But how close to the truth am I, really? How big is the difference? And how fast and composable can an implementation become in a modern C++?

Obviously, I can't just sit down and learn how to write the best and most elegant code in another language - it would take years until I reached the level of my current Julia skills. This is where the Julia challenge comes into play!

I put together a reference implementation for a problem that nicely illustrates the fundamental principles which make Julia so productive and scalable for numeric libraries. It's the foundation that allows one to freely combine packages and still get optimal performance. (If you're curious about such packages, have a look at this article: Some State of the Art Packages in Julia 1.0.)

I can't really imagine writing those in any other language, so I dare you to teach me! Use Python + Numba, Python + C, Swift, Cython, Go, Lua-Jit, Rust, D - surprise us! If all works out, this can be a great learning experience for everyone following the challenge! :)