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Virtual Threads (Project Loom)

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Virtual Threads (Project Loom) – Lightweight Concurrency in Java Java 21 brings one of the most awaited concurrency updates – Virtual Threads from Project Loom . Designed to dramatically reduce the complexity and resource cost of multithreaded programming, virtual threads enable scalable, high-throughput applications without complicated thread management. 🚀 What are Virtual Threads? Virtual threads are lightweight threads managed by the Java Virtual Machine (JVM), not the OS. Unlike platform (or "native") threads, virtual threads use minimal memory and can scale to millions of concurrent tasks. ✅ Lightweight ✅ Non-blocking friendly ✅ Familiar API (same Thread class) ✨ Why They Matter Traditional Java threads are costly in memory (1MB+ per thread) and limited in scalability. With virtual threads, you can handle massive I/O-bound workloads using simple, synchronous-style code. ...
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Real-Time Camera Input for Image Recognition

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Real-Time Camera Input for Image Recognition Imagine pointing your webcam at an object and instantly getting a prediction of what it is—just like Google Lens! In this guide, we’ll connect your browser's camera to a backend AI model built using TensorFlow or PyTorch, all in real-time. 🔧 Tech Stack HTML5 + JavaScript – to access webcam and capture frames Flask (Python) – to serve the model and process images TensorFlow or PyTorch – for the image classification model 🎬 Step 1: HTML + JS for Webcam Input Use the getUserMedia() API to stream webcam video, and capture frames as images. <video id="video" width="480" height="360" autoplay></video> <canvas id="canvas" width="480" height="360" style="display:none;"></canvas> <br> <button onclick="captureImage()">📷 Capture & Analyze</button> <p id=...
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Voice & Chatbots – AI-Assisted Conversational Apps

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Voice & Chatbots – AI-Assisted Conversational Apps Conversational AI has transformed the way users interact with applications. From customer service bots to voice-based virtual assistants, building intelligent chat interfaces is easier than ever with tools like OpenAI, Dialogflow, and Spring Boot. In this article, we'll look at how to design, implement, and integrate chatbot and voice assistants into your apps. 🚀 Why Conversational Interfaces? Improve user experience with natural interaction Automate FAQs, bookings, and support Enable voice control for accessibility and IoT 🧠 Tools for AI-Powered Conversations OpenAI GPT (ChatGPT API) – Natural language understanding & generation Dialogflow – NLP + multi-channel chatbot development Spring Boot – Backend framework to integrate services Twilio / Web Speech API – For voice-based interaction 💬 Chatbot with OpenAI + Spring Boot Using the /...
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Image Recognition with Deep Learning Frameworks

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Image Recognition with Deep Learning Frameworks Image recognition is at the heart of many real-world AI applications—from facial recognition to autonomous vehicles. In this post, we'll walk through building an image recognition system using Python-based deep learning frameworks like TensorFlow and PyTorch. We'll also touch on integrating it with a Java backend using REST APIs. 🚀 Tools & Frameworks TensorFlow / PyTorch – for deep learning model development Flask – for serving the model as an API Java (Spring Boot) – to consume the image recognition service 🧰 Step 1: Training or Using a Pretrained Model We'll use a pretrained model like ResNet50 from TensorFlow or PyTorch's torchvision. 🧠 Using ResNet50 with TensorFlow import tensorflow as tf from tensorflow.keras.applications import resnet50 from tensorflow.keras.preprocessing import image import numpy as np model = resnet50.ResNet50(weights='im...
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Building a Recommendation System in Spring Boot

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Building a Recommendation System in Spring Boot Recommendation systems are widely used in platforms like Netflix, Amazon, and Spotify to suggest relevant content to users. In this post, we will learn how to build a simple **content-based recommendation system** using **Spring Boot** and **Hugging Face API**. 💡 What is a Content-Based Recommendation System? Content-based filtering recommends items based on their features and the preferences of users. For example, it might recommend movies similar to a user's watched movie based on genre, director, or actors. **User preferences**: What the user likes (e.g., genres, artists, topics, etc.) **Item features**: Information about items (e.g., movie plots, product descriptions, etc.) 📦 Step 1: Setup Spring Boot Project Let’s start by adding necessary dependencies in our pom.xml : <dependencies> <dependency> <groupId>org.springframework.boot</groupId> ...
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Sentiment Analysis Using Hugging Face API in Spring Boot

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Sentiment Analysis Using Hugging Face API in Spring Boot In this post, we'll demonstrate how to perform sentiment analysis using Hugging Face's pre-trained models in a Spring Boot application. The app will expose a simple REST API endpoint where users can submit text, and the API will return whether the sentiment of the text is positive or negative. 📦 Step 1: Maven Dependencies To get started, we'll add the necessary dependencies to our pom.xml file for Spring Boot and HTTP client support: <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> </dependencies> ⚡ Step 2: Controller for Sentiment Anal...
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Spring Boot with AI

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Spring Boot with AI – Bringing Intelligence to Your Backend Spring Boot is a popular framework for building robust Java applications. What if we could add intelligence to those apps? With the rise of AI APIs and Java ML libraries, integrating AI into your backend is easier than ever. 🤖 What Kind of AI Can You Add to Spring Boot? Text Generation – ChatGPT-style prompts via OpenAI Sentiment Analysis – Using Hugging Face APIs Recommendation Systems Image Recognition – With deep learning frameworks Voice & Chatbots – AI-assisted conversational apps 🔌 Example 1: Using OpenAI API in Spring Boot 1. Add dependencies (Maven) <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> 2. Create a REST controller to call the AI API import org.springframework.web.bind.annotation.*; ...
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