> Types are expensive and dont tend to pay off on spikey/experimental/MVP code, most of which gets thrown away.
I find I’ve spent so much time writing with typed code that I now find it harder to write POC code in dynamic languages because I use types to help reason about how I want to architect something.
Eg “this function should calculate x and return”, well if you already know what you want the function to do then you know what types you want. And if you don’t know what types you want then you haven’t actually decided what that function should do ahead of building it.
Now you might say “the point of experimental code is to figure out what you want functions to do”. But even if you’re writing an MVP, you should know what that each function should do by the time you’ve finished writing it. Because if you don’t know who to build a function then how do you even know that the runtime will execute it correctly?
Python doesn’t have “no types,” in fact it is strict about types. You just don’t have to waste time reading and writing them early on.
While a boon during prototyping, a project may need more structural support as the design solidifies, it grows, or a varied, growing team takes responsibility.
At some point those factors dominate, to the extent “may need” support approaches “must have.”
My point is if you don’t know what types you need, then you can’t be trusted to write the function to begin with. So you don’t actually save that much time in the end. typing out type names simply isn’t the time consuming part of prototyping.
But when it comes to refactoring, having type safety makes it very easy to use static analysis (typically the compiler) check for type-related bugs during that refactor.
I’ve spent a fair amount of years in a great many different PL paradigms and I’ve honestly never found loosely typed languages any fast for prototyping.
That all said, I will say that a lot of this also comes down to what you’re used to. If you’re used to thinking about data structures then your mind will go straight there when prototyping. If you’re not used to strictly typed languages, then you’ll find it a distraction.
Right after hello world you need a list of arguments or a dictionary of numbers to names. Types.
Writing map = {}, is a few times faster than map: Dictionary[int, str] = {}. Now multiply by ten instances. Oh wait, I’m going to change that to a tuple of pairs instead.
It takes me about three times longer to write equivalent Rust than Python, and sometimes it’s worth it.
Rust is slower to prototype than Python because Rust is a low level language. Not because it’s strictly typed. So that’s not really a fair comparison. For example, assembly doesn’t have any types at all and yet is slower to prototype than Rust.
Let’s take Visual Basic 6, for example. That was very quick to prototype in even with “option explicit” (basically forcing type declarations) defined. Quicker, even, than Python.
Typescript isn’t any slower to prototype in than vanilla JavaScript (bar setting up the build pipeline — man does JavaScript ecosystem really suck at DevEx!).
Writing map = {} only saves you a few keystrokes. And Unless you’re typing really slowly with one finger like an 80 year old using a keyboard for the first time, you’ll find the real input bottleneck isn’t how quickly you can type your data structures into code, but how quickly your brain can turn a product spec / Jira ticket into a mental abstraction.
> Oh wait, I’m going to change that to a tuple of pairs instead
And that’s exactly when you want the static analysis of a strict type system to jump in and say “hang on mate, you’ve forgotten to change these references too” ;)
Having worked on various code bases across a variety of different languages, the refactors that always scare me the most isn’t the large code bases, it’s the ones in Python or JavaScript because I don’t have a robust type system providing me with compile-time safety.
There’s an old adage that goes something like this: “don’t put off to runtime what can be done in compile time.”
As computers have gotten exponentially faster, we’ve seemed to have forgotten this rule. And to our own detriment.