Getting Started - Generative AI with Phi-3-mini: Running Phi-3-mini in Intel AI PC
In 2024, with the empowerment of AI, we will enter the era of AI PC. On May 20, Microsoft also released the concept of Copilot + PC, which means that PC can run SLM/LLM more efficiently with the support of NPU. We can use models from different Phi-3 family combined with the new AI PC to build a simple personalized Copilot application for individuals. This content will combine Intel's AI PC, use Intel's OpenVINO, NPU Acceleration Library, and Microsoft's DirectML to create a local Copilot.Exploring Microsoft's Phi-3 Family of Small Language Models (SLMs) with Azure AI
Dive into the world of small language models (SLMs) with Microsoft's Phi-3 family and learn how to integrate them into real-world applications using Azure AI. Discover step-by-step guidance, practical exercises, and a Gradio-powered chatbot interface to bolster your confidence in deploying and integrating AI. Keep learning and building with Azure AIBuilding your own copilot – yes, but how? (Part 1 of 2)
Today, there’s a wide range of built-in services and features designed to enable organizations and developers to build their own copilots, able to answer questions based on their own knowledge bases and data sources. But how to choose the most suitable one for each scenario? This blog post wants to provide an overview of some of the main choices you have in the Microsoft technology ecosystem. Part 1 will look into low-code tools and out-of-the-box features, while part 2 will focus on code-heavy and extensible options.Generative AI for Beginners - Full Videos Series Released!
With so many new technologies, tools and terms in the world of Generative AI, it can be hard to know where to start or what to learn next. "Generative AI for Beginners" is designed to help you on your learning journey no matter where you are now. We are happy announce that the "Generative AI for Beginners" course has received a major refresh - 18 new videos for each lesson.Training a Time-Series Forecasting Model Using Automated Machine Learning
Imagine having the power to predict the unpredictable, to foresee the future of your business, your health, or your environment. What if you could unlock the secrets of time itself? Welcome to the world of time-series forecasting, where machine learning meets magic. Join us to discover how Automated Machine Learning can revolutionize your understanding of the future and uncover the hidden patterns that shape our world. Read on to unlock the secrets of time, and unleash the power of prediction.Master Generative AI with Azure OpenAI Service: A Comprehensive Guide for Students
Dive into the cutting-edge technology of Generative AI with our detailed guide. From understanding the role of large language models to building powerful applications with Azure OpenAI Service, this guide has everything you need. Plus, participate in our Azure OpenAI Cloud Skills Challenge and compete with peers globally while enhancing your technical skills. Start your journey with Generative AI today!Integrating Microsoft Foundry with OpenClaw: Step by Step Model Configuration
Step 1: Deploying Models on Microsoft Foundry Let us kick things off in the Azure portal. To get our OpenClaw agent thinking like a genius, we need to deploy our models in Microsoft Foundry. For this guide, we are going to focus on deploying gpt-5.2-codex on Microsoft Foundry with OpenClaw. Navigate to your AI Hub, head over to the model catalog, choose the model you wish to use with OpenClaw and hit deploy. Once your deployment is successful, head to the endpoints section. Important: Grab your Endpoint URL and your API Keys right now and save them in a secure note. We will need these exact values to connect OpenClaw in a few minutes. Step 2: Installing and Initializing OpenClaw Next up, we need to get OpenClaw running on your machine. Open up your terminal and run the official installation script: curl -fsSL https://openclaw.ai/install.sh | bash The wizard will walk you through a few prompts. Here is exactly how to answer them to link up with our Azure setup: First Page (Model Selection): Choose "Skip for now". Second Page (Provider): Select azure-openai-responses. Model Selection: Select gpt-5.2-codex , For now only the models listed (hosted on Microsoft Foundry) in the picture below are available to be used with OpenClaw. Follow the rest of the standard prompts to finish the initial setup. Step 3: Editing the OpenClaw Configuration File Now for the fun part. We need to manually configure OpenClaw to talk to Microsoft Foundry. Open your configuration file located at ~/.openclaw/openclaw.json in your favorite text editor. Replace the contents of the models and agents sections with the following code block: { "models": { "providers": { "azure-openai-responses": { "baseUrl": "https://<YOUR_RESOURCE_NAME>.openai.azure.com/openai/v1", "apiKey": "<YOUR_AZURE_OPENAI_API_KEY>", "api": "openai-responses", "authHeader": false, "headers": { "api-key": "<YOUR_AZURE_OPENAI_API_KEY>" }, "models": [ { "id": "gpt-5.2-codex", "name": "GPT-5.2-Codex (Azure)", "reasoning": true, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 400000, "maxTokens": 16384, "compat": { "supportsStore": false } }, { "id": "gpt-5.2", "name": "GPT-5.2 (Azure)", "reasoning": false, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 272000, "maxTokens": 16384, "compat": { "supportsStore": false } } ] } } }, "agents": { "defaults": { "model": { "primary": "azure-openai-responses/gpt-5.2-codex" }, "models": { "azure-openai-responses/gpt-5.2-codex": {} }, "workspace": "/home/<USERNAME>/.openclaw/workspace", "compaction": { "mode": "safeguard" }, "maxConcurrent": 4, "subagents": { "maxConcurrent": 8 } } } } You will notice a few placeholders in that JSON. Here is exactly what you need to swap out: Placeholder Variable What It Is Where to Find It <YOUR_RESOURCE_NAME> The unique name of your Azure OpenAI resource. Found in your Azure Portal under the Azure OpenAI resource overview. <YOUR_AZURE_OPENAI_API_KEY> The secret key required to authenticate your requests. Found in Microsoft Foundry under your project endpoints or Azure Portal keys section. <USERNAME> Your local computer's user profile name. Open your terminal and type whoami to find this. Step 4: Restart the Gateway After saving the configuration file, you must restart the OpenClaw gateway for the new Foundry settings to take effect. Run this simple command: openclaw gateway restart Configuration Notes & Deep Dive If you are curious about why we configured the JSON that way, here is a quick breakdown of the technical details. Authentication Differences Azure OpenAI uses the api-key HTTP header for authentication. This is entirely different from the standard OpenAI Authorization: Bearer header. Our configuration file addresses this in two ways: Setting "authHeader": false completely disables the default Bearer header. Adding "headers": { "api-key": "<key>" } forces OpenClaw to send the API key via Azure's native header format. Important Note: Your API key must appear in both the apiKey field AND the headers.api-key field within the JSON for this to work correctly. The Base URL Azure OpenAI's v1-compatible endpoint follows this specific format: https://<your_resource_name>.openai.azure.com/openai/v1 The beautiful thing about this v1 endpoint is that it is largely compatible with the standard OpenAI API and does not require you to manually pass an api-version query parameter. Model Compatibility Settings "compat": { "supportsStore": false } disables the store parameter since Azure OpenAI does not currently support it. "reasoning": true enables the thinking mode for GPT-5.2-Codex. This supports low, medium, high, and xhigh levels. "reasoning": false is set for GPT-5.2 because it is a standard, non-reasoning model. Model Specifications & Cost Tracking If you want OpenClaw to accurately track your token usage costs, you can update the cost fields from 0 to the current Azure pricing. Here are the specs and costs for the models we just deployed: Model Specifications Model Context Window Max Output Tokens Image Input Reasoning gpt-5.2-codex 400,000 tokens 16,384 tokens Yes Yes gpt-5.2 272,000 tokens 16,384 tokens Yes No Current Cost (Adjust in JSON) Model Input (per 1M tokens) Output (per 1M tokens) Cached Input (per 1M tokens) gpt-5.2-codex $1.75 $14.00 $0.175 gpt-5.2 $2.00 $8.00 $0.50 Conclusion: And there you have it! You have successfully bridged the gap between the enterprise-grade infrastructure of Microsoft Foundry and the local autonomy of OpenClaw. By following these steps, you are not just running a chatbot; you are running a sophisticated agent capable of reasoning, coding, and executing tasks with the full power of GPT-5.2-codex behind it. The combination of Azure's reliability and OpenClaw's flexibility opens up a world of possibilities. Whether you are building an automated devops assistant, a research agent, or just exploring the bleeding edge of AI, you now have a robust foundation to build upon. Now it is time to let your agent loose on some real tasks. Go forth, experiment with different system prompts, and see what you can build. If you run into any interesting edge cases or come up with a unique configuration, let me know in the comments below. Happy coding!Unlock the Power of Small Language Models with Phi-3 and Azure AI Studio
Discover the exciting world of Small Language Models (SLMs) and learn how to get started with Phi-3, a revolutionary new architecture that redefines what's possible with language models. This step-by-step guide takes you through setting up your environment, installing Phi-3 models, building your first SLM-based application, and deploying it to the cloud.
