[vchak1]

I think its important to distinguish between something that is a core piece vs all the other things that make the system usable. For example once you start adding error handling, and good error reporting, the complexity goes up by an order of magnitude. And in many cases the approach for the core does not necessarily scale out to the other contexts.

[eggy]

The right tool for the job. If you are building a huge website with input forms, videos, data collection, ML algorithms, yes then you wouldn't do the whole thing in APL or J even if you could. Python is big in ML because packages for working with data in array language ways were developed. Pandas by Wes McKinney is one example, and he studied J or q, and even tweeted: IMHO J is the best APL for data analysis because of its broad suite hash table-based functions.

I like APL and J as a scratchpad where arrays are the basic unit and not scalars. J is functional and it turned me on to that world before I touched Haskell or F#.

Aaron Hsu has a lot of great videos that speak to a lot of the usability and scaling out you mention:

https://www.youtube.com/results?search_query=aaron+hsu

I particularly like this one: https://www.youtube.com/watch?v=z8MVKianh54&t=2857s

I am able to grasp concepts or own them after coding them in APL or J even if the code isn't as fast such as how well APL applies to Convolutional Neural Networks [1,2]. I really understood the mechanics of CNNs better after working through this paper a lot more than books I had read on ANNs in general since the late 80s/early 90s. By contrast, I have coded ANNs in C and Python, and I get lost in the PL, not the concept, if that makes sense. Anyway, I am a polyglot and find people criticize J/APL/k etc. from a brief look without really trying to learn the language. I learned assembler and basic back in 1978 to 1982, and I felt the same way when I first looked at opcodes.

[1] https://dl.acm.org/doi/10.1145/3315454.3329960

[2] https://www.youtube.com/watch?v=9vIZ7d3-GBw

[7thaccount]

Bahaha. It's a small world fellow HN user. As soon as ACM opened their digital library, I started looking for interesting APL papers and found that one and thought it was beautifully done. My takeaway is that you can make purpose-built AI in APL with very little code versus calling out to a large library like Tensorflow and having no idea what's going on.