Ah yes, because Python was never used in production ever, not at Google, not at Dropbox, not ever.
The "static typing above all else" cargo cult is getting ridiculous.
You should be horrified by Dropbox's blog post on python in production: https://dropbox.tech/application/our-journey-to-type-checkin.... because this problem only existed and required $Ms of dollars to fix (even hiring the core team behind Mypy) because Python is being used instead of a typed language.
But rejecting all dynamically typed language is a poor shortcut to take. Python, Ruby, Javascript, and others have a lot of success stories.
Python is great for single developer experience and going fast as a single developer. I use it every day, willingly, in jupyter notebooks. But at scale there are no success stories. You are zoomed in on the present success of "engineering miracles" and ignoring the big picture that the application would have moved faster if they had the foresight to choose a typed language.
Hence, why i'm saying be warned on following the path of mypy+black+pyright+ruff+etc., since you will be doomed to patching on top of a fundamental flaw of the language.
I highly recommend to avoid Python for production apps especially if it is your free will (and not a matter of refactoring or rewriting existing mistakes).
Do you believe this yourself?
of course python/ruby/javascript have succeed in bringing value to the world in general -- but in this thread we're focusing on production reliability, debugging, refactoring.
as we see, dropbox is proud to admit that they hired Mypy core developers and fund the project, but guess how much they spent on the mistake of using python?
twitter, famously an advocate for Ruby, spent an ungodly amount of engineering to work around ruby, hit a wall, and switched to JVM.
Just the fact that mypy and typescript exist and have exploded in adoption is proof that eng. teams very quickly hit a sand pit when using dynamic typed languages. Mypy being funded by dropbox, Typescript funded by microsoft, shows the level of investment needed to support these dynamic languages.
And thats because of the fundamental tradeoff of dynamic typing vs static typing. That part no one can disagree on.
The only disagreeable part I believe I'm saying is where does the dynamic typing scale limit happen. As a polyglot developer, working a lot with junior developers, I can say that dynamic typing hits a scale limit of feature velocity / production support / cost much faster than the general folk are aware of.
there's this common idea in management that new grads, SREs, "less experienced devs", can jump in and contribute. it's a falacy because the code becomes unmaintainable without a large investment (more unittests, refactoring the "buggy" code blocks with mypy type annotations, some SME who continues fixing prod bugs). a Python developer will shove their head in the sand with repl'ing, testing after deploy, and settle with this dev/ex while being completely unaware of the benefits of compiler errors and warnings.
Python has the same level of complexity as another language but you'll only see all of that dirty baggage in production as runtime exceptions.