# HUE OUTSIDE: A Daily Color Hunt That Gets You Outdoors

> Source: <https://dev.to/anannyakundu07/hue-outside-a-daily-color-hunt-that-gets-you-outdoors-gme>
> Published: 2026-10-10 21:38:31+00:00

*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass](https://dev.to/challenges/hacktoberfest-week1-2026-10-05)*

**HUE OUTSIDE — Find your color. Find your world.**

What if we used technology not to spend more time staring at screens, but to notice the world around us?

That's the idea behind HUE OUTSIDE, a daily color-hunting challenge that encourages people to step outside, explore nature, and find beauty in everyday things.

Every day, users get one color to discover. Their mission is simple:

The app includes a daily challenge, a streak tracker, a calendar displaying completed days and photo memories, and a personal archive inspired by Instagram Stories archives.

Over time, the calendar becomes more than a habit tracker. It becomes a visual diary of the little things we noticed in the real world.

The goal isn't to spend more time in an app. It's to make opening the app the beginning of a small outdoor adventure.

🚀 Live demo: [https://ahsplore.github.io/hue-outside/](https://ahsplore.github.io/hue-outside/)

💻 GitHub repository: [https://github.com/ahsplore/hue-outside](https://github.com/ahsplore/hue-outside)

I built HUE OUTSIDE with the goal of combining a simple daily habit with open-source AI.

The planned technology stack includes:

The AI feature, called *Color Scout*, is designed to suggest places to look for the day's color, from flowers and leaves to objects hiding in plain sight.

The main experience remains simple: receive a color, go explore, capture two photos, and return to see your progress.

For a project like HUE OUTSIDE, AI should help people explore their surroundings, not require them to depend on a closed service for every interaction.

Using an open-weight model gives the project more flexibility. The model can be changed, run locally where supported, and adapted to the needs of the application.

Local inference can also reduce dependence on paid API calls and help keep AI interactions on the user's own machine.

I also want personal photo memories to remain private by default. The architecture is intended to keep those memories on the user's device rather than requiring them to be uploaded to an external service.

For me, open innovation means being able to experiment, understand the tools I'm using, and build an experience that can evolve without being locked into a single AI provider.

I built HUE OUTSIDE using Antigravity. Due to some system issues while setting up Ollama and integrating the AI model, I couldn't save my session through DevRelay. However, I successfully built and deployed the application.

The best part of this idea is that the result isn't just another collection of AI-generated answers. It's a collection of real moments discovered away from the screen.

**Find your color. Find your world. 🌿**