Phil Hills

Systems Architect specializing in Scientific Telemetry, Physics-Informed AI, and Quantum-Ready Data Structures. Creator of the Cube Protocol v1.2 for spatial data compression. Currently architecting the 3i-CUBE Telemetry Bridge for Lattice LightSheet microscopy automation. Extensive experience in air-gapped control systems, real-time scientific data processing, and edge-AI deployment on Google Cloud Platform.

Real-time, air-gapped control layer for Lattice LightSheet microscopy (CUBE Microscopy Platform). Enables remote automation for high-content research without network exposure.

• Architected air-gapped proxy pattern for secure microscopy control
• Implemented real-time telemetry processing for 4D scientific datasets
• Integrated with SlideBook automation for high-throughput imaging workflows
• Deployed on Google Cloud Run with edge-AI capabilities
• Designed physics-informed data compression for biological imaging

Designed and implemented a spatial data compression protocol using Z-Order curves for quantum-ready applications.

• Achieved 97%+ compression ratios using GZIP level 9 + Base64 encoding
• Implemented BLAKE3 hash verification for data integrity
• Built production web application deployed on Google Cloud Run
• Created JavaScript and Python implementations with full parity
• Designed Z-Order curve algorithms for A2AC and Quantum State Preparation

Architected supervised multi-agent systems with task routing, memory management, and structured packet exchange.

• Designed agent delegation patterns with hierarchical orchestration
• Implemented state management and inter-agent communication protocols
• Built agent mesh visualization and real-time monitoring dashboards
• Created predictable execution boundaries and safety constraints
• Integrated with Google ADK and LangChain frameworks

Created token-efficient communication protocol for AI agent interoperability.

• Designed structured packet format for deterministic agent communication
• Optimized for minimal token usage while maintaining semantic richness
• Implemented safe execution boundaries and verification systems
• Enabled cross-model communication patterns

• 180+ Open Source Repositories - Active portfolio demonstrating continuous innovation and experimentation
• Production AI Systems - Deployed serverless applications on GCP handling real-time data processing
• Protocol Standardization - Created reusable, documented protocols adopted across multiple projects
• Cross-Platform Implementation - Built identical functionality in Python and JavaScript maintaining full compatibility
• Semantic Memory Innovation - Advanced state-of-the-art in structured AI memory representations

Transitioned from traditional software engineering to specialize in AI systems architecture, focusing on the infrastructure layer that enables scalable, reliable multi-agent workflows. Deep expertise in both theoretical protocol design and practical production deployment. Track record of building systems that combine cutting-edge AI capabilities with enterprise- grade reliability.

• Self-directed learning in AI/ML, distributed systems, and protocol engineering
• Continuous experimentation and research documented across 180+ repositories
• Active engagement with cutting-edge AI research and implementation