How do Karpathy and other AI power users avoid turning their codebases into AI slop?

The Reddit post cuts off before a complete question, but the core tension is real: agentic coding loops accumulate drift because each iteration compounds small deviations from intent. Practitioners like Karpathy tend to constrain scope aggressively — short context windows per task, frequent checkpoints against a canonical spec, and treating the agent as a code generator rather than an architect. The honest answer is that no one fully solves this at scale; the mitigation is smaller autonomous units with human review at integration boundaries, not longer autonomous runs.