Browse Artificial Intelligence Blogs (41)
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.
Thomas Maurer introduces Azure Local Disconnected Operations and explains how to run Azure-style infrastructure—and selected AI workloads—inside fully disconnected or air-gapped environments for sovereignty and compliance needs.
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.
Zure summarizes recent Microsoft Fabric and Purview capabilities for metadata management and governance, covering OneLake catalog search, workspace tagging, bulk definition APIs, and how AI agents/copilots intersect with lineage, compliance, and risk controls.
John Edward shares practical ways to control Azure-based copilot and AI agent spend, focusing on token discipline, caching, model selection, and ongoing governance so LLM solutions scale without surprise bills.
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.
John Edward compares Microsoft Copilot Studio and Azure AI Agents (via Azure AI Foundry/Studio) to help architects choose between a low-code agent builder and a developer-driven platform based on flexibility, cost, scalability, and control.
Heidi Hämäläinen explains why Microsoft Purview Data Governance can feel heavy at first, and why governed metadata (glossary, catalog, data products, and security foundations) matters for scalable analytics, ML, and GenAI work—especially when you need discoverability, compliance, and trust in production.
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.
DevClass.com highlights Microsoft's switch to weekly Visual Studio Code releases and the rollout of Autopilot in Copilot Chat, offering developers new AI-driven coding experiences while raising fresh security concerns.
John Edward explores the foundations of Microsoft Copilot agent design, outlining how goals, memory, tools, and autonomy create robust, autonomous AI systems for enterprise automation.
DevClass.com reports on how Microsoft Azure CTO Mark Russinovich used Anthropic’s Claude Opus 4.6 AI model to scan 1986 Apple II machine code, finding security vulnerabilities and raising important points about AI’s expanding role in legacy code security.
John Edward provides a comprehensive look at agentic AI in IT, showing how Microsoft Azure and related services create self-healing and intelligent operations through automation, monitoring, and AI-driven incident response.
DevClass.com highlights Microsoft execs Mark Russinovich and Scott Hanselman as they examine how AI coding assistants affect the role and growth of junior software developers, emphasizing new industry and educational needs.