Pushback notwithstanding, this article is 100% correct in all PyTorch criticisms. PyTorch was a platform for fast experimentation with eager evaluation, now they shoehorn "compilers" into it. "compilers", because a lot of the work is done by g++ and Triton.
It is a messy and quickly expanding codebase with many surprises like segfaults and leaks.
Is scientific experimentation really sped up by these frameworks? Everyone uses the Transformer model and uses the same algorithms over and over again.
If researchers wrote directly in C or Fortran, perhaps they'd get new ideas. The core inference (see Karparthy's llama.c) is ridiculously small. Core training does not seem much larger either.