Dev Containers for .NET in VS Code: A Beginner‑Friendly Guide That Actually Works
What Dev Containers are really about At a high level, Dev Containers let you use a Docker container as your development environment inside VS Code. But the real idea is not “Docker for development”. The real idea is this: Move all environment complexity out of your laptop and into version‑controlled configuration. With Dev Containers: Your laptop becomes just a VS Code client Your tools, SDKs, runtimes, and dependencies live inside the container Your project defines its own development environment, not your machine This means: You can switch projects without breaking anything You can delete and recreate your environment safely New developers get the same setup without tribal knowledge Why Dev Containers are so useful for .NET projects .NET development often looks simple at first until it doesn’t. Common pain points: Different developers using different .NET SDK versions One project needs .NET 6, another needs .NET 8 Native dependencies work on one machine but not another CI runs on Linux but developers run on Windows Dev Containers solve this by: Locking the SDK version and OS used for development Running everything in a Linux container (close to CI/production) Keeping developer machines clean and stable Making onboarding almost instant: clone → reopen in container → run Once the .devcontainer folder is committed to the repo, the environment becomes part of the codebase, not a wiki page. How Dev Containers work in VS Code You don’t need deep Docker knowledge to use Dev Containers. Here’s the mental model that helped me: Your repository contains a .devcontainer folder Inside it, devcontainer.json describes the development environment VS Code reads that file and starts a container VS Code connects to the container and runs extensions inside it Your source code stays on your machine, but: the terminal runs inside the container the debugger runs inside the container the SDKs live inside the container If something breaks, you rebuild the container, not your laptop. When Dev Containers are a great choice (and when they’re not) Dev Containers are a great fit when: You work on multiple projects with different requirements Your team struggles with environment consistency You want Linux parity for CI and containerized deployments You value reproducibility over ad‑hoc local setup They may not be ideal when: You’re working on very small throwaway scripts You rely heavily on Windows‑only tooling You cannot use Docker at all in your environment For most professional .NET teams, the benefits far outweigh the cost. Docker on Windows: a choice you must make early When starting with Dev Containers on Windows, one of the first decisions you must make is how Docker runs on your machine. Both Docker Desktop and Docker Engine inside WSL work well with Dev Containers but they serve slightly different needs. Using Docker Desktop Docker Desktop is the easiest and most beginner‑friendly way to get started with Dev Containers. Pros Very quick setup with minimal configuration Comes with a graphical dashboard for containers, images, and logs Integrates smoothly with VS Code and WSL2 Easier to troubleshoot when you’re learning Cons Uses more system resources in the background Runs additional services even when you’re not actively developing May be restricted or licensed differently in some enterprise environments When to use Docker Desktop You are new to Docker or Dev Containers You want the simplest and fastest setup You value ease of use over fine‑grained control You are working on personal projects or in environments where Docker Desktop is allowed For most developers starting out with Dev Containers, Docker Desktop is the recommended entry point. Using Docker Engine inside WSL This approach installs Docker Engine directly inside a Linux distribution (like Ubuntu) running on WSL2, without Docker Desktop. Pros Lower resource usage compared to Docker Desktop Linux‑native behavior (closer to CI and production) No dependency on Docker Desktop Often preferred in enterprise or restricted environments Cons Requires manual installation and configuration Needs basic Linux and WSL knowledge No graphical UI everything is CLI‑based When to use Docker Engine in WSL Docker Desktop is not allowed or restricted You want a leaner, Linux‑first workflow You already work mostly inside WSL You want tighter control over your Docker setup This approach is ideal once you are comfortable with Docker and WSL. Note : Do not mix Docker Desktop and Docker Engine inside WSL.Pick one approach and stick with it. Running both at the same time often leads to Docker context confusion and Dev Containers failing in unpredictable ways, even when your configuration looks correct. A performance tip that makes a huge difference If you’re using Linux containers with WSL, store your code inside the WSL filesystem. Recommended: /home/<user>/projects/your-repo Avoid: /mnt/c/Users/<user>/your-repo Linux containers accessing Windows files are slower and cause file‑watching issues. Moving the repo into WSL made my Dev Containers feel almost native. First‑time setup: the simplest way to start If you’re trying Dev Containers for the first time, follow this exact order: Install Visual Studio Code Install the Dev Containers extension Install Docker Desktop (or Docker Engine in WSL) Clone your repo inside the WSL filesystem Open the folder in VS Code Run “Dev Containers: Reopen in Container” That’s it. VS Code handles the rest. Your first .NET Dev Container (hands‑on example) Tech Stack .NET 8 Web API PostgreSQL 16 Entity Framework Core + Npgsql VS Code Dev Containers Docker Compose Project Structure my-blog-api/ ├─ .devcontainer/ │ └─ devcontainer.json ├─ docker-compose.yml └─ src/ └─ BlogApi/ ├─ Program.cs ├─ BlogApi.csproj ├─ appsettings.json ├─ Models/ └─ Data/ Step 1: Create the Web API mkdir my-blog-api cd my-blog-api mkdir src && cd src dotnet new webapi -n BlogApi cd BlogApi Step 2: Add EF Core + PostgreSQL packages dotnet add package Npgsql.EntityFrameworkCore.PostgreSQL dotnet add package Microsoft.EntityFrameworkCore.Design Step 3: Docker Compose (API + PostgreSQL) Create docker-compose.yml at the repo root: version: "3.8" services: app: image: mcr.microsoft.com/devcontainers/dotnet:1-8.0 volumes: - .:/workspace:cached working_dir: /workspace command: sleep infinity ports: - "5000:5000" depends_on: - db db: image: postgres:16 environment: POSTGRES_USER: devuser POSTGRES_PASSWORD: devpwd POSTGRES_DB: devdb ports: - "5432:5432" volumes: - pgdata:/var/lib/postgresql/data pgadmin: image: dpage/pgadmin4 environment: PGADMIN_DEFAULT_EMAIL: admin@admin.com PGADMIN_DEFAULT_PASSWORD: admin ports: - "5050:80" depends_on: - db volumes: pgdata: Step 4: Dev Container configuration Create .devcontainer/devcontainer.json: { "name": "dotnet-postgres-devcontainer", "dockerComposeFile": "../docker-compose.yml", "service": "app", "workspaceFolder": "/workspace", "shutdownAction": "stopCompose", "customizations": { "vscode": { "extensions": [ "ms-dotnettools.csdevkit", "ms-dotnettools.csharp", "ms-azuretools.vscode-docker" ] } }, "postCreateCommand": "dotnet restore" } Open the folder in VS Code and run: Dev Containers: Reopen in Container Step 5: Connection string (container‑to‑container) Update appsettings.json: { "ConnectionStrings": { "DefaultConnection": "Host=db;Port=5432;Database=devdb;Username=devuser;Password=devpwd" }, "Logging": { "LogLevel": { "Default": "Information", "Microsoft.AspNetCore": "Warning" } }, "AllowedHosts": "*" } Host=db works because Docker Compose provides internal DNS between services. Step 6: EF Core Model & DbContext Post entity – Models/Post.cs namespace BlogApi.Models; public class Post { public int Id { get; set; } public string Title { get; set; } = string.Empty; public string Content { get; set; } = string.Empty; public DateTime CreatedUtc { get; set; } = DateTime.UtcNow; } DbContext – Data/BlogDbContext.cs using BlogApi.Models; using Microsoft.EntityFrameworkCore; namespace BlogApi.Data; public class BlogDbContext : DbContext { public BlogDbContext(DbContextOptions<BlogDbContext> options) : base(options) { } public DbSet<Post> Posts => Set<Post>(); } Step 7: Program.cs (Minimal CRUD) Replace Program.cs with: using BlogApi.Data; using BlogApi.Models; using Microsoft.EntityFrameworkCore; var builder = WebApplication.CreateBuilder(args); builder.Services.AddDbContext<BlogDbContext>(options => options.UseNpgsql(builder.Configuration.GetConnectionString("DefaultConnection"))); builder.Services.AddEndpointsApiExplorer(); builder.Services.AddSwaggerGen(); var app = builder.Build(); // Apply migrations on startup (dev-only convenience) using (var scope = app.Services.CreateScope()) { var db = scope.ServiceProvider.GetRequiredService<BlogDbContext>(); db.Database.Migrate(); } app.UseSwagger(); app.UseSwaggerUI(); app.MapGet("/posts", async (BlogDbContext db) => await db.Posts.OrderByDescending(p => p.CreatedUtc).ToListAsync()); app.MapPost("/posts", async (Post post, BlogDbContext db) => { db.Posts.Add(post); await db.SaveChangesAsync(); return Results.Created($"/posts/{post.Id}", post); }); app.MapPut("/posts/{id:int}", async (int id, Post input, BlogDbContext db) => { var post = await db.Posts.FindAsync(id); if (post is null) return Results.NotFound(); post.Title = input.Title; post.Content = input.Content; await db.SaveChangesAsync(); return Results.Ok(post); }); app.MapDelete("/posts/{id:int}", async (int id, BlogDbContext db) => { var post = await db.Posts.FindAsync(id); if (post is null) return Results.NotFound(); db.Posts.Remove(post); await db.SaveChangesAsync(); return Results.NoContent(); }); app.Run("http://0.0.0.0:5000"); Step 8: Run migrations (inside Dev Container) cd src/BlogApi dotnet tool install --global dotnet-ef export PATH="$PATH:/home/vscode/.dotnet/tools" dotnet ef migrations add InitialCreate dotnet ef database update Step 9: Run the API dotnet run 🔗 Open: Swagger → http://localhost:5000/swagger Posts API → http://localhost:5000/posts Common mistakes and quick fixes Mistake Symptom Fix Mixing Docker models Random failures Use only one Docker approach Code under /mnt/c Slow builds Move repo to WSL filesystem Docker not running Container won’t start Check docker info Pruning first Issues return Fix daemon/context first Common Challenges Faced Multiple Docker engines active simultaneously Docker Desktop and Docker Engine inside WSL were both present, causing conflicts. Unstable Docker CLI context Docker CLI intermittently pointed to different or broken Docker endpoints. Docker daemon appeared running but was unusable Docker commands failed with API errors despite the daemon seeming active. systemd dependency issues inside WSL Docker Engine depended on systemd, which was not consistently active after WSL restarts. Dev Containers failing during setup VS Code Dev Containers surfaced failures during feature installation and builds. Misleading Docker error messages Errors pointed to API or version issues, masking the real root cause. Cache cleanup ineffective Pruning images and containers did not resolve underlying daemon issues. Container observability confusion PostgreSQL and pgAdmin worked, but container health, volumes, and data locations were unclear. Solutions & Maintainable Settings Enforce a Single Docker Model. Use either Docker Desktop or native Docker Engine inside WSL .never both. docker version docker info Verify only one server is shown No references to dockerDesktopLinuxEngine when using native WSL Docker Explicitly Lock Docker CLI Context. Always verify and set Docker context before running Compose or Dev Containers. docker context ls docker context show docker context use default Context must point to the intended daemon (WSL or Desktop) Validate Docker Daemon Health Before Project Start.Confirm Docker is reachable before Dev Containers or Compose. docker info docker ps Must return without API / 500 / version errors Do not proceed if these fail Ensure systemd Is Enabled in WSL.Docker Engine inside WSL depends on systemd. cat /etc/wsl.conf Expected: [boot] systemd=true If not then apply changes.Restart WSL and re‑verify: wsl --shutdown systemctl status docker Start Docker Explicitly After WSL Restart.WSL restarts silently stop services. sudo systemctl start docker sudo systemctl enable docker Verify: docker ps Use Only WSL Native Filesystem for Projects.Keep project under /home/<user>/... Avoid /mnt/c/... paths. Path starts with /home/ Treat Dev Containers as a Consumer, Not the Fix. Fix Docker issues outside Dev Containers first. Pre‑check Commands docker compose config docker compose up -d Compose works before opening Dev Container Keep Dev Container Features Minimal on First Run Start with base image + required services only Add features after baseline stability docker images docker ps Verify Container Observability Explicitly.Confirm containers are healthy, ports mapped, volumes mounted. docker ps docker inspect <container_name> docker logs <container_name> Port check: ss -lntp | grep <port> Avoid Cache Cleanup as a First Fix. Do not rely on prune to fix daemon issues Only After Daemon Is Healthy docker system prune -f docker volume prune -f Establish a “Known‑Good” Baseline Checklist. Validate sequence before development starts Baseline Flow wsl --shutdown # reopen WSL sudo systemctl start docker docker context show docker info docker compose up -d code . # Only then open Reopen in Container If something breaks in between after running dev container we can stop and clean the containers and rebuild it again docker ps docker stop $(docker ps -q) docker rm -f $(docker ps -aq) docker ps Closing Thoughts Dev Containers shift local development from fragile, machine‑specific setups to reproducible, version‑controlled environments. With Dev Containers, Docker Compose, PostgreSQL, and pgAdmin, your entire .NET development stack lives inside containers.not on your laptop. SDKs, databases, and tools are isolated, consistent, and easy to rebuild. When something breaks, you rebuild containers.not machines. This approach removes onboarding friction, improves Linux parity with CI, and eliminates the classic “works on my machine” problem. Once Docker is stable, Dev Containers become one of the most reliable ways to build modern .NET applications. Key Takeaways Dev Containers treat the development environment as code .NET, PostgreSQL, and pgAdmin run fully isolated in containers pgAdmin provides clear visibility into database state and migrations Docker stability is a prerequisite.Dev Containers are not a Docker fix Onboarding becomes simple: clone → reopen in container → run Rebuild containers, not laptopsAZD for Beginners: A Practical Introduction to Azure Developer CLI
If you are learning how to get an application from your machine into Azure without stitching together every deployment step by hand, Azure Developer CLI, usually shortened to azd , is one of the most useful tools to understand early. It gives developers a workflow-focused command line for provisioning infrastructure, deploying application code, wiring environment settings, and working with templates that reflect real cloud architectures rather than toy examples. This matters because many beginners hit the same wall when they first approach Azure. They can build a web app locally, but once deployment enters the picture they have to think about resource groups, hosting plans, databases, secrets, monitoring, configuration, and repeatability all at once. azd reduces that operational overhead by giving you a consistent developer workflow. Instead of manually creating each resource and then trying to remember how everything fits together, you start with a template or an azd -compatible project and let the tool guide the path from local development to a running Azure environment. If you are new to the tool, the AZD for Beginners learning resources are a strong place to start. The repository is structured as a guided course rather than a loose collection of notes. It covers the foundations, AI-first deployment scenarios, configuration and authentication, infrastructure as code, troubleshooting, and production patterns. In other words, it does not just tell you which commands exist. It shows you how to think about shipping modern Azure applications with them. What Is Azure Developer CLI? The Azure Developer CLI documentation on Microsoft Learn, azd is an open-source tool designed to accelerate the path from a local development environment to Azure. That description is important because it explains what the tool is trying to optimise. azd is not mainly about managing one isolated Azure resource at a time. It is about helping developers work with complete applications. The simplest way to think about it is this. Azure CLI, az , is broad and resource-focused. It gives you precise control over Azure services. Azure Developer CLI, azd , is application-focused. It helps you take a solution made up of code, infrastructure definitions, and environment configuration and push that solution into Azure in a repeatable way. Those tools are not competitors. They solve different problems and often work well together. For a beginner, the value of azd comes from four practical benefits: It gives you a consistent workflow built around commands such as azd init , azd auth login , azd up , azd show , and azd down . It uses templates so you do not need to design every deployment structure from scratch on day one. It encourages infrastructure as code through files such as azure.yaml and the infra folder. It helps you move from a one-off deployment towards a repeatable development workflow that is easier to understand, change, and clean up. Why Should You Care About azd A lot of cloud frustration comes from context switching. You start by trying to deploy an app, but you quickly end up learning five or six Azure services, authentication flows, naming rules, environment variables, and deployment conventions all at once. That is not a good way to build confidence. azd helps by giving a workflow that feels closer to software delivery than raw infrastructure management. You still learn real Azure concepts, but you do so through an application lens. You initialise a project, authenticate, provision what is required, deploy the app, inspect the result, and tear it down when you are done. That sequence is easier to retain because it mirrors the way developers already think about shipping software. This is also why the AZD for Beginners resource is useful. It does not assume every reader is already comfortable with Azure. It starts with foundation topics and then expands into more advanced paths, including AI deployment scenarios that use the same core azd workflow. That progression makes it especially suitable for students, self-taught developers, workshop attendees, and engineers who know how to code but want a clearer path into Azure deployment. What You Learn from AZD for Beginners The AZD for Beginners course is structured as a learning journey rather than a single quickstart. That matters because azd is not just a command list. It is a deployment workflow with conventions, patterns, and trade-offs. The course helps readers build that mental model gradually. At a high level, the material covers: Foundational topics such as what azd is, how to install it, and how the basic deployment loop works. Template-based development, including how to start from an existing architecture rather than building everything yourself. Environment configuration and authentication practices, including the role of environment variables and secure access patterns. Infrastructure as code concepts using the standard azd project structure. Troubleshooting, validation, and pre-deployment thinking, which are often ignored in beginner content even though they matter in real projects. Modern AI and multi-service application scenarios, showing that azd is not limited to basic web applications. One of the strongest aspects of the course is that it does not stop at the first successful deployment. It also covers how to reason about configuration, resource planning, debugging, and production readiness. That gives learners a more realistic picture of what Azure development work actually looks like. The Core azd Workflow The official overview on Microsoft Learn and the get started guide both reinforce a simple but important idea: most beginners should first understand the standard workflow before worrying about advanced customisation. That workflow usually looks like this: Install azd . Authenticate with Azure. Initialise a project from a template or in an existing repository. Run azd up to provision and deploy. Inspect the deployed application. Remove the resources when finished. Here is a minimal example using an existing template: # Install azd on Windows winget install microsoft.azd # Check that the installation worked azd version # Sign in to your Azure account azd auth login # Start a project from a template azd init --template todo-nodejs-mongo # Provision Azure resources and deploy the app azd up # Show output values such as the deployed URL azd show # Clean up everything when you are done learning azd down --force --purge This sequence is important because it teaches beginners the full lifecycle, not only deployment. A lot of people remember azd up and forget the cleanup step. That leads to wasted resources and avoidable cost. The azd down --force --purge step is part of the discipline, not an optional extra. Installing azd and Verifying Your Setup The official install azd guide on Microsoft Learn provides platform-specific instructions. Because this repository targets developer learning, it is worth showing the common install paths clearly. # Windows winget install microsoft.azd # macOS brew tap azure/azd && brew install azd # Linux curl -fsSL https://aka.ms/install-azd.sh | bash After installation, verify the tool is available: azd version That sounds obvious, but it is worth doing immediately. Many beginner problems come from assuming the install completed correctly, only to discover a path issue or outdated version later. Verifying early saves time. The Microsoft Learn installation page also notes that azd installs supporting tools such as GitHub CLI and Bicep CLI within the tool's own scope. For a beginner, that is helpful because it removes some of the setup friction you might otherwise need to handle manually. What Happens When You Run azd up ? One of the most important questions is what azd up is actually doing. The short answer is that it combines provisioning and deployment into one workflow. The longer answer is where the learning value sits. When you run azd up , the tool looks at the project configuration, reads the infrastructure definition, determines which Azure resources need to exist, provisions them if necessary, and then deploys the application code to those resources. In many templates, it also works with environment settings and output values so that the project becomes reproducible rather than ad hoc. That matters because it teaches a more modern cloud habit. Instead of building infrastructure manually in the portal and then hoping you can remember how you did it, you define the deployment shape in source-controlled files. Even at beginner level, that is the right habit to learn. Understanding the Shape of an azd Project The Azure Developer CLI templates overview explains the standard project structure used by azd . If you understand this structure early, templates become much less mysterious. A typical azd project contains: azure.yaml to describe the project and map services to infrastructure targets. An infra folder containing Bicep or Terraform files for infrastructure as code. A src folder, or equivalent source folders, containing the application code that will be deployed. A local .azure folder to store environment-specific settings for the project. Here is a minimal example of what an azure.yaml file can look like in a simple app: name: beginner-web-app metadata: template: beginner-web-app services: web: project: ./src/web host: appservice This file is small, but it carries an important idea. azd needs a clear mapping between your application code and the Azure service that will host it. Once you see that, the tool becomes easier to reason about. You are not invoking magic. You are describing an application and its hosting model in a standard way. Start from a Template, Then Learn the Architecture Beginners often assume that using a template is somehow less serious than building something from scratch. In practice, it is usually the right place to begin. The official docs for templates and the Awesome AZD gallery both encourage developers to start from an existing architecture when it matches their goals. That is a sound learning strategy for two reasons. First, it lets you experience a working deployment quickly, which builds confidence. Second, it gives you a concrete project to inspect. You can look at azure.yaml , explore the infra folder, inspect the app source, and understand how the pieces connect. That teaches more than reading a command reference in isolation. The AZD for Beginners material also leans into this approach. It includes chapter guidance, templates, workshops, examples, and structured progression so that readers move from successful execution into understanding. That is much more useful than a single command demo. A practical beginner workflow looks like this: # Pick a known template azd init --template todo-nodejs-mongo # Review the files that were created or cloned # - azure.yaml # - infra/ # - src/ # Deploy it azd up # Open the deployed app details azd show Once that works, do not immediately jump to a different template. Spend time understanding what was deployed and why. Where AZD for Beginners Fits In The official docs are excellent for accurate command guidance and conceptual documentation. The AZD for Beginners repository adds something different: a curated learning path. It helps beginners answer questions such as these: Which chapter should I start with if I know Azure a little but not azd ? How do I move from a first deployment into understanding configuration and authentication? What changes when the application becomes an AI application rather than a simple web app? How do I troubleshoot failures instead of copying commands blindly? The repository also points learners towards workshops, examples, a command cheat sheet, FAQ material, and chapter-based exercises. That makes it particularly useful in teaching contexts. A lecturer or workshop facilitator can use it as a course backbone, while an individual learner can work through it as a self-study track. For developers interested in AI, the resource is especially timely because it shows how the same azd workflow can be used for AI-first solutions, including scenarios connected to Microsoft Foundry services and multi-agent architectures. The important beginner lesson is that the workflow stays recognisable even as the application becomes more advanced. Common Beginner Mistakes and How to Avoid Them A good introduction should not only explain the happy path. It should also point out the places where beginners usually get stuck. Skipping authentication checks. If azd auth login has not completed properly, later commands will fail in ways that are harder to interpret. Not verifying the installation. Run azd version immediately after install so you know the tool is available. Treating templates as black boxes. Always inspect azure.yaml and the infra folder so you understand what the project intends to provision. Forgetting cleanup. Learning environments cost money if you leave them running. Use azd down --force --purge when you are finished experimenting. Trying to customise too early. First get a known template working exactly as designed. Then change one thing at a time. If you do hit problems, the official troubleshooting documentation and the troubleshooting sections inside AZD for Beginners are the right next step. That is a much better habit than searching randomly for partial command snippets. How I Would Approach AZD as a New Learner If I were introducing azd to a student or a developer who is comfortable with code but new to Azure delivery, I would keep the learning path tight. Read the official What is Azure Developer CLI? overview so the purpose is clear. Install the tool using the Microsoft Learn install guide. Work through the opening sections of AZD for Beginners. Deploy one template with azd init and azd up . Inspect azure.yaml and the infrastructure files before making any changes. Run azd down --force --purge so the lifecycle becomes a habit. Only then move on to AI templates, configuration changes, or custom project conversion. That sequence keeps the cognitive load manageable. It gives you one successful deployment, one architecture to inspect, and one repeatable workflow to internalise before adding more complexity. Why azd Is Worth Learning Now azd matters because it reflects how modern Azure application delivery is actually done: repeatable infrastructure, source-controlled configuration, environment-aware workflows, and application-level thinking rather than isolated portal clicks. It is useful for straightforward web applications, but it becomes even more valuable as systems gain more services, more configuration, and more deployment complexity. That is also why the AZD for Beginners resource is worth recommending. It gives new learners a structured route into the tool instead of leaving them to piece together disconnected docs, samples, and videos on their own. Used alongside the official Microsoft Learn documentation, it gives you both accuracy and progression. Key Takeaways azd is an application-focused Azure deployment tool, not just another general-purpose CLI. The core beginner workflow is simple: install, authenticate, initialise, deploy, inspect, and clean up. Templates are not a shortcut to avoid learning. They are a practical way to learn architecture through working examples. AZD for Beginners is valuable because it turns the tool into a structured learning path. The official Microsoft Learn documentation for Azure Developer CLI should remain your grounding source for commands and platform guidance. Next Steps If you want to keep going, start with these resources: AZD for Beginners for the structured course, examples, and workshop materials. Azure Developer CLI documentation on Microsoft Learn for official command, workflow, and reference guidance. Install azd if you have not set up the tool yet. Deploy an azd template for the first full quickstart. Azure Developer CLI templates overview if you want to understand the project structure and template model. Awesome AZD if you want to browse starter architectures. If you are teaching others, this is also a good sequence for a workshop: start with the official overview, deploy one template, inspect the project structure, and then use AZD for Beginners as the path for deeper learning. That gives learners both an early win and a solid conceptual foundation.Announcing the IQ Series: Foundry IQ
AI agents are rapidly becoming a new way to build applications. But for agents to be truly useful, they need access to the knowledge and context that helps them reason about the world they operate in. That’s where Foundry IQ comes in. Today we’re announcing the IQ Series: Foundry IQ, a new set of developer-focused episodes exploring how to build knowledge-centric AI systems using Foundry IQ. The series focuses on the core ideas behind how modern AI systems work with knowledge, how they retrieve information, reason across sources, synthesize answers, and orchestrate multi-step interactions. Instead of treating retrieval as a single step in a pipeline, Foundry IQ approaches knowledge as something that AI systems actively work with throughout the reasoning process. The IQ Series breaks down these concepts and shows how they come together when building real AI applications. You can explore the series and all the accompanying samples here: 👉 https://aka.ms/iq-series What is Foundry IQ? Foundry IQ helps AI systems work with knowledge in a more structured and intentional way. Rather than wiring retrieval logic directly into every application, developers can define knowledge bases that connect to documents, data sources, and other information systems. AI agents can then query these knowledge bases to gather the context they need to generate responses, make decisions, or complete tasks. This model allows knowledge to be organized, reused, and combined across applications, instead of being rebuilt for each new scenario. What's covered in the IQ Series? The Foundry IQ episodes in the IQ Series explore the key building blocks behind knowledge-driven AI systems from how knowledge enters the system to how agents ultimately query and use it. The series is released as three weekly episodes: Foundry IQ: Unlocking Knowledge for Your Agents — March 18, 2026: Introduces Foundry IQ and the core ideas behind it. The episode explains how AI agents work with knowledge and walks through the main components of the Foundry IQ that support knowledge-driven applications. Foundry IQ: Building the Data Pipeline with Knowledge Sources — March 25, 2026: Focuses on Knowledge Sources and how different types of content flow into Foundry IQ. It explores how systems such as SharePoint, Fabric, OneLake, Azure Blob Storage, Azure AI Search, and the web contribute information that AI systems can later retrieve and use. Foundry IQ: Querying the Multi-Source AI Knowledge Bases — April 1, 2026: Dives into the Knowledge Bases and how multiple knowledge sources can be organized behind a single endpoint. The episode demonstrates how AI systems query across these sources and synthesize information to answer complex questions. Each episode includes a short executive introduction, a tech talk exploring the topic in depth, and a visual recap with doodle summaries of the key ideas. Alongside the episodes, the GitHub repository provides cookbooks with sample code, summary of the episodes, and additinal learning resources, so developers can explore the concepts and apply them in their own projects. Explore the Repo All episodes and supporting materials live in the IQ Series repository: 👉 https://aka.ms/iq-series Inside the repository you’ll find: The Foundry IQ episode links Cookbooks for each episode Links to documentation and additional resources If you're building AI agents or exploring how AI systems can work with knowledge, the IQ Series is a great place to start. Watch the episodes and explore the cookbooks! We’re excited to see what you build and welcome your feedback & ideas as the series evolves.Agents League: Two Weeks, Three Tracks, One Challenge
We're inviting all developers to join Agents League, running February 16-27. It's a two-week challenge where you'll build AI agents using production-ready tools, learn from live coding sessions, and get feedback directly from Microsoft product teams. We've put together starter kits for each track to help you get up and running quickly that also includes requirements and guidelines. Whether you want to explore what GitHub Copilot can do beyond autocomplete, build reasoning agents on Microsoft Foundry, or create enterprise integrations for Microsoft 365 Copilot, we have a track for you. Important: Register first to be eligible for prizes and your digital badge. Without registration, you won't qualify for awards or receive a badge when you submit. What Is Agents League? It's a 2-week competition that combines learning with building: 📽️ Live coding battles – Watch Product teams, MVPs and community members tackle challenges in real-time on Microsoft Reactor 💻 Async challenges – Build at your own pace, on your schedule 💬 Discord community – Connect with other participants, join AMAs, and get help when you need it 🏆 Prizes – $500 per track winner, plus GitHub Copilot Pro subscriptions for top picks The Three Tracks 🎨 Creative Apps — Build with GitHub Copilot (Chat, CLI, or SDK) 🧠 Reasoning Agents — Build with Microsoft Foundry 💼 Enterprise Agents — Build with M365 Agents Toolkit (or Copilot Studio) More details on each track below, or jump straight to the starter kits. The Schedule Agents League starts on February 16th and runs through Feburary 27th. Within 2 weeks, we host live battles on Reactor and AMA sessions on Discord. Week 1: Live Battles (Feb 17-19) We're kicking off with live coding battles streamed on Microsoft Reactor. Watch experienced developers compete in real-time, explaining their approach and architectural decisions as they go. Tue Feb 17, 9 AM PT — 🎨 Creative Apps battle Wed Feb 18, 9 AM PT — 🧠 Reasoning Agents battle Thu Feb 19, 9 AM PT — 💼 Enterprise Agents battle All sessions are recorded, so you can watch on your own schedule. Week 2: Build + AMAs (Feb 24-26) This is your time to build and ask questions on Discord. The async format means you work when it suits you, evenings, weekends, whatever fits your schedule. We're also hosting AMAs on Discord where you can ask questions directly to Microsoft experts and product teams: Tue Feb 24, 9 AM PT — 🎨 Creative Apps AMA Wed Feb 25, 9 AM PT — 🧠 Reasoning Agents AMA Thu Feb 26, 9 AM PT — 💼 Enterprise Agents AMA Bring your questions, get help when you're stuck, and share what you're building with the community. Pick Your Track We've created a starter kit for each track with setup guides, project ideas, and example scenarios to help you get started quickly. 🎨 Creative Apps Tool: GitHub Copilot (Chat, CLI, or SDK) Build innovative, imaginative applications that showcase the potential of AI-assisted development. All application types are welcome, web apps, CLI tools, games, mobile apps, desktop applications, and more. The starter kit walks you through GitHub Copilot's different modes and provides prompting tips to get the best results. View the Creative Apps starter kit. 🧠 Reasoning Agents Tool: Microsoft Foundry (UI or SDK) and/or Microsoft Agent Framework Build a multi-agent system that leverages advanced reasoning capabilities to solve complex problems. This track focuses on agents that can plan, reason through multi-step problems, and collaborate. The starter kit includes architecture patterns, reasoning strategies (planner-executor, critic/verifier, self-reflection), and integration guides for tools and MCP servers. View the Reasoning Agents starter kit. 💼 Enterprise Agents Tool: M365 Agents Toolkit or Copilot Studio Create intelligent agents that extend Microsoft 365 Copilot to address real-world enterprise scenarios. Your agent must work on Microsoft 365 Copilot Chat. Bonus points for: MCP server integration, OAuth security, Adaptive Cards UI, connected agents (multi-agent architecture). View the Enterprise Agents starter kit. Prizes & Recognition To be eligible for prizes and your digital badge, you must register before submitting your project. Category Winners ($500 each): 🎨 Creative Apps winner 🧠 Reasoning Agents winner 💼 Enterprise Agents winner GitHub Copilot Pro subscriptions: Community Favorite (voted by participants on Discord) Product Team Picks (selected by Microsoft product teams) Everyone who registers and submits a project wins: A digital badge to showcase their participation. Beyond the prizes, every participant gets feedback from the teams who built these tools, a valuable opportunity to learn and improve your approach to AI agent development. How to Get Started Register first — This is required to be eligible for prizes and to receive your digital badge. Without registration, your submission won't qualify for awards or a badge. Pick a track — Choose one track. Explore the starter kits to help you decide. Watch the battles — See how experienced developers approach these challenges. Great for learning even if you're still deciding whether to compete. Build your project — You have until Feb 27. Work on your own schedule. Submit via GitHub — Open an issue using the project submission template. Join us on Discord — Get help, share your progress, and vote for your favorite projects on Discord. Links Register: https://aka.ms/agentsleague/register Starter Kits: https://github.com/microsoft/agentsleague/starter-kits Discord: https://aka.ms/agentsleague/discord Live Battles: https://aka.ms/agentsleague/battles Submit Project: Project submission templateRethinking Documentation Translation: Treating Translations as Versioned Software Assets
Rethinking Documentation Translation: Treating Translations as Versioned Software Assets This article is written from the perspective of maintaining large, open-source documentation repositories in the Microsoft ecosystem. I am the maintainer of Co-op Translator, an open-source tool for automating multilingual documentation translation, used across multiple large documentation repositories, including Microsoft’s For Beginners series. In large documentation repositories, translation problems rarely fail loudly. They fail quietly, and they accumulate over time. Recently, we made a fundamental design decision in how Co-op Translator handles translations. Translations are treated as versioned software assets, not static outputs. This article explains why we reached that conclusion, and what this perspective enables for teams maintaining large, fast-moving documentation repositories. When translations quietly become a liability In most documentation projects, translations are treated as finished outputs. Once a file is translated, it is assumed to remain valid until someone explicitly notices a problem. But documentation rarely stands still. Text changes. Code examples evolve. Screenshots are replaced. Notebooks are updated to reflect new behavior. The problem is that these changes are often invisible in translated content. A translation may still read fluently, while the information it contains is already out of date. At that point, the issue is no longer about translation quality. It becomes a maintenance problem. Reframing the question Most translation workflows implicitly ask: Is this translation correct? In practice, maintainers struggle with a different question: Is this translation still synchronized with the current source? This distinction matters. A translation can be correct and still be out of sync. Once we acknowledged this, it became clear that treating translations as static content was no longer sufficient. The design decision: translations as versioned assets Starting with Co-op Translator 0.16.2, we made a deliberate design decision: Translations are treated as versioned software assets. This applies not only to Markdown files, but also to images, notebooks, and any other translated artifacts. Translated content is not just text. It is an artifact generated from a specific version of a source. To make this abstraction operational rather than theoretical, we did not invent a new mechanism. Instead, we looked to systems that already solve a similar problem: pip, poetry, and npm. These tools are designed to track artifacts as their sources evolve. We applied the same thinking to translated content. Closer to dependency management than translation jobs The closest analogy is software dependency management. When a dependency becomes outdated: it is not suddenly “wrong,” it is simply no longer aligned with the current version. Translations behave the same way. When the source document changes: the translated file does not immediately become incorrect, it becomes out of sync with its source version. This framing shifts the problem away from translation output and toward state and synchronization. Why file-level versioning matters Many translation systems operate at the string or segment level. That model works well for UI text and relatively stable resources. Documentation is different. A Markdown file is an artifact. A screenshot is an artifact. A notebook is an artifact. They are consumed as units, not as isolated strings. Managing translation state at the file level allows maintainers to reason about translations using the same mental model they already apply to other repository assets. What changed in practice From embedded markers to explicit state Previously, translation metadata lived inside translated files as embedded comments or markers. This approach had clear limitations: translation state was fragmented, difficult to inspect globally, and easy to miss as repositories grew. We moved to language-scoped JSON state files that explicitly track: the source version, the translated artifact, and its synchronization status. Translation state is no longer hidden inside content. It is a first-class, inspectable part of the repository. Extending the model to images and notebooks The same model now applies consistently to: translated images, localized notebooks, and other non-text artifacts. If an image changes in the source language, the translated image becomes out of sync. If a notebook is updated, its translated versions are evaluated against the new source version. The format does not matter. The lifecycle does. Once translations are treated as versioned assets, the system remains consistent across all content types. What this enables This design enables: Explicit drift detection See which translations are out of sync without guessing. Consistent maintenance signals Text, images, and notebooks follow the same rules. Clear responsibility boundaries The system reports state. Humans decide action. Scalability for fast-moving repositories Translation maintenance becomes observable, not reactive. In large documentation sets, this difference determines whether translation maintenance is sustainable at all. What this is not This system does not: judge translation quality, determine semantic correctness, or auto-approve content. It answers one question only: Is this translated artifact synchronized with its source version? Who this is for This approach is designed for teams that: maintain multilingual documentation, update content frequently, and need confidence in what is actually up to date. When documentation evolves faster than translations, treating translations as versioned assets becomes a necessity, not an optimization. Closing thought Once translations are modeled as software assets, long-standing ambiguities disappear. State becomes visible. Maintenance becomes manageable. And translations fit naturally into existing software workflows. At that point, the question is no longer whether translation drift exists, but: Can you see it? Reference Co-op Translator repository https://github.com/Azure/co-op-translatorLeverage AI for faster, more productive coding with GitHub Copilot
GitHub Copilot serves as an invaluable learning tool for developers, especially those who are still learning a particular programming language or framework. By providing context-aware suggestions, it helps learners understand the syntax, structure, and logic behind different code snippets. While fundamental knowledge of programming concepts and syntax services should remain a prerequisite, a developer exploring a new framework would be able to describe the functionality they want to implement, and GitHub Copilot will generate code that demonstrates how to achieve it. This accelerates the learning process and empowers developers to gain proficiency in new technologies more efficiently.AI Dev Days 2025: Your Gateway to the Future of AI Development
What’s in Store? Day 1 – 10 December: Video Link Building AI Applications with Azure, GitHub, and Foundry Explore cutting-edge topics like: Agentic DevOps Azure SRE Agent Microsoft Foundry MCP Models for AI innovation Day 2 – 11 December Agenda: Video Link Using AI to Boost Developer Productivity Get hands-on with: Agent HQ VS Code & Visual Studio 2026 GitHub Copilot Coding Agent App Modernisation Strategies Why Join? Hands-on Labs: Apply the latest product features immediately. Highlights from Microsoft Ignite & GitHub Universe 2025: Stay ahead of the curve. Global Reach: Local-language workshops for LATAM and EMEA coming soon. You’ll recognise plenty of familiar faces in the lineup – don’t miss the chance to connect and learn from the best! 👉 Register now and share widely across your networks – there’s truly something for everyone! https://aka.ms/ai-dev-days
Amazing opportunity provided by MICROSOFT
Hi everyone recently I had found an amazing opportunity provided by Microsoft to the learn which most of us don't know please share this opportunity in your connection so that they also get benefited. https://learn.microsoft.com/en-us/training/browse/?resource_type=course&wt.mc_id=studentamb_478312 Thank youAMA Spotlight: Build Smarter with Azure Developer CLI 'AZD'
Weekly AMA 'Ask Me Anything': Build Smarter with Azure Developer CLI Calling all AI engineers, developers, and builders of the future, this is your backstage pass to the tools shaping scalable, agentic AI deployments. Join Kristen Womack, Product Manager for the Azure Developer CLI (azd) Developer CLI (azd), and the engineering team behind azd for a live Ask Me Anything session every Thursday at 12:30pm PT in the Azure AI Foundry Discord. Whether you're: 🧠 Orchestrating multi-agent systems 📦 Deploying LLM-powered apps with Azure AI Foundry 🔐 Navigating least-privilege infrastructure setups 🛠️ Debugging and optimizing reproducible workflows …this AMA is your chance to connect directly with the team building the CLI that powers it all. 💡 Why Join? Real-time answers from the azd engineers and product team Deployment walkthroughs for Foundry templates, from chatbots to document processors Tips for CI/CD, debugging, and reproducibility in enterprise environments Community-first mindset: bring your feedback, challenges, and ideas Kristen Womack brings deep insight into developer experience and product strategy; this is a rare opportunity to learn from the source and shape the future of AI tooling. 🔧 Get Ready Before you join: Install azd 👉 Install Guide Explore Kristen’s work 👉 www.kristenwomack.io Join the Discord 👉 Azure AI Foundry Community 🗓️ Weekly Schedule 🕧 Thursdays at 12:30pm PT 📍 Azure AI Foundry Discord Bring your questions. Bring your curiosity. Build with the best. Additional resources: check out the AZD for Beginners course https://aka.ms/azd-for-beginnersEssential Microsoft Resources for MVPs & the Tech Community from the AI Tour
Unlock the power of Microsoft AI with redeliverable technical presentations, hands-on workshops, and open-source curriculum from the Microsoft AI Tour! Whether you’re a Microsoft MVP, Developer, or IT Professional, these expertly crafted resources empower you to teach, train, and lead AI adoption in your community. Explore top breakout sessions covering GitHub Copilot, Azure AI, Generative AI, and security best practices—designed to simplify AI integration and accelerate digital transformation. Dive into interactive workshops that provide real-world applications of AI technologies. Take it a step further with Microsoft’s Open-Source AI Curriculum, offering beginner-friendly courses on AI, Machine Learning, Data Science, Cybersecurity, and GitHub Copilot—perfect for upskilling teams and fostering innovation. Don’t just learn—lead. Access these resources, host impactful training sessions, and drive AI adoption in your organization. Start sharing today! Explore now: Microsoft AI Tour Resources.
