DEV Community: Varun Fatehpuria

Touchgrass.AI

Varun Fatehpuria — Thu, 08 Oct 2026 09:34:41 +0000

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

What I Built

I built TouchGrass AI, an open-source AI companion designed to help people spend less time on their screens and more time outdoors. 🌱

The idea is simple: use AI for a few seconds, then put the phone away.

Users can choose an activity mode, available time, energy level, outdoor setting, and personal interests. TouchGrass AI then generates a short, practical outdoor mission that they can actually go outside and complete.

The app currently includes three modes:

🌿 Nature — discover and observe things around you
🚶 Explore — generate small outdoor exploration challenges
🌱 Garden — get simple gardening and plant-care activities

It is designed for anyone who wants a simple reason to step outside, whether that's a short walk, exploring a local park, observing nature, or spending some time in the garden.

The goal isn't to make people spend more time interacting with AI.

The goal is to make the AI interaction as short as possible.

Code

💻 GitHub: [https://github.com/Varun-666/touchgrass.ai]

The repository contains the complete application, including the frontend, FastAPI backend, AI integration, prompts, fallback system, PWA support, tests, and documentation.

How I Built It

TouchGrass AI is built around open-weight AI and local inference.

The AI component uses Qwen2.5-0.5B-Instruct, accessed through the Hugging Face Transformers ecosystem.

The basic flow is:

User
  ↓
TouchGrass Web UI
  ↓
FastAPI Backend
  ↓
Qwen2.5-0.5B-Instruct
  ↓
Outdoor Mission
  ↓
Put the Phone Away
  ↓
Go Outside 🌳

The user provides a few preferences such as:

Activity type
Available time
Energy level
Outdoor setting
Interests or goals

The local AI uses these inputs to generate a structured outdoor mission containing a title, description, steps, duration, things to bring, and safety information.

The project uses:

Python
FastAPI
Hugging Face Transformers
Qwen2.5-0.5B-Instruct
HTML/CSS/JavaScript
Progressive Web App support

There is also a fallback system so the application can still provide useful outdoor activities if the AI model is unavailable.

No paid AI API or API key is required.

Why Does Open Innovation Matter?

Open innovation is important to TouchGrass AI because the project is intended to work with people in the real world, not just inside a cloud-based chat window.

🔒 Privacy

Outdoor experiences can involve personal information such as photos, gardens, homes, and surroundings.

With local inference, the project can process AI requests without requiring everything to be sent to a third-party AI provider.

📡 Offline Potential

The places where people want to use TouchGrass AI may not always have a reliable internet connection.

Using an open-weight model gives the project a path toward running the AI locally and eventually supporting completely offline experiences.

🔧 Freedom to Experiment

The AI isn't locked to a proprietary API.

Developers can experiment with:

Different open-weight models
Smaller models
Quantized models
Fine-tuned models
Different prompts
Different local hardware

This makes the project much easier to modify and extend.

💰 Accessibility

There are no per-request API costs.

Once the model is available locally, users can experiment with the application without needing a paid AI API subscription.

For me, that's one of the biggest advantages of open AI: the technology becomes something developers can actually take apart, modify, and build on.

My Agent Session

[Add your DevRelay agent session here if available]

Prize Categories

Touch Grass
Open-Source AI

Less screen. More outside. 🌱

Project TouchGrass.AI for HacktoberFest2026 Week 1

Varun Fatehpuria — Thu, 08 Oct 2026 09:30:49 +0000

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

What I Built

I built TouchGrass AI, an open-source AI companion designed to get people off their screens and into the real world.

The idea is simple:

Use AI for a few seconds. Then put the phone away. 🌱

Instead of building another AI assistant that encourages people to spend more time chatting with a model, TouchGrass AI generates small, practical outdoor missions based on:

🌿 Activity type
⏱️ Available time
⚡ Energy level
📍 Outdoor setting
🎯 Personal interests and goals

The app currently has three modes:

Nature — activities focused on observing and exploring nature
Explore — small outdoor exploration challenges
Garden — simple gardening and plant-care activities

For example, a user can select:

Explore → 30 minutes → Low energy → Park

and receive a short outdoor mission designed to be completed away from the screen.

The goal isn't to keep the user inside the app.

The goal is to make the app unnecessary as quickly as possible.

The ideal interaction is:

10 seconds on the phone → 30 minutes outside. 🌳

Code

GitHub Repository: [https://github.com/Varun-666/touchgrass.ai]

The project is completely open source and includes the application code, AI integration, prompts, fallback system, frontend, backend, and documentation.

How I Built It

TouchGrass AI is built around an open-weight AI model rather than a proprietary AI API.

The AI layer uses:

Qwen2.5-0.5B-Instruct

The model runs locally through the Hugging Face Transformers ecosystem.

The basic architecture is:

User
  │
  ▼
TouchGrass Web UI
  │
  ▼
FastAPI Backend
  │
  ▼
Local Qwen2.5-0.5B-Instruct
  │
  ▼
Outdoor Mission
  │
  ▼
📱 Put Phone Away
  │
  ▼
🌳 Go Outside

The user provides a few simple preferences such as their available time, energy level, environment, and interests.

The local model then generates a structured outdoor mission containing things like:

Mission title
Short description
Steps
What to bring
Safety notes
A suggested duration
A reason for doing the activity

I also included a fallback mission system so the application can still demonstrate its core experience if the AI model isn't available.

The project uses:

Python
FastAPI
Hugging Face Transformers
Qwen2.5-0.5B-Instruct
HTML/CSS/JavaScript
PWA support

No paid AI API or API key is required.

Why Does Open Innovation Matter?

This project is specifically designed around the idea that AI shouldn't always require sending everything to a remote server.

🔒 Privacy

Outdoor activities can involve photos, locations, gardens, homes, or other information about someone's surroundings.

A local AI architecture provides a path toward keeping that information on the user's own device instead of automatically sending it to a third-party AI provider.

📡 Offline Potential

People don't necessarily use TouchGrass AI while sitting next to a perfect internet connection.

The whole point is to use it while going outside.

Using an open-weight model means the project can be developed toward offline/local inference rather than depending entirely on an external API.

🔧 Model Freedom

Because the project isn't tied to a proprietary API, the AI component can be modified or replaced.

Developers can experiment with:

Different open-weight models
Quantized models
Fine-tuned models
Smaller models for mobile devices
Different prompting strategies

The application isn't locked into one company's AI infrastructure.

💰 Accessibility

There is no per-request AI API cost.

That makes experimenting with the project much more accessible for students, hobbyists, and other developers who want to build AI applications without maintaining a paid API subscription.

The Interesting Part

Most AI applications try to maximize the amount of time users spend interacting with them.

TouchGrass AI tries to do the opposite.

A successful interaction isn't:

"I spent 30 minutes talking to the AI."

It's:

"The AI gave me something to do, and then I put my phone away."

That's the behavior I wanted to build around for this challenge.

My Agent Session

[ADD DEVRELAY AGENT SESSION HERE IF AVAILABLE]

Prize Categories

Open-Source AI
Touch Grass

Built with open-source AI for Hacktoberfest 2026. 🌱

Less screen. More outside.