This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.
What I Built
Pocket Field Trip helps you notice something on an ordinary walk. Instead of giving you another feed to scroll, it offers a small observation task, a few cues to take outside, and a place to record what you noticed when you return.
The idea is simple: the screen should be the shortest part of the experience. You do not need a special destination or an ambitious hike. A familiar street can become a field trip.
Demo
Try the working browser app:
https://sundaysebasidian-byte.github.io/
Choose a prompt, read the observation card, step away from the screen, and return to save a short note. The browser demo is the easiest way to try the project; an Android build is also in development.
Code
The source is public under the MIT license:
https://github.com/sundaysebasidian-byte/sundaysebasidian-byte.github.io
How I Built It
The project uses an open-weight text embedding model through Transformers.js and ONNX Runtime Web. Inference runs locally in the browser through WebAssembly. The model helps match the user's input to a bounded collection of observation cards, rather than relying on an unrestricted text generator.
This keeps the activities understandable and testable. The AI helps select an appropriate starting point; the person does the observing.
The published demo has been tested with real browser inference, producing 384-dimensional embeddings across a collection of 20 cards. The interface and note-saving flow have also undergone regression testing. AI coding assistants helped with implementation and review.
Why Does Open Innovation Matter?
A small outdoor tool should not require every interaction to travel to a hosted AI service. Local inference lets Pocket Field Trip keep the matching process on the user's device and avoids a paid inference API for each prompt.
Open components also make the behavior inspectable. Developers can examine the matching logic, replace the model, or adapt the observation cards. After the required assets are downloaded and available locally, inference does not depend on a remote model endpoint.
What I Learned
The most useful AI interaction here is deliberately short. More generated text does not necessarily make an outdoor experience better. A clear suggestion and a reliable place to save one observation can be enough.
The browser version is ready to try. Android testing is still based on an emulator, so I am not claiming broad physical-device validation. The next step is testing the experience on real walks and improving it from that feedback.
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