While AI changes the way in which we write software, how do we ensure that our open-source contributions remain valuable and are welcomed by maintainers? This blog post explores this topic in the context of the Depository Trust & Clearing Corporation (DTCC) Hackathon and our contributions to the Fluxnova project.

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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.

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