How an MIT research project became a global programming language

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• Hacker News
• Do you think the criticism in the article "Why I no longer recommend Julia" about correctness is still valid? * there are too many correctness and composability bugs throughout the ecosystem to justify using it in just about any context where correctness matters *

  https://yuri.is/not-julia/

  by radiator
• When I was a grad student at MIT, my desk was across the hall from the Julia people. We even shared a lunch table. Julia is most famous for its optimizing compiler, but many people may not notice the careful work that went into designing the language itself. Matlab this was not! I remember one long conversation where one of them was teaching me about the different types of normalization of unicode. They had to pick one to canonicalize unicode variable names (more useful for a mathy programming language), and they were carefully considering the consequences of the options.
  by drhagen
• Julia is my go-to when I need a fast language that I can reason about in a functional way. The type system is fine, the pattern matching is pretty darn good, the metaprogramming story could be significantly better but it’s not bad and boy howdy it’s fast. So stinking fast.
  by ashton314
• Julia is really fun to use, and I think they are onto something with the type system: Complex types interact much better across module boundaries than in most other languages; here's a good talk on this by one of the creators:

  https://www.youtube.com/watch?v=kc9HwsxE1OY

  The biggest drawback of the language in my view is that Julia is an LLVM toolchain "pretending" to be an interpreted language (it is, technically), this often leaks as very slow first execution latency (and almost forces you to keep the interpreter open instead of just calling it on the file you're editing).

  by myrmidon
• I really want to like Julia. It has a nice type system, a good ecosystem, reasonable syntax, and it’s far faster than Python. But there are several issues with its DX that prevent me from using it: the most severe of which being the complete lack of a cache for the JIT (or JIT like system), inducing multi second compile times for scripts that run in <100ms. There are some external packages that try to solve this issue, but they are far from first-party quality, and, in my experience, are quite buggy.

  (I last tried Julia a few years ago; perhaps this has been improved since?)

  by maxall4
• I did some Julia benchmarking over the weekend against a bunch of languages if anyone is interested. The workload is an EKF on an Apple M1, I'm trying to do as many as possible in parallel:

  https://raw.githubusercontent.com/mech-lang/mech/codex/taich...

  Compared against Taichi, Halide, Futhark, Rust', Lua, LuaJIT, Numpy and pure scalar Python. Julia holds up great it can run basically as fast as you'd like! (These are not apples-to-apples comparisons, picture this as a basket of fruit)

  'The Mech results should be understood to be a lower bound on how Rust would perform.

  by cmontella
• Julia is fantastic if you do numerical work and want to write out your inner loops explicitly without sacrificing too much performance, either for pedagogical reasons or because you want to fiddle with the algorithm.

  It is a lot of fun to start from an empty file, add maybe an import LinearAlgebra, and develop things like a convolutional neural network or a Markov-chain Monte Carlo algorithm completely from scratch. And then it is very rewarding to have such a program be fast enough (looking at Python here) to train on the MNIST dataset or find reasonable estimates for critical exponents.

  I am not sure there are languages better suited than Julia for these kind of things.

  by prof-dr-ir
• Julia is such a great language, it's what really made me care about end enjoy programming. I think it's the modern language which has taken the most lessons from lisps, and has come up with some great ideas of its own too.

  There's lots to like, but I think the thing I love most about it and find it so interesting is that it's almost uniquely good at taking a piece of code and transforming it's meaning in various ways, and has so many tools for doing so. There's

    * Multiple dispatch allowing very flexible writing of generic code, and multiple dispatch isn't some tacked on, opt-in extra. Every function in the language is overloadable, and there's no performance penalty for using multiple dispatch
  
    * Parametric typing allows for a huge amount of abstraction over common 'base' types
  
    * Lispy macros let you do metaprogramming that changes the meaning of a piece of syntax
  
    * Generated functions let you intervene at compile time and lets you essentially take over the compilation pipeline and customize the code generation for any given input type signature
  
    * The abstract interpreter interface which lets one essentially take over the compiler and customize your code generation and analysis passes to your heart's content. This is used for instance to support GPUs and automatic differentiation as package offerings.

  by eigenspace