Julia bucks the trend with its static duck typing; it's one of few languages where composition really works well. If your type defines + and * you can make matrices of them and multiply them together, then do linear algebra with them or format them as spreadsheets. Numpy, despite Python's duck typing, fails here because it's a thin wrapper over C, so the types are merely proxies and not extensible. Julia's compiled and heavily optimized, so it lives up to the promise. Also, unlike most static languages, the type system looks at the body of functions to infer what types they'll support - everything is maximally generic by default.

Julia has many flaws, but they're a different kind of flaws. Mostly caused by choosing function overloads based on vibes, and reckoning with the pandemonium caused by modules being glued together in unanticipated ways.

will look into this, thanks.