# GitHub Copilot An AI coding assistant from GitHub that provides code suggestions, chat assistance, and agent-driven development features. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Migrating the GitHub Copilot runtime to Rust, using Copilot DevFeed: [Migrating the GitHub Copilot runtime to Rust, using Copilot]() Original publisher: [Read original article]() Author: Stephen Toub Published: 2026-09-17T00:26:43Z Content type: article Language: en Sources: [GitHub Engineering]() Topics: [GitHub Copilot](), [Rust](), [Agent Harness](), [GitHub Copilot SDK](), [GitHub]() Tags: [agent-harness](), [ai-agents](), [ai-ml](), [developer-experience](), [generative-ai](), [github](), [github-copilot](), [github-copilot-sdk](), [rust]() ### AI overview The GitHub Copilot agent runtime was rewritten from TypeScript on Node.js and V8 into more than 800,000 lines of production Rust. AI agents wrote most of the code across 128 pull requests, and the runtime was shipped incrementally while regressions were fixed and performance improved by orders of magnitude. ### Source excerpt A rewrite this size wasn't affordable before agents. Here's what porting the Copilot agent runtime to 800,000 lines of production Rust actually took. The post Migrating the GitHub Copilot runtime to Rust, using Copilot appeared first on The GitHub Blog. ## Building the new GitHub Copilot Inline Suggestions Model: Part One DevFeed: [Building the new GitHub Copilot Inline Suggestions Model: Part One]() Original publisher: [Read original article]() Author: Julia Gong, Ben Liggett, Ulugbek Abdullaev Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [GitHub Copilot](), [AI-assisted coding](), [coding](), [Development](), [GitHub](), [Software Engineering]() Tags: [agent](), [ai-assisted-coding](), [blog](), [coding](), [github](), [github-copilot](), [quality](), [vs-code](), [workflows]() ### AI overview GitHub Copilot unified completion-style ghost text, nearby next edit suggestions, and long-distance edits into a single "3-in-1" model. The article explains that training, evaluation, and editor design evolved together, and that the unified model can improve suggestion selection and cache additional edits for faster subsequent interactions. ### Source excerpt Explore how GitHub Copilot unified completion, next edit, and long-distance suggestions into one model for a faster, more cohesive coding experience. Read the full article ## Improving Test Coverage in the Interview Coach Application with GitHub Copilot DevFeed: [Improving Test Coverage in the Interview Coach Application with GitHub Copilot]() Original publisher: [Read original article]() Author: Aaron Powell Published: 2026-09-15T16:00:21Z Content type: tutorial Language: en Sources: [Visual Studio Blog]() Topics: [test-coverage](), [Test coverage](), [GitHub Copilot](), [App](), [Visual Studio](), [Code]() Tags: [agent](), [analysis](), [code-coverage](), [copilot](), [github-copilot](), [productivity](), [projects](), [run](), [test](), [test-coverage](), [testing](), [tests](), [unit-tests](), [visual-studio]() ### AI overview This tutorial explains how to improve test coverage in the Interview Coach application by establishing a baseline, using GitHub Copilot's Test Agent to generate unit tests, and analyzing coverage across the solution. The article reports coverage increasing from 37% to 81%. ### Source excerpt Today I want to look at how we can improve on the test coverage that we have in the Interview Coach application. This application has some unit tests in it already, but I'm sure there is room for improvement. Defining our baseline Before we start writing tests, it's a good idea to have a baseline [...] The post Today I will... improve test coverage appeared first on Visual Studio Blog. ## Build an interview coach app with the GitHub Copilot SDK DevFeed: [Build an interview coach app with the GitHub Copilot SDK]() Original publisher: [Read original article]() Author: Justin Yoo Published: 2026-09-15T16:00:11Z Content type: tutorial Language: en Sources: [Developer Blogs]() Topics: [GitHub Copilot](), [SDK](), [Model Context Protocol](), [.NET](), [Blazor]() Tags: [agent-framework](), [ai](), [blazor](), [github-copilot](), [github-copilot-sdk](), [mcp](), [microsoft-agent-framework](), [microsoft-foundry](), [model-context-protocol](), [net](), [sdk]() ### AI overview This tutorial demonstrates how to build an interview coach application with the GitHub Copilot SDK in .NET. The sample uses a Blazor chat interface, Copilot instructions and callable tools, and Model Context Protocol services for document handling and interview records. It emphasizes giving Copilot application-specific tools rather than access to shell commands or source files. ### Source excerpt An interview coach has to do more than ask questions. It needs to read a resume, follow up on an incomplete answer, and save enough context to give useful feedback at the end. Some of that work is conversation. Some of it requires calling an application service. The GitHub Copilot SDK lets you use the [...] The post Build an interview coach app with the GitHub Copilot SDK appeared first on Microsoft for Developers. ## C++ in the Age of AI: Visual Studio at CppCon 2026 DevFeed: [C++ in the Age of AI: Visual Studio at CppCon 2026]() Original publisher: [Read original article]() Author: Augustin Popa Published: 2026-09-14T19:34:43Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [C++](), [Visual Studio](), [GitHub Copilot](), [Visual Studio 2026](), [build performance](), [MSVC](), [debugging](), [Optimization](), [Refactoring](), [Artificial Intelligence]() Tags: [ai](), [build-optimization](), [build-performance](), [c-plus-plus](), [copilot](), [cppcon](), [debugging](), [github-copilot](), [msvc](), [visual-studio](), [visual-studio-2026]() ### AI overview This companion reading guide covers how Visual Studio 2026 is evolving C++ development with improved code navigation, build and runtime performance, language feature support, debugging, modernization, refactoring, and AI-assisted workflows using GitHub Copilot. ### Source excerpt At CppCon 2026, we shared how Visual Studio is evolving to help you work more effectively with large C++ codebases, improve build and runtime performance, adopt the latest language features, and incorporate AI into your existing workflows. This post is a companion reading guide for our session, C++ in the Age of AI: How Visual [...] The post C++ in the Age of AI: Visual Studio at CppCon 2026 appeared first on C++ Team Blog. ## What's New for C++ Developers in Visual Studio 2026 (18.7 - 18.10) DevFeed: [What's New for C++ Developers in Visual Studio 2026 (18.7 - 18.10)]() Original publisher: [Read original article]() Author: Augustin Popa Published: 2026-09-14T06:38:16Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [Visual Studio 2026](), [C++](), [GitHub Copilot](), [MSVC](), [AI-assisted coding](), [CMake](), [Git](), [Submodules]() Tags: [2026](), [build-optimization](), [c-plus-plus](), [cmake](), [copilot](), [git](), [modernization](), [msvc](), [submodules](), [testing](), [tools](), [visual-studio](), [visual-studio-2026]() ### AI overview This Microsoft C++ Team Blog post recaps Visual Studio 2026 versions 18.7 through 18.10 for C++ developers. It covers C++ reliability fixes, GitHub Copilot modernization for upgrading MSBuild and CMake projects to newer MSVC Build Tools, improved code navigation, discoverable MSVC Build Tools installations, Git worktrees and submodules in the IDE, and debugging improvements. ### Source excerpt Over the past few months, we have continued shipping monthly Visual Studio 2026 releases, delivering improvements across all stages of the development cycle. In this blog post, we'll recap everything that changed from version 18.7 through 18.10 (released this month) that is relevant for C++ developers. This includes new tools for maintaining and navigating C++ [...] The post What's New for C++ Developers in Visual Studio 2026 (18.7 - 18.10) appeared first on C++ Team Blog. ## Microsoft at CppCon 2026 DevFeed: [Microsoft at CppCon 2026]() Original publisher: [Read original article]() Author: Augustin Popa Published: 2026-09-14T00:16:42Z Content type: news Language: en Sources: [C++ Team Blog]() Topics: [C++](), [Microsoft](), [Visual Studio](), [Visual Studio 2026](), [GitHub Copilot](), [MSVC](), [Visual Studio Code](), [Artificial Intelligence](), [Optimization](), [CMake]() Tags: [ai](), [c-plus-plus](), [cmake](), [copilot](), [cppcon](), [github-copilot](), [microsoft](), [msvc](), [optimization](), [safety](), [survey](), [visual-studio](), [visual-studio-2026]() ### AI overview Microsoft's CppCon 2026 sessions cover modern C++ development, including AI-assisted workflows, MSVC, Visual Studio, Visual Studio Code, compiler optimization, C++ safety, and stricter compiler warnings. The article also provides conference logistics, speaker sessions, and a survey invitation. ### Source excerpt Microsoft will be at CppCon 2026 in Aurora, Colorado, which is taking place from September 14 through 18. Join us for talks from Microsoft speakers and colleagues across the C++ community and visit the Microsoft booth Monday through Thursday to meet the team and discuss the tools, technologies, and challenges that matter to you. If [...] The post Microsoft at CppCon 2026 appeared first on C++ Team Blog. ## GitHub Copilot's Project HydraFusion Promises Frontier Level Performance Through Multi-Model Routing DevFeed: [GitHub Copilot's Project HydraFusion Promises Frontier Level Performance Through Multi-Model Routing]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-13T06:06:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [GitHub Copilot](), [Model Routing](), [AI-assisted coding](), [Orchestration]() Tags: [agentic-coding](), [ai](), [ai-ml-data-engineering](), [benchmarks](), [development](), [frontier-model](), [github-copilot](), [github-hydrafusion](), [ml-data-engineering](), [model-routing](), [news](), [orchestration]() ### AI overview GitHub's Project HydraFusion research preview for Copilot orchestrates multiple models at runtime for coding tasks. Its single, cascade, and critique execution patterns aim to balance task quality, latency, and estimated cost. ### Source excerpt GitHub's Project HydraFusion is a research preview for GitHub Copilot that enhances coding intelligence through runtime model orchestration. It dynamically assembles execution plans using models from various providers. The system employs three execution patterns based on task complexity. Evaluations indicate that it achieves high task quality while significantly reducing operational costs. By Olimpiu Pop ## GitHub Copilot app for Beginners: Using the diff, terminal, and browser DevFeed: [GitHub Copilot app for Beginners: Using the diff, terminal, and browser]() Original publisher: [Read original article]() Author: Kayla Cinnamon Published: 2026-09-10T21:31:19Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [GitHub Copilot](), [AI-assisted coding](), [dev-tools](), [web applications]() Tags: [ai-coding](), [ai-ml](), [github-copilot](), [github-copilot-app](), [github-copilot-app-for-beginners](), [pull-request](), [review](), [web-browser]() ### AI overview A beginner-oriented guide to using the GitHub Copilot app's diff, terminal, and browser panels to review agent-made code changes, run a project, preview a web feature, iterate, and create a pull request. ### Source excerpt Checking agent-generated code usually means hopping between tabs. Learn how to view diffs, run terminal commands, and preview web apps side by side in the GitHub Copilot app. The post GitHub Copilot app for Beginners: Using the diff, terminal, and browser appeared first on The GitHub Blog. ## GitHub Copilot is now available in the AI SDK harness layer DevFeed: [GitHub Copilot is now available in the AI SDK harness layer]() Original publisher: [Read original article]() Author: Felix Arntz Published: 2026-09-10T17:39:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [GitHub Copilot](), [vercel ai sdk](), [AI-assisted coding](), [cursor]() Tags: [agent](), [ai](), [claude](), [claude-code](), [code](), [codex](), [coding](), [copilot](), [cursor](), [github](), [github-copilot]() ### AI overview The AI SDK harness layer adds official GitHub Copilot support through an adapter, allowing applications to run Copilot and other coding agents through the same HarnessAgent interface. ### Source excerpt The AI SDK harness layer now supports GitHub Copilot through the official @ai-sdk/harness-github-copilot adapter. The harness layer lets your application run different coding agents through the same HarnessAgent interface, so you can switch agents without changing your application code. Pass githubCopilot to HarnessAgent: Under the hood, the adapter uses @ai-sdk/harness-acp to connect GitHub Copilot to HarnessAgent through the Agent Client Protocol (ACP). Supported harnesses now include, in addition to GitHub Copilot, Claude Code, Cline, Codex, Cursor, Deep Agents, fx, Grok Build, OpenCode, and Pi, with more coming soon. Read the GitHub Copilot harness documentation to get started. Read more ## Visual Studio Code 1.137 DevFeed: [Visual Studio Code 1.137]() Original publisher: [Read original article]() Author: Visual Studio Code Team Published: 2026-09-09T17:00:00Z Content type: release Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [vs-code](), [Automation](), [GitHub](), [GitHub Issues](), [Pull Request](), [GitHub Copilot]() Tags: [2026](), [article](), [automation](), [experimental](), [github](), [github-issues](), [pull-request](), [release](), [updates](), [visual-studio](), [voice](), [vs-code]() ### AI overview Visual Studio Code 1.137 introduces scheduled automations, experimental voice interaction with agents, workspace-attached quick chats, and GitHub issues and pull request details in the Agents window. ### Source excerpt Learn what's new in Visual Studio Code 1.137 Read the full article ## Evaluating Model Cost and Quality for Developer Workflows DevFeed: [Evaluating Model Cost and Quality for Developer Workflows]() Original publisher: [Read original article]() Author: Waldek Mastykarz Published: 2026-09-09T09:14:04Z Content type: comparison Language: en Sources: [Developer Blogs]() Topics: [Claude](), [GitHub Copilot](), [Visual Studio Code](), [Testing](), [upgrade](), [Code]() Tags: [agent-experience](), [ai](), [ax](), [claude](), [comparison](), [cost](), [evaluation](), [github-copilot](), [model](), [testing](), [upgrade](), [visual-studio-code](), [windows]() ### AI overview This article compares GPT-6 Astra with Claude Sonnet 4.6 across code upgrade scenarios in GitHub Copilot Chat for Visual Studio Code on Windows. It argues that teams should evaluate models on their own repositories and workflows because higher cost did not consistently produce better results in the reported tests. ### Source excerpt The smartest model can cost five times more and deliver the same result, or even a worse one. See how evaluating your own work helps you get more value from your agent budget. The post Your work might not need the smartest model appeared first on Microsoft for Developers. ## Today I will... find hidden latency across a distributed .NET application DevFeed: [Today I will... find hidden latency across a distributed .NET application]() Original publisher: [Read original article]() Author: Justin Yoo Published: 2026-09-08T16:00:22Z Content type: tutorial Language: en Sources: [Visual Studio Blog]() Topics: [.NET](), [Processes](), [GitHub Copilot]() Tags: [copilot](), [github-copilot](), [github-copilot-profiler-agent](), [latency](), [net](), [performance](), [processes](), [productivity](), [profile](), [visual-studio](), [visual-studio-2026](), [visual-studio-profiler]() ### AI overview A Visual Studio profiling walkthrough for finding hidden latency in a distributed .NET application by isolating the process responsible for a slow interaction and determining whether it is computing or waiting. ### Source excerpt When a distributed application feels slow, the user sees one delay. The code behind that delay may run across a web frontend, backend services, databases, and external APIs. If you profile only the frontend while the backend is slow, the profiler can show that the frontend is healthy without revealing the actual bottleneck. This article [...] The post Today I will... find hidden latency across a distributed .NET application appeared first on Visual Studio Blog. ## Project HydraFusion: Frontier quality via multi-model orchestration DevFeed: [Project HydraFusion: Frontier quality via multi-model orchestration]() Original publisher: [Read original article]() Author: GitHub Staff Published: 2026-09-04T16:04:14Z Content type: release Language: en Sources: [GitHub Engineering]() Topics: [Orchestration](), [Model Routing](), [GitHub Copilot](), [AI-assisted coding](), [Optimization](), [Claude]() Tags: [agentic-coding](), [ai-ml](), [benchmarking](), [benchmarks](), [claude](), [cost](), [github-copilot](), [llms](), [optimization](), [orchestration](), [routing]() ### AI overview GitHub introduces Project HydraFusion, a GitHub Copilot research preview that selects multi-model execution workflows to balance quality, cost, and latency for coding tasks. ### Source excerpt In controlled offline evaluations, HydraFusion's selective coding workflows matched or exceeded the evaluated Opus 5 baseline while reducing estimated workflow cost. Now available as a research preview in GitHub Copilot. The post Project HydraFusion: Frontier quality via multi-model orchestration appeared first on The GitHub Blog. ## Why Short AI Coding Prompts Can Cost You More Time DevFeed: [Why Short AI Coding Prompts Can Cost You More Time]() Original publisher: [Read original article]() Author: Dean Hume Published: 2026-09-03T09:17:56Z Content type: opinion Language: en Sources: [Dean Hume]() Topics: [Prompt Engineering](), [ai-coding](), [GitHub Copilot](), [Artificial Intelligence](), [Agent Harness](), [GitHub Copilot CLI](), [Testing]() Tags: [agent-harness](), [ai](), [ai-coding](), [coding](), [copilot](), [github-copilot](), [prompt](), [testing]() ### AI overview This opinion article connects GitHub Copilot team's findings about compressing tool output with everyday AI coding prompts. It argues that optimizing a visible step, such as shortening a response or prompt, can increase total time and cost when missing details force follow-up questions, reruns, or recovery work. It recommends giving coding assistants the full problem and relevant source material, and treating instruction files carefully because rewriting guidance can change agent behavior. ### Source excerpt Ever tried to save time by keeping a prompt short - only to spend the next ten minutes answering follow-up questions because the AI didn't have what it needed? Yeah, me too. I recently came across a really interesting post from the GitHub Copilot team about making ## How we make AI coding more cost efficient without sacrificing task quality DevFeed: [How we make AI coding more cost efficient without sacrificing task quality]() Original publisher: [Read original article]() Author: Erik Kristensen Published: 2026-09-02T18:00:00Z Content type: article Language: en Sources: [GitHub](), [GitHub Engineering]() Topics: [GitHub Copilot](), [AI-assisted coding](), [GitHub Copilot CLI](), [coding](), [GitHub Copilot app](), [GitHub Copilot code review]() Tags: [agentic-ai](), [agentic-coding](), [ai](), [ai-coding](), [ai-ml](), [architecture-optimization](), [benchmarks](), [compression](), [cost](), [engineering](), [experiments](), [github-copilot](), [github-copilot-cli](), [github-copilot-code-review](), [llms](), [prompt-engineering]() ### AI overview GitHub Copilot's efficiency work focuses on total task cost and duration rather than minimizing tokens in individual tool responses. The article describes evaluating changes with coding benchmarks and controlled experiments, and explains that overly compressed output can cause agents to repeat work. ### Source excerpt Why shorter outputs can cost more, and how GitHub Copilot reduces wasted work across the complete coding task. The post How we make AI coding more cost efficient without sacrificing task quality appeared first on The GitHub Blog. ## Today I will... review GitHub PRs DevFeed: [Today I will... review GitHub PRs]() Original publisher: [Read original article]() Author: Aaron Powell Published: 2026-09-01T16:00:27Z Content type: tutorial Language: en Sources: [Visual Studio Blog]() Topics: [Pull Request](), [GitHub](), [GitHub Copilot](), [Syntax Highlighting]() Tags: [copilot](), [git](), [github](), [productivity](), [pull-requests](), [review](), [tooling](), [visual-studio](), [visual-studio-2026]() ### AI overview A tutorial on reviewing GitHub pull requests from Visual Studio. It covers opening PRs, examining diffs, adding buffered inline comments, approving or merging, and using Copilot during review. ### Source excerpt Today I will show you how we can tackle one of the most common tasks when working in a development team or managing an open-source project, and that is working with Pull Requests. While we're going to be using the GitHub tooling within Visual Studio for this, the approach would be very similar with Azure [...] The post Today I will... review GitHub PRs appeared first on Visual Studio Blog. ## GitHub Copilot app for Beginners: Automate Dependabot pull request triage DevFeed: [GitHub Copilot app for Beginners: Automate Dependabot pull request triage]() Original publisher: [Read original article]() Author: Christopher Harrison Published: 2026-08-26T20:12:53Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [GitHub Copilot app](), [pull-requests](), [Automation](), [GitHub Copilot](), [GitHub](), [Vulnerabilities]() Tags: [ai-ml](), [automation](), [copilot](), [developer](), [github](), [github-copilot](), [github-copilot-app](), [github-copilot-app-for-beginners](), [pull-request](), [workflow]() ### AI overview Learn how to create a daily automation in the GitHub Copilot app to triage Dependabot pull requests. The automation reviews open pull requests, groups updates by risk, checks CI status, and returns a summary highlighting changes ready to merge or requiring closer attention. ### Source excerpt Managing library updates can be tedious at times. Learn how the GitHub Copilot app can handle this type of repetitive task. The post GitHub Copilot app for Beginners: Automate Dependabot pull request triage appeared first on The GitHub Blog. ## Visual Studio August Update - Work Smarter Across Models and Branches DevFeed: [Visual Studio August Update - Work Smarter Across Models and Branches]() Original publisher: [Read original article]() Author: Mark Downie Published: 2026-08-25T16:00:00Z Content type: release Language: en Sources: [Visual Studio Blog]() Topics: [Visual Studio 2026](), [GitHub Copilot](), [Language models](), [AI-assisted coding]() Tags: [agents](), [copilot](), [git](), [github-copilot](), [models](), [update](), [visual-studio]() ### AI overview Visual Studio 2026's August update adds adjustable Copilot model thinking effort, organization-level custom agents, and Copilot usage access. It also improves Git workflows with worktrees and submodule support. ### Source excerpt Tune Copilot model thinking effort, share custom agents across your organization, track usage, and simplify complex Git workflows with worktrees and submodule support. The post Visual Studio August Update - Work Smarter Across Models and Branches appeared first on Visual Studio Blog. ## Visual Studio Code 1.134 DevFeed: [Visual Studio Code 1.134]() Original publisher: [Read original article]() Author: Visual Studio Code Team Published: 2026-08-19T17:00:00Z Content type: release Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [Visual Studio Code](), [GitHub Copilot](), [AI-assisted coding](), [SDKs](), [Processes]() Tags: [agents](), [copilot](), [github-copilot](), [html](), [release](), [vs-code]() ### AI overview Visual Studio Code 1.134 adds side-by-side chat organization, prompt navigation and search, local HTML preview, and an agent host that can connect an agent session across multiple VS Code windows. ### Source excerpt Learn what's new in Visual Studio Code 1.134 Read the full article ## Clearer Build Optimization Results with GitHub Copilot DevFeed: [Clearer Build Optimization Results with GitHub Copilot]() Original publisher: [Read original article]() Author: David Li Published: 2026-08-13T18:18:50Z Content type: article Language: en Sources: [C++ Team Blog]() Topics: [GitHub Copilot](), [Optimization](), [build performance](), [build times](), [C++](), [Windows](), [Visual Studio 2026]() Tags: [build-insights](), [build-optimization](), [build-performance](), [build-times](), [c-plus-plus](), [github-copilot](), [optimization](), [performance](), [visual-studio](), [visual-studio-2026](), [windows](), [workflow]() ### AI overview GitHub Copilot build performance for Windows now provides deterministic messages for optimization outcomes, including successful improvements, changes below the success threshold, unsuccessful changes, and builds that were too short to analyze. Successful results include baseline and final build times, measured improvement, and effects on iterative builds when applicable. ### Source excerpt We know that slow builds are a consistent theme in feedback from C++ developers. Your feedback helped us to shape GitHub Copilot build performance for Windows, an agent that can trace your build, find bottlenecks, apply optimizations, and measure the result. When using the agent, the clearest outcome is a straightforward win. The agent changes [...] The post Clearer Build Optimization Results with GitHub Copilot appeared first on C++ Team Blog. ## Building safe MCP servers for your PostgreSQL database DevFeed: [Building safe MCP servers for your PostgreSQL database]() Original publisher: [Read original article]() Author: Pamela Fox (noreply@blogger.com) Published: 2026-08-12T18:07:30Z Content type: article Language: en Sources: [Pamela Fox]() Topics: [Model Context Protocol](), [MCP Server](), [PostgreSQL](), [SQL](), [Python](), [GitHub Copilot](), [Open Source](), [LangChain]() Tags: [github-copilot](), [langchain](), [mcp](), [mcp-server](), [open-source](), [postgresql](), [python](), [sql]() ### AI overview This tutorial explains how to build MCP servers for PostgreSQL databases and how to control the level of agent access. It compares flexible servers that accept generated SQL with stricter servers using typed tools and templated queries, using Python and FastMCP examples. ### Source excerpt Model Context Protocol (MCP) is an open protocol that describes how agents can connect to external tools and data sources, and is now widely supported by the most popular coding agents (like GitHub Copilot, Claude Code, and Codex) and agent frameworks (like LangChain and Pydantic AI). If you want to give agents a standard way to access the data in a database, you can build your own MCP server and expose tools for the agent to query or even modify data. But you need to design your MCP server carefully, to ensure that agents can do everything that users want - but nothing that you don't want them to do! In this blog post, we'll walk through the range of ways to build MCP servers on top of a PostgreSQL database, since PostgreSQL is the most popular open source database and is production-ready with hosted offerings like Azure Database for PostgreSQL. You can apply these same principles to any database, however. There's a spectrum of ways to build MCP servers on top of a database. We'll start with the most flexible option, exploratory servers that allow the agent to generate full SQL queries, conclude with the strictest option, fully typed tools for templated queries, and explore options in the middle too. Free-form SQL Let's take a look at a simple MCP server that gives the agent as much information and control as possible. For all of our examples, we use the Python language and the FastMCP package, but SDKs are available in multiple languages. All code is available in the GitHub repository. We start off by giving the server a name, which the agent will see and consider when deciding which MCP server to invoke for a given user query: mcp = FastMCP("Bees database MCP server") For this example, my database stores observations of bees, so I name it accordingly. We then define an execute_sql tool that accepts any SQL string, executes it against the database, and returns the rows. @mcp.tool() async def execute_sql(sql: str) -> str: """Execute a SQL query against the database ## Generating AI Descriptions of Automated Pull Requests DevFeed: [Generating AI Descriptions of Automated Pull Requests]() Original publisher: [Read original article]() Author: Daniel Doubrovkine (dblock@dblock.org) Published: 2026-08-11T00:00:00Z Content type: tutorial Language: en Sources: [Daniel Doubrovkine]() Topics: [GitHub Copilot CLI](), [GitHub Actions](), [GitHub Copilot](), [Large Language Model](), [Ruby](), [Slack](), [changelog](), [Pull Request](), [Open Source](), [JSON](), [YAML]() Tags: [ai](), [automated](), [changelog](), [ci](), [cli](), [copilot](), [github](), [github-actions](), [github-copilot](), [github-copilot-cli](), [json](), [llm](), [open-source](), [pull-request](), [pull-requests](), [ruby](), [yaml]() ### AI overview A tutorial explains how the maintainer of the slack-ruby-client library uses a scheduled GitHub Actions workflow and GitHub Copilot CLI to generate meaningful commit messages, pull request bodies, and CHANGELOG entries from diffs of regenerated Slack API code. It also covers YAML indentation, prompt files, response logging, and extracting a JSON result from Copilot CLI output. ### Source excerpt The slack-ruby-client library, an open source Ruby gem I maintain, runs a scheduled GitHub Actions workflow that regenerates code from Slack's API definitions and opens a pull request with the diff. The commit message and CHANGELOG entry used to be a generic "Update API (2026-08-11)", which told a reviewer nothing about what actually changed. Here's how we taught the workflow to describe its own diffs, using GitHub Copilot CLI, which open source maintainers can get for free. The Idea The workflow already computes a diff before opening the pull request. Instead of a boilerplate commit message, we pipe that diff through an LLM and ask it to summarize what changed, then use the response as the commit message and PR body. - name: Check for changes id: changes run: | if git diff --quiet; then echo "changed=false" >> "$GITHUB_OUTPUT" else echo "changed=true" >> "$GITHUB_OUTPUT" fi - name: Prepare diff for AI summary if: steps.changes.outputs.changed == 'true' run: | git diff --stat | sed 's/^/ /' > /tmp/diff_stat.txt git diff | head -c 20000 | sed 's/^/ /' > /tmp/diff.txt The sed 's/^/ /' indent isn't decorative. actions/ai-inference substitutes template variables as raw text into a prompt YAML file before parsing it, so a multi-line diff starting at column 0 breaks the indentation of the enclosing content: |- block scalar. Pre-indenting the file to match keeps the YAML valid no matter what the diff looks like. The Prompt File actions/ai-inference supports .prompt.yml files, a small convention for keeping the system/user prompt out of the workflow YAML. messages: - role: system content: |- You write CHANGELOG entries describing an automated API update to slack-ruby-client, a Ruby gem whose Web API endpoint methods, argument validations, specs, and bin commands are code-generated from vendored Slack API method definitions (via a git submodule and rake task). Given a diffstat and a diff of the regenerated files, respond with ONLY a single JSON object (no markdown code fence ## Using the GitHub Copilot SDK for Java DevFeed: [Using the GitHub Copilot SDK for Java]() Original publisher: [Read original article]() Author: Edward Burns Published: 2026-08-10T19:30:00Z Content type: tutorial Language: en Sources: [GitHub]() Topics: [GitHub Copilot](), [Java](), [SDK](), [Jakarta EE](), [Agent Harness](), [Artificial Intelligence](), [Command-line interface](), [Spring AI]() Tags: [agent](), [agent-harness](), [ai](), [ai-ml](), [cli](), [client-library](), [engineering](), [framework](), [github-copilot](), [github-copilot-sdk](), [jakarta-ee](), [java](), [sdk]() ### AI overview This tutorial introduces the GitHub Copilot SDK for Java, a framework-agnostic client library for creating Copilot agent sessions, registering tools, sending prompts, and receiving structured responses. It demonstrates the SDK in a Jakarta EE 11 real-estate lead-management application and describes prerequisites including JDK, Maven, a GitHub Copilot subscription, and the Copilot CLI. ### Source excerpt Enterprise Java developers have a new superpower--drive GitHub Copilot from idiomatic Java code with annotations, virtual threads, and more. The post Using the GitHub Copilot SDK for Java appeared first on The GitHub Blog. [Next page]()