Obsidian-Excalidraw like note linking & visualizing in LiftOS (Whiteboard + Notes)

Phase 1: Integration of Whiteboard and Notes Apps

High-Level Architecture:

1. Backend Services:

   • Data Linking Service: Handles the linking between Whiteboard visuals and Notes (each visual/note pairs as 1 node, or each visual/note can link to a group of visuals/notes).

   • Synchronization Service: Ensures real-time updates and synchronization between the two apps.

2. Frontend Components:

   • Whiteboard UI: Enhanced to allow linking visuals to notes.

   • Notes UI: Enhanced to display linked visuals and allow toggling views.

   • Toggle Mechanism: UI element for switching between visual-heavy (Whiteboard) and text-heavy (Notes) views.

3. Data Storage:

   • Note and Visual Storage: Central repository for storing notes and visuals, possibly using a graph database for easy linking and retrieval.

Detailed Breakdown:

• Whiteboard App Enhancements:

  • Linking Capability: Users can create visuals/notes and link each element to specific notes.

  • Visual Representation: Visual elements display metadata indicating their linked notes.

• Notes App Enhancements:

  • Link Indication: Notes show links to visual elements from the Whiteboard and vice versa

  • Toggle View: Button or switch to toggle between viewing notes in text format OR visual representation.

Phase 2: AI-Generated Visuals/Notes

High-Level Architecture:

1. AI Integration Service: A service (like Albus from Google) to analyze existing notes and generate corresponding visuals or summaries.

2. Natural Language Processing (NLP) Engine: To comprehend, summarize (if needed) and generate additional content based on selected context from existing notes.

3. Visual Generation Engine: To convert textual information (Notes) into visual representations (Whiteboard Visuals).

Detailed Breakdown:

• Note Analysis: AI analyzes individual or grouped notes to identify key concepts and relationships.

• Visual/Note Generation: AI generates a visual or note summary and integrates it into the Whiteboard or Notes app.

Phase 3: Tagging System and AI-Generated Content

High-Level Architecture:

1. Central Tag System: A community-defined tagging system alongside user-defined custom tags.

2. Content Generation Engine: AI engine capable of generating diverse content forms (mind maps, presentations, research papers, blog posts, SEO analysis) based on tagged visuals/notes.

Detailed Breakdown:

• Tagging Mechanism: Users can tag visuals/notes with community or custom tags.

• Content Generation: AI uses tags to generate contextually relevant content, integrating it seamlessly into the user workflow.