ReThread

Inspiration

As passionate fashion lovers, we know the thrill of finding the perfect piece—but we also know the hidden cost of fast fashion. The industry is one of the biggest polluters, yet sustainable alternatives often feel inconvenient, expensive, or hard to find. We built ReThread because we believe style and sustainability should go hand in hand. Our goal? To give fashion-forward shoppers an effortless way to shop secondhand, without sacrificing trends or convenience.

What it does

ReThread is an AI-powered Chrome extension that seamlessly integrates with major online fashion stores. When users browse fast fashion items, our tool detects them in real-time and instantly suggests pre-owned, high-quality alternatives from secondhand platforms like ThredUp, Depop, and Poshmark. No extra effort—just smarter, more sustainable shopping at your fingertips.

How we built it

We developed ReThread using Chrome Extension Developer Mode, ensuring a seamless browsing experience that integrates directly with major fashion retail websites. Our tech stack includes:

Chrome Extension APIs – To interact with shopping sites and inject our recommendations dynamically.

Google Cloud Vision API – For image recognition and product matching, allowing us to detect fast fashion items and find similar secondhand alternatives.

JavaScript (ES6), HTML5, CSS3 – To build a responsive, lightweight, and efficient extension.

Depop, Poshmark, Mercari, Grailed – Integrated as data sources to pull real-time secondhand fashion listings.

Challenges we ran into

Pivoting Our Idea Midway – We initially started with a more complex concept, but halfway through, we had to simplify our approach. This shift meant reworking our strategy but ultimately led to a more functional and user-friendly solution.

Keyword Matching Difficulties – One of the biggest hurdles was ensuring accurate product matching between fast fashion items and secondhand alternatives. Since different platforms use inconsistent naming conventions, finding the right secondhand match wasn’t always straightforward. We had to refine our search queries, improve filtering methods, and adjust for brand-specific keywords to get better results.