Documentation
Technical guidance for teams evaluating, integrating, and deploying Curve Reality hardware platforms.
Platform Overview
Architecture, supported modules, and deployment modes
Board Selection Guide
Choosing between CB301 and CB302
Bring-Up Checklist
Initial setup for developer and integration teams
Battery Integration
M12 battery workflows and runtime considerations
Hot-Swap Operation
Continuous operation and battery replacement procedures
Power Profiles
Balancing thermals, performance, and runtime
DisplayPort over USB-C
Direct display output and compatibility considerations
Camera & Sensor Interfaces
MIPI CSI and expansion pathways
GPIO Expansion
Jetson-compatible headers and peripheral integration
JetPack & L4T Setup
Linux bring-up for Orin-based deployments
Android Support
CB301 workflows for Android-based wearable systems
Storage & Model Deployment
NVMe, datasets, and local inference packaging
This gives developers a path to continuity, recall, and personalization across device upgrades, model changes, and hybrid deployment architectures.
Teams building persistent AI products can use Curve hardware as the compute and power foundation while layering long-term memory behavior on top for more capable assistants, tools, and autonomous workflows.
Additional memory architecture patents are pending as Curve expands beyond hardware into infrastructure for persistent AI systems.