Coding Agent Runs Wild, Porting 6 Repositories Overnight at YC Agents Hackathon

This weekend at the YC Agents hackathon, a team of engineers decided to explore the boundaries of what a coding agent could do by running Claude Code headlessly in an infinite loop. The experiment, which they whimsically dubbed "RepoMirror," resulted in over 1,000 commits and six ported codebases by morning.

How We Got Here

The idea started with a simple question: What’s the weirdest way we could use a coding agent? The team settled on running Claude Code, an AI-powered coding assistant, in a continuous loop without any human intervention. They set up a basic script that would repeatedly prompt Claude to port code from one repository to another and commit the changes.

The Setup

• Agent: Claude Code
• Environment: Headless (no user interface)
• Loop: Continuous execution of porting tasks
• Repositories: A mix of open-source projects with varying complexity

The script was straightforward:

1. Fetch a repository from a predefined list.
2. Prompt Claude to port the code to a new language or framework.
3. Commit changes to the target repository.
4. Repeat indefinitely.

The Results

By morning, the team was greeted with an overwhelming 1,000+ commits and six fully ported repositories. Here’s a breakdown of what RepoMirror achieved:

• Repository A: Ported from Python to JavaScript
• Repository B: Converted from React to Vue.js
• Repository C: Migrated from Node.js to Go
• Repository D: Transformed from Flask to Django
• Repository E: Upgraded from Angular 7 to Angular 13
• Repository F: Refactored from Java to Kotlin

Technical Details

The team used a combination of Git and Claude's API to automate the process. Here are some key implementation details:

• Git Integration: The script used Git commands to clone, commit, and push changes.
• API Calls: Claude's API was invoked to generate code transformations.
• Error Handling: Basic error handling was implemented to retry failed tasks.

Performance Metrics

• Total Commits: 1,000+
• Time Taken: Approximately 8 hours
• Average Commit Time: ~45 seconds per commit

Challenges and Learnings

While the experiment was a success, it wasn't without its challenges:

• Code Quality: The quality of the ported code varied. Some repositories required manual clean-up.
• API Limits: Claude's API has rate limits, which slowed down the process at times.
• Repository Complexity: More complex repositories took longer to port and had more errors.

Future Directions

The team is excited about the potential of using coding agents in a controlled environment for tasks like code refactoring, automated testing, and continuous integration. They plan to refine RepoMirror by:

• Improving Error Handling: Enhancing the script to better manage API rate limits and failed tasks.
• Enhancing Code Quality: Implementing post-processing steps to clean up and optimize the generated code.
• Expanding Use Cases: Exploring other applications, such as automated documentation generation and security audits.

Conclusion

The RepoMirror experiment at the YC Agents hackathon demonstrated the power and potential of AI-powered coding agents when used creatively. While there are still challenges to overcome, the team is optimistic about the future of automated code transformation tools.