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docs(tac): add Memory vs Taste architecture pattern 

Folds the Memory vs Taste concept into loop_engineering.md. Memory
is raw storage/retrieval (vector DB, full histories). Taste is
higher-order judgment (significance extraction, preferences, lessons).
Applied to TAC: skill_health.py as the bridge, deploy policy as taste,
25 containers + qdrant as memory.
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artale 2026-07-09 06:48:08 +02:00
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@ -338,3 +338,16 @@ Reference: github.com/UnpaidAttention/fable5-methodology — Claude Fable 5 docu
- The "write methodology so a weaker model can execute it" pattern is what TAC's loop_engineering.md does for its agents — documented procedures with enforceable gates. - The "write methodology so a weaker model can execute it" pattern is what TAC's loop_engineering.md does for its agents — documented procedures with enforceable gates.
**Sleep-time compute reference:** Letta Sleep-time Compute (arxiv 2504.13171) formalizes what TAC's RSI loop already does — offline computation between conversational turns for memory consolidation, reflection, and insight generation. The factory's skill_health.py cron that runs every 6 hours is a primitive implementation. Advanced sleep-time compute would add structured reflection, cross-skill pattern discovery, and automated methodology refinement. **Sleep-time compute reference:** Letta Sleep-time Compute (arxiv 2504.13171) formalizes what TAC's RSI loop already does — offline computation between conversational turns for memory consolidation, reflection, and insight generation. The factory's skill_health.py cron that runs every 6 hours is a primitive implementation. Advanced sleep-time compute would add structured reflection, cross-skill pattern discovery, and automated methodology refinement.
## Memory vs Taste — two-layer agent architecture
Memory is raw storage and retrieval — full conversation histories, documents, events, vector databases (e.g. qdrant:6333, GBrain). It scales to tens of thousands of files but risks overwhelming the model with noise.
Taste is the higher-order judgment layer — selectively understanding *why* something matters, extracting significance, preferences, and lessons rather than archiving everything. It enables better decision-making and more human-like reasoning.
In practice: Hermes, OpenClaw, and Claude Code combine scalable memory (graph-based synthesis via GBrain or vector DB) with taste mechanisms to prioritize relevant insights, reducing hallucination and improving output quality over brute-force context expansion.
**Applied to TAC's factory:**
- **Memory layer:** 25 containers, 63 skills in three hermes agents, full RSI skill-health pass/fail history, qdrant vector DB, forgejo issue tracker, complete conversation logs — everything stored.
- **Taste layer:** The signed-action deploy policy (gating what `:8098` can accept), the `auto_patch_proven` threshold (3 receipts × 2 skill types), the verifier/reviewer gates in the ADW pipeline, and every correction that marks stale entries as SUPERSEDED. These are taste — judgment applied to raw data.
- The RSI loop bridges both: skill_health.py reads memory (pass rates), applies taste (below 80% = degraded), triggers action (repair). The taste layer is where TAC's value lives — without it, the factory is just an expensive archive.