Stellar

Clipkingz

Automatically Clip Streams to Short-Form in Real-Time.

Tech Stack

In building this platform, I had to carefully consider each element of the tech stack, especially because it involved cloud-based video processing. Speed and reliability were crucial to avoid any hitches during the video editing process. Additionally, scalability was a top priority to ensure that multiple users could edit videos simultaneously without any performance issues. The system was designed to enable users to automatically clip & edit streams in real time. They can then select specific clips for editing, which are processed in the cloud and returned to the user within minutes. This streamlined workflow is ideal for users looking to quickly upload their edited videos to social media.

Front-End

Next.JS

Next.js is utilized for it's fast performance, enhanced SEO, and seamless React integration, enabling the creation of interactive, modern web applications.

TailwindCSS

Tailwind CSS is employed to swiftly design and customize UI components, ensuring responsive and visually consistent layouts across the web application.

Backend

Stripe

Stripe handles all the payments securely along with webhooks to ensure subscription status is updated in the database.

Magic Auth

Magic auth was used to remove the password management and login with just the email.

MongoDB

MongoDB, a NoSQL database was used for this project as it is easily scalable, maintainable, and serverless.

Video Editing

AWS Lambda

I choose to use lambda to fire functions when necessary and those functions are connected to API endpoints with web sockets from API Gateway as a trigger. I also have cleanup functions that run to remove old videos from S3 that are triggered with Eventbridge.

AWS SQS

When a video needs to be edited they are added to a queue so they can be sent to the server to be processed, if there is an error during the process the video can be requeued and get processed no matter what.

AWS EC2

The video editing was to powerful to be ran on AWS Lambda so I had to move it to an EC2 instance to allow editing to be done in a quick manner along with multiple videos being able to be processed at once. The server only runs when videos are in the queue and is shut down during other times to preserve resources.

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