It always felt strange to me that the main implementation of something as niche and esolang-adjacent as APL is neither OSS nor casually usable commercially, but instead comes under an enterprise license.
Anyway, I had a fun time a while ago translating APL programs to NumPy. At some point you get what APL is all about, and you can move on with life without too many regrets. Turns out most of the time it's more like a puzzle to get an (often inefficient) terse implementation by torturing some linear algebra operators.
If you're after a language that's OSS, has terse notation, and rewires your brain by helping you think more clearly instead of puzzle-solving, TLA+ is the answer.
Edit: if you're curious to see at a glance what APL is all about:
APL code:
(2=+⌿0=∘.|⍨⍳N)/⍳N <- this computes primes up to N and is presented as the 'Hello world' of APL.
Equivalent NUMPY code:
```
R = np.arange(1, N + 1) # ⍳N
divides = (R[None, :] % R[:, None]) == 0 # 0=∘.|⍨⍳N
divisor_counts = divides.sum(axis=0) # +⌿
result = R[divisor_counts == 2] # (2=...)/⍳N
```
As you can see, the famous prime generator is not even the Eratostenes' sieve, but a simple N^2 divisor counting computation.
> Turns out most of the time it's more like a puzzle to get an (often inefficient) terse implementation by torturing some linear algebra operators.
solutions in APL can be very efficient if they are written in a machine sympathetic way
or in cases where the interpreter can map them onto one
for the curious:
https://aplwiki.com/wiki/Performance
https://www.youtube.com/watch?v=-6no6N3i9Tg (The Interpretive Advantage)
https://ummaycoc.github.io/wc.apl/ (Beating C with Dyalog APL: wc)
Thanks for the response. I'd interpret it as a valid technical caveat, but it feels somewhat orthogonal to what I was pointing out.
You focus on the 'often inefficient' parenthetical, yet, to me, your response highlights the puzzle nature of the thinking APL encourages. If anything, it shifts the question from 'how do I express this tersely' to a still narrower 'how do I express this tersely in a way the interpreter can also optimize'.
I think every programming language to a degree has some kind of puzzle aspect
I'm not sure APL has more or less of it compared to other languages
for example in Python, even though the language has a concept of "There should be one-- and preferably only one --obvious way" (PEP 20) it is quite multi paradigm, which I think is a strength of Python
oop, functional, imperative, …
and you get tons of libraries to choose from
e.g. numpy, pandas, polars, pytorch, keras, jax, … etc
but you still also have to figure out the algorithm and data structures you want to use (like in any language)
and you also kinda want to know (if you care about performance) how pytorch differs from numpy and how that differs from using a list with boxed values
Not saying this is not the case with APL
it definitely helps if you are familiar with the APL implementation you're using if you care about performance
I just don't think it's a disadvantage of APL over other languages
Agreed. I think I shouldn't put hard boxes around languages like 'puzzle language' vs 'abstraction/clear thinking language'.
What I was trying to point at was more specific: the way I experience APL thinking tends toward 'expression search' and 'notation compression', which feels, to me at least, somewhat at odds with clarity about the underlying problem. More often than not, I seemed to produce an APL-shaped model of the problem rather than a problem-shaped model expressed in a language.
When I first learned about APL, I was looking for new ways to think about computation. What I found was a language that rewarded deciphering APL programs and generating clever new ones. That is interesting and beautiful, but it was not quite the kind of brain-rewiring I was looking for. My original comment was targeting people in a position similar to mine and trying to set expectations about what they would learn best from APL. APL may change how you think about array expressions and how far they can go, but TLA+ is much closer to what I'd recommend if what you want is clearer thinking about programs, systems and state.
ty for the pointer to TLA+
> At some point you get what APL is all about, and you can move on with life without too many regrets.
Honestly this is how computers/software/programming feel in general these days and it’s ruined it all for me.
I basically feel the same way. In a way it is very liberating. All of those esoteric languages that were on my ever-growing todo list are now things I can let go of. Ultimately we have to ask ourselves how we want to spend our time, and now it is much harder to justify spending countless hours studying one programming language after another. We still can, of course, but we are now more "free" to do other things instead.
It's sort of sad, but really I think it is a weight off my shoulders.
BQN exists and needs more attention I think. It has some modern affordances as well.
https://github.com/mlochbaum/BQN
https://mlochbaum.github.io/BQN/doc/quick.html
It’s not truly fair to call it an esolang given that it was used by mainframe customers for decades. It’s more like a less popular product than COBOL…
but instead comes under an enterprise license.
The reason for this is because APL is quite popular in fintech, and of course that industry has no qualms about things not being free.
> Turns out most of the time it's more like a puzzle to get an (often inefficient) terse implementation by torturing some linear algebra operators.
In vector function space, no one can hear your eigen-scream.
related: blog post on fast primes in BQN
https://panadestein.github.io/blog/posts/ps.html
https://lamport.azurewebsites.net/tla/tla.html ?
Yes! I put together some explanation and TLA+ related resources in this comment, the website is one of them: https://news.ycombinator.com/item?id=48075169