Ansuman Nayak

LinkedIn

GitHub

Dribbble

Upwork

This front-end revamp of Doughful's online store prioritizes user experience and boosting sales. Designed in Figma with a responsive React.js layout, the site offers smooth navigation and lightning-fast loading times thanks to lazy loading and delightful animations. Version control through GitHub and deployment on Vercel ensure easy collaboration and future enhancements.

React.js

JavaScript

Figma

Adobe Illustrator

Dohful

Dive into the world of ideas with this Medium-inspired blog! Craft and share compelling content with ease. Built with Vite, TypeScript, and Tailwind CSS, the user-friendly interface is a joy to navigate. Secure login with JWTs keeps your writing safe, while Prisma and Neon database ensure seamless interaction. Scalable and performant thanks to AWS Cloudflare deployment, this platform fosters creativity and connection.

Vite

Tailwind

Prisma

TypeScript

CloudFlare

Inked

This feature-rich payment app, inspired by Paytm, lets users experience the ease and security of digital transactions. Built with React.js and Node.js/Express.js, it boasts robust user authentication via JWTs for a smooth, secure experience.  MongoDB stores user and transaction data, while session-based transfers ensure funds move only upon successful transactions. Tailwind CSS adds a touch of style to this user-friendly app.

Express

Node.js

MongoDb

Flux

Experience elegance at your fingertips with this luxury clothing e-commerce site. Designed in Figma and built with React.js, the user-friendly interface makes shopping for high-end fashion a breeze. The project garnered over 200 views on Dribbble, showcasing its stunning design.

HTML

Adobe Photoshop

HueHarmony

This user-centric IMDB clone boasts a modern UI designed in Figma to elevate user experience and engagement. Built with React and the lightning-fast Vite build tool, the site enjoys blazing speeds (10-100x faster!) thanks to pre-bundled dependencies. Redux acts as the central nervous system, managing state, while the TMDB API fuels the movie database. Delightful loading animations and lazy loading further optimize performance, making this a joy to navigate, especially for image-heavy movie content.

Sass

Redux

FlickFrame

This movie recommender system predicts your next favourites! It leverages TMDB data and content-based filtering powered by Python. You'll discover hidden gems based on movie similarities calculated with Cosine similarity. Built with Pandas, NumPy, and Matplotlib, the system delivers personalized recommendations in just 60-80ms!

Python

StreamLit

Movie Recommender