A Coding Guide Implementing ScrapeGraph and Gemini AI for an Automated, Scalable, Insight-Driven Competitive Intelligence and Market Analysis Workflow

A Coding Guide Implementing ScrapeGraph and Gemini AI for an Automated, Scalable, Insight-Driven Competitive Intelligence and Market Analysis Workflow

In this tutorial, we demonstrate how to leverage ScrapeGraph’s powerful scraping tools in combination with Gemini AI to automate the collection, parsing, and analysis of competitor information. By using ScrapeGraph’s SmartScraperTool and MarkdownifyTool, users can extract detailed insights from product offerings, pricing strategies, technology stacks, and market presence directly from competitor websites. The tutorial then…

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A Coding Guide for Building a Self-Improving AI Agent Using Google’s Gemini API with Intelligent Adaptation Features

A Coding Guide for Building a Self-Improving AI Agent Using Google’s Gemini API with Intelligent Adaptation Features

In this tutorial, we will explore how to create a sophisticated Self-Improving AI Agent using Google’s cutting-edge Gemini API. This self-improving agent demonstrates autonomous problem-solving, dynamically evaluates performance, learns from successes and failures, and iteratively enhances its capabilities through reflective analysis and self-modification. The tutorial walks through structured code implementation, detailing mechanisms for memory management,…

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A Step-by-Step Coding Guide to Defining Custom Model Context Protocol (MCP) Server and Client Tools with FastMCP and Integrating Them into Google Gemini 2.0’s Function‑Calling Workflow

A Step-by-Step Coding Guide to Defining Custom Model Context Protocol (MCP) Server and Client Tools with FastMCP and Integrating Them into Google Gemini 2.0’s Function‑Calling Workflow

In this Colab‑ready tutorial, we demonstrate how to integrate Google’s Gemini 2.0 generative AI with an in‑process Model Context Protocol (MCP) server, using FastMCP. Starting with an interactive getpass prompt to capture your GEMINI_API_KEY securely, we install and configure all necessary dependencies: the google‑genai Python client for calling the Gemini API, fastmcp for defining and…

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Introducing Gemini Robotics and Gemini Robotics-ER, AI models designed for robots to understand, act and react to the physical world.

Introducing Gemini Robotics and Gemini Robotics-ER, AI models designed for robots to understand, act and react to the physical world.

Research Published 12 March 2025 Authors Carolina Parada Introducing Gemini Robotics, our Gemini 2.0-based model designed for robotics At Google DeepMind, we’ve been making progress in how our Gemini models solve complex problems through multimodal reasoning across text, images, audio and video. So far however, those abilities have been largely confined to the digital realm….

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Meet AI Co-Scientist: A Multi-Agent System Powered by Gemini 2.0 for Accelerating Scientific Discovery

Meet AI Co-Scientist: A Multi-Agent System Powered by Gemini 2.0 for Accelerating Scientific Discovery

Biomedical researchers face a significant dilemma in their quest for scientific breakthroughs. The increasing complexity of biomedical topics demands deep, specialized expertise, while transformative insights often emerge at the intersection of diverse disciplines. This tension between depth and breadth creates substantial challenges for scientists navigating an exponentially growing volume of publications and specialized high-throughput technologies….

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