Author here. It's fair enough. I didn't give real-world examples; that's partially down to what I typically work on. I usually work in brownfield backend logic in closed-source applications that don't showcase well.

Two recent production features:

1. *Quota crossing detection system* - Complex business logic for billing infrastructure - Detects when usage crosses configurable thresholds across multiple metric types - Time: 4 days parallel work vs ~10 days focused without AI

   The 3-attempt pattern was clear here:
   - Attempt 1: DB trigger approach - wouldn't scale for our requirements
   - Attempt 2: SQL detection but wrong interfaces, misunderstood counter vs gauge metrics
   - Attempt 3: Correct abstraction after explaining how values are stored and consumed

Nothing glamorous, but they are real-world examples of changes I've deployed to production quicker because of Claude.