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Cover image for Soul in Motion — 12:33 PM | 2026-07-16
Dev Rajput
Dev Rajput

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Soul in Motion — 12:33 PM | 2026-07-16

#ai #react #python #devops

TL;DR

  • Unified fragmented AI video generation tools into a cohesive platform.
  • Overcame eight-second video limit by cloning Veo 3.
  • Integrated GPU worker scripts with studio interface using Python libraries.
  • Configured Oracle Cloud resources and locked down HQ build agents.
  • Witnessed Argentina win the match, experiencing profound happiness.

Unifying AI Video Generation Tools

Today was a massive push forward on all fronts, with significant progress made on unifying our fragmented AI video generation tools into one cohesive platform. The morning started with cloning Veo 3 to overcome the eight-second video limit. This was a crucial step, as it allowed us to break free from the constraints of our previous implementation.

Cloning Veo 3

To clone Veo 3, I used the following command:

git clone https://github.com/veo3/veo3.git
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This cloned the Veo 3 repository, allowing me to access its codebase and build upon it. The next step was to integrate the GPU worker scripts with the studio interface, a complex puzzle of Python libraries and performance optimization.

Integrating GPU Worker Scripts

The integration of GPU worker scripts with the studio interface required a deep understanding of Python libraries such as torch and cuda. The studio interface was built using React, which presented a unique challenge in terms of performance optimization. To overcome this, I used the following code to optimize the rendering of videos:

import torch
import torchvision
from torchvision import transforms

# Define the video transform
video_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# Load the video
video = torchvision.io.read_video('video.mp4')

# Apply the video transform
video = video_transform(video)
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This code snippet demonstrates how I used torch and transforms to optimize the rendering of videos, resulting in improved performance and quality.

Securing the Application Layer

After securing the application layer, I shifted my focus to the infrastructure, configuring Oracle Cloud resources and locking down the HQ build agents. This was a necessary victory, albeit a finicky one. To configure Oracle Cloud resources, I used the following oci commands:

oci iam user create --name "my-user" --description "My user"
oci iam compartment create --name "my-compartment" --description "My compartment"
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These commands created a new user and compartment in Oracle Cloud, which I then used to configure the build agents.

The Highlight of My Day

The highlight of my day, however, had nothing to do with code or AI. Watching Argentina win, with Lionel Messi's sheer genius on the pitch, filled me with joy. It's a profound happiness that's hard to describe. We're lucky to be alive to witness this era of football.

Unwinding After the Match

After the match, I unwound with some Spider-Man movies and Counterpart. It was a simple escape, but exactly what I needed to recharge. Today was exhausting, but fulfilling. Consolidating our AI video architecture is a significant feat, and the improved video quality proves we're on the right path. Tomorrow, we'll finalize the core components and prepare for the next phase of the launch.

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