JS AI Build‑a‑thon: Wrapping Up an Epic June 2025!
After weeks of building, testing, and learning — we’re officially wrapping up the first-ever JS AI Build-a-thon 🎉. This wasn't your average coding challenge. This was a hands-on journey where JavaScript and TypeScript developers dove deep into real-world AI concepts — from local GenAI prototyping to building intelligent agents and deploying production-ready apps. Whether you joined from the start or hopped on midway, you built something that matters — and that’s worth celebrating. Replay the Journey No worries if you joined late or want to revisit any part of the journey. The JS AI Build-a-thon was designed to let you learn at your own pace, so whether you're starting now or polishing up your final project, here’s your complete quest map: Build-a-thon set up guide: https://aka.ms/JSAIBuildathonSetup Quest 1: 🔧 Build your first GenAI app locally with GitHub Models 👉🏽 https://aka.ms/JSAIBuildathonQuest1 Quest 2: ☁️ Move your AI prototype to Azure AI Foundry 👉🏽 https://aka.ms/JSAIBuildathonQuest Quest 3: 🎨 Add a chat UI using Vite + Lit 👉🏽 https://aka.ms/JSAIBuildathonQuest3 Quest 4: 📄 Enhance your app with RAG (Chat with Your Data) 👉🏽 https://aka.ms/JSAIBuildathonQuest4 Quest 5: 🧠 Add memory and context to your AI app 👉🏽 https://aka.ms/JSAIBuildathonQuest5 Quest 6: ⚙️ Build your first AI Agent using AI Foundry 👉🏽 https://aka.ms/JSAIBuildathonQuest6 Quest 7: 🧩 Equip your agent with tools from an MCP server 👉🏽 https://aka.ms/JSAIBuildathonQuest7 Quest 8: 💬 Ground your agent with real-time search using Bing 👉🏽 https://aka.ms/JSAIBuildathonQuest8 Quest 9: 🚀 Build a real-world AI project with full-stack templates 👉🏽 https://aka.ms/JSAIBuildathonQuest9 Link to our space in the AI Discord Community: https://aka.ms/JSAIonDiscord Project Submission Guidelines 📌 Quest 9 is where it all comes together. Participants chose a problem, picked a template, customized it, submitted it, and rallied their community for support! 🏅 Claim Your Badge! Whether you completed select quests or went all the way, we celebrate your learning. If you participated in the June 2025 JS AI Build-a-thon, make sure to Submit the Participation Form to receive your participation badge recognizing your commitment to upskilling in AI with JavaScript/ TypeScript. What’s Next? We’re not done. In fact, we’re just getting started. We’re already cooking up JS AI Build-a-thon v2, which will introduce: Running everything locally with Foundry Local Real-world RAG with vector databases Advanced agent patterns with remote MCPs And much more based on your feedback Want to shape what comes next? Drop your ideas in the participation form and in our Discord. In the meantime, add these resources to your JavaScript + AI Dev Pack: 🔗 Microsoft for JavaScript developers 📚 Generative AI for Beginners with JavaScript Wrap-Up This build-a-thon showed what’s possible when developers are empowered to learn by doing. You didn’t just follow tutorials — you shipped features, connected services, and created working AI experiences. We can’t wait to see what you build next. 👉 Bookmark the repo 👉 Join the community on Join the Azure AI Foundry Discord Server! 👉 Stay building Until next time — keep coding, keep shipping!Quest 5 - I want to add conversation memory to my app
In this quest, you’ll explore how to build GenAI apps using a modern JavaScript AI framework, LangChain.js. LangChain.js helps you orchestrate prompts, manage memory, and build multi-step AI workflows all while staying in your favorite language. Using LangChain.js you will make your GenAI chat app feel truly personal by teaching it to remember. In this quest, you’ll upgrade your AI prototype with conversation memory, allowing it to recall previous interactions making the conversation flow more naturally and human-like. 👉 Want to catch up on the full program or grab more quests? https://aka.ms/JSAIBuildathon 💬 Got questions or want to hang with other builders? Join us on Discord — head to the #js-ai-build-a-thon channel. 🔧 What You’ll Build A smarter, context-aware chat backend that: Remembers user conversations across multiple exchanges (e.g., knowing "Terry" after you introduced yourself as Terry) Maintains session-specific memory so each chat thread feels consistent and coherent Uses LangChain.js abstractions to streamline state management. 🚀 What You’ll Need ✅ A GitHub account ✅ Visual Studio Code ✅ Node.js ✅ A working chat app from previous quests (UI + Azure-based chat endpoint) 🛠️ Concepts You’ll Explore Integrating LangChain.js Learn how LangChain.js simplifies building AI-powered web applications by providing a standard interface to connect your backend with Azure’s language models. You’ll see how using this framework decouples your code and unlocks advanced features. Adding Conversation Memory Understand why memory matters in chatbots. Explore how conversation memory lets your app remember previous user messages within each session enabling more context-aware and coherent conversations. Session-based Message History Implement session-specific chat histories using LangChain’s memory modules (ChatMessageHistory and BufferMemory). Each user or session gets its own history, so previous questions and answers inform future responses without manual log management. Seamless State Management Experience how LangChain handles chat logs and memory behind the scenes, freeing you from manually stitching together chat history or juggling context with every prompt. 📖 Bonus Resources to Go Deeper Exploring Generative AI in App Development: LangChain.js and Azure: a video introduction to LangChain.js and how you can build a project with LangChain.js and Azure 🦜️🔗 Langchain: the official LangChain.js documentation. GitHub - Azure-Samples/serverless-chat-langchainjs: Build your own serverless AI Chat with Retrieval-Augmented-Generation using LangChain.js, TypeScript and Azure: A GitHub sample that helps you build your own serverless AI Chat with Retrieval-Augmented-Generation using LangChain.js, TypeScript and Azure GitHub - Azure-Samples/langchainjs-quickstart-demo: Build a generative AI application using LangChain.js, from local to Azure: A GitHub sample that helps you build a generative AI application using LangChain.js, from local to Azure. Microsoft | 🦜️🔗 Langchain Official LangChain documentation on all functionalities related to Microsoft and Microsoft Azure. Quest 4 - I want to connect my AI prototype to external data using RAG | Microsoft Community Hub a link to the previous quest instructions.Quest 9: I want to use a ready-made template
Building robust, scalable AI apps is tough, especially when you want to move fast, follow best practices, and avoid being bogged down by endless setup and configuration. In this quest, you’ll discover how to accelerate your journey from prototype to production by leveraging ready-made templates and modern cloud tools. Say goodbye to decision fatigue and hello to streamlined, industry-approved workflows you can make your own. 👉 Want to catch up on the full program or grab more quests? https://aka.ms/JSAIBuildathon 💬 Got questions or want to hang with other builders? Join us on Discord — head to the #js-ai-build-a-thon channel. 🚀 What You’ll Build A fully functional AI application deployed on Azure, customized to solve a real problem that matters to you. A codebase powered by a production-grade template, complete with all the necessary infrastructure-as-code, deployment scripts, and best practices already baked in. Your own proof-of-concept or MVP, ready to scale or show off to the world. 🛠️ What You Need ✅ GitHub account ✅ Visual Studio Code ✅ Node.js ✅ Azure subscription (free trials and student credits available) ✅ Azure Developer CLI (azd) ✅ The curiosity to solve a meaningful problem! 🧩 Concepts You’ll Explore Azure Developer CLI (azd) Learn how azd, the developer-first command-line tool, simplifies authentication, setup, deployment, and teardown for Azure apps. With intuitive commands like azd up and azd deploy, you can go from zero to running in the cloud no deep cloud expertise required. Production-Ready Templates Explore a gallery of customizable templates designed to get your app up and running fast. These templates aren’t just “hello world” they feature scalable architectures, sample code, and reusable infrastructure assets to launch everything from chatbots to RAG apps to full-stack solutions. Infrastructure as Code (IaC) See how every template bundle configuration files and scripts to automatically provision the cloud resources you need. You’ll get a taste of how top teams ship secure, repeatable, and maintainable systems without manually clicking through Azure dashboards. Best Practices by Default Templates incorporate industry best practices for code structure, deployment, and scalability. You’ll spend less time researching how to “do it right” and more time customizing your application to fit your unique use case. Customization for Real-World Problems Pick a template and make it yours! Whether you’re building a copilot, a chat-enabled app, or a serverless API, you’ll learn how to tweak the frontend, swap out backend logic, connect your own data sources, and shape the solution to solve a real-world problem you care about. 🌟 Bonus Resources Here are some additional resources to help you learn more about the Azure Developer CLI (azd) and the templates available: Kickstart JS/TS projects with azd Templates Kickstart your JavaScript projects with azd on YouTube ⏭️ What next? With production-ready templates and the Azure Developer CLI at your side, you’re ready to move from “just an idea” to a deployable, scalable solution without reinventing the wheel. Start with the right foundation, customize with confidence, and ship your next AI app like a pro! Once you have your project done, ensure you submit to GitHub - Azure-Samples/JS-AI-Build-a-thonGitHub Copilot Vibe Coding Workshop
Many of us do the vibe coding these days, and GitHub Copilot (GHCP) takes the key role of the vibe coding. You might simply enter prompts to GHCP like "Build a frontend app for a marketplace of camping gear" or even simpler ones like "Give me an app for camping gear marketplace". This surely works. GHCP delivers an app for you. However, the deliverable might be different from what you initially expected. This happens because GHCP fills in uncertainties with its own imagination unless we provide clear and detailed prompts. Let's recall the basics of product lifecycle management (PLM). You're a product owner or product manager about to launch a new product or develop a new business to sell values to your prospective customers. Where would you start from? Yes, it's the fist step to perform market analysis – whether your idea is feasible or not, whether the market is profitable or not, and so on. Then, based on this analysis, you would generate a product requirements document (PRD). The PRD describes what the product or service should be look like, how it should work, what it should deliver. In addition to that, the doc should also contain user stories and acceptance criteria. The user stories define what the app should expect, how it should behave, and what it should return. The acceptance criteria defines how you test the app to accept as a final deliverable. So, is a PRD is important for vibe coding? YES, IT IS! As stated earlier, GHCP tries really hard to fill some missing parts with its full of imagination. Therefore, the more context you provide to GHCP, the better GHCP works more accurately. That's how you get more accurate results from the vibe coding. But how do you actually practise this type of vibe coding? Introducing GitHub Copilot Vibe Coding Workshop I'm more than happy to introduce this GitHub Copilot Vibe Coding Workshop, a resource available for everyone to use. It's based on a typical app development scenario – building a web application that consists of a frontend UI and backend API with database transaction. This workshop has six steps: Analyse a PRD and generate an OpenAPI document from it. Build a FastAPI app in Python based on the OpenAPI doc. Build a React app in JavaScript based on the OpenAPI doc. Migrate the FastAPI app to Spring Boot app in Java. Migrate the React app to Blazor app in .NET. Containerise both the Spring app and the Blazor app, and orchestrate them. This workshop is self-paced so you can complete it in your spare time. It's also designed to run on GitHub Codespaces, since not everyone has all the required development environment set up locally. Throughout this workshop, you'll learn: How to activate GHCP Agent Mode on VS Code, How to customise your GHCP to get the better result, and How to integrate MCP servers for vibe coding. Do you prefer a language other than English? No problem! This workshop provides materials in seven different languages including English, Chinese (Simplified), French, Japanese, Korean, Portuguese and Spanish so you can choose your preferred language to complete the workshop. It's your time for vibe coding! Now it's your turn to try this GitHub Copilot Vibe Coding Workshop on your own, or together with your friends and colleagues. If you have any questions about this workshop, please create an issue in the repository! Want to know more about GitHub Copilot? GitHub Copilot in VS Code GitHub Copilot Agent Mode GitHub Copilot Customisation MCP Server Support in VS CodeCreate Stunning AI Videos with Sora on Azure AI Foundry!
Special credit to Rory Preddy for creating the GitHub resource that enable us to learn more about Azure Sora. Reach him out on LinkedIn to say thanks. Introduction Artificial Intelligence (AI) is revolutionizing content creation, and video generation is at the forefront of this transformation. OpenAI's Sora, a groundbreaking text-to-video model, allows creators to generate high-quality videos from simple text prompts. When paired with the powerful infrastructure of Azure AI Foundry, you can harness Sora's capabilities with scalability and efficiency, whether on a local machine or a remote setup. In this blog post, I’ll walk you through the process of generating AI videos using Sora on Azure AI Foundry. We’ll cover the setup for both local and remote environments. Requirements: Azure AI Foundry with sora model access A Linux Machine/VM. Make sure that the machine already has the package below: Java JRE 17 (Recommended) OR later Maven Step Zero – Deploying the Azure Sora model on AI Foundry Navigate to the Azure AI Foundry portal and head to the “Models + Endpoints” section (found on the left side of the Azure AI Foundry portal) > Click on the “Deploy Model” button > “Deploy base model” > Search for Sora > Click on “Confirm”. Give a deployment name and specify the Deployment type > Click “Deploy” to finalize the configuration. You should receive an API endpoint and Key after successful deploying Sora on Azure AI Foundry. Store these in a safe place because we will be using them in the next steps. Step one – Setting up the Sora Video Generator in the local/remote machine. Clone the roryp/sora repository on your machine by running the command below: git clone https://github.com/roryp/sora.git cd sora Then, edit the application.properties file in the src/main/resources/ folder to include your Azure OpenAI Credentials. Change the configuration below: azure.openai.endpoint=https://your-openai-resource.cognitiveservices.azure.com azure.openai.api-key=your_api_key_here If port 8080 is used for another application, and you want to change the port for which the web app will run, change the “server.port” configuration to include the desired port. Allow appropriate permissions to run the “mvnw” script file. chmod +x mvnw Run the application ./mvnw spring-boot:run Open your browser and type in your localhost/remote host IP (format: [host-ip:port]) in the browser search bar. If you are running a remote host, please do not forget to update your firewall/NSG to allow inbound connection to the configured port. You should see the web app to generate video with Sora AI using the API provided on Azure AI Foundry. Now, let’s generate a video with Sora Video Generator. Enter a prompt in the first text field, choose the video pixel resolution, and set the video duration. (Due to technical limitation, Sora can only generate video of a maximum of 20 seconds). Click on the “Generate video” button to proceed. The cost to generate the video should be displayed below the “Generate Video” button, for transparency purposes. You can click on the “View Breakdown” button to learn more about the cost breakdown. The video should be ready to download after a maximum of 5 minutes. You can check the status of the video by clicking on the “Check Status” button on the web app. The web app will inform you once the download is ready and the page should refresh every 10 seconds to fetch real-time update from Sora. Once it is ready, click on the “Download Video” button to download the video. Conclusion Generating AI videos with Sora on Azure AI Foundry is a game-changer for content creators, marketers, and developers. By following the steps outlined in this guide, you can set up your environment, integrate Sora, and start creating stunning AI-generated videos. Experiment with different prompts, optimize your workflow, and let your imagination run wild! Have you tried generating AI videos with Sora or Azure AI Foundry? Share your experiences or questions in the comments below. Don’t forget to subscribe for more AI and cloud computing tutorials!Build AI-Ready Apps and Agents with PostgreSQL on Azure
As developers, we’re constantly looking for ways to build smarter, faster, and more scalable applications. The Microsoft Reactor series, Build AI apps with Azure Database for PostgreSQL, is a four-part livestream experience designed to help you do just that—by combining the power of PostgreSQL with Azure’s AI capabilities. Dive into the world of AI apps and agents with Azure Database for PostgreSQL in this engaging video series—your ideal starting point for building intelligent solutions and improving your workflow. Get ready to explore the fundamentals of AI and discover how vector support in databases can elevate your applications. Uncover how innovative tools like the Visual Studio Code extension for PostgreSQL and GitHub Copilot can make your database work faster and more efficient. You'll also see how to create intelligent apps and AI agents using frameworks such as LangChain and Semantic Kernel. Why This Series Matters PostgreSQL is already a favorite among developers for its flexibility and open-source strength. But when paired with Azure’s AI services, it becomes a launchpad for intelligent applications. This series walks you through how to: Orchestrate AI agents using PostgreSQL as a foundation. Enhance semantic search with vector support and indexes like DiskANN. Integrate Azure AI services to enrich your data and user experiences. Boost productivity with tools like the Visual Studio Code PostgreSQL extension and GitHub Copilot. What You'll Learn Each session is packed with practical insights: Episode 1: Laying the foundation: AI-powered apps and agents with Azure Database for PostgreSQL We introduce key AI concepts, setting the stage for a deeper understanding of Large Language Models (LLMs) and its applications, we will explore the capabilities of Azure Database for PostgreSQL, focusing on how its vector support enables advanced semantic search through technologies like DiskANN indexes. We'll also discuss the Azure AI extension, which brings powerful AI features to your data projects, helping you enrich your applications with enhanced search relevance and intelligent insights, and provide a solid foundation for leveraging these tools in your own solutions. Register here Episode 2: Accelerate your data and AI tasks with the VS Code extension for PostgreSQL and GitHub Copilot This talk will delve into how the Visual Studio Code extension for PostgreSQL can streamline your database management, while GitHub Copilot's AI-powered assistance can boost your productivity. Learn how to seamlessly integrate these tools to enhance your workflow, automate repetitive tasks, and write efficient code faster. Whether you're a developer, data scientist, or database administrator, this session will provide you with practical insights and techniques to elevate your data and AI projects. Join us to learn how to effectively use these advanced tools and take your data skills to the next level. Register here Episode 3: Build your own AI copilot for financial apps with PostgreSQL Join us to discover how to transform traditional financial applications into intelligent, AI-powered solutions with Azure Database for PostgreSQL. In this hands-on session, you'll learn to integrate generative AI for high-quality responses to financial queries using PDF-based Statements of Work and invoices, perform AI-driven data validation, apply the Azure AI extension, implement vector search with DiskANN indexes, enhance results with semantic re-ranking, use the LangChain framework, and leverage GraphRAG on Azure Database for PostgreSQL. By the end, you’ll have gained practical skills to build end-to-end AI-driven applications using your own data and projects. Register here Episode 4: Build advanced AI Agents with PostgreSQL Using a sample dataset of legal cases, we’ll show how AI technologies empower intelligent agents to provide high-quality answers to legal queries. In this session, you’ll learn to build an advanced AI agent with Azure Database for PostgreSQL, integrating generative AI for enhanced data validation, retrieval-augmented generation (RAG), semantic re-ranking, Semantic Kernel, and GraphRAG via the Apache AGE Graph extension. This practical demonstration offers insights into developing robust, intelligent solutions using your own data. Register here Join us for an inspiring and hands-on experience—don’t miss out! Get the full series details and register now: https://aka.ms/postgres-ai-reactor-seriesNew GitHub Copilot Global Bootcamp: Now with Virtual and In-Person Workshops!
From June 17 to July 10, you can learn from anywhere in the world — online or in your own city! The GitHub Copilot Global Bootcamp started in February as a fully virtual learning journey — and it was a hit. More than 60,000 developers joined the first edition across multiple languages and regions. Now, we're excited to launch the second edition — bigger and better — featuring both virtual and in-person workshops, hosted by tech communities around the globe. This new edition arrives shortly after the announcements at Microsoft Build 2025, where the GitHub and Visual Studio Code teams revealed exciting news: The GitHub Copilot Chat extension is going open source, reinforcing transparency and collaboration. AI is being deeply integrated into Visual Studio Code, now evolving into an open source AI editor. New APIs and tools are making it easier than ever to build with AI and LLMs. This bootcamp is your opportunity to explore these new tools, understand how to use GitHub Copilot effectively, and be part of the growing global conversation about AI in software development.Introducing Azure AI Travel Agents: A Flagship MCP-Powered Sample for AI Travel Solutions
We are excited to introduce AI Travel Agents, a sample application with enterprise functionality that demonstrates how developers can coordinate multiple AI agents (written in multiple languages) to explore travel planning scenarios. It's built with LlamaIndex.TS for agent orchestration, Model Context Protocol (MCP) for structured tool interactions, and Azure Container Apps for scalable deployment. TL;DR: Experience the power of MCP and Azure Container Apps with The AI Travel Agents! Try out live demo locally on your computer for free to see real-time agent collaboration in action. Share your feedback on our community forum. We’re already planning enhancements, like new MCP-integrated agents, enabling secure communication between the AI agents and MCP servers and more. NOTE: This example uses mock data and is intended for demonstration purposes rather than production use. The Challenge: Scaling Personalized Travel Planning Travel agencies grapple with complex tasks: analyzing diverse customer needs, recommending destinations, and crafting itineraries, all while integrating real-time data like trending spots or logistics. Traditional systems falter with latency, scalability, and coordination, leading to delays and frustrated clients. The AI Travel Agents tackles these issues with a technical trifecta: LlamaIndex.TS orchestrates six AI agents for efficient task handling. MCP equips agents with travel-specific data and tools. Azure Container Apps ensures scalable, serverless deployment. This architecture delivers operational efficiency and personalized service at scale, transforming chaos into opportunity. LlamaIndex.TS: Orchestrating AI Agents The heart of The AI Travel Agents is LlamaIndex.TS, a powerful agentic framework that orchestrates multiple AI agents to handle travel planning tasks. Built on a Node.js backend, LlamaIndex.TS manages agent interactions in a seamless and intelligent manner: Task Delegation: The Triage Agent analyzes queries and routes them to specialized agents, like the Itinerary Planning Agent, ensuring efficient workflows. Agent Coordination: LlamaIndex.TS maintains context across interactions, enabling coherent responses for complex queries, such as multi-city trip plans. LLM Integration: Connects to Azure OpenAI, GitHub Models or any local LLM using Foundy Local for advanced AI capabilities. LlamaIndex.TS’s modular design supports extensibility, allowing new agents to be added with ease. LlamaIndex.TS is the conductor, ensuring agents work in sync to deliver accurate, timely results. Its lightweight orchestration minimizes latency, making it ideal for real-time applications. MCP: Fueling Agents with Data and Tools The Model Context Protocol (MCP) empowers AI agents by providing travel-specific data and tools, enhancing their functionality. MCP acts as a data and tool hub: Real-Time Data: Supplies up-to-date travel information, such as trending destinations or seasonal events, via the Web Search Agent using Bing Search. Tool Access: Connects agents to external tools, like the .NET-based customer queries analyzer for sentiment analysis, the Python-based itinerary planning for trip schedules or destination recommendation tools written in Java. For example, when the Destination Recommendation Agent needs current travel trends, MCP delivers via the Web Search Agent. This modularity allows new tools to be integrated seamlessly, future-proofing the platform. MCP’s role is to enrich agent capabilities, leaving orchestration to LlamaIndex.TS. Azure Container Apps: Scalability and Resilience Azure Container Apps powers The AI Travel Agents sample application with a serverless, scalable platform for deploying microservices. It ensures the application handles varying workloads with ease: Dynamic Scaling: Automatically adjusts container instances based on demand, managing booking surges without downtime. Polyglot Microservices: Supports .NET (Customer Query), Python (Itinerary Planning), Java (Destination Recommandation) and Node.js services in isolated containers. Observability: Integrates tracing, metrics, and logging enabling real-time monitoring. Serverless Efficiency: Abstracts infrastructure, reducing costs and accelerating deployment. Azure Container Apps' global infrastructure delivers low-latency performance, critical for travel agencies serving clients worldwide. The AI Agents: A Quick Look While MCP and Azure Container Apps are the stars, they support a team of multiple AI agents that drive the application’s functionality. Built and orchestrated with Llamaindex.TS via MCP, these agents collaborate to handle travel planning tasks: Triage Agent: Directs queries to the right agent, leveraging MCP for task delegation. Customer Query Agent: Analyzes customer needs (emotions, intents), using .NET tools. Destination Recommendation Agent: Suggests tailored destinations, using Java. Itinerary Planning Agent: Crafts efficient itineraries, powered by Python. Web Search Agent: Fetches real-time data via Bing Search. These agents rely on MCP’s real-time communication and Azure Container Apps’ scalability to deliver responsive, accurate results. It's worth noting though this sample application uses mock data for demonstration purpose. In real worl scenario, the application would communicate with an MCP server that is plugged in a real production travel API. Key Features and Benefits The AI Travel Agents offers features that showcase the power of MCP and Azure Container Apps: Real-Time Chat: A responsive Angular UI streams agent responses via MCP’s SSE, ensuring fluid interactions. Modular Tools: MCP enables tools like analyze_customer_query to integrate seamlessly, supporting future additions. Scalable Performance: Azure Container Apps ensures the UI, backend and the MCP servers handle high traffic effortlessly. Transparent Debugging: An accordion UI displays agent reasoning providing backend insights. Benefits: Efficiency: LlamaIndex.TS streamlines operations. Personalization: MCP’s data drives tailored recommendations. Scalability: Azure ensures reliability at scale. Thank You to Our Contributors! The AI Travel Agents wouldn’t exist without the incredible work of our contributors. Their expertise in MCP development, Azure deployment, and AI orchestration brought this project to life. A special shoutout to: Pamela Fox – Leading the developement of the Python MCP server. Aaron Powell and Justin Yoo – Leading the developement of the .NET MCP server. Rory Preddy – Leading the developement of the Java MCP server. Lee Stott and Kinfey Lo – Leading the developement of the Local AI Foundry Anthony Chu and Vyom Nagrani – Leading Azure Container Apps roadmap Matt Soucoup and Julien Dubois – Leading the ACA DevRel strategy Wassim Chegham – Architected MCP and backend orchestration. And many more! See the GitHub repository for all contributors. Thank you for your dedication to pushing the boundaries of AI and cloud technology! Try It Out Experience the power of MCP and Azure Container Apps with The AI Travel Agents! Try out live demo locally on your computer for free to see real-time agent collaboration in action. Conclusion Developers can explore today the open-source project on GitHub, with setup and deployment instructions. Share your feedback on our community forum. We’re already planning enhancements, like new MCP-integrated agents, enabling secure communication between the AI agents and MCP servers and more. This is still a work in progress and we also welcome all kind of contributions. Please fork and star the repo to stay tuned for updates! ◾️We would love your feedback and continue the discussion in the Azure AI Foundry Discord aka.ms/foundry/discord On behalf of Microsoft DevRel Team.Quest 8: I want to automate code reviews
Ever wished your code reviews could be faster, more consistent, and maybe even… automated? In this quest, you’ll build a smart code review system powered by AI, designed to catch issues and share feedback before committing your changes. GenAIScript is a modern JavaScript extension designed for seamless AI integration. GenAIScript lets you automate repetitive tasks, orchestrate prompts, and create multi-step AI workflows, all within your coding environment. By leveraging GenAIScript, you’ll transform your development workflow by adding AI-powered code reviews. 👉 Want to catch up on the full program or grab more quests? https://aka.ms/JSAIBuildathon 💬 Got questions or want to hang with other builders? Join us on Discord — head to the #js-ai-build-a-thon channel. 🔧 What You’ll Build An automated code review agent that analyzes your staged code changes and provides actionable, best-practice feedback right inside VS Code. A custom script (using GenAIScript) that plugs into your development workflow and delivers review comments every time you make a change. 🚀 What You’ll Need ✅ A GitHub account ✅ Visual Studio Code ✅ Node.js ✅ GenAIScript extension for VSCode (installation instructions provided in the quest) ✅ GitHub Models access using PAT (refer to Quest 1 for more details on GitHub Models) 🛠️ Concepts You’ll Explore GenAIScript for AI Automation Discover how GenAIScript extends JavaScript with simple AI scripting, letting you write powerful workflows that connect to AI models without the usual complexity. You’ll see how scripts can automate tasks that once required manual effort or custom bots. Automating Code Reviews with AI Understand how AI can analyze your code changes and provide valuable feedback. Explore how automated reviews help you catch mistakes early, enforce best practices, and maintain consistent code quality across your project. Using GitHub Tokens for Secure Integration Discover how to connect GenAIScript to GitHub’s AI models by configuring a secure personal access token. This unlocks AI features for code analysis and ensures your workflow remains both powerful and protected. 📖 Bonus Resources to Go Deeper GenAIScript sample collection – automation ideas: A curated collection of sample scripts and projects showcasing how to use GenAIScript for AI-powered automation, code reviews, and custom workflows. Generative AI with JavaScript on YouTube: A YouTube series hub for developers exploring how to integrate generative AI with JavaScript. GenAIScript official docs & extension: Extension and documentation for GenAIScript . Quest - I Want to Build a Local Gen AI Prototype: This kickoff quest in the JS AI Build‑a‑thon guides you through building your very first generative AI prototype locally and entirely in JavaScript. Quest 7: Create an AI Agent with Tools from an MCP Server | Microsoft Community Hub : a link to the previous quest.
