Perspectivity: Go Outside. See Differently.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Perspectivity — Go Outside. See Differently.

What if going outside could become an opportunity to think like a researcher?

Perspectivity combines Perspective + Activity into an AI-powered field investigation experience. Instead of endlessly scrolling on a screen, users receive a real-world mission that encourages them to observe their surroundings, collect evidence, and form their own hypotheses.

Here's how it works:

• Generate a mission: Get an investigation based on your available time and experience level.
• Observe the world: Head outside and record what you notice through written or voice-to-text notes.
• Form a hypothesis: Develop your own explanation based on your observations.
• Get an AI debrief: Review the evidence, identify assumptions, explore alternative explanations, and discover what to investigate next.

The goal isn't to let AI think for you. It's to use AI to help you become a better observer and thinker.

Demo

The project currently runs locally. I haven't deployed a public demo yet.

Code

GitHub repository: https://github.com/shujahaadi/perspectivity

The project is open source under the MIT License.

The repository includes the React frontend and FastAPI backend, along with instructions for configuring the required API key and running the application locally.

How I Built It

I built Perspectivity using React and Vite for the frontend and FastAPI for the backend. Pydantic validates structured data exchanged between the frontend and backend.

For the AI component, I used Google's hosted Gemma model (gemma-4-26b-a4b-it) through the Google GenAI SDK.

Gemma powers two key parts of the experience:

1. Mission generation: It creates structured field investigations with a role, central question, mission, rules, and required observations.
2. AI debriefing: It reviews the user's observations and hypothesis, helping distinguish evidence from assumptions and suggesting useful next steps.

The backend validates the model's structured output before returning it to the frontend. I also added input validation and error handling for invalid requests.

I chose hosted inference because my development machine has limited RAM, making local inference less practical. This allowed me to focus on building the complete experience around an open-weight model rather than requiring powerful hardware.

Why Does Open Innovation Matter?

Open innovation makes it possible for developers to experiment with AI, build on open-weight models, and create applications that go beyond generic chat interfaces.

With Gemma, I could build a focused experience around real-world observation and reasoning, shaping the prompts and workflow for a specific purpose.

For me, this project was also an opportunity to learn by building: connecting an AI model to a full-stack application, validating its output, handling failures, and thinking about how AI should support human reasoning rather than replace it.

I hope Perspectivity can eventually grow into an open-source collection of field investigations that anyone can adapt, extend, and contribute to.

Prize Categories

• Best Use of Gemma — uses a hosted Gemma model for mission generation and AI-powered debriefing.