Boy I'd like to read a compact non-LLM version of the key concepts here. The signal ratio is very low, and crafted with weird LLM-isms throughout, and very hard to parse.
I've been experimenting with mapping a zettelkasten system over to my agents with a few goals in mind, not least applying the idea of more 'test time compute' to the storing of memories as a way to add useful structure that can be tapped later during retrieval. (github.com/vessenes/zet - MIT license - no warranties)
There's some good and some bad, but I think it's better than just a raw embedding memory store for agents. It's definitely better for a human in that it's navigable and understandable, while remaining useful for agents.
But, I'd really like to read more about the space and get ideas -- this blog post was just too difficult to parse for me, sadly.
I've been experimenting with mapping a zettelkasten system over to my agents with a few goals in mind, not least applying the idea of more 'test time compute' to the storing of memories as a way to add useful structure that can be tapped later during retrieval. (github.com/vessenes/zet - MIT license - no warranties)
There's some good and some bad, but I think it's better than just a raw embedding memory store for agents. It's definitely better for a human in that it's navigable and understandable, while remaining useful for agents.
But, I'd really like to read more about the space and get ideas -- this blog post was just too difficult to parse for me, sadly.