Last week, a developer let Claude run an Ralph Wiggum loop for eight hours, refactoring a healthcare app’s authentication module. The AI produced 47 commits, separated business logic from controllers, and raised test coverage from 62% to 87%. The process handled JWT handling, session management, and password reset across eight files.

Claude’s success hinged on clear success criteria—tests must pass, coverage thresholds, linting—and explicit constraints like a 20‑line limit per function. The loop ran 47 iterations, each time making changes, running tests, reading failures, and adjusting until the final commit marked <promise>COMPLETE</promise>. The run cost $23.14 in API credits, saving the developer 6‑8 hours of weekend work.

While Claude isn’t AGI, its pattern‑matching and self‑correction make it a useful junior‑developer surrogate for tasks with solid test coverage. It should stay in isolated dev environments, especially for HIPAA‑regulated healthcare code, and avoid security‑critical or subjective work. When properly guarded, autonomous loops can deliver 24/7 coding for roughly $20 a day.