These are just a few of the concepts being implemented, and many more are in the pipeline. What’s clear from this mosaic of techniques is that no one thing will magically unlock faster speeds in Python. It also shows that the path to making things faster is to figure out, whenever possible, how to make the interpreter do less—avoid type checks, for instance, or perform less reference counting.
Why faster Python must come from within Python
Before this recent wave of optimizations started landing, a common line of thought went like this: Instead of making Python itself faster, or even making a new Python runtime (PyPy), why not make a new language that’s highly compatible with Python, and gradually transition Python users over to that new language?
That’s more or less the approach fronted by projects like Mojo. Mojo offers a Python-like syntax, and even a degree of backward compatibility with existing Python. But unlike Python, it compiles by default to native machine code.
