MemoryOS
AI-Powered Semantic Second Brain & Note Retrieval Engine
“How do you build an AI second brain that understands context, not keywords?”
Case Study Index (5 Sections)↓
Problem & Product Goals
Traditional note-taking tools fail when users forget the specific keywords used when writing a note. MemoryOS leverages OpenAI vector embeddings stored in Supabase to provide real-time semantic retrieval, allowing users to query their notes naturally.
During study sessions, finding past notes often required manual searching through folders or trying multiple keyword combinations. I wanted a system that understood the semantic context of what I was looking for.
Technical Architecture & Core Engine
Design Philosophy: Focus on a single core workflow—write markdown, auto-generate embeddings in the background, and search by meaning—avoiding feature bloat during a tight hackathon timeline.
Engineering Decisions
Generating embeddings on every keystroke blocked the main thread and caused visible editor lag.
Implemented a debounced background queue that sends note contents to OpenAI embeddings endpoints only after editing pauses.
Brief delay (500ms) before newly edited text is searchable via semantic queries.
Preserved smooth 60fps markdown typing performance while maintaining search indexing.
Challenges & Solutions
36-Hour Hackathon Scope Management
Issue: Attempting to build rich text tools and complex folder hierarchies threatened completing the core semantic search flow.
Decision/Solution: Cut all peripheral features to focus exclusively on perfecting the write -> auto-embed -> semantic query pipeline.
What's Next
Key Takeaways
Scoping down to one exceptionally well-executed feature wins over long lists of half-built capabilities.
Vector search changes how personal information management tools can be structured.
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