[Teodolfo]

Julia provides no meaningful advantages. PyTorch and JAX are too good. For typical deep learning workloads, Julia will not easily have a speed advantage. Everything goes down to the same cuDNN kernels anyway.

Julia seems like an attempt at a better matlab, but the machine learning world moved to python first.

(Also 1-based indexing is almost as obnoxious as the 24/7 Julia shill brigade.)

[sidkshatriya]

> (Also 1-based indexing is almost as obnoxious as the 24/7 Julia shill brigade.)

You had me at 1-based indexing. This is sadly an anti-feature of Julia as far as I'm concerned.

I know that 1-based indexing is used in Fortran, MATLAB and Lua (and other places) -- but I just find 0-based indexing more natural to me. If I grew up using 1-based indexing I'd probably be saying the opposite but this is what I prefer now.

Julia is probably a more cleaner system overall. But then so is Deno compared to Node. The problem is that the alternative needs to be many times better not merely better. The "network" effort is quite strong in Python scientific computing this is still outweighing some of the real benefits of Julia.

It is always good to have alternatives. The quality of GCC improved when Clang came along. Julia's presence will keep Python on its toes. Both platforms will keep improving.

But, once again, Julia's 1-based indexing is something that adds unnecessary friction, for me at least.

[mickeyp]

What I miss about Pascal is its offset indices. You could start an array at 0, 1, 42, or what have you. Useful in rare circumstances!

[oivey]

Julia has this. It’s called OffsetArrays.jl. Ironically, it’s a common source of bugs because library authors don’t always anticipate them and loop over 1:length(A) rather than eachindex(A).