Numba focuses on scientific use cases speeding up most of numpy and some of scipy. Whilst it can compile some parts of pure Python (eg numeric array loops), generally you have to copy the data in to the numba side which can be slow in volume.
Numba isn't really meant for pure Python speed-ups, it gets rid of numpy specific inefficiencies.
Personally I'm pretty excited to see a fresh JIT working its way into core CPython (I'm the co-author of O'Reilly's High Performance Python).
The low hanging fruit is to implement something like a built in Cython without any type annotations. You get a speedup from not having bytecode but still have all the PyObject attribute lookups performed from native code.
