35 years on since the first public Python release and there's still so much room for improvement in its performance. These tests by Miguel are a useful quick check on that progress in 3.15 even if as he admits it's impossible to get "an objective and universal measure of the performance of a programming language".
So Claude can convert python code bases to rust or golang, and give you an easy 10x speed boost. Much better than waiting for Python performance to improve
2 benchmarks only? A very broad sense of coverage
My pet peeve are numbers with too many decimals. If you only run a benchmark three times and take the arithmetic mean you don't have five or more significant digits. At best, you have two. And for benchmarking the geometric mean is a far superior mean.
Not as fast as https://github.com/shedskin/shedskin
you should include a faster version of the fibonacci function with exponentiation by squaring
see https://oeis.org/wiki/User:Natalia_L._Skirrow/linear_recurre... (warning: old and bad and in need of revision), https://github.com/sympy/sympy/pull/30452 and https://github.com/sympy/sympy/pull/30541 for details of how to make similarly fast programs for arbitrary linear-recurrent sequences.you can also encode polynomials into integers; see https://mathstodon.xyz/@peterluschny/116320199782572958 and the following prog from https://codegolf.stackexchange.com/a/279771
both of these would be more intensive on the arithmetic side rather than control flowalso you could at least wrap the existing one in a `functools.cache`