The first problems I came to Julia to solve were ODE simulations in which it's just as critical that the stuff I write and rewrite over and over again (the actu

The first problems I came to Julia to solve were ODE simulations in which it's just as critical that the stuff I write and rewrite over and over again (the actual differential equations of my model and associated simulated controller callbacks) runs fast as it is that the pre-written stuff of the library (the solvers or integrators) runs fast. To the best of my knowledge I can't do that in python. This example (DifferentialEquations.jl in particular and SciML.ai in general) is one of the places where Julia leads the pack. When I first used Julia it felt rather strange in a lot of ways (I had used python for about 20 years), but after 2 years when I go back to python (and of course there are places where it makes more sense to use python) it feels primitive as there are a lot of very nice features of Julia that I suddenly miss.