Nature Go :Outdoor Exploration tool

Rabia Arif — Sun, 11 Oct 2026 05:15:56 +0000

This is a submission for the

Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

We keep telling people to go outside, but many outdoor apps still keep them staring at a screen.

A map app asks for constant checking. A fitness app asks for another dashboard. A nature app asks users to frame, upload, wait, read, and repeat.

The phone becomes the destination instead of the doorway.

That observation led me to build Nature Go: a screen free outdoor exploration app that turns real world nature into small, verifiable quests.

Nature Go gives an explorer a mission such as:

Find a green leaf.
Photograph tree bark.
Spot a pinecone.
Find moving water.
Discover a wildflower.
Look for moss, dewdrops, a feather, or a spiderweb.

The explorer goes outside, captures the discovery, and receives an AI verification result. The app checks whether the requested target is present, whether it is physically real, and whether the image shows genuine outdoor context.

If the discovery is verified, Nature Go saves it to the explorer’s journal, updates the daily streak and badges, and reads an educational nature fact aloud. If the image is a phone screen, printed image, indoor plant, artificial plant, or unrelated target, the app rejects it and explains why.

The screen is only needed for the shortest part of the experience: choosing a quest and confirming the result. The real activity happens outside.

The application includes:

20 nature quests
8 expedition challenges
5 achievement badges
Custom quest creation
Discovery journal with photos and confidence scores
Daily streak and longest-streak tracking
Explorer profiles
Screen-free audio mode
Camera and photo-upload flows
Demo scenarios for real discoveries and spoofed images
Admin tools for explorer profiles, official quests, and verification controls

Demo

Live application:

https://nature-go-outdoor-vision-quest.ai.studio/

Try this flow:

Open the live app.
Keep the default Green Leaf quest.
Select Real Nature — Green Leaf under the demo photos.
Observe the successful verification result.
Try Phone / Tablet Screen and compare the rejection.
Open Quests to browse the 20 nature missions.
Select Audio Walk to experience the screen-free mode.

The project workflow is:

Code

*GitHub repository: *

https://github.com/RabiaA-arif/NatureGo

The repository contains the complete React, TypeScript, Express, Firebase, quest, verification, audio, and admin-console implementation.

Local setup:
Bash
git clone https://github.com/RabiaA-arif/NatureGo.git
cd NatureGo
npm install
cp .env.example .env
npm run dev

Configure the required environment variables before testing the full AI verification and Firebase persistence flows.

How I Built It

Nature Go is built around an open-source AI verification layer rather than using AI as a decorative chatbot feature.

Gemma integration:
Model: [add the exact Gemma model name used in the submitted build]
Runtime/provider: [add the exact runtime or provider]
Inference mode: [local, Google Cloud, or another verified provider]
Source code: [add the exact public GitHub link to the Gemma implementation]

The model receives the current quest target and the captured image. It returns structured verification data:

The application uses that response to decide whether to:

Play a success or rejection sound
Narrate the nature fact
Save the discovery to the journal
Update quest completion progress
Update streaks and authenticity score
Unlock badges
Guide the explorer toward another outdoor quest

The verification policy checks three conditions:

1.Target match: The requested object or scene must be visible.

2.Physical authenticity: Screens, printed images, wallpapers, fake plants, and indoor houseplants must be rejected.

3.Outdoor context: The image should show natural daylight, soil, open air, ground, or surrounding vegetation.

The rest of the stack is:

Frontend: React 19, TypeScript, Vite, Tailwind CSS, Motion, and Lucide icons
Backend: Node.js, Express, and TypeScript
Authentication: Firebase Authentication with Google sign-in and explorer login
Database: Firebase Firestore
Local persistence: Browser local storage for cached profiles, quests, journal entries, badges, and progress
Audio: Browser SpeechSynthesis API and Web Audio API

Why Does Open Innovation Matter?

The central problem is not simply identifying a leaf. It is protecting the purpose of going outside.

A closed image API can return a label, but Nature Go needs a transparent and adaptable verification policy. The project must be able to distinguish a real outdoor discovery from a photograph of a screen showing nature.

Using Gemma as an open-weight model makes that direction possible.

It gives the project a model layer that can be inspected, served in different environments, adapted for local nature vocabulary, and replaced as better open models become available. It also creates a path toward privacy-preserving or offline exploration instead of forcing every outdoor image through a closed service.

Open innovation matters here because different communities may need different quest sets, languages, accessibility features, safety rules, and local ecological knowledge. The open foundation makes those changes possible without rebuilding the entire experience from scratch.

Most importantly, open AI helped me build a tool where AI supports the physical world instead of competing with it for attention.

The best outcome is not a user spending an hour inside Nature Go. The best outcome is a user completing one quest, hearing one nature fact, putting the phone away, and noticing something else nearby.

Prize Categories

Best Use of Gemma — Gemma is used as the open-weight vision verification layer that powers target matching, anti-spoof checking, outdoor-context verification, and structured nature-fact responses.

Links

Live demo:

https://nature-go-outdoor-vision-quest.ai.studio/

Source code:

https://github.com/RabiaA-arif/NatureGo

Challenge:

https://dev.to/challenges/hacktoberfest-week1-2026-10-05

devchallenge #hf26challenge #gemma

AI-Based-Layoff-Informator

Rabia Arif — Sun, 04 Oct 2026 17:16:08 +0000

This is my submission for the Hacktoberfest Weekend Challenge: Build for a Friend.

What I Built

My roommate works in software development and often felt anxious about layoff news.

So I built AI-Based Layoff Informator—a simple Django app that collects recent tech layoff news, summarizes it with AI, and sends a clear briefing by email.

Instead of checking multiple websites every day, users receive:

Important layoff updates
Company names and affected employees
Reported reasons
Links to original sources

The goal is to stay informed without doom-scrolling.

Demo

🎥 Video demo: [https://youtu.be/WvyyZVjem9Ehttps://youtu.be/WvyyZVjem9E]

The demo shows the complete flow—from entering an email address to receiving the AI-generated layoff briefing.

Code

🔗 GitHub: AI-Based Layoff Informator

How I Built It

I used:

Python and Django
Google News, Bing News, TechCrunch, and Hacker News RSS feeds
OpenRouter for AI summarization
SMTP for email delivery
SQLite for logging delivery results
Django tests for reliability

The application filters duplicate and unrelated headlines, validates AI responses, preserves source links, and sends both HTML and plain-text emails.

Why Does Open Innovation Matter?

Open-source tools made it possible to combine news feeds, AI, Django, and email into one useful product.

Instead of building another chatbot, I created a tool that solves a real emotional problem for someone close to me. Open innovation also makes it easier for others to improve the project with scheduled briefings, unsubscribe support, user preferences, and more news sources.

Why This Project Matters

This project is personal. I built it to help my roommate—and other tech workers—stay informed about layoffs in a calmer, more organized way.

Thank you for checking out my project!

DEV Community: Rabia Arif

Nature Go :Outdoor Exploration tool

Demo

Code

How I Built It

Links

devchallenge #hf26challenge #gemma

AI-Based-Layoff-Informator

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

Why This Project Matters