I agree with you, this is not an in depth look, could have been much more rigorous.

But then I think in some ways it's a much more accurate depiction of my use case. I mainly write monte-carlo simulations or simple scientific calculations for a diverse set of problems every day. And I'm not going to write a fast algorithm or use an unfamiliar library for a one-off simulation, even if the sim is going to take 10 minutes to run (yes I use scipy and numpy, but often those aren't the bottlenecks). This is for the sake of simplicity as I might iterate over the assumptions a few times, and optimized algorithms or library impls are not as trivial to work on or modify on the go. My code often looks super ugly, and is as laughably unoptimized as the bubble sort or fib(40) examples (tail calls and nested for loops). And then if I really need the speed I will take my time to write some clean cpp with zmq or pybind or numba.