Fieldquest: Tiny Outdoor Quests, Powered by Local AI

Pranjal KUMAR — Tue, 06 Oct 2026 20:33:07 +0000

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

What I Built

I built Fieldquest, a small web app that turns the time, energy, and kind of activity you’re in the mood for into a tiny outdoor quest.

Choose whether you want to notice things, move a little, or make something; set aside 5, 15, 20, or 45 minutes; and Fieldquest gives you a few simple steps to try. The suggestions are designed to start near wherever you are, need no special gear, and leave the screen behind once you head out.

Fieldquest is for anyone who wants a low-pressure nudge outside—whether that means a walk around the block, a moment on a balcony, or a closer look at a nearby patch of green. No streaks, scores, or account required. Small counts.

Demo

file:///C:/Users/Pranjal%20kumar/Pictures/Screenshots/index.html

The demo mode works without an AI model. To try locally generated quests, run Ollama with the gemma3:1b model on your computer.

Code

PRANJALKUMAR1307 / fieldquest

Fieldquest

A little outside, your way. Fieldquest turns your available time, energy, and mood into a short outdoor micro-adventure. The goal is to make the screen the shortest part of the experience.

Run it

Open index.html in a browser for the built-in demo mode. No account, API key, install, or network connection is required for the demo.

To generate quests with an open-weight model, run Ollama, download Gemma 3 1B, and serve this folder on localhost:

ollama pull gemma3:1b
ollama serve
python -m http.server 8000

Open http://localhost:8000. If the browser cannot reach Ollama, allow local browser access by setting OLLAMA_ORIGINS=* for the Ollama process, then restart it. The app calls Ollama's local chat endpoint directly; there is no app server and no cloud model API.

Why open-source AI?

The model can run on the user's own machine. Their starting point, energy, and preferences stay in their browser and…

View on GitHub

Built with plain HTML, CSS, and JavaScript. No framework or third-party JavaScript dependencies.

How I Built It

Fieldquest is a static, responsive website. Its controls let you choose a starting place, time, energy level, and activity style.

For AI-generated quests, the site sends those choices to a local Ollama instance running Gemma 3 1B. The model returns a short title, description, and set of steps. If Ollama or the model isn’t available, Fieldquest uses built-in quests instead, so the core experience still works offline.

I kept the suggestions low-pressure and practical: no required equipment, no trespassing, no picking plants, and no assumptions about local weather or wildlife.

Why Does Open Innovation Matter?

The local model makes the experience more private and adaptable. A person can run quest generation on their own machine, keep their choices on-device, and choose which model to use. There’s no hosted AI account or closed API required for the local setup.

Open models also make the project easier to experiment with: the prompt and model can be changed, and the built-in demo mode remains available when local inference isn’t set up. That makes the AI an option people can control, while keeping the app useful either way.

My Agent Session

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Prize Categories

Best Use of Gemma

Sidequest: A Local AI Hangout Planner for a Friend

Pranjal KUMAR — Sun, 04 Oct 2026 20:09:49 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built Sidequest, a local-first hangout planner for the friend who wants to spend time together but could use a little help making the plan.

Share their interests, location, mood, budget, and how much time you have. Sidequest creates a thoughtful, personalized outing with a few activities and a small extra touch.

Demo

Run Sidequest locally by following the setup instructions in the repository README. Once the server is running, open http://127.0.0.1:8000.

pranjalkumar1307.github.io

Code

View the Sidequest GitHub repository

How I Built It

Sidequest uses the open-weight Qwen 2.5 3B model through Ollama for local inference. The frontend uses HTML, CSS, and JavaScript, with a lightweight Python server connecting the app to Ollama.

The friend’s details stay on the device: the browser sends them to the local server, which passes the prompt to the local model. No hosted AI API or account is needed.

Why Does Open Innovation Matter?

Open innovation makes local, private AI applications like Sidequest possible. Running an open-weight model on your own computer gives you control over personal details and lets you use the project without depending on a hosted AI service or API key. The code and model choice can also be inspected and adapted.

My Agent Session

Prize Categories

Microsoft Azure Fundamentals

DEV Community: Pranjal KUMAR

Fieldquest: Tiny Outdoor Quests, Powered by Local AI

What I Built

Demo

Code

PRANJALKUMAR1307 / fieldquest

Fieldquest

Run it

Why open-source AI?

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories

Sidequest: A Local AI Hangout Planner for a Friend

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories