As such, I do think the article misses the point somewhat. Of course, if there's a numpy function that does what you want, you'd use it in real life. But what if there isn't? The nice thing about Julia is that the function can be written in Julia itself, and fast.
> It is important to note that these benchmark implementations are not written for absolute maximal performance (the fastest code to compute fib(20) is the constant literal 6765). Rather, all of the benchmarks are written to test the performance of specific algorithms implemented in each language. In particular, all languages use the same algorithm: the Fibonacci benchmarks are all recursive while the pi summation benchmarks are all iterative; the “algorithm” for random matrix multiplication is to call the most obvious built-in/standard random-number and matmul routines (or to directly call BLAS if the language does not provide a high-level matmul), except where a matmul/BLAS call is not possible (such as in JavaScript). The point of these benchmarks is to compare the performance of specific algorithms across language implementations, not to compare the fastest means of computing a result, which in most high-level languages relies on calling C code.