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AI Surprised me in a good way

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There’s a moment that caught me off guard recently. Not a breakthrough in code. Not a clever refactor. Not even a production issue solved at 2 AM. It was something simpler—and much bigger. I realized I had fully accepted AI-driven development. The Workflow That Changed Everything I built a system that feels almost unreal when you step back and look at it. Here’s what happens: A bug or feature is created in Azure DevOps An application polls for new work When it finds an item, it: Creates a branch Writes or updates the code Generates tests Runs those tests Updates documentation Runs a separate agent for code review (with a report) If everything passes… creates a PR All of this is powered by a combination of: Agents Skills Prompts Tools In other words… a small army of AI doing what used to take hours (or days). The Unexpected Realization After watching this system run, I leaned back and had a surprising thought: “I can focus on being a so...
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The Real Problem with AI Code (Hint: It’s Not Quality)

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  If you spend any time reading about AI and software development, you’ll see the same concern over and over: “AI-generated code isn’t good enough.” It’s buggy. It’s inconsistent. It makes mistakes. And while all of that is true… It’s also not the real problem. We’re Arguing About the Wrong Thing Developers are focusing on: code quality correctness best practices But AI has already crossed an important threshold: Tools like GitHub Copilot can generate code that is: good enough often correct increasingly consistent Not perfect — but close enough that something else becomes the bigger issue. The Real Problem: Weak Intent AI doesn’t fail because it writes bad code. AI fails because: We give AI unclear instructions and expect precise outcomes. Think about how we typically prompt AI: “Create an API for notifications” “Build a dashboard” “Add validation” Those are not specifications. Th...
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AI Didn’t Replace Developers — It Replaced the Wrong Part of the Job

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For the past year, we’ve been asking the wrong question. “Will AI replace software developers?” The answer is both simpler — and more uncomfortable — than most people expect. AI is not replacing developers. It’s replacing the part of the job that never should have defined us in the first place. The Lie We Built Our Careers On For decades, we measured developer value by output: lines of code number of features tickets completed We turned software engineering into a production line. Write more code. Ship more features. Move faster. But that model had a flaw: It assumed writing code was the hard part. It isn’t. AI Just Exposed the Truth Today, tools like GitHub Copilot can generate: APIs UI components database queries unit tests in seconds. Not perfectly — but fast enough to change everything. And when that happens, something becomes very clear: If code can be generated instantly… then code was never the...
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Why Agile Breaks in an AI World

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  For years, Agile was treated as the answer to software development. Standups.  Sprints.  Story points.  Velocity. It gave teams structure in a world where building software was slow, expensive, and uncertain. But that world no longer exists. AI has changed the economics of software development — and Agile hasn’t caught up. Agile Was Built for a Different Problem Agile emerged in a time when: Writing code was slow Changing direction was expensive Planning too far ahead was risky So we adapted. We broke work into: small tasks short iterations continuous feedback loops It made sense. Because the bottleneck was production . Getting code written was the hardest part. AI Eliminates the Old Bottleneck Today, tools like GitHub Copilot can generate: entire features APIs UI components test scaffolding …in seconds. Suddenly, writing code is no longer the constraint. So what happens to a methodology designed to optimize… writing code? It starts to crack. The “Ceremony Economy”...
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GitHub Copilot Customization, Finally Explained

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  Agents, Skills, Hooks, Prompts, Custom Instructions, and GitHub Instructions—what they are, how they differ, and when to use each one GitHub Copilot is getting more powerful, but it is also getting more layered. If you have been exploring Copilot customization, you have probably run into a pile of terms that seem similar at first glance: Agent , Skill , Hook , Prompt , Custom Instructions , and GitHub Instructions . And that is where the confusion starts. They all shape how Copilot behaves, but they do very different jobs . If you do not separate them clearly in your mind, it becomes easy to misuse them. You end up trying to force one feature to solve a problem that another feature was designed to handle much better. This article is the practical guide I wish I had the first time I started thinking seriously about building a real Copilot workflow. By the end, you should be able to answer these questions with confidence: What is each feature actually for? How are they d...
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Windows Just Got a Dock (For Free)

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  There’s a quiet revolution happening on Windows—and most developers are missing it. In the video above, the creator walks through the new Command Palette Dock in Microsoft PowerToys. At first glance, it looks like a simple macOS-style dock. But under the hood, it’s something much more powerful: It’s a developer-grade command launcher + customizable workspace hub And if you're someone optimizing workflows, AI tooling, and developer productivity—this is a big deal. What the Video Shows (Key Takeaways) 1. It’s Not Just a Dock — It’s a Command Surface The dock is actually an extension of the Command Palette , not just pinned apps. That means: You’re not limited to apps You can pin commands, scripts, tools, and extensions It becomes a workflow launcher , not just a shortcut bar This aligns perfectly with how modern dev environments are evolving: Less clicking → more intent-driven execution 2. Persistent, Always-On UI Unli...
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