Show HN: Continual Learning with .md
I have a proposal that addresses long-term memory problems for LLMs when new data arrives continuously (cheaply!). The program involves no code, but two Markdown files. For retrieval, there is a semantic filesystem that makes it easy for LLMs to search using shell commands. It is currently a scrappy v1, but it works better than anything I have tried. Curious for any feedback!
Discussion Highlights (4 comments)
sudb
I really like the simplicity of this! What's retrieval performance and speed like?
namanyayg
I've seen a lot of such systems come and go. One of my friends is working on probably the best (VC-funded) memory system right now. The problem always is that when there are too many memories, the context gets overloaded and the AI starts ignoring the system prompt. Definitely not a solved problem, and there need to be benchmarks to evaluate these solutions. Benchmarks themselves can be easily gamed and not universally applicable.
alexbike
The markdown approach has a real advantage people underestimate: you can read and edit the memory yourself. With vector DBs and embeddings the memory becomes opaque — you can't inspect or correct what the model "knows". Plain files keep the human in the loop. The hard part is usually knowing what +not+ to write down. Every system I've seen eventually drowns in low-signal entries.
dhruv3006
I love how you approached this with markdown ! I guess the markdown approach really has a advantage over others. PS : Something I built on markdown : https://voiden.md/