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.

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?)

• adgjlsfhk1

  today at 3:30 AM

  a between session cache had been merged for 1.14 (release expected within 6-12 months).

  • JanisErdmanis

    today at 4:28 AM

    What is this magic, how does it differ from PkgImages Julia already has?

• bandrami

  today at 3:48 AM

  [dead]

Thought that it would be about Lisp....

• manwe150

  today at 3:48 AM

  It was about lisp ;)

  Secret mode: ./julia —-lisp

Julia uses 1-based indexing. It's competes with R and Matlab for the same set of users. Both R and Julia have their core functions written in C++. Absolutely nothing new.

From my experience, grad students use Julia when their PI thinks a new programming language will help differentiate their next NSF proposal among vast funding requests.

• adgjlsfhk1

  today at 3:51 AM

  > Both R and Julia have their core functions written in C++. Absolutely nothing new.

  imo this isn't really a good summary. Julia is one of the 3 languages to have done an exascale HPC run https://arxiv.org/pdf/2309.10292v1 (Fortran and C are the other 2). Some parts of the compiler are written in c++ (the llvm interface), but doing codegen with llvm is much more similar to C/Rust/Fortran than R/Matlab

• skew-aberration

  today at 5:00 AM

  It kinda feels like Julia competes for the people who write the libraries for R and Matlab. Writing fast and elegant ODE solvers, etc in Julia seems to be easier than the others and they've attracted a lot of academics for that reason.

Yet another example of MIT taking far too much credit for something...

• slwvx

  today at 4:51 AM

  I've seen JuliaHub taking credit for all of Julia before. I don't think there's anything new in this article

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