Blog
Thoughts on AI hardware, edge computing, and SaaS web development.
Edge AI Inference at Scale: Architecture Patterns for Production
Dr. Elena Voss · June 5, 2026
Production edge AI requires a fundamentally different architecture than cloud inference. Here are the patterns that work.
Building the Ultimate AI Workstation for Deep Learning in 2026
Marcus Chen · May 28, 2026
The right workstation setup can cut your training iteration time in half. Here's what we recommend for deep learning in 2026.
Real-Time Computer Vision on Edge Devices: A Technical Deep Dive
Dr. Elena Voss · May 15, 2026
Achieving real-time computer vision on resource-constrained edge devices requires careful optimization across the entire pipeline.
Federated Learning on Edge Devices: Privacy-Preserving AI at Scale
Dr. Elena Voss · May 2, 2026
Federated learning enables training across distributed edge devices while keeping data local. Here's how to implement it in production.
Edge AI Hardware Comparison: NVIDIA Jetson vs. Google Coral vs. Intel Movidius in 2026
Marcus Chen · April 20, 2026
Choosing the right edge AI hardware platform is critical. We benchmark the three leading options across real-world workloads.
Architecting SaaS Platforms That Serve Edge AI Deployments
Sarah Kim · April 8, 2026
The cloud platform that manages your edge devices is as important as the devices themselves. Here's how to architect it.
Power Optimization Techniques for Edge AI: From Milliwatts to Megawatts
Dr. Elena Voss · March 25, 2026
Power is the single most constrained resource in edge AI. Here are the techniques we use to maximize performance per watt.
Edge AI Security: Best Practices for Protecting Distributed Inference Systems
Marcus Chen · March 12, 2026
Edge AI devices are physically accessible and often deployed in untrusted environments. Here's how to secure them.