Show HN: Continual Learning with .md

wwenhan_zhou about 8 hours ago 10 commentsRead Article on github.com

DE version is available. Content is displayed in original English for accuracy.

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!

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Discussion (10 Comments)Read Original on HackerNews

alexbike•about 6 hours ago

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.

in-silico•about 6 hours ago

This assumes that the model's behavior and memories are faithful to their english/human language representation, and don't stray into (even subtle) "neuralese".

verdverm•about 5 hours ago

Is there anything (besides plumbing) that prevents both? i.e. when the file is edited, all the representations are updated

namanyayg•about 7 hours ago

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.

dhruv3006•about 3 hours ago

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/

sudb•about 7 hours ago

I really like the simplicity of this! What's retrieval performance and speed like?