Step-by-Step Guide to Installing Hadoop 3.5 on WSL2 Ubuntu in 2025

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A updated technical guide details how to install Hadoop 3.5.x on WSL2 Ubuntu, addressing common pitfalls that older 2020–2023 tutorials overlook. The setup requires Windows 10 (build 19041+) or Windows 11 with WSL2 enabled, Java 17, and approximately 3GB of free disk space. A key WSL2-specific issue is that SSH does not start automatically on launch, requiring users to manually run the service each session or configure it to auto-start via .bashrc. The guide also warns against storing Hadoop files on the Windows filesystem under /mnt/c/, as cross-boundary I/O significantly degrades performance. Configuration steps cover core Hadoop XML files, environment variables, and SSH key setup needed for even a single-node local cluster.

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Developer Builds Multilingual English Practice Voice Agent for Indian Users

A developer created SpeakEasy AI, a voice-based AI assistant designed to help Indian users practice English through natural spoken conversations. Built as part of the Murf AI Voice for Bharat Challenge, the tool supports English, Hindi, and Hinglish to accommodate users with varying levels of English confidence. The system uses Python, LiveKit Agents, Murf AI for voice generation, and a FastAPI and Next.js stack, combining speech-to-text, text-to-speech, and LLM-based conversation. A conversational routing feature allows the main agent to redirect users to specialist agents, such as a dedicated maths practice assistant. The project also includes a basic call analytics dashboard and is planned for further development with improved memory, more specialist agents, and expanded multilingual support.

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ProgrammingDEV Community ·

Salesforce Admin Exam Guide: Security Models, Flow Automation, and Data Architecture

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ProgrammingDEV Community ·

Graph Theory in C#: BFS, DFS, and Real-World Problem Solving Explained

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ProgrammingDEV Community ·

PawSense AI Uses Google Gemini to Analyze Dog Photos and Generate Canine Profiles

A developer has built PawSense AI, a full-stack web application submitted for DEV Community's Weekend Challenge: Dog Days Edition. The app allows users to upload a dog photo or use a webcam, after which Google Gemini's multimodal vision analyzes the image to produce a detailed canine profile. Features include breed identification, personality archetypes, a simulated 'dog thoughts' translator with text-to-speech, a personalized daily care schedule, and a downloadable ID card called a PawPort. The frontend is built with React 19 and TypeScript, while a Node.js and Express backend handles communication with the Gemini API. The application is live on Google AI Studio and uses IndexedDB for persistent client-side storage of scanned dog profiles.

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