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