Did #julialang end up kinda stalling or at least plateau-ing lower than hoped?
I know it’s got its community and dedicated users and has continued development.
But without being in that space, and speculating now at a distance, it seems it might be an interesting case study in a tech/lang that just didn’t have landing spot it could arrive at in time as the tech-world & “data science” reshuffled while julia tried to grow … ?
Can a language ever solve a “two language” problem?
So at my previous employer I developed using Julia a custom ML model which ran, but the performance just wasn’t good enough for what I needed despite trying to aggressively optimize. I ended up rewriting in Rust (and calling through R) which ended up being like 10x faster. At my current job I program a mixture of Rust and Python.
If Julia were more peformant then it could potentially be an alternative to Python/R users having to learn Rust - but if you’re looking for top performance, some of your codebase is already written in R/Python, and you’re already willing to learn another language, then learning something like Rust naturally seems the better choice over Julia.
The one thing I did like about Julia - it took barely anytime at all to build a working prototype.
@festus
In general, how much more performant would you say Rust is or can be than Julia? Any good resources on this?
What’s interesting about this take is that it targets the whole “two language” thing and implies that it might be a fool’s errand.