The art of simplicity

While visiting multiple organizations and talking to colleagues about integrating AI into their software development lifecycle, I noticed something: The approaches couldn’t have been more different. Some teams were embedding AI deeply into every step of development—coding, testing, documentation, even architectural decision-making. Others were deliberately cautious, limiting AI to narrow, controlled use cases. Opposites. And yet, both felt… reasonable. That’s when it clicked for me: We are all beginners. Not in the dismissive sense. Not in a “we don’t know anything” kind of way. But in the ways as described inside the Dreyfus Model of Skill Acquisition .   The Dreyfus model, briefly The Dreyfus model describes how people acquire skills through five stages: Novice – Rely on rules and rigid guidelines Advanced Beginner – Start recognizing patterns, but still need support Competent – Can plan, prioritize, and make conscious de...

With the introduction of agent skills , we can teach our AI agent to handle our most repetitive and specialized workflows. After adding context through an agent.md file, integrating tool calls using MCP, creating our own Agents, this is a logical next step in defining your AI enabled software development lifecycle. Here's everything you need to know to get started. What are agent skills? Agent skills are folders of instructions, scripts, and resources that GitHub Copilot can load automatically when relevant to your prompt. Think of them as reusable "playbooks" you write once and invoke repeatedly — without having to re-explain the context every time. Unlike custom instructions , which set broad coding guidelines that apply across nearly every task, skills are meant for specialized, on-demand capabilities: things like running a specific test suite, converting file formats, generating components, or following a custom deployment checklist. How to create your firs...

GitHub Copilot CLI is my 'go-to' coding agent when I work directly from your terminal. It understands my codebase, proposes edits, runs commands, and helps me move faster without leaving the command line. As I care about privacy, offline workflows, or custom model experimentation, I decided to try Copilot CLI entirely on local LLMs using Ollama. No cloud dependency. No API keys. Just my machine, a local model and my workflow. In this post, I’ll walk through how to set it up, and how to use it effectively. Why combine Copilot CLI with Ollama? Copilot CLI gives you a powerful agentic interface for your codebase. Ollama gives you a fast, local model runtime with support for dozens of open models. Together, you get: Local-first AI coding:  keep your code and prompts on your machine Predictable performance:  no rate limits or network delays Model flexibility : swap between Qwen, Llama, Mistral, Gemma, and more Agentic workflows:  Copilot CLI can edit...

Yesterday I tried deploying a Docker image to Azure Container Apps and hit a wall with a cryptic error about an invalid operating system.  It took me a lot of time to find the root cause and fix it.Here's what happened, why it happens, and the exact command to fix it. The error After pushing an image to Azure Container Registry and pointing a Container App at it, the deployment failed with this message: Selected tag uses an invalid operating system ''. Error when deploying an Azure Container App image. The error is frustrating because it gave me almost nothing to work with; an empty string where the OS name should be. The image built and pushed just fine, so what's going on? Root cause The culprit is provenance attestation — a feature that Docker BuildKit enables by default when using docker buildx . When provenance is enabled, Docker generates an extra manifest layer containing build metadata. This results in a multi-platform image manifest (an OCI image i...

Security vulnerabilities in your dependencies are one of those things where I know that I should address them promptly, but the process of hunting down the right package version, understanding the scope of the issue, and making the change without breaking anything can turn a five-minute fix into a frustrating rabbit hole. With the Visual Studio March 2026 update, that workflow just got a whole lot smoother. GitHub Copilot can now help you fix NuGet package vulnerabilities directly from Solution Explorer, turning what used to be a manual research task into a guided, in-editor experience. What's new? When Visual Studio detects a vulnerability in one of your NuGet packages, you'll now see a Fix with GitHub Copilot link alongside the vulnerability notification in Solution Explorer. One click is all it takes to kick off the process: Copilot analyzes the vulnerability, identifies the appropriate dependency updates, and implements them for you — without disrupting the rest of y...

With the Data API builder, you can easily generate an API on top of an existing database. However typing out the configuration settings in the dab-config.json isn't much fun. The auto-entities features I talked about before can certainly help, but that is not always the right solution. With the integrated GUI in the MSSQL extension for Visual Studio Code, you can replace the manual JSON configuration with a visual interface that handles entity selection, CRUD permission mapping, API type targeting, and Docker-based local deployment — all without leaving the editor. This post covers exactly what the UI does, what it generates, and where it falls short. Entry points The DAB configuration view is accessible from two places: Object Explorer — right-click a database node → Build Data API (Preview)... Schema Designer — Design API button (top-right toolbar) or the Backend icon in the left panel Both open the same configuration surface. Entity selection Tables a...