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...
In an era where artificial intelligence can draft emails, write code, and answer complex scientific questions with astonishing speed, a common refrain echoes in classrooms: "Why should I bother learning this if I can just a sk the AI?" While AI is an invaluable tool, reducing human learning to a mere memory exercise misses a crucial point: true understanding transcends rote memorization. The Engineering Gauntle t Consider the journey of an engineering student. For two grueling years, they are put through a mathematical gauntlet of differential equations, multi-variable calculus, an d linear algebra. These subjects are presented as fundamental truths that must be mastered with pencil and paper. Then, often in their junior year, they encounter Numerical Analysis . In this course, they are introduced to approximation algorithms—methods that use iterative calculations to estimate solutions to the very problems they just spent years learning to solve exact ly by ...