Julia is a language that you write more or less like python, but in cases where python would chase a million pointers to figure out what the type of a variable is, get an instance variable, call a method, etc, Julia (if written correctly) does just in time compilation. The first time it has to call a function on a combination of types it hasn't seen before, it compiles the function for those specific types (into LLVM IR, and then native code) and stores the compiled code so that subsequent dispatches of that function with those types are instantaneous, calling directly into the compiled code. You do not need to add type annotations for this to happen; at runtime, Julia checks the types of variables (which may have even been determined when the function was compiled, eliding the runtime check, if it was used in a context where the types were statically known; see below) to determine which version of a function to call.

This is a strength, as the only thing you have to do to get the performance of fully compiled code (modulo things like GC and bounds checking) is make sure your functions are “type stable”, i.e., their type information can be determined statically. (In fact, in Julia you get better performance by writing more, smaller functions because you'll have more regions within which the types are statically inferrable.) But it's also a weakness because the first time you start up a REPL and call a bunch of functions, each of those functions has to be compiled from scratch, which takes a long time (google “Julia time to first plot”).

Julia has other niceties such as very flexible math (promotion between every pair of numeric types) and a lisp-like macro system with homoiconic code, which make writing numerical code and scientific algorithms highly ergonomic.