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Cover image for Soul in Motion — 5:29 PM | 2026-07-28
Dev Rajput
Dev Rajput

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Soul in Motion — 5:29 PM | 2026-07-28

#ai #devops #python #automation

TL;DR

  • Designed an automated digital media pipeline for Kathaverse, an animation channel project, to generate personalized children's videos.
  • Collaborated with Claude to create a digital assembly line for custom videos in the ₹499–₹999 range.
  • Implemented a tester tier and refined internal logic for ELI, an emotional support application.
  • Planned stress-testing with my brother to gather feedback before scaling.

Building an Automated Digital Media Pipeline

Today was a great day, and I'm excited to share my progress on Kathaverse, our animation channel project. My goal is to create a pipeline that can generate personalized children's videos, similar to the Magicbox model, but with a focus on rapid monetization. I've been working closely with Claude to design a digital assembly line that can produce custom videos in the ₹499–₹999 range.

Our pipeline is built using a combination of AWS S3, AWS Lambda, and Amazon Rekognition. The process starts with uploading a set of pre-rendered scenes to S3. These scenes are then used as input for our Lambda function, which uses Rekognition to generate personalized video content. The output is a custom video that can be downloaded directly from S3.

# Upload pre-rendered scenes to S3
aws s3 cp scenes/ s3://kathaverse-scenes/

# Run the Lambda function to generate personalized video content
aws lambda invoke --function-name generate-video --payload '{"scenes": ["scene1", "scene2"]}' output.json
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By the end of the morning, the core scenes were rendering, and the automated pipeline was up and running. This is a significant milestone for Kathaverse, and I'm excited to see how it will perform in production.

Refining ELI, Our Emotional Support Companion

In the afternoon, I switched to working on ELI, our emotional support application. Building an AI companion requires a balance of empathetic design and efficient backend infrastructure. I implemented a tester tier, which allows us to test new features and updates without affecting production users.

I also cleared out stuck background sessions, which was a necessary step to ensure that our application remains stable and responsive. Additionally, I refined the internal logic that tracks retention and costs, which will help us better understand how users interact with ELI.

# Implementing the tester tier using Flask
from flask import Flask, request
app = Flask(__name__)

@app.route('/test', methods=['POST'])
def test():
    # Process test request
    pass

if __name__ == '__main__':
    app.run(debug=True)
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One of the highlights of my day was getting some great news: my brother has agreed to stress-test the application, which will provide invaluable feedback before we scale. This is a huge step forward for ELI, and I'm grateful for his support.

Catching Up and Learning

During my breaks, I explored text-to-speech models for our animation channel and watched some Vercel deployments. I also caught up on Boardwalk Empire, a guilty pleasure that provides a nice contrast to my work on cloud-based AI servers.

It's days like today that remind me why I build – it's exciting to turn abstract ideas into functional platforms. Seeing tangible progress on both projects is a great motivator, and I'm looking forward to continuing this work in the coming days.

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