> I suggest learning about C++ ranges and CUDA algorithms. I’m familiar. > Parenthesis and brackets have nothing to do with how high level a language happens

> I suggest learning about C++ ranges and CUDA algorithms. I’m familiar. > Parenthesis and brackets have nothing to do with how high level a language happens to be. I would argue readability and expressiveness do, though. When someone is talking about high level languages in the context of Python, they don’t mean in terms of instructions, registers, and interrupts. > BLAS in JavaScript would certainly be faster than Python, this is what is being discussed here. No, it explicitly is not. You originally replied to my comment that it was impressive how Julia in this case was competitive with Fortran and much faster than Python. You agree that no JavaScript JIT is going to make it as fast as C/C++/Fortran, right? > Again the dynamism excuse. Julia is a dynamic language, so I’m not sure what excuse you think I’m making. The reality is that the languages you cited generally are not competitive with C/C++/Fortran. The reality is that none of the languages you cited are even being considered for those applications. The reality is that languages that have had massive investment in a JIT like JavaScript are still relatively slow. Julia seems to be different than the other dynamic languages you cited in this regard. Why do you think the relatively small investments in Julia have been able to achieve this while others, often with more resources, have failed? I think it probably has something to do with the language design.