The differential fuzzing approach is clever — way safer than a big-bang rewrite. Running both versions in parallel to catch edge cases before switching over is how you actually ship rewrites without breaking production. The 160k to 90k LOC drop is impressive, but the real engineering win is the validation strategy.
On binary size, static linking with LTO should handle most of the bloat without needing custom stdlib builds.
This looks solid, the type safety and automatic escaping alone would've saved me hours debugging Cypher strings. The batching is smart too. Curious how it handles variable-length paths though, since that's where most ORMs get messy. The AI agent memory use case makes a lot of sense.
This is exactly what I've been screaming about, AI coding assistants are basically playing guess the impact with our production code. The fact that you're exposing actual call graphs and blast radius through MCP tools instead of making Claude hallucinate dependencies is huge from my pov. Installing this now to test with our multi-repo setup. Does the telemetry integration for dead code detection require specific instrumentation? does it hook into existing APM tools?
I don't get it. Why wouldn't I just use a Vector DB (Pinecone/Weaviate) for this? Just retrieve relevant chunks and stick them in the prompt. Feels like you're reinventing the wheel here.
On binary size, static linking with LTO should handle most of the bloat without needing custom stdlib builds.