I’ve noticed something similar, though I don’t think it’s literally “time of day” so much as changing system conditions.

My working theory is that under higher load, the model is more likely to: - take broader interpretive leaps - attempt larger refactors instead of minimal diffs - “explain its way forward” after a wrong turn rather than reset cleanly

That shows up as rabbit holes and self-reinforcing iterations, especially on codebases where local consistency matters more than global cleverness.

What’s helped a bit for me: - explicitly asking for minimal, localized changes - telling it not to refactor unless necessary - breaking requests into smaller steps and locking earlier decisions

It could also be variance from routing, context window pressure, or subtle prompt drift rather than a predictable nightly degradation, but the pattern of “overconfident refactor spirals” feels real.

A like-for-like experiment with the same prompt and context at different times would be interesting, though hard to fully control.