> There is a rush in public to condense and summarize many authoritative publications to find patterns, or to replace a human expert with automated results.. yet that is fundamentally different than taking multiple incomplete perspectives to add to a human library-owners knowledge and investigations. It is subtle to speak it but not subtle in its implications.. taking "data as facts" and condensing them or reordering them or rewriting an output based on them, using automation, is different than a human mind taking in many inputs for human mind knowledge and enabling new outputs from a human author.

You nailed it! Thanks for noticing the divergence!

> Would an LLM-driven "Personal Library" require manually annotated textual interpretation of each curated item, or could it derive personal interpretations from user history and the uniqueness of curated items/sets?

I’ve personally found that tagging is less robust than LLM embeddings (mainly due to dimensionality), but human appended thoughts about a source — also embedded — serve even better as tags.

Example: “this is a quote about dinosaurs…” (Old way of doing things) Tags: dinosaurs, jurassic, history Query: “dinosaurs” > results = 1…

(New way of doing things) Embedded Quote: [0.182…] User Added Thought: “this dinosaur reminds me of a time i went to six flags with my cousins and…” Embedded User Added Thought: [0.284…]

Query: “dinosaurs” > results = 2 (indexes = sources, thoughts)

The "thoughts" index can do a second layer cosine similarity search and serve as a tag on its own to fetch similar concepts. Basically a tree search created by similarity from user input/feedback loops.

Personal computing paves the way for personal library science

Personal Library Science

neat! one thing i’d really love tooling for: supporting multi user apps where each has their own siloed data and embeddings. i find myself having to set up databases from scratch for all my clients, which results in a lot of repetitive work. i’d love to have the ability one day to easily add users to the same db and let them get to embedding without having to have any knowledge going in