There is a similarity, but there is also a difference. The general difference (among PyPy/Pyjion/Pyston) is "what to do with CPython C API". As far as I understand:
* Pyjion says "we gonna add a JIT API to CPython" - so no touching the C API at all (or the object model)
* Pyston says "we gonna implement our own object model supporting CPython C API", but they seem to have copied large portions of CPython. This is a semi-rigid approach compared to Pyjion, because it's not runtime-swappable
* PyPy says "we gonna implement a fake layer on top of our own object model" which means that while C API is feasible (and mostly implemented), it's gonna be always a compatibility layer with slower approach, but the underlaying data types/GC can change at will
I kinda wish we could get more wood behind a single arrow -- like I understand they have 80k lines in C extensions, but that has to be something like 8k lines if rewritten in Python -- and then that gives you access to pypy and now you can contribute to making that better instead of doing a new thing.
Based on the slides discussing Pypy performance, it looks to me that they're not confident that rewriting those 80k lines of C would provide sufficient performance. It might, but if you're not sure that's a lot of risk to take on.
