The framing of "linter" vs "scanner" is an interesting design choice. Linters typically run synchronously in the editor and flag as you type, which means they n

The framing of "linter" vs "scanner" is an interesting design choice. Linters typically run synchronously in the editor and flag as you type, which means they need to be fast and have low false positive rates to avoid training developers to ignore them. Scanners run on demand or at commit time and can afford more thorough analysis. For AI-generated code specifically, the false positive problem is harder than for human-written code. AI tends to generate code that looks like common patterns but has subtle structural differences a merge function that iterates keys without filtering, a CORS middleware that reflects the origin header, an auth check that verifies identity but not authorization. These aren't syntactically unusual, so rules tuned for human code patterns miss them. Curious what your approach is for the false positive rate on AI-generated patterns specifically. We've been running SafeWeave (MCP-native, runs inside Cursor/Claude Code) and the main tuning challenge has been distinguishing "AI wrote this correctly" from "AI wrote this in a way that looks correct but has a structural flaw."