Definitely agree with the C++ corner cases thing being a challenge - at times I felt like I was writing another compiler on top of the C++ type system to handle the corner cases. But again, I used powerful abstractions to handle that. For instance, my library uses boost graph library and djikstra's algorithm to handle C++'s tangled web of automatic type conversions.
Hats off to you sir. Oh, I also do the occasional motorcycle maintenance as well.
We're very happy with the results. It's a little bit slower than hand parsing the headers, but the robustness more than compensates for it. We set up a unity build (just for the headers) to make up the speed difference.
Later, when I had to write native code to speed up Python, I always used plain old C and swig worked flawlessly.
The key was to handle the resulting Python module as a low level C wrapper effectively allowing us to write "managed C" in Python, which we encapsulated neatly in a library with a much more pythonic API. We then naturally went all the way up and implemented SQL-like API (no parser, just methods) and a Django model subset. A SQL subset parser and DBAPI compliance were planned features. All of that allowed us to develop new features in Python and on Django while maintaining compatibility with the old components (also thanks to Django multi-db support) and making the planned switch to a complete SQL database a drop-in replacement.
