用 DSPy 打造夜间自调试 AI 循环：让系统在睡眠中自我改进

In short : A nightly loop finds failed AI tasks, debugs them automatically using DSPy, and iterates until the prompts work. The system wakes up smarter than it went to sleep.

This week I chatted with an acquaintance who mentioned a board game. I caught half the title & looked for the full title & Amazon link using my AI in Asana.

The system tried with Gemini & failed. The failover to Claude also failed. Rather than continuously iterating with the AI until it worked, I created a Ralph Wiggum loop.

Geoffrey Huntley coined this pattern. Named after the persistently clueless Simpsons character, the idea is simple : keep pushing the model against its failures until it dreams a correct solution just to escape the loop. The system is deterministically bad in an undeterministic world. Iteration beats perfection.

Now an AI loop runs each night. It finds all tasks with “failed” in them. It creates a plan to debug & iterates until the prompt solves the task.

So far this naive system is working pretty well. There is a risk it might begin to oscillate between two optimal states, but I haven’t observed that in the few days it’s been running. It’s something I’m watching.

AI creates software cheaply; excellence requires iteration. Implicit feedback loops are how you get there.

This self improving loop ensures the system wakes up smarter than it went to sleep. So do I.

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