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Harsha Vardhan

Full Stack Developer

Hi, I’m Harsha Vardhan — a Full Stack Developer from Vizag, India. I build clean, modern web applications using TypeScript, React.js, and Node.js, with a strong focus on performance, scalability, and intuitive user experience.

I also work at the intersection of software and machine learning, using Python and data-driven approaches to create intelligent features that solve real problems. My goal is to combine solid engineering with practical AI to build products that are both impactful and thoughtfully designed.

Projects(6)

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SourceSurf

building
SourceSurf is a sophisticated project discovery and curation platform designed to serve as the central hub for a multi-service open-source ecosystem. It solves the fragmentation of high-quality repository discovery by aggregating data streams from specialized microservices. The platform features a robust webhook architecture that ingests enriched metadata—including real-time star counts, fork activity, and GSoC history—directly into a structured PostgreSQL database. Key technical achievements include designing an asynchronous data pipeline to handle high-concurrency ingestion from external scrapers and implementing a flexible schema that standardizes diverse metadata formats. SourceSurf provides developers with a unified dashboard for exploring curated, trending projects, backed by a scalable Node.js/Express backend and a type-safe TypeScript implementation that ensures data integrity across the entire discovery workflow.
Project
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Quorum

building
Quorum is a comprehensive full-stack Q&A platform tailored specifically for the developer community, facilitating technical knowledge sharing and collaborative problem-solving. Built with a unified TypeScript stack, the application features a React-based frontend and a robust Express backend. The platform implements complex threaded discussions, real-time interaction, and a developer-centric UI optimized for code snippet sharing and technical discourse. Technically, Quorum solves the challenge of maintaining end-to-end type safety between the client and server through shared TypeScript interfaces, significantly reducing integration bugs and runtime errors. The system manages a relational database of questions, answers, and user profiles, utilizing optimized PostgreSQL queries to handle nested discussion trees and high-traffic user interactions, providing a seamless and responsive experience for technical developers.
Project
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OSS Trends Scraper

building
The OSS Trends Scraper is a high-performance Python microservice engineered to automate the discovery and enrichment of trending GitHub repositories. Built with FastAPI and an entirely asynchronous pipeline, it utilizes BeautifulSoup4 and httpx for high-concurrency web scraping of the GitHub Trending pages. Once a repository is discovered, the service enriches the record by interfacing with the official GitHub REST API to fetch deep metadata, including owner profiles and precise activity metrics. To solve the challenge of GitHub's aggressive rate limiting, the service implements an intelligent scheduling and batching system using APScheduler. Enriched data is 'upserted' into a PostgreSQL database (hosted via Supabase) using asyncpg to ensure non-blocking I/O. The microservice then automatically synchronizes this data with the SourceSurf backend via secure webhooks, providing a continuous stream of fresh, high-quality open-source leads while maintaining extreme operational efficiency.
Project
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SOON

Price Pulse

building
Price Pulse is a modern real-time price tracking application built on Next.js, designed to monitor and analyze price fluctuations across various e-commerce platforms. The application leverages Server-Side Rendering (SSR) and Edge-compatible middleware to deliver high-performance data fetching and request filtering. It provides users with automated monitoring of product URLs, visual price history charts, and instant notifications for significant price drops. One of the primary technical challenges solved was the reliable extraction of data from dynamic e-commerce sites that rely heavily on client-side JavaScript. This was achieved through a robust background scraping layer that periodically validates product data and stores historical 'pulses' in a MongoDB database. The frontend utilizes custom React hooks and TypeScript-based charting libraries to transform raw historical data into actionable trend analysis, helping users make data-driven purchasing decisions.

Education

Gayatri Vidya Parishad College of Engineering (A)

Narayana Junior College (CBSE)