Browse GitHub Copilot Blogs (20)

Jesse Houwing summarizes GitHub’s update that GitHub Copilot can now keep inference processing and associated data within US or EU data residency regions, and shows the enterprise/org policy you must enable to restrict Copilot to data-resident models.

Emanuele Bartolesi shows how to point GitHub Copilot CLI at an Azure AI Foundry (Azure OpenAI) deployment using a BYOK-style setup, including how to deploy a model, build the correct endpoint URL, set the required environment variables, and validate the connection.

Emanuele Bartolesi explains how to run GitHub Copilot CLI against a local LLM via LM Studio’s OpenAI-compatible API, including the exact PowerShell environment variables needed to avoid cloud fallback and when this offline setup is (and isn’t) worth using.

Hidde de Smet explains how Spec-Kit’s extension system works, highlights useful community extensions, and walks through the Ralph Loop extension, which runs a GitHub Copilot agent in iterations to implement tasks from `tasks.md`, commit changes, and track context in `progress.md`.

Harald Binkle explains the latest Visuals MCP update, adding a chart tool that lets AI agents render single charts and full dashboards directly inside GitHub Copilot Chat in VS Code, with Storybook examples and export options for turning analysis into shareable visuals.

Randy Pagels explains a simple GitHub Copilot workflow: before asking for an implementation, prompt Copilot to ask clarifying questions so you uncover assumptions, edge cases, and missing requirements early—leading to better prompts and better code changes.

Jesse Houwing clarifies GitHub Copilot’s April 24 interaction-data policy change, explaining which subscription tiers may have interactions used for training, what is and isn’t included (like private repos), and practical ways enterprises can enforce license tiers and lock down developer environments.

Emanuele Bartolesi explains how to make GitHub Copilot less “agreeable” and more useful by adding a repo-level voice instructions file that pushes Copilot to be direct, critical, and focused on correctness and maintainability.

Jesse Houwing explains why he rebuilt the Azure DevOps Marketplace publishing tasks from v5 to v6, focusing on faster builds, stronger testing, GitHub Actions support, and more secure authentication (OIDC/workload identity) while using GitHub Copilot’s Coding Agent to accelerate the rewrite.

Randy Pagels shares practical tips for developers to maximize GitHub Copilot's effectiveness by providing better context and intent, rather than relying on longer prompts.

Hidde de Smet explains practical frameworks and real-world techniques for effective prompt engineering and context engineering with LLMs and agent tools, including GitHub Copilot, helping AI practitioners move from vague queries to reliable, production-grade results.

Harald Binkle explores how to extend AI agents with Visuals MCP, letting tools like GitHub Copilot render interactive tables, lists, and images inside VS Code using React, TypeScript, and a flexible MCP server.

Emanuele Bartolesi shows how to use GitHub Copilot as a guardrail for generating strict Conventional Commit messages in VS Code and JetBrains Rider, with concrete instruction snippets you can paste into each IDE to make the output consistent and automation-friendly.

John Edward details how native Mermaid diagram support in Visual Studio 2026, enhanced by GitHub Copilot, empowers developers to visualize, generate, and maintain documentation seamlessly within their coding workflow.

Jesse Houwing shares his journey improving GitHub Actions versioning with the release of 'actions-semver-checker' v2, leveraging Copilot Agent and automated testing to streamline and automate release management.

Emanuele Bartolesi shares the GitFlow setup he actually enforces on GitHub, including strict branch protection, PR habits, release/tag rules, and how he wires it to GitHub Actions, environments, and basic security checks so the workflow holds up under real release and hotfix pressure.

Randy Pagels shares practical guidance for developers on building confidence in GitHub Copilot’s coding output by combining smart trust with targeted verification.

Harald Binkle provides a thorough comparison of GitHub Copilot’s extension mechanisms—Custom Agents, Skills, and MCP Tools—guiding developers on selecting the best option for AI-powered coding workflows.

Randy Pagels outlines actionable strategies for reviewing GitHub Copilot's code output with a senior developer's mindset, emphasizing intent, assumptions, and iterative improvement.

Randy Pagels discusses effective strategies for balancing human judgment and automation through GitHub Copilot, highlighting when to take the lead and when to delegate to maximize development workflow efficiency.