is one more argument against Python. You don't need FFI to use a Fortran library from Fortran, and I (and many physicists) have found Fortran better suited to HPC than Python since... the day Python came to existence. And no, many other scripting languages have wrappers, and no, scientific computing is not restricted to ML which the only area Python can be argued to have most wrapper libraries to external code. Language matters, and two-language problem is a real problem, and you can't make it go away by closing your ears and chanting "doesn't matter! doesn't matter!" Julia is a real step toward solving this problem, and allows you to interact with libraries/packages in ways that is not possible in Python + Fortran + C/C++ + others. You are free to keep pretending that problem doesn't exist. You are making disparaging and hyperbolic claims about hyperbolic claims without proper attribution, and when asked for source, you cry foul and sadly try to appear smart by saying "you're acting dumb". You should take on your advice and instead of "acting dumb", explicitly cite what "promises" or "bombastic claims" you are referring to. This is what I asked you to do, but instead of doing it, you are doing what you are doing, which is interesting.
• blindseer 1y ago
  > 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: https://discourse.julialang.org/t/julia-used-to-prototype-whatsapp-audio/115626/4 https://discourse.julialang.org/t/julia-used-to-prototype-wh... Julia is failing at its core conceit. 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.