[endoftime5]

Just wanted to jump in here and say that for numerical computing Julia’s ecosystem of libraries are absolutely fantastic! DifferentialEquations.jl alone was worth switching to Julia!

Although I spent a long time optimising numerical code with Numba the speedup I got (whilst significant) wasn’t really comparable to the speed of a Julia implementation.

[spectramax]

I love some of their libraries as you mentioned. Just that when the language itself is painful to write code and debug it, the whole value proposition is diminished.

The core developers are of Julia are very smart folks, they want to develop a great language that's fast and easy to use. They missed the opportunity to restrict syntax, provide useful exception message (just look at Rust! it is a thing of art when you get an exception, it is beautiful), and generally provide good documentation.

For example, just creating a Julia local registry requires significant overhead and time investment. Spinning up a registry should not take more than 30 mins.

All these aspects of Julia are prohibitive and in my opinion, Julia should not be used in any company or production use until perhaps version 2. People who have dealt with Julia issues will tell you the truth - not the academicians or researchers. The people that maintain infrastructure/maintenance support for Julia apps are almost ready to quit their jobs. It sucks so bad.

[byt143]

>The people that maintain infrastructure/maintenance support for Julia apps are almost ready to quit their jobs. It sucks so bad.

Any specific examples?