SlothOps: An Autonomous AI Coding Factory That Works While You Sleep
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady What I Built SlothOps is an autonomous software-maintenance system that takes repetitive GitHub issue-to-PR work off a developer's hands. Instead of manually investigating every issue, tracing dependencies,
Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 馃Ε
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
SlothOps is an autonomous software-maintenance system that takes repetitive GitHub issue-to-PR work off a developer's hands. Instead of manually investigating every issue, tracing dependencies, implementing fixes, running tests, and writing PR descriptions, you queue up eligible issues and let the factory work through them.
The goal is simple: wake up to investigated issues, isolated code changes, test results, and draft pull requests ready for human review. This is not another chatbot suggesting code snippets. It is a persistent engineering workflow that carries tasks across multiple steps, recovers from failures, and preserves progress without requiring a human to supervise every tool call.
The project is deliberately conservative about irreversible actions. It prepares code and draft PRs, but it does not merge its own work. Reliability and verifiability matter more than PR volume.
Working project under active development. The current development target is Recon-OS, a repository with multiple issues involving data loaders and document ingestion. The factory has been used to prepare feature PRs for plain text, Markdown, JSON, HTML, CSV, and remote URL ingestion.
Live end-to-end demonstration with real workers and review feedback is the next milestone. Recent smoke tests encountered provider timeouts and free-tier limits, which is exactly the kind of failure the orchestration layer is designed to handle gracefully.