I've long been a big fan of APL, since I learned it many years ago. I started by studying on the classic book by Gilman and Rose and trying to use the scarce fr

I've long been a big fan of APL, since I learned it many years ago. I started by studying on the classic book by Gilman and Rose and trying to use the scarce freeware/shareware implementations available at the time. At the time it was hard or impossible to get anything working for free on a Linux computer. Nowadays there is a great, free, and actively maintained implementation: GNU APL. That, combined with the widespread use of Unicode and the APL support in Linux/XOrg keyboard definitions, make it so much easier to get started than in the past. I have used APL mostly for fun, that is, for learning the language itself, solving mathematical puzzles, and taking part in "code golf" games. I find the language exquisite, especially its programming model based on multi-dimensional arrays, its graphical symbols, and its grammar. But it is somewhat "impractical" to use nowadays. J is a great successor (designed by the same mastermind behind APL) but I found it harder to learn (and to read) due to its ASCII "line-noise" look and to the prevalence of point-free or tacit function definitions, which I find inferior in readability. I'm only now learning NumPy and TensorFlow, and I bet their authors were inspired by APL. Does anybody have any benchmarks of J's performance against NumPy? As for K, I wouldn't recommend picking it up. It's closed-source and documentation-less, meaning good look if you run into any trouble; its error messages are notoriously infuriating; it overloads its operators with a ton of meanings, depending on context, even more so than APL and J; and its fundamental programming paradigm is not based on multi-dimensional arrays. On the other hand, writing an APL-like frontend for TensorFlow looks like a nice project!