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.