I would really appreciate your input!
1. Top end model on high/xhigh thinking (last time I did it it was Sol xhigh I think)
2. Make sure it creates some representative fixtures of different sizes and sets up a good testing, profiling and benchmarking loop that doesn't require my input.
3. Make sure it has access to reference implementation code
Edit: Oh and one obvious pitfall that for some reason I still have to remind even smart models of from time to time: make sure it knows not to try to parallelize its benchmark runs. I've occasionally had an agent struggle to figure out absolutely nonsensical data because it tried to run multiple tests on the same compute hardware simultaneously.
Can you tell us what kind of gain you got ie. what was the speed bump
Keep in mind though that this is also sometimes use case dependent. Off the shelf implementations are generally pretty good overall, but can have pathological behavior on specific workload shapes you care about. So do this as a somewhat later optimization, and particularly when you see performance characteristics that don’t seem to make any sense.