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I see the potential to speed up code a lot. Say that you could translate Python to C++ or Nim that would speed up the program a lot. You could profile and translate select hot often used code paths. This has the potential too save a lot of energy by using more efficient programs.


Cython already translates Python to C. And if the translation introduces a bug, the Cython developers can find a solution.

This ML crap is inexplicable, and nearly guaranteed to introduce subtle bugs and performance regressions.


That seems like a lot of effort when Julia is both high-level and efficient already.


Ok but I have 3 million lines of Python code, so that doesn't help me. I'm not gonna spend a year or two writing everything in Julia.


Good thing you don't have to: https://github.com/JuliaPy/PyCall.jl


I hear that you can use Python from Julia and vice versa. You could technically get started immediately and strangle the python away.


Let's rewrite everything in Julia.


There's no need to rewrite anything: https://github.com/JuliaPy/PyCall.jl

But yeah, I won't start any new projects in Python.




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