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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...

In the age of cutting-edge technology, the ability to clone your voice is no longer a futuristic dream. With advancements in Text-to-Speech (TTS) technology, you can create a digital replica of your voice using open-source tools like SWivid's F5-TTS. Whether you're a tech enthusiast, a content creator, or someone interested in preserving their voice, this guide will walk you through the process step-by-step. If you're interested in watching, then here is the recording: What is SWivid's F5-TTS? SWivid's F5-TTS is an open-source Text-to-Speech system that uses deep learning algorithms to synthesize speech. It leverages a powerful neural network to create highly realistic and natural-sounding voices.  The best part?  It’s accessible to anyone with a bit of tech know-how and a willingness to experiment. Why Clone Your Voice? Cloning your voice can have numerous applications: Accessibility: Create personalized voice assistants. Content Creation: Enhance your videos, podc...

In this article we will see how we can run our agents locally which means we will be using OpenAI Swarm framework but still we will not be paying anything to OpenAI as we will not be utilizing OpenAI's API key. Using OpenAI's Swarm but not using OpenAI's key, got confused?  Well, we will be achieving this using Ollama :) Now, before we proceed, if you have not watched my earlier video on what is OpenAI-Swarm and how to get started with it, I would recommend you check this one here:  Here is link of the GitHub repository containing Swarm's source code and implementation details. What Are We Trying To Do? We will create our own agent utilizing  OpenAI-Swarm framework using Ollama and the open-source model Llama3.2:1b . This agent will run locally on our machine without the need to any API key from OpenAI . Setting Up The Things Install Swarm We need to install Swarm from GitHub as it is still in experimental stage and that can be done by running below command: pip...