Don't worry. Not everyone knows it...

Imagine you're at a busy airport, and planes from different airlines are landing and taking off. To keep everything running smoothly, air traffic controllers decide which runway each plane should use. Now, think of AI models as those planes—each one has different strengths, speeds, and capabilities. The Azure Model Router  acts like an air traffic controller for AI models, ensuring that every request gets handled by the best model available. What is Azure Model Router? Azure Model Router is a smart AI system that automatically selects the best AI model to respond to a request. Instead of developers manually choosing which AI model to use, the Model Router does it for them, optimizing for speed, cost, and accuracy. It’s part of Azure AI Foundry , a platform that helps businesses and developers deploy AI models efficiently. Why Do We Need It? AI models come in different types—some are great at answering questions, others are better at reasoning, and some are super-fast but less detai...

AI models are powerful, but their utility is often limited by their inability to interact with external systems efficiently. The Model Context Protocol (MCP) is designed to bridge this gap, allowing AI models to integrate seamlessly with external tools, APIs, and real-time data sources. By standardizing this interaction, MCP enables AI assistants to provide more informed, precise, and interactive responses.                                                            image generated from copilot What is MCP? MCP is an open protocol designed to improve how applications communicate context to Large Language Models (LLMs) . It allows AI models to access relevant information from external sources dynamically, reducing reliance on static training data and enhancing responsiveness. MCP supports multiple interaction method...

In the ever-evolving world of artificial intelligence, combining powerful tools can open up new avenues for innovation and efficiency. Today, we're diving into how to use Google Gemini with Semantic Kernel —a match made in AI heaven. Whether you're an AI enthusiast, developer, or data scientist, this guide will walk you through the integration process step-by-step, ensuring you harness the full potential of these technologies. If you're more interested in watching the entire process, then here is the video: What is Google Gemini? Google Gemini is a suite of generative AI models designed to handle multiple types of data, including text, images, and audio. Its multimodal capabilities make it a versatile tool for a wide range of applications, from natural language processing to creative content generation. Introduction to Semantic Kernel Microsoft Semantic Kernel is an open-source development kit designed to help developers integrate AI models into their applications. It s...

Are you fascinated by AI and looking for an easier way to interact with it?  Great news!  You can now use your phone to call ChatGPT for free. Yes, you heard that right! Anyone in the USA can simply dial the number 1-800-242-8478 and start talking with ChatGPT instantly. Here is my video on this:

OpenAI has expanded its AI-powered search capabilities by rolling out ChatGPT Search to all users, both free and paid. This enhancement enables users to access real-time information directly within the ChatGPT interface, streamlining the process of obtaining up-to-date data without navigating to external search engines. Key Features of ChatGPT Search Real-Time Information Access Users can now retrieve current data, including news updates, weather forecasts, sports scores, and stock market trends, all within the ChatGPT environment.  Enhanced User Interface The search functionality has been integrated with a more traditional search engine appearance, featuring location-based searches that display lists of results, images, ratings, operating hours, and detailed information such as maps and directions directly within the app.  Direct Links to Sources Responses now include links to relevant web sources, allowing users to delve deeper into topics of interest.  Access and Avail...

Are you fascinated by the capabilities of Hugging Face models but unsure how to run them locally?  Look no further!  Here, we will explore the simplest and most effective way to get Hugging Face models up and running on your local machine using Ollama . For a complete walkthrough check out my latest video on "How to Run Hugging Face Models Locally Using Ollama".  This video covers everything from installation to running an example, ensuring you have all the information you need to get started: Happy coding!

In the rapidly evolving field of artificial intelligence, generating accurate and contextually relevant responses is crucial. Ollama , a lightweight and extensible framework, combined with the powerful Llama 3.2 model, provides a robust solution for generating AI model responses in JSON format. This article explores how to leverage these tools to create efficient and effective AI responses. In case, if you are interested in knowing every single bit then here is my video recording: Setting Up Ollama and Llama 3.2 Before diving into the specifics of generating responses, it's essential to set up Ollama and Llama 3.2 on your local machine. Ollama offers a straightforward installation process, and you can download the necessary models from the Ollama library.  Import required packages In order to get started with code, first we need to import the required packages: from ollama import chat from pydantic import BaseModel Generating Responses in JSON Format JSON format is a structure...