Building a model rocket that can become a real rocket

Each stage builds on the previous one without requiring architectural rewrites. We're planning for the space station while building the model rocket.

Basic move that everything else builds on

Input → Parallel Processing → Smart Storage → Semantic Retrieval
         ├─ Embedding (semantic understanding)
         └─ LLM Parser (structure extraction)

• Input: CLI/simple TUI for text input
• Processing: Dual-track (embeddings + parsing)
• Storage: SQLite with JSON metadata + vector columns
• Retrieval: Semantic search + temporal queries
• Output: Basic report generation from templates

1. Modular parser system - Easy to add new domain schemas
2. SQLite with JSON - Can migrate to graph DB later
3. Abstract storage layer - Swap backends without rewriting
4. Plugin-ready architecture - Even if not exposed yet

• Can capture thoughts without friction
• Finds relevant content that keyword search would miss
• Generates useful daily summary
• All pieces are modular and testable

Adding style and complexity

Input → Type Detection → Specialized Processing → Rich Storage → Multi-Modal Retrieval
         ├─ URLs: Crawl & Summarize
         ├─ Images: Vision + Alt Text
         ├─ Code: Diff Analysis
         └─ Voice: Transcription

• Smart Input Router: Detects input type automatically
• Specialized Processors:
  • Web crawler for links
  • Computer vision for images
  • Git integration for code
  • Voice transcription
• Enhanced Storage:
  • Graph relationships between globules
  • Richer metadata schemas
  • Version tracking
• Advanced Outputs:
  • Blog post generation
  • Code diary formatting
  • Business dashboards
  • Custom report templates

• Background workers for async processing
• WebSocket API for real-time updates
• Plugin system goes live
• Cloud sync option (encrypted)

Each processor is a plugin to the core system. The ollie architecture doesn't change - we just add new input adapters and output formatters.

Multiple inputs working in harmony

Passive Monitoring → Event Stream → Semantic Layer → Proactive Insights

• Passive Input Sources:

  • File system monitoring
  • Browser activity
  • Clipboard monitoring
  • ActivityWatch integration
  • Calendar integration
  • Email monitoring (with permission)

• Event Correlation:

  • "You edited this file while reading these docs"
  • "This meeting relates to these code changes"
  • Pattern detection across sources

• Proactive System:

  • Notifications for patterns
  • Auto-categorization
  • Suggested connections
  • Anomaly detection

• Event streaming architecture (Kafka-lite)
• ML models for pattern detection
• Privacy-preserving analytics
• Federated learning prep

The full vision realized

• OS-level integration
• Universal semantic search across all computer activity
• Time travel through digital life
• Collaborative intelligence network
• Natural language computer control

Because every previous stage created the foundations:

• Stage 1: Semantic understanding
• Stage 2: Multi-modal processing
• Stage 3: Ambient capture
• Stage 4: Just connecting it all

1. Embedding/Parser Duality - This is core to everything
2. Storage Abstraction - Must handle future graph needs
3. Plugin Architecture - Even if hidden, must exist
4. Performance Baseline - Sub-100ms for operations

• Beautiful UI (CLI is fine)
• Multi-user support
• Advanced visualizations
• Cloud features

• Tight coupling between components
• Storage decisions that lock us in
• Over-engineering the MVP
• Feature creep in Stage 1

1. Each stage must provide standalone value

   • Ollie: Replaces note-taking
   • Kickflip: Replaces multiple tools
   • Tre Flip: New capability (ambient capture)
   • 360: Paradigm shift

2. No architectural rewrites between stages

   • Plan the interfaces from day 1
   • Abstract the right things
   • Leave hooks for future features

3. User value before technical elegance

   • Ship the ollie even if tre flip isn't designed
   • Get feedback early and often
   • Let usage patterns guide development

4. Build for extensibility, ship for simplicity

   • Core must be plugin-ready
   • But MVP shouldn't expose complexity
   • Progressive disclosure of power

• Week 1-2: Core capture + storage working
• Week 3-4: Semantic search beating keyword search
• Week 5-6: Report generation providing value

• Month 1: Input router + 2 specialized processors
• Month 2: Graph relationships + rich queries
• Month 3: Plugin ecosystem + cloud option

Before moving to next stage:

1. Current stage is daily-active useful
2. Architecture supports next stage without rewrites
3. Performance meets targets
4. Users are asking for next stage features

We're not building features. We're building toward a world where:

"Computers understand what you mean, not just what you type"

Every stage moves us closer to this vision.