Yes, some code won't work -- numba has a nice compiler that will show you errors if it cannot infer the type of even a single variable at compile time (which us

Yes, some code won't work -- numba has a nice compiler that will show you errors if it cannot infer the type of even a single variable at compile time (which usually happens the first time you call your function at run-time). The argument of "some code won't get faster" is null, since you typically only want to use `@njit`, which ensures that you're in `nopython` mode. I guess that's a double edged sword, in that when it says `nopython`, it really does mean no python. This means you can only use features from the python interpreter that the numba team has re-implemented in LLVM IR. --- IIRC `@njit` does involve an overhead in `lowering` the types from python -> LLVM when the first njit function in the call graph is invoked, but not after that. All this means that if you use `for-loops` in nopython mode, they are guaranteed to run faster, at least in my experience.