# GitHub Copilot Published articles for GitHub Copilot. 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. ## Visual Studio Code 1.139 (Insiders) DevFeed: [Visual Studio Code 1.139 (Insiders)]() Original publisher: [Read original article]() Author: Visual Studio Code Team Published: 2026-09-16T13:22:12.332134Z Content type: release Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [Visual Studio Code](), [releases](), [Release notes](), [GitHub Copilot CLI](), [Command-line interface](), [CSS]() Tags: [extension](), [github](), [github-copilot](), [release](), [release-notes](), [terminal](), [visual-studio-code](), [vs-code]() ### AI overview Visual Studio Code 1.139 (Insiders) release notes describe updates added between September 11 and September 15, 2026. Changes include Cargo completions in terminal suggestions, experimental Agent Merge support for creating and reviewing pull requests, CSS color previews, searchable Remote workspace menus, improved Copilot context routing, and other editor and API updates. ### Source excerpt Learn what is new in Visual Studio Code 1.139 (Insiders) Read the full article ## 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. ## 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 ## Marketing ops as code: Automating events from planning to follow-up on GitHub DevFeed: [Marketing ops as code: Automating events from planning to follow-up on GitHub]() Original publisher: [Read original article]() Author: Tomoko Tanaka Published: 2026-09-11T18:26:10Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [AI-assisted coding]() Tags: [ai-ml](), [api](), [automation](), [cli](), [events](), [github](), [github-actions](), [github-copilot](), [github-issues]() ### AI overview A GitHub marketing lead describes using GitHub Copilot and GitHub Actions to automate event operations from a GitHub Issue, including setup, daily registrant processing, and post-event cleanup. ### Source excerpt If you can write down how you do your work, you can automate it. Here's what I did to support GitHub's APAC marketing team. The post Marketing ops as code: Automating events from planning to follow-up on GitHub appeared first on The GitHub Blog. ## 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 ## Harness rebuilt its Git repository for nonstop AI agent traffic DevFeed: [Harness rebuilt its Git repository for nonstop AI agent traffic]() Original publisher: [Read original article]() Author: Frederic Lardinois Published: 2026-09-09T11:00:00Z Content type: news Language: en Sources: [The New Stack]() Topics: [Code review](), [AI-assisted coding](), [Pull Request](), [GitHub Copilot]() Tags: [ai](), [ai-agent](), [ai-agents](), [code](), [developer-tools](), [devops](), [git](), [github-copilot](), [podcast](), [pull-request](), [review](), [video]() ### AI overview Harness discusses rebuilding its Git repository and launching AI Code Review to address the review bottleneck created by increased pull-request volume from coding agents. ### Source excerpt As one of Harness's field CTOs, Martin Reynolds spends much of his time asking engineering leaders one question with no The post Harness rebuilt its Git repository for nonstop AI agent traffic appeared first on The New Stack. ## 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. ## Microsoft's September 2026 Patch Tuesday addresses 964 CVEs (CVE-2026-81963, CVE-2026-85880) DevFeed: [Microsoft's September 2026 Patch Tuesday addresses 964 CVEs (CVE-2026-81963, CVE-2026-85880)]() Original publisher: [Read original article]() Author: Research Special Operations Published: 2026-09-08T18:07:55Z Content type: news Language: en Sources: [Tenable Blog]() Topics: [Microsoft](), [cve-2026-85880](), [.NET](), [ASP.NET](), [GitHub Copilot](), [Visual Studio Code](), [OpenSSH]() Tags: [asp-net-core](), [cve-2026-81963](), [cve-2026-85880](), [github-copilot](), [microsoft](), [net](), [openssh](), [patch-tuesday](), [security](), [september-2026](), [update](), [updates](), [visual-studio](), [windows]() ### AI overview Microsoft's September 2026 Patch Tuesday release addresses 964 CVEs, including two zero-days exploited in the wild. The release rates 104 vulnerabilities as critical and 860 as important. ### Source excerpt 104Critical 860Important 0Moderate 0Low Microsoft addresses 964 CVEs, smashing July's release as the largest Patch Tuesday release. This month's updates include patches for two zero-days that were exploited in the wild. Microsoft patched a record 964 CVEs in its September 2026 Patch Tuesday release, with 104 rated critical and 860 rated as important. This month's update includes patches for: .NET .NET and Visual Studio ASP.NET Core Active Directory Certificate Services (AD CS) Active Directory Domain Services Active Directory Federation Services (AD FS) Audio Video Control Transport Protocol Azure Arc Azure CycleCloud Azure HDInsights BranchCache Connected Devices Platform Service (Cdpsvc) Data Sharing Service Client GitHub Copilot and Visual Studio Code Graphic Fonts HID class driver IP Helper Internet Storage Name Service Kernel Streaming WOW Thunk Service Driver Microsoft Account Microsoft Authenticator Microsoft Azure Attestation service and Device Health Attestation Service Microsoft Azure CLI Microsoft COM for Windows Microsoft Dynamics 365 Microsoft Exchange Server Microsoft Graphics Component Microsoft Install Service Microsoft JScript Microsoft Local Security Authority Server (lsasrv) Microsoft Office Microsoft Office Access Microsoft Office Excel Microsoft Office Outlook Microsoft Office PowerPoint Microsoft Office Publisher Microsoft Office SharePoint Microsoft Office Word Microsoft Standard XPS Microsoft Teams for Android Microsoft Trace Data Helper Microsoft UxTheme Library (uxtheme.dll) Microsoft WDAC OLE DB provider for SQL Microsoft WebP Image Extension Microsoft Windows Codecs Library Microsoft Windows Media Foundation Microsoft Windows PDF Microsoft Windows SCSI Class System File Microsoft Windows Search Component Microsoft Windows Speech OpenSSH for Windows Power Automate Push Message Routing Service RPC Runtime Reliable Multicast Transport Driver (RMCAST) Remote Desktop Client Remote Desktop Gateway Service Role: DNS Server Role: Windows Fax Serv ## 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. ## GitHub Copilot app for Beginners: Run several agents at once DevFeed: [GitHub Copilot app for Beginners: Run several agents at once]() Original publisher: [Read original article]() Author: Kayla Cinnamon Published: 2026-09-03T16:00:00Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [AI Chat]() Tags: [agents](), [ai](), [ai-agents](), [ai-ml](), [copilot](), [developer](), [developer-tools](), [development](), [git](), [github](), [github-copilot](), [github-copilot-app](), [github-copilot-app-for-beginners]() ### AI overview A beginner tutorial on using the GitHub Copilot app to run several AI agent sessions in parallel. It explains that sessions are isolated through separate Git worktrees and retain their own context, then demonstrates concurrent feature work, accessibility review, and testing. ### Source excerpt Learn how to run parallel agents in the GitHub Copilot app, and experience the moment it stops feeling scary and starts feeling powerful. The post GitHub Copilot app for Beginners: Run several agents at once 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. ## A Model Portfolio for cost-efficient AI across the software development lifecycle DevFeed: [A Model Portfolio for cost-efficient AI across the software development lifecycle]() Original publisher: [Read original article]() Author: Praveen Sidda Published: 2026-09-01T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [AI Development](), [AI Models](), [Model Routing](), [sdlc](), [Agile](), [Developer experience](), [Compression](), [Requirements](), [Testing](), [Deployment](), [Code review]() Tags: [agentic-sdlc](), [ai](), [ai-cost-optimization](), [ai-models](), [code-review](), [compression](), [deployment](), [github-copilot](), [llm](), [overhead](), [requirements](), [software-development]() ### AI overview The article examines whether routing software development tasks across a portfolio of AI models can reduce costs compared with using one premium model. It reports that lower-cost models handled well-defined tasks, while premium models were reserved for architecture, implementation, and code review; context compression reduced token usage but risked losing important technical details. ### Source excerpt Image generated by AIWhat one controlled experiment taught me about matching model capability to developer work Topic: Can intelligently routing developer tasks across different AI models outperform relying on a single premium model? In this article, I put that question to the test by mapping software development lifecycle (SDLC) stages to a portfolio of AI models and comparing the outcomes. Motivation As AI becomes embedded throughout the AI-Native Development Lifecycle (AIDLC), an evolution of the traditional Software Development Lifecycle (SDLC), its cost is no longer tied to a single prompt. A single developer task can involve multiple model calls, each carrying source files, conversation history, tool definitions, and generated output. Applying the most capable model to every interaction is straightforward, but it also consumes premium model capacity on tasks that less expensive models can often complete just as effectively. This raises an important question for engineering organizations: How can teams reduce the cost of AI-assisted development without compromising quality, reliability, or the developer experience? My first instinct was to reduce token consumption. Context compression appeared to be the most direct path to lowering inference costs by shortening prompts. Although it reduced token usage, it also introduced risk. Important constraints and technical details could be lost, affecting downstream tasks. Source code, stack traces, and active instructions proved to be especially poor candidates for lossy compression. That experience shifted my focus. The objective was not to process fewer tokens, but to complete developer tasks successfully at a lower overall cost. I then experimented with model allocation. Lower-cost models handled well-defined tasks such as requirements synthesis, planning, routine test generation, deployment artifacts, and final summaries, while premium models were reserved for architecture, implementation, and code review. This appro ## How Uno Platform uses .NET, MCP, and AI to build high quality apps DevFeed: [How Uno Platform uses .NET, MCP, and AI to build high quality apps]() Original publisher: [Read original article]() Author: Sam Basu Published: 2026-08-27T17:00:00Z Content type: article Language: en Sources: [.NET Blog]() Topics: [.NET](), [AI-assisted coding]() Tags: [ai](), [ai-agents](), [api](), [browser](), [c-sharp](), [cross-platform](), [developer-stories](), [docs](), [documentation](), [github-copilot](), [http](), [mcp](), [model-context-protocol](), [net](), [server](), [servers](), [skiasharp](), [tooling](), [uno-platform](), [xaml]() ### AI overview Uno Platform describes using two C# Model Context Protocol servers to give AI agents current documentation and the ability to inspect and drive running cross-platform .NET apps. ### Source excerpt How Uno Platform built two Model Context Protocol servers in C# so AI agents can ground themselves in real documentation and then see and drive a running cross-platform .NET app. The post How Uno Platform uses .NET, MCP, and AI to build high quality apps appeared first on .NET Blog. ## VSLive! @ Microsoft HQ: Developer Takeaways and Must-Watch Sessions DevFeed: [VSLive! @ Microsoft HQ: Developer Takeaways and Must-Watch Sessions]() Original publisher: [Read original article]() Author: James Rempt Published: 2026-08-27T14:00:09Z Content type: article Language: en Sources: [Visual Studio Blog]() Topics: [AI-assisted coding](), [.NET](), [Developer Tools](), [debugging](), [WinForms]() Tags: [ai](), [events](), [github-copilot](), [modernization](), [net](), [visual-studio]() ### AI overview A VSLive! @ Microsoft HQ recap highlights developer discussions about AI-assisted development, Visual Studio fundamentals, and .NET modernization. ### Source excerpt VSLive! @ Microsoft HQ 2026 was one of the most energizing VSLive! events I've attended. What stood out was the level of engagement over five days of learning and conversation from July 27 through July 31. Developers came ready to dig into the future of Visual Studio, .NET, and AI-assisted software development, and many Microsoft engineers and product managers were there to have those conversations directly. If you weren't able to [...] The post VSLive! @ Microsoft HQ: Developer Takeaways and Must-Watch Sessions 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 Code 1.135 DevFeed: [Visual Studio Code 1.135]() Original publisher: [Read original article]() Author: Visual Studio Code Team Published: 2026-08-26T17:00:00Z Content type: release Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [AI-assisted coding](), [AI Chat](), [SDKs]() Tags: [agents](), [claude](), [copilot](), [github-copilot](), [release](), [vs-code]() ### AI overview Visual Studio Code 1.135 adds support for continuing recent Copilot or Claude agent sessions from other applications, an experimental Rubber Duck second-opinion feature, streamlined Agents-window controls, and per-model chat token usage details. ### Source excerpt Learn what's new in Visual Studio Code 1.135 Read the full article [Next page]()