Ah, the statefulness foot-gun. It'll solve all your problems... if your problem is having feet!

Ok, that's a little mean, there's nothing quite as convenient and simple as a REPL or notebook for ad-hoc caching, but sometimes you need the opposite and the language really needs to support that.

I often chain unix tools, domain-specific binary executables and python scripts in shell. Julia feels very slow for this type of workflow. A few years ago there were discussions about whether Julia could replace Python. No way just due to Julia's long startup time.

Ditto.

I can't wait for Julia to murder Python but before it can do that it needs to push all delays out of the interactive parts (to include both REPL and cold launch) and into the package management parts, where people will tolerate delays. Anaconda decided to stick a full SAT solver into the package management and it's every bit the shitshow that you'd expect, frequently taking 30 minutes to solve and failing to converge, but people tolerate it because it's in the package management.

I have been using anaconda much less often due to its long resolving time... That said, I fully agree with you that Julia should cache precompiled binaries. Lacking this key functionality is a misstep from the very beginning.

That's my least favorite thing about R - it just looooves to save state unless you configure it otherwise. This is also why I've had trouble adopting Julia in the past - I've adopted the habit of constantly resetting my workspace or working from scripts to avoid exactly this issue, so my habits are somewhat Julia-hostile.

I'm giving it another go though, since it seems like it could be really useful for algorithm-adjacent research.