🌿 IRL Quest: Touch Grass, Complete Quests, Level Up!

Rudra Singh Bora — Sat, 10 Oct 2026 16:19:54 +0000

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

What I Built

IRL Quest is a gamified real-life mission app built around a simple idea: make getting away from screens feel like a real-life adventure!

The app encourages users to step outside, try small activities, explore their surroundings, and complete real-world challenges. Instead of just scrolling through a screen, users can choose a mission, complete it in real life, earn XP, and unlock achievements.

✨ Key Features

🎯 Real-Life Missions: Discover small challenges designed to encourage offline activities.
🤖 AI-Generated Missions: Generate new missions using a locally running AI model.
⭐ XP & Leveling: Earn experience points by completing missions.
🏆 Achievements & Ranks: Track progress and celebrate milestones.
⏱️ Mission Timer: Keep track of time spent on a quest.
💾 Local Progress: Save progress in the browser using Local Storage.

IRL Quest is for anyone who wants to make everyday activities more engaging, build healthier screen habits, and add a little fun to real life.

The goal is simple: use technology to motivate people to spend more time experiencing the world beyond their screens. 🌱

Code

💻 GitHub Repository:

https://github.com/rudra5002-code/IRL-Quest

The project source code is available on GitHub. Feel free to explore the implementation, share feedback, and suggest improvements!

How I Built It

I built IRL Quest using:

HTML — app structure
CSS — styling and layout
JavaScript — mission logic, XP, achievements, timers, and UI interactions
Ollama — local inference for AI-generated missions
Qwen3 4B — the open-weight language model used to generate missions
Browser Local Storage — saving progress locally

For AI mission generation, the app sends a request to the locally running Ollama API. The model generates a mission in JSON format, which the application validates before displaying it to the user.

Note: AI-generated missions require Ollama and the Qwen3 4B model to be installed locally. Generation speed depends on your computer's hardware. The AI feature may not work until the local setup is complete.

Why Does Open Innovation Matter?

Open innovation matters because it makes AI experimentation more accessible to developers and creators.

By using an open-weight model through Ollama, I could experiment with AI-powered features locally instead of depending entirely on a hosted, closed AI API.

For a project like IRL Quest, the idea is especially meaningful: AI helps create the missions, but the real experience happens away from the screen.

Open tools make it easier to experiment, share what we learn, and build on each other's work. 🌍

🌿 Touch grass. Complete quests. Earn XP. Level up IRL.

Built for the Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass.

NextStep: A Productivity Coach That Helps You Decide What to Do Next

Rudra Singh Bora — Sun, 04 Oct 2026 18:45:07 +0000

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

What I Built

I built NextStep, a simple productivity app designed for a friend who often knows he has a lot to do, but gets stuck deciding what to do first.

The problem wasn't a lack of motivation. There were just too many things competing for attention.

So I built NextStep around one simple idea:

Don't figure out everything. Just figure out what to do next.

NextStep lets my friend quickly capture his thoughts and tasks, prioritize them by importance and timing, work through focused sprints, and track his progress throughout the day.

The most important feature I added for this challenge is AI Coach.

When my friend feels confused or overwhelmed about a goal, he can describe the situation in his own words. The AI Coach, powered by the open-weight Gemma model, turns that situation into three simple, actionable next steps.

Each AI-generated step can then be added directly to the task list.

This means the AI isn't just giving advice — it helps turn confusion into something my friend can actually do.

Demo

Live Demo:
https://newstep2.onrender.com

Try the AI Coach section and enter something like:

"I have an exam tomorrow and five chapters left. I don't know where to start."

The AI Coach will generate three practical next steps, which can be added directly to the NextStep task list.

Code

GitHub Repository:
https://github.com/rudra5002-code/newStep

The project is open source and contains the frontend, Express backend, AI Coach integration, and setup instructions.

How I Built It

I built NextStep as a lightweight web application using:

HTML
CSS
JavaScript
Node.js
Express
Hugging Face Inference Providers
Google's Gemma 2 2B IT, an open-weight model

The core AI feature is the AI Coach.

The flow is:

The user describes what's confusing or overwhelming.
NextStep sends the situation to the Express backend.
The backend sends the request to Gemma through the Hugging Face Inference API.
Gemma is instructed to return exactly three short, practical next steps.
NextStep displays those steps to the user.
The user can add any generated step directly to their task list.

I deliberately made the AI output task-oriented rather than creating another general-purpose chatbot.

The goal is not to have a long conversation with AI.

The goal is to answer one question:

"What should I do next?"

I deployed the application on Render, with the AI request handled securely through the backend so the Hugging Face token is not exposed in the browser.

Why Does Open Innovation Matter?

Open innovation matters here because the AI model is part of the product experience, not just an external black box.

I used Gemma, an open-weight model, as the reasoning layer behind NextStep's AI Coach. That gives the project more flexibility than building the feature around a completely closed model.

Using an open-weight model makes it possible to think about future improvements such as:

running the model locally
experimenting with different model versions
fine-tuning the model for productivity workflows
changing the inference provider
having more control over how the AI behaves

For a productivity tool dealing with someone's personal thoughts, goals, and plans, having more control over the AI stack is especially valuable.

Open models also make experimentation more accessible to individual developers. I was able to take an open-weight model like Gemma and turn it into a focused feature for a real person and a real problem.

Prize Categories

I am entering the following partner categories:

Best Use of Render

NextStep is deployed and running on Render as a live web application. The application uses a Node.js/Express backend to securely handle the AI Coach requests.

Live Demo:
https://newstep2.onrender.com

Best Use of Gemma

The AI Coach is powered by Google's Gemma 2 2B IT, an open-weight model.

Gemma is used as the core AI component that transforms an overwhelming situation into three practical next steps that the user can turn into tasks.

DEV Community: Rudra Singh Bora

🌿 IRL Quest: Touch Grass, Complete Quests, Level Up!

What I Built

✨ Key Features

Code

How I Built It

Why Does Open Innovation Matter?

NextStep: A Productivity Coach That Helps You Decide What to Do Next

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

Prize Categories

Best Use of Render

Best Use of Gemma