I'm failing to grasp how it solves/replaces what vector db were created for in the first place (high-dimensional neighborhood searching, where the space to be searched grows by distance^dimension)
replies(4):
The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history?
How it works:
Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual changes in understanding, not just similarity scores.
This is very much a PoC - rough edges everywhere, not production ready. But it's been working surprisingly well for personal use. The entire index for a year of conversations fits in ~100MB RAM with sub-second retrieval.
The cool part: You can git checkout to any point in time and see exactly what the AI knew then. Perfect reproducibility, human-readable storage, and you can manually edit memories if needed.
GitHub: https://github.com/Growth-Kinetics/DiffMem
Stack: Python, GitPython, rank-bm25, OpenRouter for LLM orchestration. MIT licensed.
Would love feedback on the approach. Is this crazy or clever? What am I missing that will bite me later?
Then mention she is 10,
a few years later she is 12 but now i call her by her name.
I have struggled to get any of the RAG approaches to handle this effectively. It is also 3 entries, but 2 of them are no longer useful, they are nothing but noise in the system.