Building Pi With Pi

2026-05-24 · Source: Armin Ronacher's Thoughts and Writings · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

The article "Building Pi With Pi," written on May 24, 2026, details challenges encountered while using the AI agent "Pi" to develop the "Pi" project. A key issue is the influx of "slop issues" in the tracker, where AI "clankers" generate 95% of content, often containing confident but inaccurate diagnoses. This misleads Pi agents, despite a custom "/is" command. The author advocates for concise, factual issue reports. AI-generated code also frequently over-engineers solutions by handling bad states rather than preventing them, increasing system complexity and violating core invariants. The project's GitHub tracker experiences high volume, with 2,504 of 3,145 external issues/PRs auto-closed in 90 days, with only 26% of issues and 8% of PRs ultimately accepted. This is attributed to users submitting low-quality, AI-assisted content. Pi employs "careful parallelism" with "/is" and "/wr" commands for automated issue analysis and wrap-up. The piece concludes by emphasizing that AI increases code volume but not maintainers, underscoring the need for stronger foundations and human collaboration in open source.

Key takeaway

For Open Source Maintainers and AI Engineers dealing with increasing AI-generated contributions, you must establish clear guidelines for issue reporting, demanding factual observations over AI-derived diagnoses. Prioritize architectural integrity by preventing bad states rather than allowing AI to introduce complex local workarounds. While tools like Pi's "/is" can automate analysis, your focus should remain on fostering human collaboration and maintaining strong project foundations to avoid a deluge of low-quality "slop" that fragments effort and diminishes project value.

Key insights

AI-generated content in issue trackers and code introduces significant complexity and reduces quality in open source projects.

Principles

• Issue reports should be condensed to observed facts, not AI-generated diagnoses.
• Correct fixes prevent bad states, rather than handling them with added complexity.
• Open Source value derives from human collaboration, not isolated machine work.

Method

Pi uses "/is" to analyze GitHub issues, instructing agents to independently verify behavior. A "prompt-url-widget" provides UI context. "/wr" handles wrap-up: updating changelog, drafting comments, committing, and pushing.

In practice

• Implement strict issue reporting guidelines for AI-assisted submissions.
• Prioritize preventing bad states over adding local defenses in code.
• Utilize automated tools like Pi's "/is" for parallel issue investigation.

Topics

• AI Agents
• Open-Source Development
• Issue Tracking
• Code Quality
• Automated Software Engineering
• Human-AI Collaboration

Code references

• earendil-works/pi

Best for: CTO, VP of Engineering/Data, AI Architect, AI Engineer, Software Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Armin Ronacher's Thoughts and Writings.