Shourya Shinde

Posted on Oct 6

FieldNotes - DEV Challenge Week 1 Hacktoberfest

#devchallenge #hf26challenge

Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass Submission 🌿

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

What I Built

🌿 Field Notes — Trade Scrolling for Noticing

Field Notes is a local-first, AI-powered outdoor mission generator designed to help people spend less time scrolling and more time experiencing the world around them.

Instead of opening another app and consuming more content, users tell Field Notes:

⚡ How much energy they have — Slow & Soft, Curious, or Let's Roam
🌳 What's nearby — Anywhere, Park, Streets, or Garden
📍 Optionally, something they can see from their door

Field Notes then uses a locally running open-weight AI model to generate a small, personalized outdoor mission.

For example:

The Overlooked Corner

Take the most familiar route near you, but turn once where you normally wouldn't. Find a corner, doorway, tree, or view you've never properly looked at.

Each mission includes:

A short title
A practical activity
An estimated time
Something to look for
Something to take away from the experience

The idea is simple:

The AI shouldn't keep you on the screen. It should give you a reason to leave it.

Field Notes is designed for anyone who feels stuck in a scrolling loop, wants a low-pressure break from technology, or simply needs a small reason to step outside.

Demo

GitHub Repository: ShouryaShinde/FieldNotes

Project Screenshots:

Code

The complete project is open source on GitHub:

Field Notes — GitHub Repository

The project currently uses:

HTML
CSS
Vanilla JavaScript
Node.js
Express
Ollama
Llama 3.2 3B

How I Built It

The original prototype used a hardcoded collection of outdoor missions.

I changed that architecture so missions are generated dynamically by an open-weight AI model running locally.

Tech Stack

Frontend: HTML, CSS, Vanilla JavaScript
Backend: Node.js + Express
AI Runtime: Ollama
AI Model: Llama 3.2 3B
Communication: REST API
Output: Structured JSON
Inference: Local

The architecture is:

User
  ↓
Field Notes Web UI
  ↓
Vanilla JavaScript
  ↓
POST /api/mission
  ↓
Node.js + Express
  ↓
Ollama
  ↓
Llama 3.2 3B
  ↓
Structured JSON Mission
  ↓
Field Notes UI

When a user submits their preferences, the browser sends information such as:

{
  "energy": "medium",
  "place": "park",
  "location": "a quiet lane with two neem trees"
}

The Express backend constructs a prompt around those preferences and sends it to the local Llama 3.2 3B model through Ollama.

The model returns structured JSON containing:

{
  "title": "Follow the moving shade",
  "description": "Walk slowly through the park...",
  "time": "25 min",
  "look": "Places where sunlight meets shadow",
  "leave": "One detail you would normally miss"
}

The frontend then renders the generated mission using the existing mission card.

This means the application no longer depends on a fixed list of missions. Every interaction can produce a new activity based on the user's context.

Why Does Open Innovation Matter?

Open innovation made it possible to build Field Notes around local AI rather than a closed AI API.

That matters because the project's entire philosophy is local-first.

With a locally running open-weight model:

🔐 User-provided context can stay on the device.
💸 There are no per-request AI API costs.
🔑 No proprietary AI API key is required.
🧪 The model can be replaced and experimented with.
🛠️ Developers have more control over the AI layer.
🌱 The application can continue working without depending on a cloud AI provider.

A closed API could certainly generate outdoor missions. But using an open-weight model changes what is possible.

The AI doesn't need to be a remote service that users continuously send their information to. It can simply be another component running locally alongside the application.

That makes the technology fit the purpose of the project:

Use technology to help people disconnect from technology.

Prize Categories

Best Use of GitHub Copilot

GitHub Copilot was used during the initial development of Field Notes to help scaffold the project, implement the initial frontend structure, and iterate on the application's functionality.

Copilot helped accelerate the transition from the initial concept and UI prototype into a working web application, while the AI mission-generation layer was later implemented using Ollama and Llama 3.2 3B.

The project is open source and available here:

https://github.com/ShouryaShinde/FieldNotes

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