Part 4: Deterministic Networks for Physical AI
Why best-effort networking is not enough for Physical AI, and how Deterministic Ethernet (TSN) solves the temporal synchronization problem.
Part 5: The Memory Wall — Storing Reality for World Models
How to curate, store, and process continuous multimodal reality streams for World Model learning and simulation.
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01 / LATEST THINKING
Recent essays and technical notes.
Part 3: The Compute Split - Edge vs Cloud in Physical AI
Physical AI cannot rely on cloud latency for real-time control. Explore how edge inference and cloud simulation divide the compute, data, and learning workloads.
Part 2: World Models Hit the Data Ingestion Wall
The AI Infrastructure Shift: Why World Models Change Everything
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Edge & AI Infrastructure
Edge computing, AI infrastructure, distributed inference, data pipelines and AI-ready platforms.
Career Notes
Engineering careers, principal-level growth, interviews, relocation and lessons from a long technical career.
Distributed Systems
Architecture, reliability, state, coordination, scalability and the systems that keep modern platforms running.
Engineering Leadership
Architecture decisions, technical leadership, mentoring, organizational engineering and lessons from building systems.
Kubernetes & Cloud Infrastructure
Kubernetes, containers, networking plugins, cloud-native infrastructure and platform engineering.
Networking & Telecom
Networking architecture, Linux networking, telecom systems, routing, security and programmable infrastructure.
03 / THE AUTHOR BEHIND THE WRITING