> Have you ever tried to iterate developing any serious class of algorithms in C++?
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.
> Yes, and it's not so bad.
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.
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.