The Insomniac Coder

For those of us who have spent years, or even decades, maintaining and extending large, legacy .NET applications, the current AI boom presents a fascinating, yet daunting, challenge. The business sees the "magic" of AI and, naturally, wants to sprinkle it into our existing products. The request is often simple: "Can we add AI to do X?". But as architects and developers, we know it's never that simple. Bolting a modern, stateless, and often slow-to-respond Large Language Model (LLM) onto a battle-hardened, stateful ASP.NET Web Forms application is a classic case of architectural impedance mismatch. A direct API call from a code-behind file during a postback is a recipe for disaster. It will lead to hanging UI, terrible user experience, and a maintenance nightmare. So, how do we bridge the gap between our monolithic, reliable workhorses and the brave new world of generative AI? The answer isn't a rewrite; it's smart architecture. Here are a couple of patte...

 The tech world is in the midst of a quiet shockwave. It’s not just about new tools or faster processors; it’s a fundamental shift in the core skills required to build software. For decades, the primary bottleneck has been implementation: the sheer effort of translating an idea into thousands of lines of correct, performant code. Today, that bottleneck is vanishing. A recent, striking example comes from Jaana Dogan , a Principal Engineer at Google, who revealed that a coding agent ( Claude Code ) was able to generate in one hour what her team had spent a year trying to build. This isn't just about an AI writing code faster; it's a signal that the most valuable skill in software engineering is shifting from how to code to how to articulate the problem . The Compression of Friction As industry observer Thomas Power noted, the real power of these AI agents isn't just their coding speed. It's their ability to compress a massive amount of organizational friction. A clear p...