[cookieperson]

Diffeqs precompilation is particularly heinous if you aren't on a serious machine. but I think I misspoke. I thought we were discussing package compilation. Unfortunately though, even if we are discussing precompilation theres a ton of cost there even if you go beyond packages. Time to first anything is often brutal. See the discussions on time to first gradient... This is often up to the developer to work out, but it's a challenge that's pretty unique to Julia itself.

When I see people describing viable alternatives to python and or C I personally look at C++ and Rust. Julia's GC is good for most academic embarrassingly parallel number crunching things, but is rough for large scale applications. I've only been able to use Julia in a vacuum for research. For product development, every effort I've seen has eroded insanely fast due to things other languages control much more easily. Those languages can often also do the math fast enough too, especially when the cost of failed experiments is accounted for. All those wait three hours to find out your first gradient descent iteration had a type error that propagated to 1000 compute nodes($) moments are gone. It just can't happen in other paradigms, and in some paradigms it's far less likely to happen and when it does the cost is minor because the cost of compilation was already amortized.