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points by alexbike 22 hours ago | hide | 0 comments

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.

The editability is surely an underrated advantage, both for the program itself and the memories it generated.

I think in terms of noise, it is less problematic here because not everything is being retrieved. The agent can selectively explore subsets of the tree (plus you can edit the exploration policy by yourself).

Since there is no context bloat, it is quite forgivable to just write things down.

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".

Having run a Markdown memory system with Claude for over a year, I don't think I've seen any evidence of neuralese. That's even with Claude being regularly encouraged to write "reflections" on each session, including automated sessions, and weekly summaries of those reflections.

The bigger problem is avoiding what I call the Memento Effect. I won't spoil the movie for anyone, but Memento involves a character who cannot make new memories, so he has to take meticulous notes about everything. But if any of those notes are vague or incorrect, they still get accept as truth when next reviewed. So you really need your Markdown memory to be pristine and mustn't allow it to become polluted.

I think what's missing is a benchmark that measures how well the memories contribute to future interactions.

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