I'll be happy to give you a live personalised intro in https://apl.chat or head over to https://challenge.dyalog.com/ for an automated guided introduction (with a chance of winning a prize).

https://xpqz.github.io/learnapl/intro.html is a good place to start learning.

ChatGPT is pretty good at correcting my mistakes. Give it a snippet of not quite working code with no explanation and it almost always correctly identifies what I am trying to do and explains where I went wrong. Its corrected code almost never works but its explanation of why my code failed gets me my answer and that is what I really want. A year ago it could not help me in the slightest, it has improved quite a bit. The biggest issue with AI and APL seems to be that it does not quite understand the differences between the various versions of APL and seems to view APL2, Dyalog and GnuAPL as all the same language.

Edit: It probably would be useless to someone at all competent in APL and whose problems are more complex than their own failings.

True. Due to its lack of verbosity, APL is not at all forgiving for any substitution or transposition in its symbols, which can cause great differences in program output, so it is quite improbable for LLMs to generate a working program, unless it is identical with one from its training set.

What I have said matches exactly what another poster said about his experience in using a LLM with APL: "Its corrected code almost never works".

The LLM recognizes the problem that must be solved by the code, but it fails to generate the right APL symbol string.

I doubt that here a coding agent that attempts to verify the generated code by compiling it can help, because the LLM will generate eventually some syntactically-correct symbol string, but which will implement a different function than desired.

Only a complete feedback loop, with a battery of varied tests for the executable program produced by the generated code, which can verify if it really implements the desired functionality, can be used to filter the results for a working program.

APL is greatly superior to almost all programming languages that are popular today, for writing expressions involving arrays (this includes expressions that do not involve arrays in other languages, but which could be made simpler by using arrays in APL).

However, the original APL has defects, due mainly to the fact that it was an incomplete programming language, e.g. when compared to the other contemporaneous IBM language, i.e. PL/I.

What one needs is a programming language with modern program structures, data types and data type definition facilities, but also with an expression syntax matching the power of APL expressions.