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?
I think it really strongly depends on what you’re programming - I know in some instances Julia’s performance can be nearly identical to languages like Rust. I suspect in my case it related to Julia being a garbage collected language, as my algorithm involved creating very large dynamic structures in memory before serializing them, clearing the memory, and building another one. Since Rust has no garbage collector it knew exactly when and what to drop from memory. In my case I had roughly a 10x(!!) speed-up. Funny enough an even earlier version of that algorithm was programmed in Java, and Julia was roughly 10x faster that it, so Julia isn’t the worst of the pack.
