Thanks. I am guessing you have to try stuff and build tacit experience. No other way, just get stuck in and try stuff, then try and learn bits from others?

The models are very good now so the feedback cycle on these meta adjustments is much tighter. Yesterday I was able to one shot a Liquid Glass, HIG-compliant and localized DICOM image viewer (frame by frame and looping video) with Apple Intelligence for de-jargoning the series details. Took 30 minutes. But the app had 60% CPU because it was not caching the decoded JPEGs. I can do a point in time fix for that of course, but the more interesting thing is why that misaligned code was permitted to be generated in a “done” artifact in the first place. What other misaligned code from a perf perspective might there be? And how do I intervene into the system that produced this software to make these misalignments statically not meet acceptance criteria?

Basically yea. It is the only way to learn how to outrun your priors on what “high ambition” looks like. The labor that goes into implementation is an uncapped resource now.