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Comment by jampekka

1 day ago

Maybe because Python can reasonably used to make actual applications instead of just notebooks or REPL sessions.

8 comments

jampekka

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yellowapple  1 day ago

What's stopping Julia from being reasonably usable to make actual applications? It's been awhile since I've touched it, but I ain't seeing a whole lot in the way of obstacles there — just less inertia.

  • zem  34 minutes ago

    I was excited about julia as an application development language when it first came out, but the language and ecosystem seem to be targeting long-running processes. there was just a ton of latency in build time and startup time for things like scripts and applications, so I moved on.

  • fc417fc802  18 hours ago

    Presumably inertia and ecosystem size (but that's a follow on of inertia). When Julia came out Python already had traction for ~most things.

    Keep in mind that it went with 1 based indexes to make the switch easy for Matlab types. I'm not sure if that was a good or bad move for the long term. I'm sure it got some people to move who otherwise wouldn't have but conversely there are also people like me who rejected it outright as a result (after suffering at the hands of 1 based indexing in Matlab I will never touch those again if I have any say in the matter).

    I've considered switching to it a few times since seeing that they added variable indexes but Python works well enough. Honestly if I were going to the trouble of switching I'd much rather use Common Lisp or R5RS. The nearest miss for me is probably Chicken, where you can seamlessly inline C code but (fatally) not C++ templates.

    If I ever encounter "Chicken, except Rust" I will probably switch to that for most things.

    • seertaak  10 hours ago

      That's part of the answer, but there's a bit more to it IMO.

      The syntax is a bit weird; python, swift, rust, and zig feel more parsimonious.

      I absolutely love multimethods, but I think the language would have been better served by non-symmetric multimethods (rather than the symmetric multimethods which are used). The reason is that symmetric multimethods require a PHD-level compiler implementation. That, in turn, means a developer can't easily picture what the compiler is doing in any given situation. By contrast, had the language designers used asymmetric multimethods (where argument position affects type checking), compilation becomes trivial -- in particular, easily allowing separate compilation. You already know how: it's the draw shapes trick i.e., double-dispatch. So in this case, it's trivial to keep what compiler is "doing" in your head. (Of course, the compiler is free to use clever tricks, such as dispatch tables, to speed things up.)

      The aforementioned interacts sensitively with JIT compilation, with the net outcome that it's reportedly difficult to predict the performance of a snippet of Julia code.

  • zamalek  1 day ago

    It's actually better suited IMO, being a compiled language. I'm not sure how anyone could consider the current train wreck of getting python code just to run "actual applications." uv is great and all, but many of these "actual applications" don't use it.