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Yeah, you should be able to anything - I've written a Pattern Matcher with PIF, which is about as non-Wave as you can get. But to get the full speed benefits, and for actually improving your own gun, I'd definitely recommend collecting your own data with your own attributes and bullet power scheme. Adding attributes should be pretty easy, really, just adding them to the Wave class then the reads and writes - but then again I wrote the code. =)

I also have an updated TripHammer that I haven't posted yet, after the big Diamond refactor. The code is a lot cleaner and integrates better with Diamond's code, just modifying the gun data manager to write the data files. It also interpolates waves for skipped turns (as Diamond does now), which is kind of cool.

A WaveSim based challenge could be fun, kind of like the Netflix challenge. Not really sure how many would be interested, though, and we'd have to come up with some kind of rule set to prevent over-fitting. Maybe a training data set, then you submit your code to an App Engine instance to run against the real data set, which is how Netflix did it I think.

This makes me think... I wonder if the data collection and conversion to attributes should be made into seperate steps. The data collection bot could collect the raw data, and a seperate program could convert it to the attributes one's gun uses. That way you get the full speed benefits without having to modify the data collection bot or collect new data.