> but it's much smarter than "shove markdown files into directories".
Is it, though? I mean, is there evidence "bunch of markdown files" is bad while "database the model has to be instructed how to use" is good? `rg` is fast as hell. Markdown is the LLMs native tongue. It does require maintenance of the Markdown files to keep them current, but maybe explicit management is fine. The models can do the grunt work.
BMDF (Bunch of Markdown Files) can be checked into the git repo, they travel to any developer on the project without any setup or special auth, any agent and any model can read them with no special tools to install, and humans can easily poke around and read them, too. And, they can be part of the PR review process, documenting the code and intentions.
I can't come up with good arguments for why a database or search index would be better than documentation in Markddown for any of my projects.
Because a bunch of markdown files is just RAG, and RAG is unintelligent, so the results are not great. If you want a smarter AI, it needs to have not-dumb memory. That's why this article (and the summary I posted) covers multiple kinds of memory, multiple ways of managing different memories, multiple ways of finding memories, a way to pick the best memory, and a way to manage memories long-term (and among multiple users). Now the memory isn't dumb, so the results are better. (And the article shows you why it's better)
tl;dr https://www.elastic.co/search-labs/blog/agent-memory-elastic...
> If you want a smarter AI, it needs to have not-dumb memory.
Who says? According to what metric? How would you prove that assertion?
> Now the memory isn't dumb, so the results are better. (And the article shows you why it's better)
But, it doesn't. It explains what they built, and how it behaves. It does not show why it's better than any other alternative for making models "smarter", somehow.
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