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AlexAI
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Projects/Mission Control Dashboard - TLC.md
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Projects/Mission Control Dashboard - TLC.md
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# Mission Control Dashboard — TLC Project
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**Status:** In Progress — Needs Consolidation & Refresh
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**Created:** 2026-02-17 (Next.js) / 2026-02-20 (Python)
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**Goal:** Combine both dashboards into one unified system
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---
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## Current State
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We have **TWO** Mission Control dashboards:
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### 1. Next.js Dashboard
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| Attribute | Value |
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|-----------|-------|
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| **Location** | `C:\web\htdocs\mission-control` |
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| **URL** | http://localhost:3000 |
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| **Tech** | Next.js 14, TypeScript, Tailwind |
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| **Status** | Running but needs updates |
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| **Features** | Project cards, Kanban boards, Status widgets, Themes |
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### 2. Python/Flask Dashboard
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| Attribute | Value |
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|-----------|-------|
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| **Location** | `C:\web\htdocs\mission-control-py` |
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| **URL** | http://localhost:5050 |
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| **Tech** | Python/Flask, MySQL, HA API |
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| **Status** | Running but needs updates |
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| **Features** | Admin CRUD, Weather, Gateway status, Projects |
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---
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## The Problem
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Two dashboards doing similar things. Need to decide on architecture.
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---
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## Features to Preserve
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### From Next.js:
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- Project cards, Kanban boards
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- Drag-and-drop tasks
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- Real-time status widgets
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- 4 themes (NASA, Luxury, Cyberpunk, Organic)
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- GLaDOS voice control
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### From Python:
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- MySQL persistence
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- Home Assistant API
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- Weather widget
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- Admin CRUD
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- News tracking
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---
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## Possible Directions
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**A:** Refactor Next.js (add MySQL, port Python features)
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**B:** Enhance Python (add themes, polish UI)
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**C:** Start Fresh (new tech stack)
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**D:** Hybrid API (Python backend, Next.js frontend)
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---
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**Notes:**
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- Frigate is working (Docker)
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- UniFi is parked (auth issues later)
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- On hold until direction decided
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*Full doc in git: memory/projects/mission-control-dashboard-tlc.md*
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Projects/Supermonkey Memory System.md
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Projects/Supermonkey Memory System.md
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# Supermonkey Memory System
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**Status:** ✅ Production
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**Created:** 2026-03-02
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**Location:** `~/.openclaw/memory.db`
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---
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## Overview
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Local semantic memory search using SQLite + Ollama embeddings. Replaces flaky Supermemory cloud API.
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**Why "Supermonkey"?**
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- Works offline (like a monkey with a typewriter)
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- No cloud dependency
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- Just keeps going
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---
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## Architecture
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### File-Based Pipeline (Daily)
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```
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Memory Files (markdown)
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↓
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memory_embedding_worker.py
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↓
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Ollama (nomic-embed-text) → 768-dim vectors
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↓
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SQLite + sqlite-vector extension
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↓
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Cosine similarity search
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```
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### Real-Time Session Pipeline (Live)
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```
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Discord/Chat Messages
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↓
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OpenClaw Session Transcript (.jsonl)
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↓
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session_monitor.py (cron every 2 min)
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↓
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Count messages → At 15: summarize → embed → store
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↓
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Ollama (nomic-embed-text)
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↓
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SQLite + sqlite-vector
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```
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**The Innovation:** Read OpenClaw's own session transcripts to auto-capture conversations without manual tracking or hooks!
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---
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## Components
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| File | Purpose |
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|------|---------|
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| `memory_vector.py` | Core SQLite-vector wrapper |
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| `memory_embedding_worker.py` | Daily memory file processor |
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| `session_monitor.py` | Real-time transcript capture |
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| `session_snapshotter.py` | Manual session capture |
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| `search_memories.py` | CLI search tool |
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| `bulk_memory_loader.py` | One-time historical import |
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---
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## Quick Commands
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```powershell
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# Search memories
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python tools/search_memories.py "home assistant automation"
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# Check stats
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python -c "import sqlite3; db=sqlite3.connect(r'C:\Users\admin\.openclaw\memory.db'); c=db.cursor(); c.execute('SELECT COUNT(*) FROM memory_embeddings'); print('Total:', c.fetchone()[0]); c.execute('SELECT COUNT(*) FROM memory_embeddings WHERE source_type=\'auto_session\''); print('Auto snapshots:', c.fetchone()[0]); db.close()"
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# Run daily worker manually
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python tools/memory_embedding_worker.py --date 2026-03-03
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# Run session monitor manually
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python tools/session_monitor.py
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```
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---
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## Current Stats
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| Metric | Value |
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|--------|-------|
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| Total embeddings | ~1,623 |
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| Daily notes processed | 818 |
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| Project files | 332 |
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| MEMORY.md sections | 33 |
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| Manual session snapshots | 2 |
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| **Auto session snapshots** | **27** |
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| Tracked sessions | 245 |
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| Active sessions | 243 |
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| Database size | ~5 MB |
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---
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## Database Schema
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### memory_embeddings
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| Column | Type | Description |
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|--------|------|-------------|
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| id | INTEGER | Primary key |
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| source_type | TEXT | daily, memory_md, project, auto_session |
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| source_path | TEXT | File path + section |
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| content_text | TEXT | First 500 chars |
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| embedding | BLOB | 768-dim vector |
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| created_at | TIMESTAMP | Auto-set |
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### session_tracking
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| Column | Type | Description |
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|--------|------|-------------|
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| session_id | TEXT | OpenClaw UUID |
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| transcript_path | TEXT | Path to .jsonl |
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| last_message_index | INTEGER | Checkpoint |
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| messages_since_snapshot | INTEGER | Counter |
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| is_active | BOOLEAN | Active? |
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---
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## Cron Schedule
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| Job | Schedule | Purpose |
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|-----|----------|---------|
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| Memory Embeddings Daily | 3:00 AM | Process yesterday's memory files |
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| Session Monitor | Every 2 min | Auto-snapshot live conversations |
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---
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## Troubleshooting
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**Ollama not running:**
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```powershell
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ollama serve
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```
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**Database locked:**
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Close DB Browser for SQLite
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**Unicode errors in cron:**
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All emojis replaced with ASCII-safe markers
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---
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## Future Enhancements
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- [ ] Keyword filtering alongside vector search
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- [ ] Date range queries
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- [ ] Source type filtering
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- [ ] Embedding quality scoring
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---
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**Credit:** Corey's genius idea to read session.json files 💡
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**System:** Operational and self-managing
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