Say 👋 to GPT-5. You can use OpenAI’s new model in VS Code to write, test and deploy code with GitHub Copilot - and develop agents using the Azure AI Foundry extension all within your editor.
Microsoft Developer
Software Development
Redmond, WA 368,614 followers
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The Microsoft Developers community is a resource hub built for passionate developers. Follow along for tips, tricks, research and more to help you fuel innovation and build apps that users love.
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https://developer.microsoft.com/en-us/
External link for Microsoft Developer
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They’ve helped shape the digital age — inventing such transformational technology as RSS feeds, Google Docs, Kubernetes and Python. Now these pioneering technologists have chosen Microsoft as the place to solve hard problems and build what’s next. See what they’re up to: https://msft.it/6041sFEs5
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In our third Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation. RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain. The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases. In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search. 📌 This session is a part of a series. Learn more here: https://aka.ms/PythonAI/2 #MicrosoftReactor #learnconnectbuild
Python + AI: Retrieval Augmented Generation
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In the final session of our Python + AI series, we're diving into the hottest technology of 2025: MCP, Model Context Protocol. This open protocol makes it easy to extend AI agents and chatbots with custom functionality, to make them more powerful and flexible. We'll show how to use the official Python FastMCP SDK to build an MCP server running locally and consume that server from chatbots like GitHub Copilot. Then we'll build our own MCP client to consume the server. Finally, we'll discover how easy it is to point popular AI agent frameworks like Langgraph, Pydantic AI, and Semantic Kernel at MCP servers. With great power comes great responsibility, so we will briefly discuss the many security risks that come with MCP, both as a user and developer. 📌 This session is a part of a series. Learn more here: https://aka.ms/PythonAI/2 #MicrosoftReactor #learnconnectbuild
Python + AI: Model Context Protocol
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For the penultimate session of our Python + AI series, we're building AI agents! We'll use many of the most popular Python AI agent frameworks: Langgraph, Semantic Kernel, Autogen, Pydantic AI, and more. Our agents will start simple and then ramp up in complexity, demonstrating different architectures like hand-offs, round-robin, supervisor, graphs, and ReAct. If you'd like to follow along with the live examples, make sure you've got a GitHub account. 📌 This session is a part of a series. Learn more here: https://aka.ms/PythonAI/2 #MicrosoftReactor #learnconnectbuild
Python + AI: AI agents
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For the final week of our Python + AI series, we're focusing on the technologies needed to build AI agents, starting with the foundation: tool calling (also known as function calling). We will define our tool call definitions using both JSON schema and Python function definitions, and send those tool definitions to the LLM. We will discover how to properly handle tool call responses from LLMs, enable "parallel" tool calling, and iterate over multiple tool calls. It is absolutely essential to understand tool calling before diving into agents, so do not miss this foundational session. If you'd like to follow along with the live examples, make sure you've got a GitHub account. 📌 This session is a part of a series. Learn more here: https://aka.ms/PythonAI/2 #MicrosoftReactor #learnconnectbuild
Python + AI: Tool calling
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Now that we're more than halfway through our Python + AI series, we're covering a crucial topic: how to use AI safely, and how to evaluate the quality of AI outputs. There are multiple mitigation layers when working with LLMs: the model itself, a safety system on top, the prompting and context, and the application user experience. Our focus will be on Azure tools that make it easier to put safe AI systems into production. We'll show how to configure the Azure AI Content Safety system when working with Azure AI models, and how to handle those errors in Python code. Then we'll use the Azure AI Evaluation SDK to evaluate the safety and quality of the output from our LLM. 📌 This session is a part of a series. Learn more here: https://aka.ms/PythonAI/2 #MicrosoftReactor #learnconnectbuild
Python + AI: Quality and safety
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In our fifth stream of the Python + AI series, we'll discover how to get LLMs to output structured responses that adhere to a schema. In Python, all we need to do is define a @dataclass or a Pydantic BaseModel, and we get validated output that meets our needs perfectly. We'll focus on the structured outputs mode available in OpenAI models, but you can use similar techniques with other model providers. Our examples will demonstrate the many ways you can use structured responses, like entity extraction, classification, and agentic workflows. If you'd like to follow along with the live examples, make sure you've got a GitHub account. 📌 This session is a part of a series. Learn more here: https://aka.ms/PythonAI/2 #MicrosoftReactor #learnconnectbuild
Python + AI: Structured outputs
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Our third stream in the Python + AI series is all about vision models! Vision models are LLMs that can accept both text and images, like GPT 4o and 4o-mini. You can use those models for image captioning, data extraction, question-answering, classification, and more! We'll use Python to send images to vision models, build a basic chat-on-images app, and build a multimodal search engine. If you'd like to follow along with the live examples, make sure you've got a GitHub account. 📌 This session is a part of a series. Learn more here: https://aka.ms/PythonAI/2 #MicrosoftReactor #learnconnectbuild
Python + AI: Vision models
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Your guide to GitHub Universe 2025 👇
Your GitHub Universe 2025 agenda is ready! ✨ The full schedule is now live. Check out what's planned and build your personal calendar. 🗓️ Explore the sessions.👇 https://lnkd.in/gx9qYUEi