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Gonna drop my own link here, because I really think the UX I’m working on is truly novel. Inspired by the commonplace book format, I take highlights from Kindle and embed them in a DB [1]. From there I build (multiple) downstream apps but the central one, Commonplace Bot [2] is a bot that serves as a retrieval and transformer for said highlights. It has changed the way I read books. I now get to link ideas from books I read in 2018 to books I read last week. I don’t need to always have a query either, as I added a hypothetical question as an entry point allowing for the UX of finding an idea to be as simple as typing “wander”. Finally, since quotes are dense, short, and generally context free, I enable a bunch of transformations like Anki quizzes, and art from quotes, and using the quote itself as a centroid to search its neighbors, etc.

[1] - https://github.com/bramses/quoordinates [2] - https://github.com/bramses/commonplace-bot




I love this. I have my commonplace book in Roam Research. Search in Roam is not perfect and I have wondered lately if there was a way to get all of the content into a graph DB and then query using LLMs. But I haven't had time to tinker with it - I am sure open source libraries exist that do exactly this.

Can your library take all highlights from Readwise or just Kindle? I use Readwise Reader quite a bit and will love something that takes everything I save + all highlights + other places (Roam Research, Email, Calendar) etc. and I can just ask it questions.


You definitely could! Funnily enough, I have a function named "justBooks()" [1] that filters the Readwise export to just book type tags, but you could use the entire export, or whatever upstream method you want. I think much like journaling, every one's use case will be catered to their own tasks/quotes/ideas, but allow me to share centralized advice. You'll definitely need: 1) a database that supports vectors, I use Postgres 2) a low friction way to get your "new" highlights from your reading practice, I use Readwise 3) an llm to "cache" transformations [2]. This transformation does an insane amount of work, and takes it to the next level in terms of utility, I wouldn't skip it.

[1] - https://github.com/bramses/quoordinates/blob/1b9d1fadaded98b... [2] - https://github.com/bramses/quoordinates/blob/1b9d1fadaded98b...


This is really cool! I could see it being excellent for anyone who writes or gives speeches very often, a great way to quickly access the knowledge one builds up over a lifetime of reading. Love it!




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