I think the biggest thing that is "light years ahead" is that numpy is built right in and insanely extensible. Libraries can be very lightweight / maintainable because they don't need to roll their own numpy like tensorflow pytorch etc. do (which makes them depend on bigcorps to improve and maintain). In julia somehow everything is compatible and composable. On the other hand counting from 1 is is quite a big issue for me and leads to constant errors when I switch between python and julia. Also the startup time it takes is a huge bummer. They should have an interpreted mode for debugging (as long as the debugger is unusable for e.g. breakpoints)

Is Julia better for these use-cases? I'd interested and would love some links/examples.

"Please don't think that Julia is only useful for 1. custom units 2. custom GPU kernels 3. Custom array types 4. custom bayesian priors. 5. AD through custom types 6. Task based parallelism 7. symbolic gradients with modeling toolkit 8. Agent based modeling 9. physics informed neural networks 10. abstract tables types ..."