Now try doing these tests on arrays of user defined types. Or use one of those fast functions as the kernel of an ODE solver and compare to julia. Anyone can game microbenchmarks on arrays of Float64s, doing so shouldn't impress anyone. If Numpy can do it, you know it's not impressive.

The point of Julia is that Float64 and Int aren't special. They're on the same footing as whatever types you cook up, and our performant multiple dispatch system lets you build a vibrant interacting package ecosystem filled with user defined types doing targeted overloads of other functions.

It's the multiple dispatch and composability of julia code that matters. The important part of the performance of Julia is that we can get the ecosystem benefits of writing pure julia code without paying with runtime performance penalties.