This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
Every day, people walk through their neighbourhoods, local parks, and outdoor trails. They see overflowing bins, littered green spaces, illegal dumping, or polluted waterways — and usually do one of two things: scroll past it or feel helpless to fix it.
EcoQuest is a mobile-first, community-driven environmental action web app built for the Hacktoberfest 2026 Week 1 "Touch Grass" challenge. Its goal is simple: help people turn outdoor observations into concrete, actionable community reports in under 30 seconds.
Instead of leaving environmental stewardship to bureaucratic delay or passive social media complaints, EcoQuest provides an immediate bridge between noticing a problem and doing something about it safely.
Key Features
- 📱 Mobile-First Outdoor UX: Built for real-world outdoor use with quick camera capture, drag-and-drop file uploads, responsive card layouts, and touch-friendly controls.
- 🌿 Smart Categorisation: Classifies reports into 5 key outdoor categories:
- Litter & Debris
- Overflowing Public Bins
- Neglected Green Spaces
- Water Pollution
- Illegal Dumping
- 🛡️ Confidence & Honest Uncertainty Disclaimers: AI output is never treated as infallible truth. Every report displays an explicit confidence score and an uncertainty notice stating that results are automated suggestions and not independently verified.
- 👷 Safe Next-Action Guidance: Gives immediate, safety-first recommendations (e.g., "Wear puncture-resistant gloves," "Report hazardous bulk items to the municipal council," "Organize a small neighborhood sweep").
- 🔄 Community Action Workflow: A live community feed that tracks issues across clear lifecycle statuses: Open ➔ In Progress ➔ Resolved, empowering neighbors to take ownership and celebrate resolved cleanups.
- 🔒 Privacy-Preserving Architecture: Strict client-side EXIF stripping via HTML5 Canvas prevents accidental leakage of home coordinates or camera metadata. Location is strictly general and user-described (no invasive GPS tracking).
- ⚡ Zero-Barrier Sample Data Mode: Works immediately offline out of the box with zero external API keys required, making the app 100% testable and demonstrable anywhere.
Demo
-
Local Running App: Accessible at
http://localhost:5173 -
Cover Art & Visuals:
public/cover.jpg(included in repository)
To run the live interactive demo locally:
git clone https://github.com/balabhadra3141/Hacktoberfest-Week1.git
cd Hacktoberfest-Week1
npm install
npm run dev
Visit http://localhost:5173 and click "Load samples" to immediately populate the interactive feed, submit new camera reports, and test status transitions.
Code
- Repository: https://github.com/balabhadra3141/Hacktoberfest-Week1
- License: MIT License
How I Built It
EcoQuest is engineered with modern, performant, and privacy-first web tools:
Tech Stack
-
Frontend: React 19, TypeScript, Vite, Tailwind CSS v4, Lucide React,
date-fns - Backend Proxy: Lightweight Node.js server (keeps API keys completely server-side)
- Storage: Browser LocalStorage with persistent CRUD state management
Swappable AI Architecture (AIProvider Pattern)
A core design tenet of EcoQuest is that the UI is completely decoupled from the AI provider. In src/lib/providers.ts, all model interaction adheres to a strict contract:
export interface AIProvider {
name: string;
isOpenWeight: boolean;
licenseUrl?: string;
analyze: (payload: CreateReportPayload) => Promise<AIAnalysis>;
}
This interface enables two swappable operational modes:
- Sample Provider (Default): A deterministic, zero-dependency offline engine that simulates realistic analysis without any API keys or network latency.
-
Vision-Language Provider (Prototype): A lightweight proxy calling Google Gemini 1.5 Flash via a backend server. The API key is stored securely in
.env.localand never exposed to the client. - Pluggable Open-Weight Models: Designed specifically to be swapped out for self-hosted open-weight vision models such as LLaVA-1.6, InternVL2, or CLIP.
Why Does Open Innovation Matter?
Environmental challenges are fundamentally local, shared, and community-owned. Building environmental infrastructure on closed, proprietary AI creates severe risks:
- Community Data Sovereignty: When citizens document civic issues, that data should belong to the community and local authorities — not be funneled into proprietary training datasets or held hostage behind commercial paywalls.
- Auditability & Algorithmic Trust: If an AI model prioritizes or deprioritizes reports, citizens have a right to inspect the prompts and weights. With open-weight models, community members can audit the system to ensure it doesn't bias against underserved neighborhoods.
- Longevity & Independence: Commercial API endpoints can be deprecated, rate-limited, or price-hiked at any moment. Open-weight models ensure that community tools will continue running decades from now on local hardware.
- Zero-Telemetry Privacy: Environmental reports frequently contain background imagery of public roads or parks. Running open-weight vision models locally or on community servers means sensitive spatial imagery never leaves the municipal boundary.
Open innovation ensures that technology serves as public infrastructure, not private gatekeeping.
My Agent Session
This project was built iteratively using an advanced agentic coding pair-programming workflow:
- Rapid architecture design, domain model drafting, and schema definitions.
- Developing robust validation, error states, and EXIF-stripping sanitization.
- Implementing a strict separation between frontend state and backend proxy inference.
- Testing and validating builds with full TypeScript checking and zero warnings.
Prize Categories
- Primary: Hacktoberfest Open-Source AI Challenge Week 1: "Touch Grass"
- Individual Submission: Created and submitted individually.
Thank you to the Hacktoberfest and DEV teams for promoting open-source innovation and inspiring developers to get outside and touch grass!
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