GPL code generation licensing
An AI agent generated a tidy Go implementation of a rope data structure for your closed-source SaaS editor. It compiles, passes tests, and ships. Months later a license-scanning audit flags that the implementation is near-identical to a GPL-3.0 library on GitHub, down to variable names and comment phrasing. Walk through how you'd respond, and what you'd change in your AI-coding process so this doesn't recur.
provenance_match_percent(generated_lines: list[str], reference_lines: list[str]) → int[["func (r *Rope) Insert(idx int, s string) {"," if idx < 0 || idx > r.length {"," return"," }"," r.root = insertNode(r.root, idx, s)","}"],["func (r *Rope) Insert(idx int, s string) {"," if idx < 0 || idx > r.length {"," return"," }"," r.root = insertNode(r.root, idx, s)","}"]]out100[["package rope","","type Rope struct {"," root *node","}"],["package tree","","type Tree struct {"," root *node","}"]]out33[[],["anything"]]out0Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it (tests, edge cases, reading critically), and how you’d re-prompt or decompose to get it right.
Vibe & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.