The APL Challenge (opens in new tab)

You can embed GNU APL. I did it once with Clojure[1], tho it wasn't too popular. If they hired you to do Python should probably do that instead of embedding APL into Python. (I don't follow my own advice)

I would love to embed Dyalog but last time I tried I wasn't smart enough. If anyone from Dyalog feels like throwing cycles at it let me know!

[1] https://github.com/jjtolton/libapl-clj

Well, there's KlongPy[1]. While not APL, it is built on top of Python and manages to turn NumPy from "Iverson Ghost"[2] status into Klong[3], a proper array language.

[1] https://github.com/briangu/klongpy

[2] https://analyzethedatanotthedrivel.org/2018/03/31/NumPy-anot...

[3] https://t3x.org/klong/

What aspects of APL do you want to use at your job ?

Adám is awesome. I'm an avid follower of his criminally underviewed YouTube channel[1].

[1]: https://youtube.com/@abrudz

Large parts of Sweden's health system run on APL; Deutsche Bank also uses lots of APL; Denkmark has an appreciable number of companies using APL. IMHO, the problem isn't the scalability of APL (Dyalog, specifically) but more that good, maintainable, solid APL violates what the industry holds up as Best Practices etc. Trying to apply typical software architecture methodologies to APL definitely evokes the feeling you describe.

However, APL in my experience enables radical simplicity for building large applications. Learning to leverage that means that you need to first unlearn lots of ingrained problem-solving habits and ways of thinking as a software engineer. It's admittedly a steep hill to climb.

A page? I sure hope not. The whole training and inference code for a U-Net architecture in APL is only about 30 lines, with inference direction only taking up 4 or so lines. Assuming you know APL, the code is actually really straightforward and readable, too! Not to mention that it's only ~2.5x slower than a comparable PyTorch implementation.

[0]:https://www.dyalog.com/uploads/conference/dyalog22/presentat...

People have done it. For eg, https://github.com/BobMcDear/trap and few others that I can't find the links of right now.

When I got into BQN recently, I had the same thought and tried my hand at recreating LLM building blocks and saw the compactness APL-likes afford first-hand: https://www.chandergovind.org/blog/5126/aipasi/Array-program...

Conversely, after many years of dabbling I still consider myself a complete novice at APL and array programming. However, my studying of array langs made learning LLMs fairly trivial. In particular, studying APL's inner product from the Mastering Dyalog book was the key to understanding the self-attention mechanism from Attention is All You Need and how it communicates semantic information across the positionally encoded tokens.

I'd still probably struggle to do anything mildly complex in APL, though, sadly. It hasn't fully clicked yet, but maybe one day!