"Geoffrey Huntley's Ralph Wiggum loop is probably the cleanest expression of this idea I've seen, and it's becoming more popular quickly. In his demonstration video, he describes creating specifications through conversation with an AI agent, and letting the loop run. Each iteration starts fresh: the agent reads the specification, picks the most important remaining task, implements it, runs the tests. If they pass, it commits to Git and exits."
"If you think about it, that's what human prompting already looks like: prompt, wait, review, prompt again. You're shaping the code or text the way a potter shapes clay: push a little, spin the wheel, look, push again. The Ralph loop just automates the spinning, which makes much more ambitious tasks practical. The key difference is how state is handled. When you work this way by hand, the whole conversation comes along for the ride. In the Ralph loop, each iteration starts clean."
An iterative, stateless AI loop reads a formal specification, selects the highest-priority task, implements code, runs unit tests, and commits only when tests pass. Each iteration starts with an empty context and loads persistent state from files on disk. Flushing conversational context between runs prevents token accumulation and reduces noise, preserving signal across hundreds of iterations. The approach leverages easily-checkable outputs like test results and type checks to allow autonomous progress with minimal supervision. Automating the iteration cycle makes larger, more ambitious development tasks practical by maintaining clarity and repeatability in each run.
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