From AI Slop to AI Empowerment: Raising the Bar for Open-Source Contribution

AI-generated code is flooding open-source projects with low-quality 'slop' submissions, frustrating maintainers at projects like Godot, cURL, and tldraw. Drawing from a DTCC Hackathon experience contributing to the Fluxnova workflow platform, the author outlines a responsible approach to AI-assisted open-source contributions: breaking tasks into small units, communicating with maintainers before submitting, trimming verbose AI-generated code, writing focused unit tests, keeping documentation current, and thoroughly reviewing all AI output before submission. The core argument is that the effort imbalance — where AI makes it cheap to produce code but expensive to review it — is what drives maintainer frustration, and contributors must spend at least as much time reviewing AI code as a reviewer would.