Inspiration
Great work is connected.
Anything you ever make is produced by drawing on everything you've ever consumed.
Being a better creator, whether in video production, frontend development, or freestyle dance, begins with being a better consumer.
So how can we be better consumers?
By leaving precise fragments of the best work we find across the internet for our future selves -- all in one place.
Truth is, in most creative domains, a video is worth a million words.
That's why we built Fragments, your second brain for video that helps you clip, save, and organize the exact moments that spark your ideas.
Core Features
1. Cross-Platform Clipping System
Chrome Extension Integration: Hover-to-record button appears on all video elements across the web (instagram, tiktok, youtube, netflix etc.)
Smart Recording UI: Dark overlay with timer, stop/delete controls, and a metadata form that enables you to leave specific tags and notes for every clip (fragment).
2. AI-Powered Video Analysis & Tagging
Frame Extraction: Frames are extracted every 2 seconds for fragments using OpenCV
Cohere Vision AI Integration: Based on extracted frames, Cohere's vision AI model generates video summaries and intelligent tags
3. Multi-Format Video Processing Pipeline
Format Optimization: The original WebM video files are converted using FFmpeg to GIFs & MP4s, stored in S3, and then stored with the video metadata in DynamoDB.
4. Interactive Video Gallery
GIF Preview Gallery: Quick browsing of all fragments with various filters
Metadata Display: Upon drill-down, tags, notes, source URLs, and AI-generated descriptions are shown next to each fragment
Privacy Controls: Each fragment has a public/private visibility setting
5. Real-Time Second Brain Search
OpenSearch Integration: All data in DynamoDB is indexed to enable global fuzzy search across titles, descriptions, tags, notes, and AI-generated content to enable quick finding of any fragment
Real-Time Sync: DynamoDB streams automatically update search index
6. Content Discovery
Community Curation: Find popular collections and fragments from the community
Social Network: Follow creators you like to see their latest fragments in a feed
How we built it
Tech Stack
Frontend: Next.js 15, React 19, TypeScript, Tailwind CSS, Chrome Extension API, MediaRecorder API, MutationObserver API
Backend: Python, FastAPI, DynamoDB, Boto3 (AWS SDK), OpenCV, FFmpeg
Infrastructure: AWS (S3, Lambda, DynamoDB, OpenSearch, CloudWatch, MediaConvert), Vercel, Auth0
AI: Cohere AI (c4ai-aya-vision-32b), Computer Vision (OpenCV)
Challenges we ran into
- Screen recording videos on different sites
- Debugging auth0
- Setting up AWS permissions (across team and aws services)
- Latency and load times
- Using Cohere to analyze clips for instructions
Accomplishments that we're proud of
- Finishing the MVP and having a working demo
- Chrome extension has a smooth UI and actually works across platforms!
- Landing page visual hook
- OpenSearch indexing + DynamoDB Streams integration
- AWS Infrastructure stack
What we learned
Technical Learnings
- By default, all aws services have no permissions.
- Service-to-service delegation requires even more permissions!
- Lambda default timeout is 3s, which is way too short for most cases
- OpenSearch automatically creates inverse indices for each field in a document (and thus DynamoDB table if indexing on one), which helps make search really quick!
Personal Learnings
- A great idea requires going deep in a field. This is best done when at least one person on the team already has a lot of context in that field!
What's next for Fragments
Most importantly, we'll be working on this in our free time to get a beta version out to early users!
Here are a few features we'd like to add:
- add quick video editing tools to crop and trim clips before saving
- introduce an algorithmic feed of recommendations with two sections: a "for you" page, and a following page
- add collaborative Collections, letting friends and teams build shared libraries of inspiration together
- convert WebM files to WebP on top of GIF + add lazy loading for quicker load times
Built With
- amazon-web-services
- cohere
- css
- figma
- html
- javascript
- nextjs
- opencv
- python



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