DEV Community: Nazia Ahmed

Grounded: Building an App That Turns “I Should Go Outside” Into an Adventure 🌿

Nazia Ahmed — Mon, 12 Oct 2026 06:59:50 +0000

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

What I Built

We spend so much time online looking for something interesting to do that we sometimes forget there’s an entire world waiting outside our doors.

That thought inspired Grounded, a real-world adventure discovery platform I'm building.

💡 The idea

Grounded helps people break out of repetitive routines and discover small, meaningful experiences based on their interests, available time, and budget.

Not every adventure needs to be a mountain trek or an expensive trip. It could be a ten-minute walk, finding an unfamiliar street, photographing something beautiful, discovering a quiet café, or watching the sunset.

The goal is simple: make going outside feel exciting, accessible, and rewarding.
🌱 What I'm building

Personalized discovery: Find activities that match your mood, interests, time, and budget.

Adventure planning: Turn a free afternoon into a practical itinerary.

Explore your world: Discover nature, photography spots, hidden gems, and local experiences.

Community adventures: Find activities to share with friends.

Gamified exploration: Earn XP and unlock achievements as you complete adventures.

🛠️ The technical direction

I'm building the frontend with React and TypeScript, with a focus on interactive discovery, thoughtful UI design, and a smooth user experience.

The longer-term direction includes AI-assisted adventure planning and an architecture that can connect to an open-weight language model and backend services.

I'm developing the experience incrementally, starting with a functional frontend and sample adventure data before connecting real AI services.

Demo

Code

https://github.com/PragyaTandi/grounded-ai

How I Built It

I built Grounded, a real-world adventure discovery platform, using React, TypeScript, and Vite. The idea is to help people break out of repetitive routines and discover small, meaningful experiences outside their homes, whether that means exploring a nearby park, trying street photography, finding a hidden café, or taking an unplanned walk.

The frontend focuses on interactive, mood-based discovery, personalized activity planning, and a gamified exploration experience. I'm designing the architecture so that a dedicated recommendation service can eventually connect the interface to an open-weight language model for more personalized adventure suggestions.

My approach is to start with a functional application and structured adventure data, then introduce AI capabilities as modular services rather than making the entire product dependent on a single model or provider. This makes the project easier to test, extend, and adapt to different inference environments.

Why Does Open Innovation Matter?

Open innovation matters for Grounded because I want the product to remain adaptable, accessible, and independent of a single AI provider.

Using open-weight models could allow me to experiment with local inference, customize recommendation workflows, and evaluate different models for generating useful, context-aware adventure plans. It could also make it possible to run certain AI features on a user's own hardware, reducing dependence on paid, closed APIs and giving developers greater control over data handling.

More importantly, open innovation lets developers learn from existing tools, build on community contributions, and share improvements rather than reinventing everything from scratch. For a project like Grounded, that flexibility creates room to experiment with personalized recommendations while keeping the core experience usable even when an AI service is unavailable.

I see open innovation not just as a way to reduce costs, but as a way to make AI-powered experiences more transparent, adaptable, and accessible to more people.

My Agent Session

Prize Categories

perspective

Nazia Ahmed — Mon, 05 Oct 2026 06:59:00 +0000

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

What I Built

My friend is really good at taking photographs of me. I am terrible at taking photographs of her. i mean she says i dont get her sense of style and perspective.

That made me wonder: what does she see through her camera that I don't?
So I built perspective an AI-powered photography reflection tool that compares two collections of photographs:

Photos my friend took of me
Photos I took of my friend

Instead of simply judging which photographs are "better", the project tries to understand the different perspectives behind them.

The goal is to identify patterns in composition, framing, camera angle, distance, subject placement, environment, lighting, and visual mood, and turn those patterns into something understandable in human language.

Eventually, the system will produce insights such as:

Her visual perspective: She seems to give the surrounding environment an important role in the photograph.

My visual perspective: I tend to make her the clear center of attention and focus more tightly on her.

And then go one step further:

What can I learn from her eye?

The point isn't to teach someone to copy another photographer. It is to help them notice what they naturally overlook.

I also want the project to become a small memory-making tool, where the photographs and the insights around them can become a record of how two people saw the same moments differently.

Demo

The project currently runs locally as a Streamlit application.

The application allows the user to upload photographs from both photographers and send them to a locally running vision model for analysis.

A demo prototype will be added here:

Code

https://github.com/nazya-ux/perspective

How I Built It

The application is built with Python and Streamlit.

The core AI component is Qwen3-VL 4B, an open-weight vision-language model running locally through Ollama.

The current pipeline is:

Photograph
↓
Streamlit upload
↓
Ollama
↓
Qwen3-VL
↓
Visual interpretation
↓
Photographer's Eye

The model is prompted to analyze photographs in terms of composition, framing, camera angle, subject placement, background, lighting, candid/posed character, visual mood, and what the photographer appears to be paying attention to.

The next layer of the project will aggregate these observations across both collections and generate a comparison of:

Her perspective
My perspective
Visual philosophy
Emotional character suggested by the photographs
What each photographer tends to notice
What one photographer might learn from the other
A meaningful memory interpretation

The important part is that the AI isn't supposed to take the photograph for you.

It doesn't tell the photographer exactly where to stand or how to pose someone. It acts more like a visual mirror that helps the photographer understand their own habits and perspective.

Why Does Open Innovation Matter?

This project would be very different if the entire experience depended on a closed vision API.

Using an open-weight model running locally makes the project possible in a much more personal way.

These photographs can be deeply personal. They may contain someone's face, home, friends, locations, and private memories. With local inference, the photographs don't need to be sent to a remote commercial AI service just to understand their visual patterns.

Open models also make the project replaceable and experimentable.

I can change the vision model, experiment with prompts, modify the analysis pipeline, or eventually fine-tune the system without rebuilding the entire application around a proprietary API.

Most importantly, open innovation lets me build an AI system that is not trying to replace the photographer.

It helps someone understand how they already see.