This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
NatureQuest AI is a full-stack, gamified outdoor exploration platform built around a single mission: Explore More, Scroll Less.
Modern screen fatigue is real. Most fitness and wellness trackers only count steps or screen hours without giving people a compelling, adventurous reason to step outside and observe the real world.
NatureQuest AI changes that by turning your local environment into an interactive outdoor RPG:
1)Dynamic Local Micro-Quests: Procedurally generated nature quests categorized by difficulty, duration, and sensory focus (e.g., “Find tree bark moss facing north”, “Photograph an urban pollinator at work”, “Identify canopy leaf venation”).
2)Anti-Cheat AI Media Forensics (NeuralEye Engine): Anyone can download a photorealistic Midjourney, DALL-E, or Gemini image from their couch. NatureQuest uses an open-weight vision transformer ensemble to inspect submitted photos for real optical camera characteristics, rejecting synthetic AI diffusion, digital screen captures, and recycled images.
3)Gamified Outdoor Habits: Earn XP, level up through outdoor ranks (from Seedling Scout to Canopy Guardian), build consecutive outdoor streaks, and unlock nature badges.
4)AI Dynamic Quest Generation: Generates contextual outdoor exploration missions tailored to current weather, surroundings, and seasons using open-weight local LLMs.
Demo
Code
NatureQuest AI — Explore More, Scroll Less
A production-grade, gamified outdoor exploration platform that rewards users for spending time in nature and documenting authentic discoveries while combating screen addiction.
🌟 Core Product Vision & Philosophy
In an era saturated with endless algorithmic feeds and generative AI deepfakes, NatureQuest AI shifts focus from the virtual world to the living, physical world.
The Golden Rule:
Inspect your mission, put your phone in your pocket, and explore the living outdoors.
Users select real-world quests, step outside, record authentic photographs of their discoveries, and submit them to NeuralEye™—a multimodal media forensics pipeline that verifies camera sensor optical capture, detects diffusion artifacts, and enforces strict cryptographic duplicate protections.
🛠️ Tech Stack & Architecture
Frontend
-
Framework: React 19 + TypeScript + Vite
-
Styling: Tailwind CSS with custom editorial nature design tokens (
forest-800, forest-900, lime, ivory)
-
Icons & Motion: Lucide Icons…
.
How I Built It
- Open-Weight AI Architecture
NatureQuest is built entirely on open-source, open-weight AI rather than proprietary black-box APIs:
NeuralEye Multi-Layer Forensics (haywoodsloan/ai-image-detector-deploy):
Architecture: SwinV2 (Hierarchical Shifted-Window Vision Transformer) fine-tuned for high-accuracy discrimination between physical sensor photons and synthetic diffusion artifacts.
Ensemble Defense:
Layer 1 (Fast-Path Provenance): Inspects binary byte arrays for C2PA cryptographic manifests (Google Imagen, Adobe Firefly), imperceptible SynthID watermarks, and visible 4-point generative corner stars in under 5ms.
Layer 2 (Vision Transformer): Evaluates frequency domain artifacts and lighting consistency using SwinV2 running on CPU inference.
Anti-Duplicate Hashing: Uses perceptual difference hashing (dhash) and SHA-256 to prevent users from recycling previous quest photos.
Procedural Quest Generation (Qwen/Qwen2.5-1.5B-Instruct via Ollama):
An open-weight Apache 2.0 licensed SLM (Small Language Model) generates micro-quests with structured JSON outputs specifying title, category, sensory observation guidelines, difficulty, and XP rewards.
Runs locally or on edge containers without per-token API costs or third-party data tracking.
- High-Performance Engineering Highlights
Client-Side Image Optimization: Smartphone cameras produce 10MB+ raw files. We built an in-browser canvas compressor that scales photos to 1600px and 82% JPEG quality before transmission. This dropped upload payload sizes by 95% (from 10MB down to ~300KB), reducing verification latency from 30s to under 1s.
Background Pipeline Pre-warming: The SwinV2 PyTorch weights are pre-warmed in a background thread on application boot so end-users never experience cold-start latency.
Why Does Open Innovation Matter?
Open innovation is the backbone of NatureQuest AI for three critical reasons:
1)Privacy for Physical Locations & Personal Captures: Asking users to upload personal outdoor photos to proprietary closed-source APIs means their real-world routines, geolocation EXIF data, and surroundings could be used to train closed models. Open-weight models deployed on self-hosted infrastructure guarantee user privacy.
2)Deterministic, Inspectable Forensics: A verification system must be transparent. If an image is rejected, open-source weights and inspectable C2PA/SynthID heuristics allow us to explain why an image failed (e.g. generative artifacts vs. resolution issues), rather than hiding behind an opaque corporate score.
3)Zero Paywalls & Accessibility: Health and outdoor wellness tools should not be tied to expensive API subscription tiers that shut down when pricing doubles. Open-weight models like Qwen 2.5 and open Hugging Face vision transformers ensure anyone can run and self-host NatureQuest anywhere.
Prize Categories
Render — Best Use of Render**
NeuralEye uses AI-powered image detection to distinguish AI-generated images from authentic photographs, supporting our nature-exploration platform. We plan to use Render to deploy and host the backend inference API, enabling reliable access to the AI detection model for image verification.
