Why does Merge Mind use vector embeddings instead of simple keyword matching?

Keyword or pattern matching can only catch issues that were explicitly anticipated and written into a rule. Vector embeddings instead capture the meaning and structure of code, so similar logic can be recognized even if it's phrased or named differently.

• Lets Merge Mind find code that behaves like this rather than code that contains this exact string
• Makes it possible to compare new changes against the team's actual historical patterns instead of a fixed external rulebook
• Scales better as a codebase grows, since similarity search stays fast even across large embedding sets

This is also why Merge Mind can catch team-specific inconsistencies, like deviating from an established validation pattern, that a generic linter with static keyword rules would never be configured to look for.

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