Aside 2:
Anecdotally we found that Pi performs more or less on par with native harnesses at lower cost on decently specified prompts. It is also phenomenal at context cacheing especially on Deepseek models (its hard to precisely attribute credit here are my understanding is this is a DS speciality).
But it fails much worse on poorly drafted prompts. I'm generalising but native harnesses seem to be better kind of flailing along on those.
of course it is; these entire systems are about priming the LLM to perform X task; if it's a coding harness, giving it very little priming for a task is going to make it grasp at straws.