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[Sovereignty P2] Auto-Crystallizer for Groq Reasoning Chains #961

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opened 2026-03-22 18:23:03 +00:00 by perplexity · 0 comments
perplexity commented 2026-03-22 18:23:03 +00:00
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Part of #953 (The Sovereignty Loop) — P2 priority

Governing Principle

"Every Dollar Spent Must Reduce Future Dollars" — Extracts rules from API responses automatically.

Goal

When Timmy calls Groq (or any cloud API) for a decision, the response includes a reasoning chain. Automatically analyze that chain for extractable rules. If the reasoning reveals a pattern ("I chose to heal because health was below 30%"), extract it as a local rule that preempts the next Groq call.

Implementation

  1. Create src/timmy/sovereignty/auto_crystallizer.py:
    • crystallize_reasoning(llm_response, context) -> list[Rule]
    • Pattern extraction: identify if/else conditions, thresholds, preferences
    • Rule storage in data/strategy.json and rules.py
  2. Integration: wrap every Groq/cloud API call so the response passes through auto-crystallizer before returning
  3. Rule confidence tracking: new rules start at 0.5 confidence, increase with successful application

Cost Reduction Trajectory

Week 1: ~720 Groq calls/hr ($0.40/hr) → Target: <20 calls/hr (<$0.01/hr)

Effort Estimate

3 days

Cross-references

  • #953 (Sovereignty Loop — Section III.5)
  • #882 (Model Tiering Router — cascade config becomes crystallization target)
  • #904 (Autoresearch — auto-crystallizer is an autoresearch metric)
## Parent Part of #953 (The Sovereignty Loop) — P2 priority ## Governing Principle > "Every Dollar Spent Must Reduce Future Dollars" — Extracts rules from API responses automatically. ## Goal When Timmy calls Groq (or any cloud API) for a decision, the response includes a reasoning chain. Automatically analyze that chain for extractable rules. If the reasoning reveals a pattern ("I chose to heal because health was below 30%"), extract it as a local rule that preempts the next Groq call. ## Implementation 1. Create `src/timmy/sovereignty/auto_crystallizer.py`: - `crystallize_reasoning(llm_response, context) -> list[Rule]` - Pattern extraction: identify if/else conditions, thresholds, preferences - Rule storage in `data/strategy.json` and `rules.py` 2. Integration: wrap every Groq/cloud API call so the response passes through auto-crystallizer before returning 3. Rule confidence tracking: new rules start at 0.5 confidence, increase with successful application ## Cost Reduction Trajectory Week 1: ~720 Groq calls/hr ($0.40/hr) → Target: <20 calls/hr (<$0.01/hr) ## Effort Estimate 3 days ## Cross-references - #953 (Sovereignty Loop — Section III.5) - #882 (Model Tiering Router — cascade config becomes crystallization target) - #904 (Autoresearch — auto-crystallizer is an autoresearch metric)
gemini was assigned by Rockachopa 2026-03-22 23:31:04 +00:00
gemini was unassigned by Timmy 2026-03-24 19:33:47 +00:00
Timmy closed this issue 2026-03-24 21:54:42 +00:00
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Reference: Rockachopa/Timmy-time-dashboard#961
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