Maati: a little tree that nudges you back outside

Naman Goel — Sun, 11 Oct 2026 17:22:55 +0000

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

What I Built

Maati turns “take a break” into a small mission that fits the time of day. It’s for developers and anyone who can look up from a screen and realize hours have passed.

A floating cherry blossom tree in the optional Windows desktop companion tracks active screen time. When it’s time for a break, the companion suggests one of three kinds of reset:

Daytime: a seed-planting activity when the crop score is promising, or a simple nature observation quest otherwise.
Early evening: a nearby outing, with local place suggestions and directions.
Late evening: an indoor reset, like making tea or getting water and taking ten minutes with lofi or ambient audio.

You can accept a mission, then record its completion in the web app with a photo and caption. The community feed and scrapbook make those real-world breaks something you can look back on and share.

Demo

Live app: Maati

The first visit may take a little while if the free service has gone idle.

Code

Maati on GitHub

How I Built It

The web portal uses Next.js, TypeScript, Tailwind CSS, and Framer Motion. A Fastify API handles accounts, profiles, missions, and posts; PostgreSQL stores the user and community data.

For seed guidance, Maati loads the real Crop Recommendation CSV into a scikit-learn classifier. The free Render demo trains a lightweight model at startup using temperature and humidity. Its score is an experimental crop-recommendation signal—not a calibrated probability that a seed will germinate.

The mission flow uses Backboard for structured advice, Open-Meteo for location and weather, and Google Maps services for place directions and transit details. Render hosts the web app, API, and predictor as separate services.

Why Does Open Innovation Matter?

Keeping the crop predictor in an open, replaceable service made it possible to adapt Maati to the limits of a free deployment. The model and its input features are inspectable, and I can swap the predictor without rebuilding the rest of the app.

There’s also a TabPFN implementation in the repository for a higher-resource setup. The current free Render demo uses scikit-learn instead, because that model fits the available resources better. The advice and maps portions still use hosted services, so Maati is a hybrid project rather than a fully local or offline assistant.

Prize Categories

Best Use of Render — Render hosts Maati’s web app, API, and crop-prediction service.

The submission rules ask builders to list categories their project genuinely uses. The free deployment currently runs scikit-learn, so I’d only add Best Use of TabPFN if you can also demonstrate the TabPFN path running. I left out Tinker, Gemma, and Backboard categories because the current deployed build doesn’t use them in the qualifying setups described by the challenge. Challenge details and categories

HacktoberFest 2026

Naman Goel — Fri, 09 Oct 2026 06:53:04 +0000

Let's log the Journey here.
Day 1, Oct 9, 12:22
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DEV Community: Naman Goel