Fourth Industrial Revolution

Symbolic Generative AI Knowledge Bot Symbolic Generative AI Knowledge Bot This is a symbolic AI chatbot designed to provide knowledge about Generative AI concepts, such as LLMs, GANs, Transformers, Datasets, and Applications. This chatbot uses symbolic reasoning to infer answers from a defined knowledge base. Get GitHub code here . Learn how it works on YouTube here . Features Dynamic reasoning based on entities and relationships from the knowledge base. Fallback responses for unmatched queries. Easily extensible knowledge base (in JSON format). Type a query about GenerativeAI (e.g., "Tell me about LLMs"). No capitalization needed! Supported terms: GenerativeAI, Datasets, LLMs, Diffusion Models, GANs, Transformers, Applications, Ethics Send Clear Tip: ask using natural language, e.g., "Tell me about GANs" or "What are the limitations of GenerativeAI?" Small d...

Summary : Software Testing Knowledge Copilot is a secure, deterministic, multi-agent AI system that helps QA Engineers instantly discover trusted software testing knowledge from an internal repository. Unlike general-purpose AI assistants, it operates entirely on a local dataset, eliminating cloud API costs, protecting intellectual property, and delivering reliable, traceable technical guidance. View the Local Multi-Agent System video below. Then, read on. There are two ways that you can get this Knowledge Copilot: Kaggle notebook (recommended option): You can copy / edit or download my Software Testing Knowledge Copilot on Kaggle here . On Kaggle, you can read the detailed Knowledge Copilot Project Writeup here . GitHub: You can clone this Knowledge Copilot from (that uses a public Software Testing dataset ) on GitHub here . Introduction: Why Teams Need Their Own AI Knowledge Copilot Every project team builds an enormous knowledge base over time. For example, a soft...

Summary : In Session 2 of our Agentic AI curriculum, we move beyond simple reasoning loops and explore how LangGraph enables structured, stateful, and production-ready multi-agent systems. Want to learn by running a Multi-Agent system? Run the Agentic AI Session 2 notebook in Google Colab. In Session 1 , we learned how to make an AI think using the ReAct loop. That was a major milestone. But thinking alone is not enough. When you step into the world of Multi-Agent Systems, structure becomes important. Without architecture, agents quickly turn into tangled scripts that collapse under production pressure. In Session 2, we move from simple Python loops to a structured architectural system using LangGraph. 1. Why LangGraph? Moving Beyond the Loop In the first session , a basic Python for loop was enough to drive reasoning. It worked well for small experiments. But complexity can grow fast. The moment multiple agents must collaborate, retry, or self-correct, simp...