January 12, 2026
The Dumbest Smart Way
to Ship Code
How a bash while loop named after a Simpsons character is letting developers ship $50K projects for $300 in API costs.
It's called the Ralph Loop. Named after Ralph Wiggum from The Simpsons— the character known for being simple, earnest, and persistently trying until something works.
In mid-2025, Australian developer Geoffrey Huntley was tending to his goats in the countryside when he had a realization about AI coding tools. Every time AI-generated code errored, the entire session would stall. Manual intervention was mandatory.
What if AI could act like a human—see the results of its own failed run and automatically retry based on that feedback?
The Code
Huntley's solution was almost disappointingly simple. In its purest form, Ralph Loop is just this:
while :; do cat PROMPT.md | claude ; done
That's it. An infinite bash loop that feeds the same prompt to an AI coding agent, over and over. But here's the clever part: progress doesn't persist in the AI's context window—it lives in your files and git history.
When the context fills up, you get a fresh agent with fresh context, picking up where the last one left off. The codebase becomes the memory.
"Deterministically bad in an undeterministic world."
Each individual iteration might produce garbage. But run enough iterations with clear success criteria, and the model eventually converges on something that works. Failures are predictable, and predictable failures can be fixed by tuning the prompt.
What Makes It Work
- 1Objective success criteria. Progress isn't measured by the AI saying 'this looks good.' It's measured by tests passing, builds succeeding, and health checks completing.
- 2State lives in files. The codebase is the memory. Git history is the log. Context window resets don't lose progress.
- 3Failures are expected. The system assumes most iterations will fail. That's fine. Predictable failures inform prompt improvements.
- 4Humans set strategy, AI executes. PRDs and acceptance criteria come from humans. The AI handles implementation.
The bottleneck was never the AI.
It was the human in the loop.
The Results
Geoffrey Huntley ran Claude in a Ralph Loop for three months with a simple prompt: create a programming language like Go, but with all lexical keywords swapped to Gen Z slang.
The result: a fully functional compiler with interpreted and compiled modes, producing binaries for macOS, Linux, and Windows via LLVM. Completely autonomous. No human code review during execution.
A team at a YC hackathon set up Ralph loops before going to sleep. By morning, they had 6 complete repositories to demo—each with functional code, tests, and documentation. The AI ran overnight while they rested.
The Evolution
Ryan Carson, founder of Treehouse (which taught over a million people to code), took Huntley's minimal approach and added structure. His 3-file system has earned over 5,000 GitHub stars because it gives developers a process to follow.
Carson's 3-File System
The human-written specification. What the product should do, acceptance criteria, constraints.
Guides the AI to create a step-by-step task list from the PRD. Parent tasks and subtasks.
Controls agent execution. Iterative code-test-commit workflow until all tasks complete.
The key insight: you're not "vibe coding" one giant prompt. You're giving the agent testable, bite-sized tickets and letting it execute like an engineering team. The AI handles implementation. Humans handle strategy and acceptance criteria.
The big idea is this: give the agent clear acceptance criteria and let it iterate until the tests pass. Not until the AI thinks it's done.
AI doesn't need to be smart.
It needs to be persistent.
Why This Matters
The Ralph Loop reveals something important about the current moment in software development. The constraint isn't AI capability—it's human bandwidth.
Before Ralph
Every AI error required human intervention. Context resets lost progress. Developers became bottlenecks in their own workflows.
After Ralph
Errors are expected and handled automatically. Progress persists across sessions. Developers can sleep while code ships.
This isn't about replacing developers. It's about changing what developers do. Less time babysitting AI outputs. More time on architecture, strategy, and the parts that require human judgment.
What would you build if the AI could run all night?
What could you ship if you weren't the bottleneck?
Share this perspective
Sources: Geoffrey Huntley on the CURSED language · Ryan Carson's 3-File System Tutorial · Ralph Loop GitHub Implementation
More Insights
Perspectives on product design, technology, and the forces shaping how people interact with the things we build.
The tooling for autonomous AI development is evolving rapidly. The teams that learn to work alongside persistent agents—not just prompt-response models—will define how software gets built in the next decade.