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Find XO: AI Agents Need Document Records More Than Memory (liao.gg)

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Writing language: Korean Read in the original language

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- Existing memory plugins divide conversation logs into thousands of pieces, store them in a vector database, and retrieve a few most similar ones to attach upon user commands. - This method only assesses similarity of information and cannot ensure that the data is always accurate or up-to-date; it also fails to reflect the project's overall context or changes. - The author argues that these memory plugins solve the wrong fundamental problem; what agents really need is not 'memory' but well-organized and continually updated 'documents'. - The author developed an open-source plugin called Operator Memory that allows agents to read collections of documents in Markdown format, update them when necessary, and work accordingly.
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Hacker News ↗ / 148 votes / 90 comments

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It's noticeable that existing memory plugins relying solely on vector similarity make it hard to verify if the information is always up-to-date. If memory is document-based, it seems possible to capture the project's background and its changes, but Operator Memory's structure, which relies on manual document viewing and updates, does have certain limitations regarding automation. I'm also curious about how naturally document updates occur in actual workflows when using such tools.
Writing language: Korean

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