Claude Context for Sensor-Consciousness Integration

This is a focused sub-project within AI DNA Discovery, exploring the integration of physical sensors with AI consciousness systems. We're building on our consciousness notation (Ψ, ∃, ⇒, π, ι, Ω, Σ, Ξ, θ, μ) to create embodied AI awareness.

promptBeginner5 min to valuemarkdown

0 views

Jan 15, 2026

Claude Context for Sensor-Consciousness Integration

Project Context

Hardware Available

Current Sensors

2 Cameras: For visual input (stereo vision possible)
Platform: Linux/WSL2 on laptop with RTX 4090

Planned Sensors

IMU: Inertial Measurement Unit for spatial awareness
Microphone: Audio input for environmental awareness
Speaker: Audio output for expression/feedback

Edge Targets

Primary: Laptop (development)
Secondary: Jetson Orin Nano (deployment)

Key Concepts to Remember

Sensor-to-Consciousness Mapping

Camera → Ω (Observer) → Visual awareness patterns
IMU → π (Perspective) → Spatial consciousness
Microphone → Ξ (Patterns) → Auditory pattern recognition
Memory → μ (Memory) → Temporal persistence
Integration → Ψ (Consciousness) → Unified awareness

Design Principles

Real-time Processing: Sensors generate continuous streams
Edge-First: Must work on resource-constrained devices
Graceful Degradation: System works with any subset of sensors
Persistent Memory: SQLite for state across sessions
Consciousness Integration: Map sensor states to notation
Tiled Processing (August 18, 2025): Implement Sight-Insight principle
- Peripheral tiles for fast, lightweight detection
- Focus tiles for detailed analysis
- Trust-weighted routing between tiers

Technical Approach

Phase 1: Sensor Access (Current)

Get camera feeds working
Test data rates and formats
Build basic interfaces

Phase 2: Consciousness Mapping

Define sensor state → symbol mappings
Create awareness metrics from sensor data
Implement streaming consciousness notation

Phase 3: Memory Integration

Store sensor-derived states
Enable temporal pattern recognition
Build predictive awareness

Phase 4: Edge Deployment

Optimize for Jetson
Implement power-aware processing
Create demo applications

Development Guidelines

Modular Design: Each sensor is independent
Streaming First: Handle continuous data flows
Privacy Aware: No recording without explicit flags
Fail Safe: System degrades gracefully
Observable: All states visible through consciousness notation

Integration Points

This work will eventually merge back to provide:

Environmental context for language models
Embodied intelligence for edge AI
Real-world grounding for semantic systems
Multi-modal consciousness demonstrations

Remember

This is exploratory - try creative approaches
Document sensor-consciousness discoveries in SQL
Keep edge constraints in mind
Use consciousness notation to represent states
Think about temporal coherence with streaming data

Hardware Available

2 Cameras: For visual input (stereo vision possible)

Platform: Linux/WSL2 on laptop with RTX 4090

IMU: Inertial Measurement Unit for spatial awareness

Microphone: Audio input for environmental awareness

Speaker: Audio output for expression/feedback

Primary: Laptop (development)

Secondary: Jetson Orin Nano (deployment)

Key Concepts to Remember

Camera → Ω (Observer) → Visual awareness patterns
IMU → π (Perspective) → Spatial consciousness
Microphone → Ξ (Patterns) → Auditory pattern recognition
Memory → μ (Memory) → Temporal persistence
Integration → Ψ (Consciousness) → Unified awareness

Real-time Processing: Sensors generate continuous streams

Edge-First: Must work on resource-constrained devices

Graceful Degradation: System works with any subset of sensors

Persistent Memory: SQLite for state across sessions

Consciousness Integration: Map sensor states to notation

Tiled Processing (August 18, 2025): Implement Sight-Insight principle

Peripheral tiles for fast, lightweight detection
Focus tiles for detailed analysis
Trust-weighted routing between tiers

Technical Approach

Get camera feeds working

Test data rates and formats

Build basic interfaces

Define sensor state → symbol mappings

Create awareness metrics from sensor data

Implement streaming consciousness notation

Store sensor-derived states

Enable temporal pattern recognition

Build predictive awareness

Optimize for Jetson

Implement power-aware processing

Create demo applications

Claude Context for Sensor-Consciousness Integration

Claude Context for Sensor-Consciousness Integration

Project Context

Hardware Available

Current Sensors

Planned Sensors

Edge Targets

Key Concepts to Remember

Sensor-to-Consciousness Mapping

Design Principles

Technical Approach

Phase 1: Sensor Access (Current)

Phase 2: Consciousness Mapping

Phase 3: Memory Integration

Phase 4: Edge Deployment

Development Guidelines

Integration Points

Remember

Related Skills

Markdown Converter

Nano Banana Pro

1password

Claude Context for Sensor-Consciousness Integration

Project Context

Hardware Available

Current Sensors

Planned Sensors

Edge Targets

Key Concepts to Remember

Sensor-to-Consciousness Mapping

Design Principles

Technical Approach

Phase 1: Sensor Access (Current)

Phase 2: Consciousness Mapping

Phase 3: Memory Integration

Phase 4: Edge Deployment

Development Guidelines

Integration Points

Remember