I want to like J, but I find that most of my programs get too cluttered with @: all over the place. I see the k-means example shown here has a similar problem (7 usages of @: in only 4 lines of J). I find implicit programming a more elegant paradigm, but lambdas with pre-declared argument names (like x,y,z in k or α and ω in Dyalog APL) more comfortable to work with (and, at least for the time being, more productive).
I think part of the reason is that J's syntax for function composition is too heavy. In an improved visualization of J like the one shown here, I would find very interesting to try something like, for example, composition by simple juxtaposition and trains with under(or over)lining, maybe even playing with colors to indicate if verbs form a train or other composition. I understand that is a quite different problem from just substituting @: with o̲.
Nevertheless, very nice work. It is interesting to see the English and APL versions next to each other. The video is also very good.
I think part of the reason is that J's syntax for function composition is too heavy. In an improved visualization of J like the one shown here, I would find very interesting to try something like, for example, composition by simple juxtaposition and trains with under(or over)lining, maybe even playing with colors to indicate if verbs form a train or other composition. I understand that is a quite different problem from just substituting @: with o̲.
Nevertheless, very nice work. It is interesting to see the English and APL versions next to each other. The video is also very good.