That particular quote is from the "Epigrams on Programming" article by Alan J. Perlis, from 1982. Lots of ideas/"Epigrams" from that list are useful, and many languages have implemented lots of them. But some of them aren't so obvious until you've actually put it into practice. Full list can be found here: https://web.archive.org/web/19990117034445/http://www-pu.inf... (the quote in question is item #9)
I think most people haven't experienced the whole "100 functions on 1 data structures instead of 10 on 10" thing themselves, so there is no attempts to bring this to other languages, as you're not aware of it to begin with.
Then the whole static typing hype (that is the current cycle) makes it kind of difficult because static typing kind of tries to force you into the opposite of "1 function you can only use for whatever type you specify in the parameters", although of course traits/interfaces/whatever-your-language-calls-it helps with this somewhat, even if it's still pretty static.
> static typing kind of tries to force you into the opposite
The entire point being to restrict what can be done in order to catch errors. The two things are fundamentally at odds.
Viewed in that way typed metaprogramming is an attempt to generalize those constraints to the extent possible without doing away with them.
I would actually expect array languages to play quite well with the latter. A sequence of transformations without a bunch of conditionals in the middle should generally have a predictable output type for a given input type. The primary issue I run into with numpy is the complexity of accounting for type conversions relative to the input type. When you start needing to account for a variable bit width things tend to spiral out of control.
"APL is like a diamond. It has a beautiful crystal structure; all of its parts are related in a uniform and elegant way. But if you try to extend this structure in any way - even by adding another diamond - you get an ugly kludge. LISP, on the other hand, is like a ball of mud. You can add any amount of mud to it and it still looks like a ball of mud."
-- https://wiki.c2.com/?JoelMosesOnAplAndLisp
some of us think in those terms and daily have to fight those who want 20 different objects, each 5-10 deep in inheritance, to achieve the same thing.
I wouldn't say 100 functions over one data structure, but e.g. in python I prefer a few data structures like dictionary and array, with 10-30 top level functions that operate over those.
if your requirements are fixed, it's easy to go nuts and design all kinds of object hierarchies - but if your requirements change a lot, I find it much easier to stay close to the original structure of the data that lives in the many files, and operate on those structures.
Seeing that diamond metaphor, and then learning how APL sees "operators" as building "functions that are variants of other functions"(1), made me think of currying and higher-order functions in Haskell.
The high regularity of APL operators, which work the same for all functions, force the developer to represent business logic in different parts of the data structure.
That was a good approach when it was created; but modern functional programming offers other tools. Creating pipelines from functors, monads, arrows... allow the programmer to move some of that business logic back into generic functions, retaining the generality and capacity of refactoring, without forcing to use the structure of data as meaningful. Modern PL design has built upon those early insights to provide new tools for the same goal.
if I could write haskell and build an android app without having to be an expert in both haskell and low level android sdk/ndk, I'd be happy to learn it properly.
It is! (https://github.com/cnlohr/rawdrawandroid) - and that's just writing a simple an android app in C. If you want to access the numerous APIs to do anything useful, it's more pain.
