Everyone's talking about AI infrastructure like it's the next gold rush. Here's what they're not telling you: most enterprise AI infrastructure projects fail not because of technology limitations, but because organizations are solving the wrong problems entirely. The Infrastructure Theater The industry

In late 2022, I had emergency open heart surgery. Not a metaphorical "rough patch" — my heart literally stopped in the hospital. I coded, was resuscitated, and spent the following months trying to put my life and health back together. A few months later, at a large, well-known tech

The Kubernetes ecosystem just pulled its biggest rug since Docker changed their licensing. Ingress NGINX is being retired in March 2026, and it powers roughly half of all Kubernetes deployments. If you're a CTO who signed off on "cloud-native transformation" in the last five years, you&

Here's what nobody wants to say out loud: your cloud security posture is probably worse today than it was three years ago. Not because you're doing less—you're doing more. You've deployed every acronym in the catalog: CSPM, CWPP, CNAPP, SIEM, SOAR.

Your AI agent project is going to fail. Not because the technology doesn't work—it does. Not because your team isn't smart—they are. It's going to fail because you're building the wrong thing, for the wrong reasons, with the wrong expectations.

Introduction Artificial Intelligence (AI) and Machine Learning (ML) are no longer just buzzwords floating around tech conferences and boardroom discussions. They have become integral components of modern enterprise architectures, driving innovation and operational efficiencies. Yet, as AI/ML technologies mature, so does the complexity of their deployment, particularly in cloud

Introduction Artificial Intelligence (AI) and Machine Learning (ML) continue to redefine how businesses operate, offering unprecedented opportunities for innovation and efficiency. The cloud has become the quintessential platform for deploying AI/ML solutions due to its scalability, flexibility, and cost-effectiveness. This guide provides a detailed examination of AI/ML infrastructure

Introduction Artificial Intelligence (AI) and Machine Learning (ML) are no longer just buzzwords floating around tech conferences and boardroom discussions. They have become integral components of modern enterprise architectures, driving innovation and operational efficiencies. Yet, as AI/ML technologies mature, so does the complexity of their deployment, particularly in cloud

2025 was supposed to be the year AI changed everything. The promises were bold: AI agents would "materially change the output of companies," scientific breakthroughs would accelerate, and the white-collar workforce would be transformed. Instead, we got something more interesting—and more useful—than the revolution we were