The "verify their own reality" point resonates. I stumbled on a post recently where someone documented getting an OCR model from 90% to 98% accuracy - turns out most of the gain came from discovering their training labels were 27% wrong, not from model tweaks. The interesting bit was their finding that running AI verification in parallel resulted in 2% correction rate, but sequential processing caught 65%. That kind of hard-won, numbers-backed insight is what makes technical blogs worth reading vs the flood of tutorial content.

  https://devguide.dev/blog/teaching-ai-to-distrust-itself