Yawn. You can have all those wrapped under a high level API, with much nicer implementations, with more support, more documentation, more eyeballs (due to more adoption) in a "slow" language.
You can even have them run faster than Julia's and in more stable and mature implementations (like there's LAPACK).
* 95% of your users can't contribute or even understand what's going on, because they don't understand the language it's written in
* even if you do understand, you have to write in a much less expressive language (you came using Python for a reason, but end up doing all the hard work in C)
* performance gets lots at the boundary between the languages (which is why Julia nearly always outperforms Numpy)
* error messages and bugs often fall through the crack of the two languages
The fundamental argument "you can just use Python, you just have to write all the hard stuff in not-Python" is very strange to me.
That's the very benefit of a larger ecosystem: you don't. Whereas with a smaller ecosystem, you often do.
>95% of your users can't contribute or even understand what's going on, because they don't understand the language it's written in
That's even more of a problem with Julia, since the code written in Julia will be unfamiliar to both first level users (much much fewer use Julia than Python) and to library developers (fewer know Julian than C/C++, which such Python libs are written in).
You 'can' have those things, but 'should' you? Julia is well designed, so 'nicer' is not a given. Julia is also quite a high level language with excellent metaprogramming facilities. Otherwise, everything you listed is based on adoption level.
Why use a "slow" language if you don't have to? Especially when the faster language is actually better designed...
As opposed to what? Wait for Julia to catch up in 10 years?
If so, yes, you absolutely should get those things from where they're already available.
>Why use a "slow" language if you don't have to?
Because that's where the action is, and who gets those niceties first.
Julia hasn't even fixed their slow startup/load situation all these years...
Julia seems to be gathering momentum rather than losing it. The future looks bright if trends continue!
(As to the startup time problem, significant progress is being made... Have you looked at standalone binaries?)
import simulation from 'simulation_code_written_in_c'
simulation.run()
What value does the above code have?
Code that e.g. can be configured to load data in a particular way, and then run in a particular way, and is then run, hella fast in C, does. Numpy is not:
import nympy numpy.run
Neither is pandas and others...
You might as well ask "what is the point of glue languages". Just ask anybody who ever embedded e.g. Python/Lua/Guile/etc in a C/C++ program (which has similar mechanics)...