NatureDex: A Little Less Scrolling, a Little More Exploring

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

What I Built

I built NatureDex, a pocket field guide for the things you notice outside.

You don’t need a planned hike or a rare discovery. Start with the tree beside your house, a flower along your usual route, or an unfamiliar leaf. Take a picture, keep walking, and learn about it when you get back.

NatureDex is for curious beginners, casual walkers, and anyone who wants a reason to look more closely at their surroundings.

The experience follows a simple rhythm:

• Capture: photograph something that catches your eye.
• Keep walking: save photos and notes in Walk mode, even without internet.
• Reconnect: when your laptop is available, the open phone app automatically sends waiting photos for identification.
• Discover: review possible matches and save sightings to your personal Field Guide.
• Explore again: choose easy, medium, or hard expeditions and earn achievements along the way.

Your Field Guide grows from your discoveries, with photos, notes, a sighting timeline, and optional map locations.

I gave the app a retro, pixel-art look to make collecting discoveries feel playful. The goal is to make the screen a small part of the outing.

Take a photo. Pocket the phone. Keep walking.

Demo

Open NatureDex

NatureDex has an installable mobile web interface. Set up the laptop companion and scan its private QR code once to pair your phone. After that, you keep using the same app address.

Load the app before heading outside so offline Walk mode is ready. Identification uses your own laptop; the hosted site provides the interface.

The sample collection shows NatureDex’s pixel illustrations. Your own Field Guide grows from the sightings you save.

Code

NatureDex on GitHub

The repository includes the app, model setup, and phone-to-laptop connection instructions.

How I Built It

NatureDex’s identification engine uses Imageomics’ open-weight BioCLIP 2 through pybioclip. A model built for biological images gives the app its core recognition capability.

The frontend uses React, TypeScript, and Vite, hosted on Render. A FastAPI backend runs on the laptop, with SQLite storing saved sightings. Offline captures stay in browser storage on the phone until they can be processed.

The most important design decision was separating taking a photo from identifying it.

You can walk somewhere with no signal and collect photos without waiting for the model. Later, when your phone and laptop are connected, reopen NatureDex and the queued photos transfer to the laptop. Results come back to the phone for review.

No signal needed to collect a discovery. Identification happens when you reconnect.

The laptop companion starts at Windows sign-in. Tailscale Funnel provides its HTTPS connection without requiring a custom domain, so pairing doesn’t mean entering a new link every session. Photos travel to the laptop through that connection; there is no cloud photo queue.

My GPU has gone bad, so I ran BioCLIP on my laptop’s CPU. To avoid holding several gigabytes of RAM all day, the model runs in a separate worker that exits after three idle minutes. The connection stays available, and the next photo automatically reloads the model.

NatureDex also treats identification as a suggestion. It shows alternative matches, makes uncertainty visible, and provides photo-quality hints. Walk results need review before joining the Field Guide.

Why Does Open Innovation Matter?

Open weights let me build the experience around a model I can run myself.

I don’t need a third-party AI API for every photograph or a per-identification payment. Once the weights are downloaded, inference can run locally without internet access. The remote phone connection still needs internet, but capturing photos outside does not.

That freedom shaped NatureDex: the phone handles the outdoor moment, while a laptop handles the large model. I can choose where inference happens, experiment with other models, and change how predictions are presented.

It also meant a broken GPU didn’t end the project. I could keep building with the CPU I already had.

For NatureDex, open innovation made this workflow possible: go outside without a connection, collect what catches your eye, and come home to learn about it using a model you control.

A little less scrolling. A little more exploring.

Prize Categories

• Best Use of Render: Render hosts NatureDex’s mobile web interface, while the open-weight identification model runs on the user’s own laptop.