Open source is a chicken-and-egg problem. If you want success stories, look at c/c++/fortran/perl/python/R/julia, old and new. I don't have a solution. I just blame IBM and all others who monetized on APL in a short dozen years and didn't manage to hold their ground.
In an alternate universe we would see current fortran users writing APL.
More realistically, had anyone write an APL compiler that gets some applications to run on any of the top 50 supercomputers with even one percent of its peak performance, APL would still have a chance.
I also don't know if I would consider C/C++ or Python or the others success stories. They all have an ecosystem around them that allows for developers to be funded doing work on these projects, but only because of other products that are out there. Other products produce money, and are making enough to allow some companies and individuals to fund core technology improvements. That's different and a harder model to work with, because it requires massive scale to be effective.
We're working on it! We're getting close already. If you have a naively parallel algorithm that is good for doing CSP style decompositions on, then you can compile your individual unit programs using Co-dfns and then using Dyalog APL Isolates to distribute the bulk computation across the distributed computing cloud. It's more work than it should be right now, but it does work for the right types of problems.
In an alternate universe we would see current fortran users writing APL.
More realistically, had anyone write an APL compiler that gets some applications to run on any of the top 50 supercomputers with even one percent of its peak performance, APL would still have a chance.