Daily Log: 2026-01-23

🧠 Context Summary

Status: Mission Complete | Focus: AI Augmentation & Networking

Today marked a pivot from pure infrastructure management to AI-Augmented Operations. We deployed a "Local Brain" on starfleet-compute to reduce token costs and improve context awareness. Additionally, the network is now fully Dual-Stack (IPv4/IPv6).

🚧 Current Projects

1. Fleet Expansion: Onboarding enterprise-dev (Complete).
2. Network Upgrade: IPv6 & Subnet Routing (Complete).
3. Federation Library: Local RAG System (Complete).

📋 Fleet Health Report (05:30 PM)

• memory-alpha: 🟢 Healthy. Acting as Gateway & Subnet Router.
• starfleet-compute: 🟢 High Load. Running Ollama + ChromaDB (AI Cluster).
• enterprise-dev: 🟢 Online. Stirling-PDF active.
• Network: Direct IPv6 path active (pdf-direct).

📝 Actions Taken

• AI Infrastructure (The Local Brain):
  • Deployed: Ollama, ChromaDB, and Open WebUI on starfleet-compute.
  • Models: Pulled phi3:mini (CPU Optimized) and nomic-embed-text.
  • Tooling: Created summarize_logs.py (Streaming) and index_codebase.py (Semantic Indexer).
  • Integration: Verified query_library.py allows semantic search of the codebase/docs from the CLI.
• Networking:
  • Optimized: Recommended UDP 443 for HTTP/3 on Direct Gateway.
  • Validated: Confirmed "Search-Only" mode for RAG is instant (<1s), while "Generation" on CPU is too slow (>60s).

💡 Strategic Notes

• RAG Capability: The system can now instantly retrieve relevant config snippets or daily logs via semantic search (python3 tools/query_library.py).
• CPU Limit: The i7-3720QM is too slow for real-time generation (Chat) but excellent for retrieval (Search) and background summarization.

⏭️ Next Steps

• Use query_library.py in future sessions to find context.
• Monitor starfleet-compute RAM usage with the new AI load.