jarvis-memory/memory/2026-02-10.md

2026-02-10 — Daily Memory Log

Qdrant Memory System — Manual Mode

Major change: Qdrant memory now MANUAL ONLY.

Two distinct systems established:

• "remember this" or "note" → File-based (daily logs + MEMORY.md) — automatic, original design
• "q remember", "q recall", "q save", "q update" → Qdrant kimi_memories — manual, only when "q" prefix used

Commands:

• "q remember" = store one item to Qdrant
• "q recall" = search Qdrant
• "q save" = store specific item
• "q update" = bulk sync all file memories to Qdrant without duplicates

Redis Messaging — Manual Mode

Change: Redis agent messaging now MANUAL ONLY.

• No automatic heartbeat checks for Max's messages
• No auto-notification queue processing
• Only manual when explicitly requested: "check messages" or "send to Max"

New Qdrant Collection: kimi_memories

Created: kimi_memories collection at 10.0.0.40:6333

• Vector size: 1024 (snowflake-arctic-embed2)
• Distance: Cosine
• Model: snowflake-arctic-embed2 pulled to 10.0.0.10 (GPU)
• Purpose: Manual memory backup when requested

Critical Lesson: Immediate Error Reporting

Rule established: When hitting a blocking error during an active task, report IMMEDIATELY — don't wait for user to ask.

What I did wrong:

• Said "let me know when it's complete" for "q save ALL memories"
• Discovered Qdrant was unreachable (host down)
• Stayed silent instead of immediately reporting
• User had to ask for status to discover I was blocked

Correct behavior:

• Hit blocking error → immediately report: "Stopped — [reason]. Cannot proceed."
• Never imply progress is happening when it's not
• Applies to: service outages, permission errors, resource exhaustion

Memory Backup Success

Completed: "q save ALL memories" — 39 comprehensive memories successfully backed up to kimi_memories collection.

Contents stored:

• Identity & personality
• Communication rules
• Tool usage rules
• Infrastructure details
• YouTube SEO rules
• Setup milestones
• Boundaries & helpfulness principles

Collection status:

• Name: kimi_memories
• Location: 10.0.0.40:6333
• Vectors: 39 points
• Model: snowflake-arctic-embed2 (1024 dims)

New Qdrant Collection: kimi_kb

Created: kimi_kb collection at 10.0.0.40:6333

• Vector size: 1024 (snowflake-arctic-embed2)
• Distance: Cosine
• Purpose: Knowledge base storage (web search, documents, data)
• Mode: Manual only — no automatic storage

Scripts:

• kb_store.py — Store web/docs to KB with metadata
• kb_search.py — Search knowledge base with domain filtering

Usage:

# Store to KB
python3 kb_store.py "Content" --title "X" --domain "Docker" --tags "container"

# Search KB
python3 kb_search.py "docker volumes" --domain "Docker"

Test: Successfully stored and retrieved Docker container info.

Unified Search: Perplexity + SearXNG

Architecture: Perplexity primary, SearXNG fallback

Primary: Perplexity API (AI-curated, ~$0.005/query)
Fallback: SearXNG local (privacy-focused, free)

Commands:

search "your query"        # Perplexity → SearXNG fallback
search p "your query"      # Perplexity only
search local "your query"  # SearXNG only
search --citations "query" # Include source links
search --model sonar-pro "query" # Pro model for complex tasks

Models:

• sonar — Quick answers (default)
• sonar-pro — Complex queries, coding
• sonar-reasoning — Step-by-step reasoning
• sonar-deep-research — Comprehensive research

Test: Successfully searched "top 5 models used with openclaw" — returned Claude Opus 4.5, Sonnet 4, Gemini 3 Pro, Kimi K 2.5, GPT-4o with citations.

Perplexity API Setup

Configured: Perplexity API skill created at /skills/perplexity/

Details:

• Key: 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
• Endpoint: https://api.perplexity.ai/chat/completions
• Models: sonar, sonar-pro, sonar-reasoning, sonar-deep-research
• Format: OpenAI-compatible, ~$0.005 per query

Usage: See "Unified Search" section above for primary usage. Direct API access:

python3 skills/perplexity/scripts/query.py "Your question" --citations

Note: Perplexity sends queries to cloud servers. Use search local "query" for privacy-sensitive searches.

Sub-Agent Setup (Option B)

Configured: Sub-agent defaults pointing to .10 Ollama

Config changes:

• agents.defaults.subagents.model: ollama-remote/qwen3:30b-a3b-instruct-2507-q8_0
• models.providers.ollama-remote: Points to http://10.0.0.10:11434/v1
• tools.subagents.tools.deny: write, edit, apply_patch, browser, cron (safer defaults)

What it does:

• Spawns background tasks on qwen3:30b at .10
• Inherits main agent context but runs inference remotely
• Auto-announces results back to requester chat
• Max 2 concurrent sub-agents

Usage:

sessions_spawn({
  task: "Analyze these files...",
  label: "Background analysis"
})

Status: Configured and ready

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Stored for long-term memory retention