As media consumption grows, keeping track of what to watch, what we’re currently watching, and what we’ve finished across dozens of platforms has become a chaotic experience.
For the PromptWars: Virtual Challenge, I decided to solve this personal pain point by building StreamTrack—a unified multimedia tracker for web series and movies. But this hackathon wasn't just about building an app; it was about changing how we build apps.
Instead of writing every line of code manually, I embraced intent-driven development using Google Antigravity. Here is a look at my "Build-in-Public" journey, from a blank canvas to a live application deployed on Google Cloud Run.
💡 The Concept: What is StreamTrack?
StreamTrack is designed to be a central hub for all media tracking. The core features include:
Unified Dashboard: A landing page displaying trending movies and web series to aid discovery.
Watchlist Management: A dedicated space to queue up shows and movies to watch next.
Watched History: A logged history of completed content.
Discovery Tools: Quick access to popular actors and top-rated shows to find new favorites.
⚙️ The Paradigm Shift: Building with Google Antigravity
The PromptWars challenge required moving away from traditional syntax-heavy coding and leveraging AI-assisted prototyping. Using Google Antigravity, the development process felt like I was directing an engineer rather than typing out boilerplate.
Here is how the workflow went:
Prompting the Architecture
Inside the Antigravity Agent Manager, I started by feeding it my core intent. Rather than setting up React components manually, I wrote comprehensive prompts detailing the UI structure, the sidebar navigation, and the specific data categories (Trending, Watchlist, History). Antigravity translated these intents into a functional frontend architecture in minutes.Version Control & GitHub Integration
Once the local prototype was looking good and the components for the Dashboard and Watchlist were rendering correctly, it was time to secure the code. I initialized a Git repository, committed the Antigravity-generated code, and pushed it to my public GitHub repository to maintain version history and prep for deployment.Deploying to Google Cloud Run
A local app is great, but a live app is better. For the backend hosting, I utilized Google Cloud.
Using the Google Cloud CLI (gcloud), I connected my GCP project and deployed the application as a Cloud Run service. Cloud Run took care of the containerization and scaling automatically.
Within minutes, StreamTrack was live and accessible via a public URL!
🚧 Challenges & Learnings
The biggest learning curve was shifting my mindset. As a developer, the instinct is to jump into the files and fix a bug manually. With intent-driven development, the challenge is learning how to write better, more precise prompts to guide the AI to correct the UI or state management issues on its own. It’s less about knowing the exact syntax, and more about system design and clear communication.
🎯 Final Result & Links
Building StreamTrack using Google Antigravity was a massive productivity boost. It allowed me to focus on the user experience and feature set rather than getting bogged down in configuration files.
You can check out the final results here:
🌐 Live Application (Cloud Run): StreamTrack Live Demo
💻 GitHub Repository: [Insert your GitHub Repo Link Here]
A huge shoutout to Hack2skill, Google Cloud, and Google for Developers for organizing this hackathon. This new way to build is definitely the future!
Let me know what you think of the app in the comments below! 👇
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