The Ralph Loop

Ralph Wiggum Loops
A Practitioner's Guide to Autonomous AI Coding Your AI coding assistant forgets everything after 32,000 tokens. What if that was a feature, not a bug?
When AI coding assistants work on long tasks, they degrade. Context windows fill with errors. Compaction introduces drift. Conversations that began with precise code generation end in generic, contradictory suggestions.
Ralph Wiggum Loops turn forgetting into a superpower. Spawn a fresh AI instance for each task, record results to the filesystem, and let the next instance start clean. Memory persists via git commits - not via an increasingly polluted context window.
This guide takes you from first principles to advanced troubleshooting:
The science : Why 11/12 models fall below 50% accuracy at 32K tokensThe workflow : PRDs, task sizing, build/plan/reverse modesImplementation : Setup for Claude Code and snarktank/ralphPitfalls : Security, the "fire and forget" myth, when not to use RalphTroubleshooting : Non-converging loops, cost optimisation, recoveryAlternatives : Goose, Ralphy, Ralph Orchestrator, Vercel AI SDK
Whether you're encountering Ralph Loops for the first time or debugging your fiftieth iteration, this book provides the unified resource the community has been missing.
Harry Munro is a Chartered Engineer and founder of the School of Simulation. Author of Vibe Modelling (2025), his background in probabilistic modelling for London Underground taught him that accounting for real-world variability matters more than theoretical perfection.
"Compaction is the devil."
- Geoffrey Huntley, creator of Ralph Wiggum Loops
For software developers using AI coding tools * Beginner to Advanced

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