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  Google just dropped their  biggest Al updates ever during Google 1/O 2025.  Here are 13 new Al updates you can't miss: Gemini Live. You can now turn on your camera, point at anything, and talk to Gemini about it in real time Imagen. Google's best image model yet Veo 3. The first video model with native sound generation Deep Research Project Astra. A JARVIS-like research prototype exploring the capabilities of a universal Al assistant Google Flow. Al filmmaking tool for creators Agent Mode. A new feature in the Gemini app that lets you state a goal, and Gemini will handle the steps to achieve it Google Jules. Jules is an Al-powered coding assistant that can read your code, write tests, fix bugs, and update dependencies Al Mode in Search. Al Mode transforms Google Search into a conversational assistant Real-time speech translation in Google Meet Google Beam. An Al-first video communication platform that turns 2D video stream...

Worth investing time on learning the Model context protocol using a free course provided by huggingface.   https://huggingface.co/learn/mcp-course/unit0/introduction

RAG (Retrieval-Augmented Generation) focuses on enhancing AI responses by retrieving external data, while MCP (Model Context Protocol) standardizes how AI interacts with various data sources and tools. Overview of RAG Definition: RAG is an AI architecture that improves the accuracy and relevance of responses generated by large language models (LLMs) by pulling in up-to-date information from external sources, such as databases or APIs, before generating a reply. 2 Functionality: When a user submits a query, RAG retrieves relevant content from connected data sources and appends this information to the input prompt, enriching the model's context with real-world relevance. This helps reduce inaccuracies and hallucinations in AI responses by grounding them in verifiable sources. 2 Use Cases: RAG is particularly useful in scenarios where real-time data is crucial, such as customer support, news aggregation, and any application requiring current information. 3 Sources Overview of MCP Defi...

How to deploy Ollama  Installation: Download Ollama:   Get the Ollama package from the  GitHub repository .   Install Dependencies:   Ensure you have any required dependencies, including libraries for your specific model.   Verify Installation:   Use  ollama --version  to confirm Ollama is installed correctly.   2. Model Deployment and Usage: Pull the Model:   Use the  ollama pull <model_name>  command to download the desired model.   Run the Model:   Use  ollama run <model_name>  to initiate the model's execution.   Interacting with the Model:   Ollama provides an API at  http://localhost:11434/api/generate  for interacting with the model.   Optional: Web UI:   Explore  Open WebUI  for a user-friendly interface to manage and interact with models.   Optional: Custom Applications:   Build custom applications using libraries like FastAPI and Gr...

 Artificial intelligence has become a cornerstone of modern technology, with tools like DeepSeek R1 and ChatGPT leading the charge in transforming how we interact with machines. While both are powered by advanced AI, they cater to different use cases and employ distinct technical architectures. In this article, we’ll explore the technical underpinnings of DeepSeek R1, compare it with ChatGPT, and highlight their unique capabilities. --- What is DeepSeek R1? DeepSeek R1 is an AI-driven search and conversational platform designed to deliver real-time, context-aware, and highly personalized results. Unlike traditional search engines, which rely on keyword matching and static datasets, DeepSeek R1 leverages cutting-edge natural language processing (NLP), machine learning (ML), and real-time data integration to provide dynamic and accurate responses. The "R1" in its name stands for Real-time, Relevance, and Reliability, reflecting its core strengths. It is built to handle complex ...

The podcast is auto-generated by https://notebooklm.google.com   Techbytes-madhukar.com is a blog created by Madhukar Rupakumar where he shares his insights and findings on various technology-related topics. [1] The blog features articles categorized by labels such as ".NET", "AI", "Apple products", "Blockchain", "Cloud technology", and many more. [2] Rupakumar, a Principal Systems Engineer at Hewlett Packard Enterprise with expertise in storage products, uses his platform to discuss a wide array of subjects related to technology and software. [1] The blog contains posts covering topics like:Linux commands for beginners. [3] Interview preparation guides for software engineers. [4] Free AI/ML LLM Fundamentals Courses. [5] Cloud computing and data storage terminology. [6] Free courses on various topics such as Generative AI, React, Angular, SEO, and data science. [7] Learning resources for data structures and algorithms. [8] The blog also...

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