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?

> the more interesting thing is why that misaligned code was permitted to be generated in a “done” artifact in the first place

It's refreshing to me to hear slop being challenged. From first principles, why ought smart models put out slop, as opposed to self-consistent content?