This tutorial explains how to build a simple AI agent using OpenAI and Google Colab. The process involves obtaining an OpenAI API key, setting up a Google Colab notebook, and installing the necessary libraries. The guide walks through the steps of writing and executing code blocks to initialise the API key, create a weather agent, and then test the agent’s functionality by querying the weather in different cities. Even when encountering a minor issue with the API key, the agent successfully provides the requested information, demonstrating the core steps for creating a basic OpenAI agent. Additional resources like a PDF and audio explanation are mentioned for further assistance.

 🎓Use AI to Convert lecture videos into detailed study materials

This guide illustrates how to leverage Artificial Intelligence (AI) to enhance the learning process from educational videos. It outlines a step-by-step method using Google’s Gemini application, demonstrating how users can upload lecture videos and prompt the AI to generate comprehensive study materials. These materials include detailed outlines, notes, examples, and timestamps for each topic, effectively transforming video content into a structured format. Furthermore, the guide explains how to request the AI to create interactive quizzes with answer keys and explanations, significantly aiding in self-assessment and understanding. The included pro tip suggests compiling these AI-generated materials from multiple lectures to build a comprehensive study library.

🛠️ Building Iterative AI Agents with LangGraph and Gemini

This tutorial provides a step-by-step guide to building an iterative AI workflow agent using the LangGraph library and Google’s Gemini 1.5 Flash model. It explains the core concepts of LangGraph, including State, Nodes, and Edges, highlighting how conditional edges enable cyclical workflows for iterative refinement. The guide details the prerequisites and setup process, such as obtaining a Gemini API key and installing necessary libraries. A significant portion is dedicated to designing and implementing the agent’s workflow as a graph with distinct nodes for routing, analysis, research, response generation, and validation. Finally, it covers running, testing, and observing the agent’s execution through streaming, and suggests potential extensions for further development.

🤝 The Model Context Protocol: Unifying AI Communication

This audioBook introduces the Model Context Protocol (MCP), an open standard designed to enable seamless communication between AI agents and external data sources or tools, much like a universal adaptor. It addresses the “N×M” integration problem where connecting numerous AI models to various tools creates an unsustainable development burden. The show explains MCP’s three-tier architecture consisting of a Host (the user-facing application), an MCP Client (which manages sessions), and an MCP Server (which translates requests for data sources like databases or APIs). Furthermore, it details how MCP uses JSON-RPC 2.0 for messaging and supports dynamic discovery of capabilities, alongside critical security principles for implementation. Finally, it distinguishes MCP from other standards, highlighting its complementary role with the Agent-to-Agent (A2A) Protocol for multi-agent collaboration.

📊 Creating Visual Infographics with Google Gemini

In this tutorial, you will learn how to use Google Gemini’s deep research feature to analyze complex topics and automatically generate visual infographics from research findings using its new visualization capabilities.

🤖Top 20 Agentic AI System Design Patterns: A Hands-On Guide to Building Intelligent Systems

Master the future of autonomous systems with this essential guide to the Top 20 Agentic Patterns. Designed for AI engineers and system architects, this hands-on book provides practical blueprints for building intelligent, adaptive, and autonomous agents. Learn how to apply proven design patterns—from perception-action loops to belief-desire-intention (BDI) models—to develop powerful agentic systems that reason, learn, and act.

🚀 Whether you’re building intelligent assistants, robotics control systems, or AI workflows, this book will help you design agent-based architectures that scale.

💤 Neurosymbolic AI – A solution to AI hallucinations

Neurosymbolic AI combines the statistical strengths of neural networks with the logic-based precision of symbolic reasoning. By integrating structured knowledge bases and symbolic rules, it aims to drastically reduce AI hallucinations and improve reasoning fidelity in complex domains like law, science, and healthcare.

🧠 Build Your AI Therapist with Gemini or ChatGPT

This tutorial and sources form a blueprint for an enhanced AI therapist using Google Gemini, building upon an original concept for a mental clarity system. The aim is to move beyond basic stress reduction and equip users with psychological skills grounded in established therapeutic frameworks like Cognitive Behavioural Therapy (CBT), Internal Family Systems (IFS), Acceptance and Commitment Therapy (ACT), and Narrative Therapy. The tutorial provides detailed guidance on implementing modular features such as a cognitive-emotional audit, belief restructuring engine, inner dialogue facilitator, and a new module for values-driven action. Crucially, they emphasise ethical considerations, clearly stating the tool’s scope as a self-help aid and not a replacement for professional therapy, while offering techniques for mastering interaction with AI to facilitate these processes.

Building an Automated Multilingual Translation Tool with Google Colab and OpenAI

The Challenge We’re Solving: Efficient and Scalable Multilingual Content

In today’s globalized world, the ability to present content in multiple languages is crucial for reaching a wider audience. However, manual translation is time-consuming and can be expensive, especially when dealing with large volumes of text or frequent updates. While professional human translation offers the highest quality, AI-powered translation provides a rapid and cost-effective solution for many common business needs, such as translating website elements, product descriptions, or internal communications. The challenge lies in creating a workflow that is not only fast but also easily manageable and scalable without requiring constant manual intervention like copying and pasting text into translation tools.

AI-Powered Proposal Creation with Google Colab, Google Sheets, and OpenAI

The Challenge We’re Solving: Overcoming Proposal Writer’s Block and Repetitive Customization

The process of creating compelling client proposals often involves a significant investment of time and effort. Many proposals share common structural elements and boilerplate content, yet each requires careful personalization to resonate with the specific client and project. This repetitive customization—adjusting cover letters, tailoring project summaries, and itemizing relevant features—can become a bottleneck. Furthermore, a common hurdle is “writer’s block,” where drafting engaging and persuasive text from scratch for each new proposal can be a daunting task, slowing down the entire sales or project initiation cycle . The core challenge is to balance the need for efficiency through standardization with the demand for personalization that makes a proposal stand out. This automation aims to address the common scenario where proposals are approximately “80% the same every time, but also need to customize each one”.

Advanced E-mail Automation with Google Colab, Gmail API, and OpenAI

The Challenge We’re Solving: Extracting Key Information from Complex Emails and Triggering Actions

Many business processes rely on information communicated via email. However, when this information is embedded within emails that have inconsistent formatting or a “nightmare” structure, manual extraction becomes a significant bottleneck . The example provided in the source material highlights a common scenario: receiving notification emails from a third-party platform  where crucial data points like a subscriber_id and an email address are present but difficult to parse reliably using traditional rule-based automation. The structure of these emails can vary – sometimes a user photo is present, sometimes not; URL parameters might change – making it nearly impossible for simple “if A, then B” logic or regular expressions to consistently find the needed data . Manually sifting through these emails, especially at scale, is not only tedious and error-prone but also prevents timely action based on the extracted information . This use case perfectly illustrates a domain where AI, particularly Large Language Models (LLMs), can excel by understanding the context and semantics of unstructured text to perform intelligent data extraction.

🤖 Automating Meeting Documentation with Zapier and Copilot

In this tutorial, you will learn how to create an automated system with Zapier Agents and Copilot that can turn meeting recordings into transcripts, summaries, and actionable task lists in Google Docs or Word Doc.

The tutorial provides step-by-step guides on automating project meeting documentation using different tools. The first section details how to use Zapier Agents to process meeting recordings into transcripts, summaries, and action lists within Google Docs. The second section focuses on leveraging Microsoft Copilot within Teams to automatically generate meeting summaries, notes, and action items directly from the meeting itself. This part outlines the prerequisites and steps for scheduling and using Copilot-enabled Teams meetings. Both methods offer ways to streamline meeting follow-up and can be further integrated with other automation tools like Power Automate for expanded functionality.