Have you ever tried to iterate developing any serious class of algorithms in C++?
> Once you know what you actually want it to do
When is that exactly? Even the last few months of LLM land development show very clearly how everything is rapidly evolving (and will very likely continue for quite some time).
Numerical linear algebra stabilized decades ago so you do have low-level libraries in C++ (or even fortran) but there is quite some distance between an LLM and linear algebra.
Yes, and it's not so bad. A lot of ML deployments have been based on C/C++ for inference anyway (with Python driving the training). So that's really nothing new. I.e. most Python research code is not deployable in terms of quality / performance.
In the absence of alternatives its workable. Libraries like eigen or armadillo help a lot. But Python with numpy has been extremely popular for a good reason.
Not to mention, to really protect source code and all the sugar around the training systems, it's going to be a good investment to get out of hobby land and manage your own memory and just code them in C/C++.
It strikes me that the hobby AI ethos aligns very well with scripting languages in that they both assume the availability of endless resources to push things a little dirtier and messier and see if anything interesting emerges. Which is great for hobby AI. It's probably not the future, though, unless resource availability outpaces the imagination of people to write more and more bloated scripts to accomplish what's already been proven.