This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
🌿 Lifeform — Explore. Discover. Build.
What I Built
Lifeform is a nature-exploration game that encourages people to step outside, discover living things, and build a digital collection of their discoveries.
The idea is simple: instead of spending more time scrolling, go outside and notice the living world around you.
With Lifeform, you can photograph a plant, bird, insect, fungus, or other animal and ask an AI naturalist to identify it.
Here's how the experience works:
- Explore: Head outdoors and find something interesting.
- Discover: Upload a photo and receive an AI-generated identification, confidence level, and description.
- Collect: Add new discoveries to your collection.
- Build: Earn XP, level up, complete daily missions, and track your progress.
- Reflect: Record observations in your Field Journal and build exploration streaks.
Lifeform combines nature discovery with game mechanics to make outdoor exploration feel rewarding.
I'm building this as a learning project to explore how open-weight AI can support a real-world activity rather than simply keep people on a screen.
AI identifications can be wrong, so results should be treated as suggestions rather than scientific confirmation.
Demo
🌐 Live demo: https://lifeform.onrender.com
The deployed app lets you try the image-scanning experience and explore the main features.
Code
💻 GitHub repository: https://github.com/Aayush264/lifeform/tree/deploy
How I Built It
I built Lifeform using HTML, CSS, JavaScript, Node.js, Express, MongoDB, and open-weight Gemma models.
The frontend provides the discovery interface, collection, missions, and Field Journal. The Express backend handles API requests and image uploads, while MongoDB Atlas stores player data.
AI identification was the most interesting part of the project.
During local development, I used Gemma 3 4B through Ollama. This allowed me to experiment with image analysis on my own computer.
For the hosted version, I integrated Gemma 4 through Google's GenAI API. Since my free web-hosting instance doesn't have enough memory to run the local model itself, hosted inference made it possible to offer AI scanning in the deployed app.
I deployed the web application on Render and connected it to MongoDB Atlas.
Getting everything working taught me about API integration, environment variables, database authentication, network access, and debugging deployment failures.
Why Does Open Innovation Matter?
Open-weight AI gave me an opportunity to experiment with a vision model without having to build an AI model from scratch.
Using Gemma locally through Ollama let me test prompts, learn how image analysis behaves, and build my application around the model's capabilities.
For deployment, I chose hosted Gemma inference so that the public demo could run without requiring every visitor to install a local model.
This taught me that open-weight models offer flexibility: developers can experiment locally, change models, and choose how to run inference based on their resources.
The deployed version still requires an internet connection and uses a hosted API, so it isn't completely offline. I'd like to explore more local-first options in the future.
For me, open innovation made this project possible as a learner: I could start small, build a real application around an open-weight model, and learn how to take it from local development to a working online demo.
Prize Categories
- Best Use of Gemma: Gemma powers Lifeform's image-identification feature, using Gemma 3 4B locally and Gemma 4 through a hosted API.
- Best Use of MongoDB Atlas: Atlas serves as the database for player data in an application built around open-weight AI.
What's Next?
I'd like to continue improving identification results, outdoor missions, and the discovery experience.
The goal is to make the app useful for encouraging people to look more closely at the nature around them — and then put their phones away and enjoy it.
Step outside. Notice more. Discover something new. 🌱
Top comments (1)
Really liked the idea. Will definitely use it whenever I will come across any life form which I want to know about.
Great for a curious person like me ☺️