Everyone Just Discovered Loop Engineering. REAP Got There First — and It's Ready When You Are

In June 2026, "loop engineering" went viral. Stop prompting your agent — design the loop that prompts it. Ralph Wiggum loops running Claude for hours. Overnight runs. Millions of views. My honest reaction: finally, everyone's here. I've been running my entire development process as AI loops since this February — months before the trend had a name — and one project is now 70+ loop iterations deep , and the tool that runs those loops is itself built by those loops. So this is a field report. Not "loops are amazing" (they are) and not "loops are hype" (they're not). Just the seven things that turned out to actually matter once you live inside a loop long enough — including the ones the infinite-loop crowd is about to learn the hard way. Context: the tool is REAP ( https://reap.cc ), an open-source pipeline I built on top of Claude Code / OpenCode. It exists because I needed these seven lessons encoded in software, not in my discipline. First, what the loop people get right Credit where due — the core insight of loop engineering is correct: One-shot prompting doesn't scale. Iteration beats a perfect mega-prompt every time. Files beat context windows. State that matters must live on disk, not in the conversation. Fresh context each iteration prevents the slow rot of a 400-message session. The leverage moved. Your job really is designing the system around the agent now. I agree with all of it. Now here's what months of actually living inside the loop adds. Lesson 1: A goal is not a loop spec The naive loop is: final goal + while true . It works for tasks where the environment can say "done" — make tests pass, finish a mechanical migration. Even Ralph loop advocates admit it: vague criteria = infinite loop, judgment-heavy work doesn't converge. But almost everything interesting in software is judgment-heavy. So instead of one goal driving infinite iterations, I got much better results from one bounded goal per iteration , chosen fresh each time by comparing long-term visio