As someone coming from a background of running Fortran programs with runtimes of multiple days, and looking into Julia at some point last year, the original blo

As someone coming from a background of running Fortran programs with runtimes of multiple days, and looking into Julia at some point last year, the original blog post was quite obviously bogus, regarding the performance complaints. Also, 1-based indexing is more than a bonus for numerical languages! IMO, C/C++ are terrible languages for numerical computing. In a way, Python is terrible too, except that it's great at gluing together existing numerical code, and doing high-level modeling, data analysis and visualization. These are quite important, of course, but very distinct from the actual numerics (the "two-language problem" Julia aims to bridge) All that being said, Julia's JIT-slowness can be a little annoying during development and testing, when you run Julia scripts on small example data, and have to wait 20 extra seconds every time you run the script! It would be really nice if Julia cached more of its compilation, so that any script being run repeatedly and invoking the same code paths with the same types would only cause compilation the first time.