I am a machine learning library developer and I don’t share your feelings. For example the specific example you cite, I feel, should never be something scientists or engineers actively think about, only language implementers.
Once you make that distinction, then whether you write it as a Cython module exposed in Python or you can use native language features to do it in Julia, nobody cares. It’s encapsulated away from people who use numerical libraries, as it should be.
I also spend time developing these “runtime overhead avoidant” backend numerical libraries, and I would say I’ve seen no significant reason to prefer Julia over Cython.
Don’t getme wrong, Julia is great, just not offering anything fundamentally different. And since there’s already a critical mass of people with engineering and optimization experience in the Cython & Python extension module stack, I’d expect that community to continue dominating Julia just by attrition alone.
Once you make that distinction, then whether you write it as a Cython module exposed in Python or you can use native language features to do it in Julia, nobody cares. It’s encapsulated away from people who use numerical libraries, as it should be.
I also spend time developing these “runtime overhead avoidant” backend numerical libraries, and I would say I’ve seen no significant reason to prefer Julia over Cython.
Don’t getme wrong, Julia is great, just not offering anything fundamentally different. And since there’s already a critical mass of people with engineering and optimization experience in the Cython & Python extension module stack, I’d expect that community to continue dominating Julia just by attrition alone.