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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Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)

Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)

Real-world data is often costly, messy, and limited by privacy rules. Synthetic data offers a solution—and it’s already widely used: LLMs train on AI-generated text Fraud systems simulate edge cases Vision models pretrain on fake images SDV (Synthetic Data Vault) is an open-source Python library that generates realistic tabular data using machine learning. It learns…

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Step-by-Step Guide to Build a Customizable Multi-Tool AI Agent with LangGraph and Claude for Dynamic Agent Creation

Step-by-Step Guide to Build a Customizable Multi-Tool AI Agent with LangGraph and Claude for Dynamic Agent Creation

In this comprehensive tutorial, we guide users through creating a powerful multi-tool AI agent using LangGraph and Claude, optimized for diverse tasks including mathematical computations, web searches, weather inquiries, text analysis, and real-time information retrieval. It begins by simplifying dependency installations to ensure effortless setup, even for beginners. Users are then introduced to structured implementations…

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A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA Optimization

A Step-by-Step Coding Guide to Efficiently Fine-Tune Qwen3-14B Using Unsloth AI on Google Colab with Mixed Datasets and LoRA Optimization

Fine-tuning LLMs often requires extensive resources, time, and memory, challenges that can hinder rapid experimentation and deployment. Unsloth AI revolutionizes this process by enabling fast, efficient fine-tuning state-of-the-art models like Qwen3-14B with minimal GPU memory, leveraging advanced techniques such as 4-bit quantization and LoRA (Low-Rank Adaptation). In this tutorial, we walk through a practical implementation…

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