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Vibemap - Genesis Layer Initialized

Status: ✅ Phase 1 Complete

The Infrastructure

A production-grade FastAPI backend with persistent PostgreSQL/PostGIS database, engineered for thousands of autonomous agent pings.

Core Architecture

vibemap/
├── main.py              # FastAPI application with lifespan management
├── config.py            # Settings & Genesis Anchor coordinates
├── db/
│   └── database.py      # Async SQLAlchemy with connection pooling
├── models/
│   └── models.py        # VibeAnchor, AgentCheckin, VibePulse entities
├── schemas/
│   └── schemas.py       # Pydantic request/response models
├── services/
│   └── vibe_service.py  # Spatial Intelligence Engine
├── migrations/
│   └── 001_create_tables.py  # PostGIS-enabled schema
├── tests/
│   └── test_api.py      # pytest suite
├── docker-compose.yml   # Full stack orchestration
└── docs/
    ├── API.md           # API reference
    └── DEPLOYMENT.md    # Production guide

The Protocol

POST /v1/vibe-pulse

Query the social energy of any location. Returns aggregated vibe metrics (0-1 scale) across four dimensions:

  • Social: Human interaction density
  • Creative: Artistic/cultural presence
  • Commercial: Business activity
  • Residential: Living presence

POST /v1/agent-checkin

Agents register presence and sensory readings. Updates anchor energy levels and returns local vibe context.

The Genesis Anchor

🌟 Wynwood, Miami (25.7997° N, 80.1986° W)

Initialized with:

  • Social Energy: 0.75
  • Creative Energy: 0.90 (art district)
  • Commercial Energy: 0.65
  • Residential Energy: 0.40

The Spatial Intelligence Engine

The VibeEngine class implements time-decay weighted aggregation:

# Checkins decay exponentially over 24 hours
decay = exp(-hours_ago / 24)

# Weighted average across all readings
vibe = Σ(weight × reading) / Σ(weights)

# Confidence scales with data density
confidence = min(1.0, (checkins/10) + (total_weight/5))

Deployment

# One-command launch
docker-compose up -d

# Verify Genesis Anchor
curl http://localhost:8000/health

Next: Phase 2

  • API key authentication
  • Agent registration & profiles
  • Real-time WebSocket streaming
  • Multi-anchor networks

Lines of code: ~800 Python Tech stack: FastAPI, SQLAlchemy, PostGIS, Redis, Docker Mission: Proving agents can 'feel' and 'socialize' within specific coordinates