This article is so hopelessly mis-informed, that it's practically hard to read without screaming "that's not how any of this works!" Most of their main arguments - type hints can be wrong, they can be ignored, and they don't inform you in a useful way about program state because of this - all evaporate as soon as you start using the correct tooling.

My stack is pycharm, mypy, pydantic, sqlalchemy stub, several mypy plugins, black, isort, tabNine completion, and a bunch of custom scripts/Make to automate the common tasks. I'm not even sure I'm on the same planet as the author. I can practically spot incorrect type hints, mismatched types, weakly defined interfaces, from orbit, that's how well they are highlighted. Since every type is known, between pycharm and tabnine I can type 2-5 keys and spit out entire lines of code. And since it's all typed, automatic refactor is a breeze.

I remember the dark ages, before type hints. It was rough. I lived in the REPL. Write 3 lines, run, Exception. Write 3 more lines, run, NoneType error. Ad nauseum. Now I write for hours on end and don't wince when I hit execute. And I don't have to go bug coworkers with questions like "hey what keys are in this dict when we redirect from the oauth endpoint".

Sure, I have my gripes, but it's mostly with the lack of power of the type system, like lack of higher kinded types. But I'm confident it'll get there.

Come to the typed side. We have less mental fatigue and screaming at neverending runtime oopsies.

As long as you don't have to rely on 3rd party libraries, sure, type hints are fine.

Many popular libraries, however, don't have type hints or use them inconsistently (looking at you numpy, pytorch, tensorflow, scipy, ...).

Always great fun to see function signatures that use type hints for the parameters and return Any.

So it really depends what kind of code you write - web frameworks might be fine, data science and ML is definitely not.

You can write type stubs for other libraries. Over past year my team had a 100k line codebase that we’ve gotten mostly typed. But we still use a lot of other untyped libraries like tensorflow. So in past couple months we’ve started adding type stubs to the parts of the api we use. While tensorflow/numpy/etc are massive libraries most people only use a small subset of api. You can write type stubs for the subset you use. It definitely takes time but I find it very helpful to have useful type checking of tensorflow so I bit bullet and started writing stubs. I explored upstreaming them to type shed but got lazy on dealing with CI issue although I hope to revisit it later.