Hey everyone! I’m Naitik Prajapati, a 15-year-old student (Class 9) and developer from India.
Over the last few months, I have been diving deep into cloud architecture and artificial intelligence. As a participant in the Google Cloud Arcade program, I wanted to challenge myself to build something fully functional that bridges frontend development with cutting-edge AI models.
Today, I’m sharing how I built and deployed my personal autonomous AI app portfolio using React, Google AI Studio, and the Gemini API, all hosted completely for free.
** 🛠️ The Tech Stack & Architecture**
To make this ecosystem highly responsive and cost-effective, I chose:
- Frontend Framework: React (Fast, component-based layout)
- AI Engine: Google AI Studio (Leveraging Gemini Pro and Gemini Flash orchestrations)
- Hosting Platform: Vercel (For high-speed global CDN delivery)
You can check out the live, fully indexed production build right here:
👉 Naitik Prajapati Portfolio
Building with Google AI StudioGoogle AI Studio is an absolute game-changer for developers looking to prototype fast. I structured my development environment to test distinct prompts, adjust safety settings, and seamlessly export system instructions straight into my code logic.By orchestrating the Gemini API, my project portfolio doesn't just display static text; it understands contexts, pulls developer data dynamically, and showcases my progress across the Google Cloud Platform (GCP) landscape.
☁️ Integrating the Google Cloud Arcade ProfileOne of my favorite milestones has been earning over 60+ Google Cloud Skill Badges through the Arcade program.To bridge this data with my portfolio, I structured my deployment to link directly to my official Google Developer Profile. By anchoring my public badge credentials straight to my web domain, I created an unforgeable, machine-readable validation loop that proves my cloud capabilities.
📈 Next Steps for My ArchitectureMy journey with Deep Tech is just getting started. Over the coming weeks, I plan to:Integrate fully autonomous multi-agent systems using the Gemini API.Fine-tune advanced prompt caching patterns to keep API latency under 200ms.Keep grinding and accumulating cloud badges in the Google Skills framework.I’d love to connect with other developers, builders, and tech enthusiasts!Live Portfolio: naitikprajapati.vercel.appSource Code: Find my active repositories over on GitHub.Drop your feedback or questions below—I'd love to hear how you are utilizing Google AI Studio in your own projects!
Top comments (1)
building and deploying a live portfolio at fifteen is a strong start. for the next version, add a small page that shows the prompt version, model name, response time, and failure state for each ai feature. keep api keys on the server, add a simple rate limit, and link each badge to its public source. these details will make the project easier to trust and easier for others to learn from.