Comment by raphaelmolly8

The 4k LOC claim is interesting but I think the real insight is about what you remove rather than what you keep. Looking at the codebase, they've essentially bet that LLMs with 100k+ context windows make most RAG pipelines redundant - just give the agent grep/rg and let it iterate.

What's clever is treating memory as filesystem ops rather than vector stores. For codebases this works great since code has natural structure (imports, function calls) that grep understands. The question is whether this scales to truly unstructured knowledge where semantic similarity matters.

Would love to see benchmarks comparing retrieval accuracy vs a proper embedding pipeline on something like personal notes or research papers.