Wilding - Touch Grass Challenge

Megh Shah — Mon, 12 Oct 2026 06:51:08 +0000

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

Wildling: Notice More. Scroll Less.

What I Built

We carry our phones everywhere. Sometimes, even when we step outside, we spend more time looking at a screen than noticing what's around us.

I wanted to explore a different use for AI: instead of giving people another reason to stay online, what if it gave them a reason to put their phones away?

That idea became Wildling, a nature companion that turns an ordinary walk, garden visit, or break in a nearby park into a small adventure.

Users choose an interest and how much time they have. Wildling helps them explore through simple observation quests: notice different leaf shapes, listen to the sounds around you, find patterns in shadows, or look for details you would usually walk past.

After the walk, users can record what they actually noticed in a personal field journal.

The goal isn't to identify everything in nature or turn the outdoors into another productivity challenge. It's to help people slow down, become curious, and pay attention to the world around them.

The screen should be the shortest part of the experience.

Demo

Try Wildling here:

Replace this placeholder with your live deployment URL.

Code

Source code: https://github.com/Megh-Shah-08/wildlife-touch-grass-challenge

The project is designed around a simple principle: the outdoor experience should remain useful without requiring a paid AI service or an account.

How I Built It

Wildling is built as a mobile-first web application, with a focus on a simple quest-to-journal experience.

The core flow is straightforward:

Choose an interest, environment, and available time.
Generate or select a nature quest.
Take the activity outdoors and work through short observation missions.
Return and record a personal observation.
Keep the entry in a private field journal.

For the AI layer, the intended approach is to use an open-weight language model through a local inference runtime such as Transformers.js. This makes it possible to generate personalized quests on the user's device instead of depending on a hosted, proprietary LLM API.

Built-in quests provide a fallback when local inference is unavailable. They must be clearly distinguished from model-generated quests.

The application is designed around local data storage, so observations can remain on the user's device rather than being uploaded to a cloud service.

[Before publishing: name the exact model and runtime you actually implemented, and remove any statements about local inference or offline functionality that you have not verified.]

Why Does Open Innovation Matter?

For Wildling, open innovation isn't just about choosing a different model. It changes the relationship between the user and the application.

1. AI without a mandatory paid inference API

A compatible open-weight model can make personalized quest generation possible without relying on a paid proprietary LLM endpoint. That makes the experience more accessible and gives the developer more control over running costs.

2. Privacy by design

Outdoor observations can be personal. A note about someone's daily walk, garden, or surroundings doesn't need to be sent to a remote AI service just to become a thoughtful journal entry.

Local inference can keep that processing on the user's device, provided the implementation does not transmit the data elsewhere.

3. Freedom to experiment

Open-weight models make it possible to evaluate different model sizes, runtimes, and generation strategies. A lightweight model may be more suitable for a mobile-first nature companion than a much larger hosted model.

The trade-offs matter, too: browser compatibility, download size, memory use, latency, and output quality all affect the experience.

4. Technology that serves the real world

The most important design decision is not which model generates the most impressive paragraph. It is whether the technology helps someone notice something meaningful outside and then lets them put their phone away.

That's the kind of AI I wanted to explore: useful, understandable, and designed to give people more control rather than more screen time.

Open innovation makes that exploration possible without making a proprietary AI service the centre of the product.

Prize Categories

Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass

Thanks to the organizers for a challenge that asks us to think about what AI can help people do away from their screens.

StudySprint — Stop Planning. Start Studying. 📚

Megh Shah — Sun, 04 Oct 2026 16:54:20 +0000

What I Built
I built StudySprint, a lightweight AI-powered study planner for a college friend who struggles with deciding what to study first when there are multiple subjects, deadlines, and limited time.
Users can add their subjects, topics, deadlines, difficulty, and estimated study time. StudySprint then creates a prioritized study plan.
It also includes:

✅ Task completion tracking
📊 Progress tracking
⏱️ Focus timer
💾 Local persistence
🤖 AI-powered prioritization
🔄 Fallback prioritization when AI is unavailable The goal is simple: spend less time planning and more time studying.

Demo
🔗 Live Demo: https://study-sprint-26.lovable.app/

Code
🔗 GitHub: https://github.com/Megh-Shah-08/study-sprint-26

How I Built It
Built using ReactJS, TypeScript, and Tailwind CSS.
The AI layer is designed around an open-weight model through Ollama, which receives the student's tasks and available time and generates a prioritized plan.
I also added a rule-based fallback using deadlines, difficulty, and estimated time so the app remains functional without AI.

Why Does Open Innovation Matter?
Using an open-weight model means the AI doesn't have to depend entirely on a proprietary API. It can be run locally, customized, and adapted for different use cases.
For a personal tool containing study schedules and tasks, local AI also provides a path toward keeping that information on the user's own device.

My Agent Session
Built with the help of Lovable.dev.

Prize Categories

Open Innovation
AI / Open-Weight AI

Built for a friend. Made to solve a real problem. 🚀

DEV Community: Megh Shah

Wilding - Touch Grass Challenge

Wildling: Notice More. Scroll Less.

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

Prize Categories

StudySprint — Stop Planning. Start Studying. 📚