J

JavaScript

A simple Budget Tracker and Expense Calculator that helps users record income and expenses, calculate balances, and monitor spending. Designed with a clean, user-friendly interface to make personal finance management easy and efficient.

Offline-first, CPU-first AI reader companion that narrates and analyzes stories locally using Ollama and structured feedback JSON.

Interview Simulator

A real-time AI interview coach built with a Bun backend and Groq AI integration. This project includes:

browser-based job description and persona setup resume upload and text extraction for PDFs/DOCX/TXT live streamed interview assistant replies via SSE transcript export and review features frontend mic support and keyboard shortcuts

Use this file as the folder-level description for the project.

BusTrack Smart is an AI-powered smart public transportation platform that provides real-time bus tracking, crowd prediction, ETA estimation, multilingual accessibility, and AI chatbot assistance to improve urban mobility and passenger experience.

MyDocVault is a secure, offline-first AI platform designed to help users store, organize, understand, and manage government documents in a privacy-preserving environment. The application enables users to upload documents such as Aadhaar, PAN, Passport, Driving Licence, Voter ID, educational certificates, and other official records, while ensuring that all processing happens locally without relying on external servers or internet connectivity.

Powered by an intelligent document-aware assistant, MyDocVault extracts key information, summarizes documents in simple language, answers user queries based solely on locally stored documents, and provides guidance on the documents required for various government services. The system never fabricates information, clearly identifies missing details, and prioritizes user privacy by keeping all data offline.

Key Features 🔒 Offline-first and privacy-focused document management 📄 Secure storage and organization of government documents 🤖 AI-powered document understanding and summarization 🔍 Automatic extraction of important document details 📋 Government service document requirement guidance 📂 Intelligent document search and retrieval ⚠️ Identification of missing or incomplete documents 🛡️ Local processing with no external API or internet dependency 💬 Simple, conversational AI assistant for document-related queries 📊 Structured JSON extraction for document metadata

Tech Stack: React.js, Node.js, Express.js, SQLite, OCR, Local AI Models, HTML, CSS, JavaScript

Goal: To provide citizens with a secure, intelligent, and completely offline digital document vault that simplifies document management while ensuring maximum privacy and accessibility.

Offline-first lecture transcription and structured study data extraction CLI/PWA.

Uncover Truth is a full-stack, AGPLv3-licensed logic puzzle web application built using a modern JavaScript monorepo architecture. The project combines a React-based frontend, a Node.js/Express backend, and shared game logic to deliver an interactive puzzle-solving experience focused on reasoning and deduction.

The system is designed with production-grade engineering practices, including automated testing, code quality enforcement, security scanning, and CI/CD compliance pipelines using GitLab.

⚙️ Key Features 🧩 Interactive logic puzzle gameplay with shared puzzle engine ⚛️ Modern frontend built with React and Vite 🖥️ Lightweight Node.js + Express backend 📦 Monorepo structure with shared utilities between client and server 🧪 Automated testing using Vitest with coverage reporting 🔍 Code quality enforcement via ESLint, Prettier, and Knip 🔐 Security scanning using Gitleaks and npm audit 📊 CI/CD pipeline with compliance checks (GitLab-ready) 📄 Open-source governance: LICENSE, CONTRIBUTING, SECURITY, CODE_OF_CONDUCT 🏗️ Architecture client/ → React frontend (Vite) server/ → Express backend API shared/ → Shared puzzle logic used by both client and server test/ → Unit and integration tests CI/CD → GitLab pipeline with lint, security, test, and compliance stages 🚀 Purpose

This project is designed to demonstrate real-world full-stack engineering practices, focusing on:

Clean architecture in monorepos Secure and compliant development workflows Automated testing and coverage enforcement Production-ready CI/CD pipelines Open-source governance standards

SafeHer – A women's safety mobile application that provides SOS alerts, live location sharing, emergency contact management, and real-time emergency assistance to enhance personal security.

Lifeline AI is an AI-powered emergency response platform providing real-time emergency intelligence, ambulance coordination, hospital connectivity, SOS management, and disaster response monitoring.

Project Overview: The CPU-First EMS is a highly resilient web platform designed specifically for distributed teams and field workers operating in environments with unreliable internet access.

Key Technical Achievements:

Offline-First Reliability: Implemented robust client-side storage to queue submissions and track attendance locally, ensuring zero data loss during outages and enabling automatic synchronization when reconnected.

Architectural Overhaul: Successfully migrated the application from a local Streamlit script to a scalable, production-ready REST API using Flask and Gunicorn with multi-threaded workers.

Modern User Experience: Built a responsive, dynamic frontend using Vanilla JavaScript and the Fetch API, styled with a modern glassmorphism aesthetic.

Production-Ready Deployment: Engineered a secure, multi-stage, non-root Docker build to streamline deployment and ensure consistent environments across local and production servers.

WaterWatch is a community-driven water quality monitoring and reporting platform that enables users to collect, visualize, and analyze environmental data. The platform provides interactive dashboards, field data collection workflows, and automated reporting tools to support sustainable water resource management and environmental awareness.

SyllabusAI is an AI-powered personalized learning curriculum generator that creates custom week-by-week learning plans using the groq API.

What it does:

Users enter their learning topic, skill level, available hours/week, goals, and preferred learning style The groq LLM generates a structured syllabus with weekly themes, objectives, curated resources, projects, and checkpoints Users can track progress, enrich resources with YouTube search, and export syllabi as Markdown Tech Stack:

React 18 frontend with vanilla CSS and design tokens Express proxy server for API key management and resource search groq API for AI curriculum generation Instrument Serif + Inter typography Quality & Standards:

ESLint + Biome + Prettier for code quality (0 errors) Jest + React Testing Library with 80% coverage threshold Husky pre-commit hooks + GitHub Actions CI/CD Security scanning: Gitleaks, CodeQL, Dependabot Full documentation: README, CONTRIBUTING, USER_MANUAL, CODE_OF_CONDUCT Key Features: Interactive week cards, resource enrichment, Markdown export, responsive UI, server-side API security, progress tracking.

Recruiters deal with hundreds of unstructured resumes in PDF and Word formats. Extracting consistent, comparable data requires expensive cloud AI APIs or tedious manual effort. This project proves that a local, quantized small language model (SLM) running on a commodity CPU can do the same job — privately, cheaply, and completely offline.

Memento AI – A CPU-first, offline AI assistant that transforms documents, images, audio, video, and text into structured knowledge using local LLMs, SQLite, and Retrieval-Augmented Generation (RAG), with

LedgerLens Drop in a photo or PDF of a receipt/invoice and get clean, structured ledger data — merchant, date, tax/GST, line items, and total — as JSON, exportable to CSV. Runs entirely on CPU and works with the network switched off.

Open-source crowdsourced wildfire and smoke tracking dashboard built for communities.

Offline Medical Report Structurer

Project Description

Offline Medical Report Structurer is an AI-powered healthcare application designed to transform unstructured medical reports into organized, structured, and easy-to-understand data without requiring an internet connection. The system processes medical documents such as laboratory reports, prescriptions, discharge summaries, and diagnostic reports, automatically extracting key information including patient details, diagnoses, medications, test results, and clinical observations.

By leveraging Natural Language Processing (NLP) and document parsing techniques, the application converts complex medical text into a standardized format that improves accessibility, readability, and data management. Since the solution operates entirely offline, it ensures patient privacy, data security, and usability in low-connectivity environments such as rural clinics, hospitals, and healthcare camps.

Key Features

Offline processing with no internet dependency Automated extraction of medical information Structured JSON/database output for easy integration Support for multiple medical report formats Secure and privacy-focused data handling Quick search and retrieval of patient information User-friendly interface for healthcare professionals

book waste management system

live link : https://study-sprint-ai.vercel.app/

PulseLink is a blood donor matching platform that connects patients with nearby compatible donors during emergencies. Built using React, FastAPI, and MySQL.