The Insomniac Coder

I've spent countless hours in my career writing and reviewing Product Requirements Documents (PRDs). They are the bedrock of traditional software development—the detailed maps that are supposed to guide us from an idea to a finished feature. We debate every user story, define every edge case, and get sign-off from every stakeholder. It's a process built on the assumption that meticulous planning prevents poor execution. But what if the execution part suddenly became... instantaneous? We're standing at the edge of a new era, one where LLM agents can take a well-defined prompt and generate not just code, but entire features, in a fraction of the time it used to take. This isn't science fiction anymore. For many tasks, the bottleneck in development is no longer the coding; it's the specification. We're now facing the " PRD Paradox ": the curious situation where writing the instructions takes longer than it takes an AI to follow them. The Old Certainty...

If you've spent any time with tools like ChatGPT or GitHub Copilot , you know that getting them to produce some code is easy. But moving from generating simple, isolated functions to creating reliable, production-quality code that fits into a larger system requires a deeper level of skill. This is the discipline of prompt engineering , and it's rapidly becoming a core competency for the modern developer. Basic prompting is just giving the model a simple instruction. Advanced prompting is about controlling the model's output with precision, guiding its "thought process," and giving it the context it needs to perform like a senior developer. Let's move beyond the basics. 1. The Persona Pattern : Prime the Model for Success The first and simplest step to getting better output is to tell the model who it should be. Before you even ask it to do something, give it a role. This primes the model's virtual "cortex," focusing it on the specific domain and...

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 ...

 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...

  For most of industrial history, "intelligence" was the scarcest and most expensive resource. To get a legal contract reviewed, a codebase audited, or a marketing strategy drafted, you had to hire a skilled human. This created a linear relationship between growth and headcount: to do twice as much "thinking," you needed twice as many "thinkers." We are now entering the era of the AI-Human Cost Inversion . Recent data shows that while a human expert costs between $15 and $60 per hour, an advanced AI agent performs equivalent information-processing tasks for as little as $0.10 to $0.50 per hour. When the cost of running an AI agent falls below the cost of a human worker, we don't just get "faster humans." We get a different kind of economy entirely. 1. From "Service" to "Utility" Historically, professional services (law, accounting, consulting) were rival goods . If a consultant was working on your project, they couldn'...