Akshra Jain

Posted on Oct 8

GreenMate AI

#devchallenge #hf26challenge

🌱 GreenMate AI --- AI That Gets You Off the Screen

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

What I Built

🌱 GreenMate AI

GreenMate AI is an open-source, AI-powered outdoor activity planner
designed to help people spend less time on screens and more time in
the real world.

Most AI applications encourage users to stay inside an app and interact
with a screen. GreenMate takes the opposite approach: it uses AI to
create a personalized outdoor mission and then encourages the user to
put the phone away and go outside.

The app is designed for students, developers, remote workers, and anyone
who wants a simple push to take a break from screens.

How it works

The user chooses:

😊 Current mood
⏱️ Available time
🚶 Preferred activity
🌱 Difficulty
📍 Outdoor environment/location

GreenMate then uses a locally running open-weight AI model to
generate a personalized mission.

For example:

30-Minute Nature Reset

🚶 Walk for 15 minutes\
🌿 Find 3 different plants\
🐦 Listen for birds\
🌳 Spend 5 minutes observing your surroundings\
📵 Avoid checking your phone for the final 10 minutes

Once the mission starts, the app encourages the user to stop interacting
with the application:

📵 Mission started --- put your phone away and enjoy the outside.

The goal is not to maximize app usage. The goal is to make the screen
the shortest part of the experience.

Who is it for?

GreenMate AI is especially useful for:

🎓 Students who spend long hours studying or coding
💻 Developers and remote workers
📱 People trying to reduce unnecessary screen time
🌳 People who want simple outdoor activity ideas
👨‍👩‍👧 Families looking for small outdoor challenges
🌱 Anyone who wants to reconnect with their surroundings

Demo

🌐 Live Demo

[Add deployed application link here]

🎥 Video Demo

[Add demo video link here]

Suggested demo flow

Open GreenMate AI.
Select Stressed as the mood.
Select 30 minutes.
Select Walking.
Select Easy.
Click Generate My Green Mission.
Show the AI-generated outdoor mission.
Start the mission.
Show the "Put your phone away" screen.
Complete the mission and show XP/badge progress.

Code

GitHub Repository

[Add GitHub repository link here]

The project is open source and structured so that contributors can add:

New outdoor missions
New challenges
New open-weight AI models
Additional languages
Accessibility improvements
Weather providers
Offline features
New map/routing providers
UI improvements
Tests and documentation

How I Built It

GreenMate AI is built as a full-stack application with a strong focus on
open-source AI and local inference.

Technology Stack

Layer Technology

Frontend React + TypeScript
Build Tool Vite
Styling Tailwind CSS
Backend Python + FastAPI
AI Runtime Ollama
AI Model Configurable open-weight model
Database SQLite
Maps Leaflet + OpenStreetMap
Weather Open-Meteo (optional)
Routing Open routing solution such as OSRM
Version Control Git + GitHub

Architecture

                         ┌─────────────────────┐
                         │       User          │
                         │ Mood / Time / Goal  │
                         └──────────┬──────────┘
                                    │
                                    ▼
                         ┌─────────────────────┐
                         │   React Frontend   │
                         │  Mission Interface  │
                         └──────────┬──────────┘
                                    │
                                    ▼
                         ┌─────────────────────┐
                         │    FastAPI Backend  │
                         │   REST API Layer    │
                         └──────┬───────┬──────┘
                                │       │
                  ┌─────────────┘       └──────────────┐
                  ▼                                    ▼
        ┌──────────────────┐                  ┌────────────────┐
        │      Ollama      │                  │     SQLite     │
        │  Local Inference │                  │ User Progress  │
        └────────┬─────────┘                  └────────────────┘
                 │
                 ▼
        ┌──────────────────┐
        │ Open-Weight Model│
        │ Mission Planning │
        └──────────────────┘

AI Workflow

The AI is not used as a generic chatbot.

Instead, the application sends structured preferences to the local
model:

Mood: Stressed
Time: 30 minutes
Activity: Walking
Difficulty: Easy
Environment: Park

The model generates structured mission information such as:

{
  "title": "30-Minute Nature Reset",
  "duration_minutes": 30,
  "difficulty": "Easy",
  "activity_type": "Walking",
  "steps": [
    "Walk for 15 minutes",
    "Find three different plants",
    "Observe your surroundings for five minutes",
    "Complete a five-minute return walk"
  ],
  "challenges": [
    "Find something yellow",
    "Listen for three different sounds",
    "Stay screen-free for ten minutes"
  ],
  "screen_free_minutes": 25
}

The structured response makes the AI output predictable and easy for the
frontend to display.

Local AI

The core AI functionality is designed around Ollama and an open-weight
model.

This means the project can run AI locally instead of requiring a
proprietary AI API for the core mission-generation feature.

The model is configurable so that users can experiment with compatible
open-weight models.

Why Does Open Innovation Matter?

Open innovation is particularly important for GreenMate AI because the
project is designed to work close to the user rather than keeping their
information inside a remote AI service.

🔐 1. Privacy

GreenMate can run the core AI locally.

A user's preferences do not need to be sent to a proprietary AI provider
just to generate an outdoor activity.

Location is also treated separately from the AI generation process so
that precise location information does not need to be sent to the AI
model.

🧩 2. Model Choice

With an open-weight approach, users can choose or replace the model
instead of being permanently tied to one proprietary provider.

This makes experimentation easier for students and developers.

🛠️ 3. Customization

The model and prompts can be adapted for different use cases.

For example, contributors could create versions focused on:

Hiking
Gardening
Bird watching
Running
Cycling
Nature photography
Family activities
Local-language outdoor missions

🌐 4. Community Contribution

Because the project is open source, contributors can improve the
application without needing access to a private AI platform.

Someone could contribute a new model adapter, another person could
improve the mission-generation prompt, and another could add
accessibility or multilingual support.

💻 5. Local and Offline Possibilities

A local model makes it possible to explore an important idea:

What if an AI outdoor assistant could continue working even when
internet connectivity is limited?

This is especially relevant for hiking, rural areas, parks, and outdoor
environments.

🔓 6. Transparency

Using open technologies makes the architecture easier to inspect and
understand.

As a B.Tech CSE project, this also makes the system valuable from an
educational perspective because students can learn how:

Frontend
   ↓
Backend
   ↓
Local AI Runtime
   ↓
Open-Weight Model
   ↓
Structured AI Output

actually works.

The Touch Grass Philosophy

GreenMate is intentionally designed around a slightly unusual product
principle:

The best session is the session where the user leaves the app.

Most applications optimize for:

More clicks
More sessions
More screen time
More engagement

GreenMate optimizes for:

More outdoor time
More movement
More observation
More screen-free minutes
More connection with the real world

The application therefore has an intentional "exit the app" moment.

After starting a mission, the user sees:

🌱 YOUR MISSION HAS STARTED

📵 Put your phone away.

Go outside.
Complete your mission.
Enjoy the real world.

We'll be here when you return.

Privacy & Safety

GreenMate is designed with privacy and responsible use in mind.

Privacy principles

Local AI is preferred for the core AI functionality.
Precise location is not required for AI mission generation.
Location access requires explicit user permission.
Sensitive location information should not be unnecessarily stored.
API keys should never be committed to the repository.
Environment variables are used for configuration.

Safety principles

GreenMate should:

Recommend realistic activities.
Avoid dangerous activities.
Avoid encouraging users to enter restricted or private areas.
Avoid medical diagnosis or medical claims.
Consider weather when weather data is available.
Encourage users to follow local safety rules.
Provide fallback suggestions when location or routing services are unavailable.

My Agent Session

<!-- Optional. Add your DevRelay agent session here if available. -->

[Add DevRelay agent session link here]

Prize Categories

<!-- Add the applicable partner prize categories here after checking the challenge page. -->

[ ] Partner Category 1
[ ] Partner Category 2
[ ] Partner Category 3

Project Roadmap

✅ MVP

[x] Project concept
[x] Open-weight/local AI architecture
[x] Personalized outdoor mission generation
[x] Mood and time-based activity planning
[x] Screen-free mission concept

🚧 In Development

[ ] React frontend
[ ] FastAPI backend
[ ] Ollama integration
[ ] Structured AI responses
[ ] Leaflet/OpenStreetMap integration
[ ] SQLite progress tracking
[ ] Gamification
[ ] Offline fallback

🔮 Future Scope

📱 Android/iOS application
🎙️ Voice-based mission generation
👁️ Local vision model for plant recognition
🐦 Bird identification
⌚ Smartwatch integration
🌐 Multilingual support
🗺️ Better offline maps
🧠 Personalized local models
👥 Community-created outdoor challenges
♿ Improved accessibility

What I Learned

Building GreenMate AI helped me explore several areas of computer
science:

Full-stack application development
REST API design
Local AI inference
Open-weight AI models
Prompt engineering
Structured AI output
Privacy-aware application design
Maps and geolocation
Database design
Git and GitHub workflows
Open-source contribution practices

Most importantly, the project changed the way I think about AI.

AI does not always have to make us spend more time with technology.

Sometimes the best use of AI is to help us put the technology down.

Conclusion

🌱 GreenMate AI

AI that gets you off the screen and into the real world.

GreenMate AI combines open-weight AI, local inference, maps, and
gamification to create a simple goal:

Use AI → Start a mission → Put the phone away → Go outside.

The project is built with open technologies so that developers and
students can inspect it, modify it, replace the AI model, and contribute
new ideas.

Because sometimes, the smartest thing an AI can tell you is:

📵 Put your phone away. Go touch grass. 🌱

Team

Project: GreenMate AI

Team Members:

[@YOUR_DEV_USERNAME] --- Developer / AI / Project Lead
[@TEAMMATE_USERNAME] --- [Role]
[@TEAMMATE_USERNAME] --- [Role]

<!-- For a team submission, list the DEV usernames of all teammates here. -->

License

This project is open source.

License: [Add your chosen open-source license, e.g. MIT]

🌱 GreenMate AI --- AI That Gets You Off the Screen

What I Built

🌱 GreenMate AI

How it works

Who is it for?

Demo

🌐 Live Demo

🎥 Video Demo

Suggested demo flow

Code

GitHub Repository

How I Built It

Technology Stack

Architecture

AI Workflow

Local AI

Why Does Open Innovation Matter?

🔐 1. Privacy

🧩 2. Model Choice

🛠️ 3. Customization

🌐 4. Community Contribution

💻 5. Local and Offline Possibilities

🔓 6. Transparency

The Touch Grass Philosophy

Privacy & Safety

Privacy principles

Safety principles

My Agent Session

Prize Categories

Project Roadmap

✅ MVP

🚧 In Development

🔮 Future Scope

What I Learned

Conclusion

🌱 GreenMate AI

📵 Put your phone away. Go touch grass. 🌱

Team

License

Links