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.

> That's pretty much a solved problem in Python now - thanks to the well developed ecosystem,

I don't understand this argument. The same C and Fortran libraries that make numeric Python fast can be called just as easily from any other programming language, just like in Python.

> you don't really have to leave Python to write fast numerical code.

Well, you need to, for example, if you want to write a new numeric algorithm to call from python. As in, not use a numerical solver, but write a numerical solver from first principles. For that, Python is mostly useless and you'd need to use C or Fortran (or Julia).