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.

(author)

I’d really like to properly get into APL though. My plan is to solve a bunch of problems on Kattis [3].

I'm really enjoying this way of learning a new language in the age of LLMs - starting with easy problems on an online code judge website and work with an LLM to come up with/explain simple solutions. It gives me dopamine hits, lots of reps, allows me to start coding right away, and is a nice way to slowly ramp up difficulty and get practice with different features of the language.

[1] https://github.com/ebanner/dyalog-mode

[2] https://github.com/lokedhs/gnu-apl-mode

[3] https://open.kattis.com

https://www.dyalog.com/uploads/documents/MasteringDyalogAPL....