> Where Python shines, though, is the prototyping phase when you have yet to find an appropriate model. We have yet to see this sort of easy & convenient protot

> Where Python shines, though, is the prototyping phase when you have yet to find an appropriate model. We have yet to see this sort of easy & convenient prototyping in C++. +1. To produce a highly-optimized C/C++ kernel that utilizes the CPU to the fullest extent, it requires tremendously amount of talent and expertise. For example, not everyone can write a hand-vectorized kernel with AVX2 intrinsics (outside a few specialized applications like 3D graphics, media encoding, and the likes), and even fewer people can exploit the underlying feature of the algorithm for optimization, such as producing usable output at greatly reduced numerical precision. The power of LLM provides strong motivation to drive the brainpower of countless programmers all over the world to do just that. New techniques are proposed and implemented on a monthly basis, with people thinking and applying every possible trick on the LLM optimization problems. In this regard, moving from Python to C is totally reasonable. In comparison, right now I'm working on optimizing a niche open-source scientific simulation kernel with a naive C codebase. Before me, there were hardly any contributors in the last decade. Python has its place because not everyone has a level of resource and expertise comparable to ML. In particular, when the bulk of the data processing of a Python script is in done in a function call to a C++ or FORTRAN kernel like scipy, the differences between naive C and naive Python code (or Julia code if you're following the trend) are not that much, especially when it's a one-off project for just publishing a single paper.