Summary of Recall Documentation
Thesis: Recall is an AI-powered tool designed to help users effectively manage, synthesize, and retain information consumed from diverse online sources, acting as a personal AI encyclopedia.
Key Points:
- Content Capture: Recall functions as a Read-It-Later app, supporting Articles, PDFs, and Google Docs (with podcasts and others forthcoming).
- AI Interaction: Provides concise or detailed AI summaries and allows users to chat only with the specific stored content for relevant insights.
- Knowledge Graph: Built on a graph database that automatically links related content using extracted keywords.
- Augmented Browsing: Newly implemented feature that resurfaces content connections while browsing. Users can also manually link content in the Recall Notebook.
- Notebook Functionality: Saved summaries and chats reside in an editable Recall Notebook for personal note-taking and customization.
- Organization: Content is automatically tagged based on existing tags and core focus, maintaining the user's intended knowledge structure.
- Retention Tools: Employs spaced repetition and active recall strategies via a built-in quiz/review function to improve knowledge retention.
Notable Data:
- Currently supports Articles, PDFs, and Google Docs for primary capture.
Actionable Insights:
- Utilize the chat feature strictly for specific content queries, not general internet searches.
- Regularly use the "Review" function to engage with personalized spaced repetition schedules for better long-term memory.
- Leverage the automatic tagging and manual linking to build a robust, interconnected knowledge base.