Yeah, Julia’s type system is really just not designed for the sort of error catching patterns people seem to want static type systems for. Instead, Julia’s type system is designed for enabling multiple dispatch which I’d argue is a much greater boon than the dubious claims of error catching due to static type systems.

However, you can simulate static typing with the @inferred macro from Test.jl. It will throw an error if all types are not infer able ahead of time which is essentially static typing.

How much do you leverage the REPL when you're developing? In my experience with "old Python" (I've yet to update my resume with new Python) I developed in an incremental manner making heavy use of the REPL, I never had the pains I do in static languages without a REPL story. Your comment to me reads like "Nowadays with modern tooling [that lets me develop Python like Java developers develop Java], it's terrific." My day job is Java with a powerful IDE that is crucial for my productivity, but this is because I'm trapped in the "write a comet mass of code interacting with a solar mass of other code and lean on the compiler and IDE tools in addition to my reason to tell me it has a chance of working" way of developing rather than sending small chunks of code to a REPL, testing immediately, and building things up. If I had to (or only knew how to) develop old Python in the Java way I'd probably go mad too. TDD can almost sometimes substitute for a REPL in JavaLand, but it has its own flaws... (And yes modern Java now has something like a REPL, my work environment unfortunately hasn't updated to support it. Soon.)

this. I find it hilarious that people keep comparing a python release from 2010 with something that was released in O(months). Modern python - mypy, attr/dataclasses has come a long way. Numba has also come a long way since it's initial release.