re: performance, I am guilty of making a general statement when I should have made a much more specific one: It's not too hard to beat Java for numerical applic

re: performance, I am guilty of making a general statement when I should have made a much more specific one: It's not too hard to beat Java for numerical applications. The root of the situation is that it's pretty easy for the dynamic languages to choose an unboxed representation on the back-end while still exposing a reasonably generic interface to the programmer. The ergonomic payoff there is immense. I submit, for example, that the just obscenely rich ecosystem of high performance data analysis packages that have been built on top of numpy just could not happen in Java. Java forces you to either accept boxing, or write specialized overloads for every specific combination of argument types you want your function to be able to handle. Java does have some specialized numeric libraries, generally built using code generation, but they aren't widely used because the ergonomics aren't great, and building any additional general-purpose tooling on top of them requires still more code generation. And that's just not a fun way to work. It's brittle.