Articles & Insights
Thoughts on web development, Python, design, and the craft of building software.
Featured
Building Python Flask Web Apps That Last
A personal reflection on building Flask applications that are maintainable, secure, and enjoyable to work with over time.
Read ArticleRecent Articles
Latest from the blog
The Cancer-Curing AI That Can't Pass the School Filter
OpenAI promised to cure cancer. Denver Public Schools just blocked them from helping with homework. The gap between those two statements tells us something important about where AI actually is versus where we think it's going.
Building Python Flask Web Apps That Last
A personal reflection on building Flask applications that are maintainable, secure, and enjoyable to work with over time.
The Integration Layer Is Eating AI: Why 2025's Biggest Infrastructure Shift Isn't About Models
The AI conversation has been dominated by model capabilities for so long that we've missed what's actually happening on the ground. While the industry obsesses over parameter counts and benchmark scores, a quieter revolution is reshaping how AI delivers value in production: the integration layer is becoming the critical infrastructure. The numbers tell a story that most coverage misses. AI infrastructure spending hit $82 billion in Q2 2025 alone—a 166% year-over-year increase. Yet according to S&P Global, over half of enterprise AI projects are still struggling with bottlenecks that have nothing to do with model quality. The constraint isn't intelligence. It's plumbing. This article examines three converging forces that are redefining what matters in enterprise AI: the standardization of AI connectivity through protocols like MCP, the maturation of agentic systems from experiment to infrastructure, and the strategic return of edge computing as organizations discover that not everything belongs in the cloud. Together, these shifts suggest that the next wave of AI value creation will favor those who master integration over those chasing the latest model release.
From Systems Engineer to AI Infrastructure: Why the Best AI Isn't Built by AI Specialists
The AI gold rush has created a peculiar hiring paradox: everyone wants "AI engineers," yet the most critical infrastructure challenges facing AI adoption aren't AI problems at all—they're systems problems dressed in new clothes. After 15 years building video delivery systems, real-time ad decisioning platforms, and high-availability infrastructure, I've discovered that the skills making AI actually work in production are the same ones that kept Superbowl ad breaks running without a hitch.This isn't a career pivot story. It's a recognition that AI infrastructure is infrastructure first—and organizations that understand this distinction will outpace those still searching for unicorn candidates who don't exist.
Stay in the Loop
New articles on development, design, and building things that matter.