MongoDB plan

docs/continual-learning/plan.md

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+# Continual Learning Plan
+
+Status: draft  
+Goal: prove PodMan learns from outcomes in the hackathon demo
+
+## Must-Have Demo Loop
+
+1. Observe two engineers touching the same file.
+2. Store the observation and git state in MongoDB.
+3. Predict a collision.
+4. Send a card or Hermes message.
+5. Record accept or dismiss outcome.
+6. Adapt `team_model`.
+7. Show the learned graph edge or changed future behavior.
+
+## Build Order
+
+### R1: Make exact recall reliable
+
+- Normalize file paths.
+- Build stable memory signatures.
+- Look up prior accepted and dismissed outcomes.
+- Prefer exact recall over vector recall.
+
+### R2: Make outcomes update memory
+
+- Accepted real collision creates or strengthens ownership.
+- Accepted real collision creates `learned_from`.
+- Dismissed outcome lowers confidence or suppresses route.
+
+### R3: Expose loop data to the graph
+
+- Add optional loop snapshot.
+- Add optional activity stream.
+- Keep existing `PodGraph` fields stable.
+
+### R4: Show the observatory
+
+- Render observe/store/predict/outcome/adapt.
+- Show recent activity.
+- Make selected-node detail explain why memory changed.
+
+### R5: Prepare a clean demo chain
+
+- Ensure one collision -> intervention -> accepted outcome exists.
+- Ensure repeated signature recalls prior memory.
+- Verify graph shows learned ownership.
+
+## Nice-to-Have
+
+- Atlas Vector Search over memory summaries.
+- Confidence scoring per ownership edge.
+- Per-file memory timeline.
+- Strategy promotion tied to outcomes.
+
+## Cut
+
+- Raw screenshot storage.
+- Full autonomous training.
+- Broad dashboard metrics.
+- Multi-pod learning generalization.
+
+## Acceptance Criteria
+
+- A judge can see what changed in memory.
+- The second similar event behaves differently.
+- Exact MongoDB records prove the loop.
+- The graph remains legible with real data.
+