What This Solution Provides This guide establishes a development workflow that enables you to switch between your Mac and iPhone while maintaining terminal session continuity. Using Tailscale for secure networking, Zellij for terminal multiplexing, and Terminus as an iOS SSH client, you can start work on your laptop and continue

After spending the last several years architecting AI workloads across AWS, Azure, and GCP, I've come to a sobering conclusion: our current cloud infrastructure was built for yesterday's applications, not tomorrow's AI revolution. While cloud providers rush to slap "AI-powered" labels on

Having built teams for over two decades, I've watched onboarding evolve from handwritten manuals to polished video libraries. But as AI reshapes how we work, it's time to ask some hard questions about our current approaches. Are Onboarding Videos Still Worth It? The honest answer is

After 25 years in technology leadership, I’ve witnessed hiring practices evolve from whiteboard algorithms to LeetCode marathons. While I haven’t been actively hiring lately, the conversations I’m having with fellow CTOs and the rapid advancement of AI tools are making one thing crystal clear - the traditional

As artificial intelligence continues to transform industries, the demand for specialized cloud infrastructure has reached unprecedented heights. The next generation of AI applications requires not just raw computational power, but a platform that is purpose-built for reliability, efficiency, and sustainability. The Rise of AI-First Cloud Platforms Traditional cloud solutions, while

The shift toward AI workloads has fundamentally changed how we approach cloud infrastructure design. Traditional architectures, optimized for web applications and microservices, often fall short when handling the unique demands of AI training and inference. Drawing from my experience architecting large-scale AI infrastructure, this article explores the technical considerations and

A deep dive into the data engineering, automation, and machine learning powering next-generation marketing measurement platforms—covering scalable data workflows, experiment automation, and the technical challenges of building a true system of record for marketing analytics.

In today's rapidly evolving AI landscape, organizations are increasingly faced with the challenge of building robust, scalable infrastructure to support their machine learning initiatives. While cloud-managed ML services offer convenience, many enterprises require more control, flexibility, and cost optimization than these services can provide. This is where Kubernetes

In the ever-evolving cloud landscape, event-driven serverless architectures have emerged as a powerful paradigm for building scalable, resilient, and cost-effective systems. By combining the operational simplicity of serverless computing with the flexibility of event-driven design, organizations can create systems that respond dynamically to changes, scale automatically, and minimize infrastructure costs.