Python

Introduction If you've ever found yourself digging through product manuals, company wikis, or lengthy documents just to find a simple answer, you know the pain. The fact you're reading this suggests you're interested in how Generative AI can make that process less painful. Stick around for a few minutes, and I'll walk you through how we built a smarter FAQ bot using Google's Gemini API, Retrieval Augmented Generation (RAG), and structured output. This isn't just another chatbot; it's designed to give reliable, context-aware answers based only on provided information, minimizing the risk of making things up (hallucination). This example uses Google Car manuals, but the principles apply anywhere you have a set of documents you need to query effectively. I'm sharing my journey building this; it's a practical demonstration, not a definitive guide, so adapt the ideas to your needs! The Problem: Dumb Bots and Information Overload Traditional search methods or basic chatbots often fall short when dealing with specific document sets: ...

Building a Smart Dustbin with IoT Introduction This project involves the development of a Smart Dustbin using Arduino, Python, Kotlin (Android App), and Azure IoT to monitor and manage waste levels in real-time. By integrating sensors, cloud computing, and mobile applications, this system enhances waste management efficiency. Components & Technologies Used Arduino Uno & ESP8266 WiFi Module for sensor data collection. Ultrasonic Sensors to measure garbage levels. Servo Motor to control the bin lid. Python & Tkinter GUI for real-time monitoring. Kotlin Android App for mobile notifications. ThingSpeak & Azure IoT Hub for cloud-based monitoring. Hardware Implementation Arduino Code for Garbage Level Detection & Smart Lid Control The Arduino Uno, paired with an HC-SR04 ultrasonic sensor, measures the bin’s fill level. The servo motor automatically opens the lid when an object is detected nearby. ...

Introduction If you've ever found yourself digging through product manuals, company wikis, or lengthy documents just to find a simple answer, you know the pain. The fact you're reading this suggests you're interested in how **Generative AI** can make that process less painful. Stick around for a few minutes, and I'll walk you through how we built a smarter FAQ bot using Google's Gemini API, Retrieval Augmented Generation (RAG), and structured output. This isn't just another chatbot; it's designed to give **reliable, context-aware answers** based *only* on provided information, minimizing the risk of making things up (hallucination). This example uses Google Car manuals, but the principles apply anywhere you have a set of documents you need to query effectively. I'm sharing my journey building this; it's a practical demonstration, not a definitive guide, so adapt the ideas to your needs! The Problem: Dumb Bots and Information Overload Traditional search methods or basic chatbots often fall short when dealing with specific document sets: ...