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If agents are the primary consumers of your codebase, then our idea of what 'good code' looks like might be the wrong standard. I'm questioning decades of received wisdom about code quality.
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95% of enterprise AI pilots fail, yet individual practitioners report real gains. A quadrant for mapping the problem space, the March of Nines for understanding why demos don't become production systems, and the outer loop for getting better over time.
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We're talking about cognitive debt. We should also be talking about operational debt: code generated faster than teams can earn the knowledge to run it.
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My full process for AI-augmented development, built from daily use in production. Field reporting from a practitioner, not theory.