# Pat's System Vision — 2026-03-09

## Immediate Priorities
1. Get Perception Engine (video swarm) fully operational
2. Store ALL transcribed/analyzed data into unified knowledge store
3. Improve swarm UI/UX — see agents working, see output being written live
4. Distribute knowledge to all agents automatically

## Unified Knowledge Store
- Already have: Memory Concierge RAG store (port 8102, SQLite + nomic embeddings, 6215 chunks)
- Need: All video transcriptions, analysis, research → auto-ingested into this store
- Need: A small model (0.8b) always running, scanning for useful data to surface to agents
- All agents should be pointed toward this store as their primary knowledge source

## Self-Improving Research Pipeline (cascading intelligence)
- Run 1-2 research agents daily, autonomous
- Architecture (small → medium → large):
  1. **Swarm layer (0.8b x many):** Query RAG/SQL DB broadly, find candidate topics
  2. **Filter layer (2b or 4b):** Narrow to ~7 topics, provide sources/tags/summaries for each
  3. **Decision layer (122B):** Evaluate topics, choose research paths, identify what papers to find
  4. **Researcher (122B + web):** Find actual papers, conduct full research pipeline
- Self-improving: system should understand what good research looks like
- All decisions, paths, and findings feed back into the knowledge store
- Research should be informed by what we already know (RAG-first)

## Key Principle
- Videos Pat processes are research-dense → capture everything
- Knowledge compounds: today's video understanding feeds tomorrow's research
- The system should get smarter over time, not just process and forget