GitHub Copilot CLI Extensions Revamped with Custom Commands and Governance

I reported a bug to GitHub. They fixed it in 2 days—then revamped their entire extension system. Here's what happened: While using GitHub Copilot CLI's extension system, I discovered a critical issue: creating a hook in an extension would override all global hooks. This broke my hook flows—the system I use to harden security across all my repositories. So I filed an issue. Within one week: • Root cause identified • Fix shipped to production • Complete extension system overhaul released The new capabilities are significant: → Custom slash commands now supported in the SDK → UI elicitation dialogs for structured user input → In-session management via /extensions command → Multi-language SDK support (Node.js, Python, Go, .NET) → Hot reload without full session restart This isn't just a bug fix. It's a signal. GitHub is treating Copilot CLI extensions as a first-class extensibility platform. For teams building internal tooling, security enforcement, or custom workflows—this changes the game. The speed of iteration here is remarkable. From power-user secret to documented, multi-language platform in 9 days. We're entering an era where developer feedback directly shapes the AI tools we use daily. If you're not experimenting with Copilot CLI extensions yet, now is the time. Full story in the video. Link in comments. #GitHubCopilot #DeveloperExperience #DevTools

You assign a GitHub issue before lunch. By the time you're back — there's a pull request waiting. That's the GitHub Copilot Coding Agent. GitHub Copilot has evolved far beyond autocomplete. The Coding Agent now works asynchronously in the background — fixing bugs, writing tests, refactoring code — and hands you a ready-to-review PR when it's done. Here's what just shipped: 🎛️ Model picker — Choose Claude Opus, Claude Sonnet, GPT-Codex-Max, or let Auto decide. Pick the right model for the complexity of each task. 🔍 Self-review — The agent reviews its own diff before tagging you. By the time you're looking at it, someone already went through it once. 🔒 Built-in security — Code scanning, secret scanning & dependency vulnerability checks — all before the PR opens. Free with Copilot coding agent. 🔌 MCP servers — Plug in external tools, databases, and context via Model Context Protocol. Your agent now has eyes beyond the repo. The agent boots a VM, clones your repo, RAG-indexes your codebase, and starts coding. You track every step in session logs. Your branch protections, CI/CD approvals, and security posture? Untouched. Think of it as having a junior dev who never sleeps, never skips tests, and always opens a clean PR. What low-to-medium complexity tasks would you hand off to an agent first? Drop a comment 👇 #GitHubCopilot #AI #CodingAgent #SoftwareEngineering #DevTools #AgenticAI #GitHub

GitHub Copilot Pulls Drawstring On Tighter Developer Usage Limits GitHub Copilot is popular. The AI-powered code completion tool (originally developed by GitHub and OpenAI) works to give software application developers a so-called “AI pair programmer” buddy that offers suggested code snippets and (when called upon) entire functions – and it happens directly within an engineer’s Integrated Development Environment (IDE) of choice. All of which means that GitHub Copilot isn’t just popular in terms of total usage; the tool is reporting an increase in patterns of high concurrency (individual developers performing similar operations, but more likely different developers requesting the same types of functions) and intense usage among power-users....

For the past few weeks I've been putting together something I wish I'd had when I first started using GitHub. A complete, beginner-friendly GitHub Handbook — written so anyone can understand it, even with zero coding background. 📘 Volume I — The Foundation (27 pages) History of Git and GitHub, every core concept explained with real examples, all the main features, Git command cheat sheet, workflows, alternatives, best practices, and common mistakes. 📗 Volume II — Beyond the Basics & The Age of AI (18 pages) Advanced topics as pointer-cards (rebase, Actions deep dive, security stack, APIs, Enterprise features) plus a comprehensive, current look at how AI is reshaping GitHub — Copilot, agents, Autofix, Spark, MCP, and what it all means for software careers. Both volumes are available as HTML and Word documents. A few things I learned putting this together: → GitHub in 2026 is not the same platform it was five years ago. It has quietly become an AI company that happens to host Git repositories. → Copilot has performed 60M+ code reviews and auto-fixed 460,000+ security vulnerabilities in the past year. → The skills that matter most are shifting — from typing speed toward specification writing, code review, and judgement. If you're learning GitHub, mentoring someone who is, or just curious about where the platform is heading — drop a comment and I'll share it. #GitHub #Git #SoftwareDevelopment #AI #GitHubCopilot #LearningInPublic #DeveloperTools

GitHub was built for 10 engineers pushing 100 commits a week. Your AI agents don't care about that constraint. We've watched teams hit API rate limits before their morning standup. We've watched latency kill agent feedback loops mid-task - the agent is waiting on a response while context evaporates. We've watched the world's most important developer platform strain under a workload it was never designed for. GitHub is remarkable software but it was designed for humans. The gap between "designed for humans" and "works for agents" is enormous: → Rate limits tuned for human hands, not automated pipelines → CI latency acceptable for a dev refreshing a PR, catastrophic for an agent mid-loop → Review interfaces built for human eyes, not machine-readable output → No native concept of agent identity or trust The infra layer for the agentic era isn't GitHub with a better API wrapper. It's a new primitive. Built from scratch. For machines. Guess what? That's what we're building with @Mesa.

GitHub adds Stacked PRs to speed complex code reviews A new feature to facilitate code reviews and prepare for an AI-driven surge in code changes. My PoV included in InfoWorld news today. https://lnkd.in/gF9kzM42

How GitHub Copilot Runs Safely in Docker Sandbox with MicroVMs Click here for source https://lnkd.in/get9BvdN Do you want to know more click here https://lnkd.in/exH3zxjM GitHub Copilot + MicroVMs via Docker Sandbox explained! In this video, I show how a local GitHub Copilot agent can run inside an isolated Docker Sandbox powered by MicroVMs for safer AI coding and agentic refactoring. You’ll learn how Docker Sandbox gives GitHub Copilot access to a private Docker daemon inside a MicroVM, so you can build images, modernizing legacy applications. I also cover how Docker Sandbox helps preserve the same workspace paths across the host and sandbox, why that matters for real projects, and how this setup can support GitHub Copilot CLI workflows with better isolation, security, and developer productivity. If you want to understand GitHub Copilot, MicroVMs, Docker Sandbox, secure AI coding agents, and agentic refactoring in a practical way, this video is for you. Topics covered: - github copilot - local github copilot agent - microvms - docker sandbox - github copilot cli - agentic refactoring - secure ai coding - docker build and docker compose in sandbox - legacy app modernization - docker desktop sandbox workflow github copilot github copilot cli github copilot tutorial github copilot explained github copilot agent local github copilot agent github copilot microvms github copilot docker sandbox microvms microvm docker sandbox docker sandboxes docker sandbox tutorial docker sandbox explained docker desktop docker build docker compose ai coding agent ai coding assistant agentic refactoring secure ai coding secure coding agent local ai agent sandboxed ai agent private docker daemon isolated docker daemon docker socket risk no docker socket secure docker workflow legacy code modernization java modernization dotnet modernization containerized testing secure developer workflow microvm isolation github copilot local setup docker sandbox copilot github copilot docker desktop modernizing legacy apps ai refactoring #githubcopilot #microvms #dockersandbox #githubcopilotcli #localgithubcopilotagent #agenticrefactoring #aicoding #secureaicoding #dockertutorial #dockerdesktop #microvm #sandbox #dockerbuild #dockercompose #legacycodemodernization

Over the past few weeks, I’ve been using Claude Code and GitHub Copilot more actively. At first, I did what most of us do. I gave a single, big prompt and expected a clean, perfect solution. Sometimes it worked. Most of the time, it didn’t. The output was either too generic, slightly off or missing important pieces. And I realised the issue wasn’t the tool. It was how I was asking. Then I made one simple change. Instead of giving one large instruction, I started breaking my task into smaller, clear sub-tasks and feeding it step by step. The difference in output was immediate. Here’s a simple example. "Build a simple expense tracker app for daily use. It should help users log expenses quickly and track spending over time.” Then I broke it down: 1) Create input fields for date, category, and amount 2) Add a button to save each expense 3) Store data locally (local storage or database) 4) Show a list of all expenses 5) Add total spending summary 6) Include basic category-wise breakdown 7) Keep UI simple and mobile-friendly Now the output becomes structured, usable, and much closer to what you actually need. When you break down your thinking, the tool simply follows. This small habit didn’t just improve the output. It made me think more clearly about the problem itself. Structure your thoughts, because better input doesn’t just give better output, it builds better thinking. #claude #claudecode #github #copilot #githubcopilot #prompting #promptbreakdown #vscode #vibecoding

Three GitHub repos blew up this week. All three solve problems you probably have right now. 1. microsoft/markitdown Converts PDFs, Word docs, HTML, and images into clean Markdown. If you're building anything with LLMs and need to feed documents into a pipeline, this replaces your messy parsing scripts. One install. Works. 2. coleam00/Archon Defines your AI coding workflow in YAML. Think GitHub Actions but for coding agents. Plan, implement, validate, review, PR. Same steps every time. No more "I got different results than yesterday." Each run happens in an isolated git worktree so nothing bleeds across tasks. 3. multica-ai/multica If you're running multiple Claude Code or Codex sessions and manually switching terminals to track progress, Multica treats them like actual teammates. They claim tasks, report blockers, share skills across the team. Your code stays local. Their servers only coordinate state. None of these require you to change how you work. They slot into what you're already doing and remove the friction you've been tolerating. All three are open source. #AIAssistedDevelopment #GenAI #DeveloperTools #OpenSource #GitHubTrending

Most developers are using GitHub Copilot wrong. It’s not about better prompts. It’s about better context. Copilot performs based on what you feed into it — not what you ask it. Here’s what actually makes a difference: • Instructions → enforce coding standards • Skills → inject domain knowledge • Agents → simulate specialized roles • MCP → connect external systems I applied this in my project by defining clear backend rules and structuring responses consistently across modules. Result: more predictable, cleaner, and reusable code. Prompt engineering gets attention. Context engineering gets results. #GitHubCopilot #AI #SoftwareEngineering #Java #FullStackDeveloper #ContextEngineering GitHub Microsoft