WildWalk: an open-source AI tool that gets you off your screen

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

What I Built

WildWalk is a free, open-source web app that helps people step outside. You type your city, and it:

1. Pulls live temperature, humidity and air quality from Open-Meteo.
2. Scores how good a walk would be right now, using a TabPFN model.
3. Finds nearby parks and gardens with SerpApi.
4. Writes a short walk guide with Gemma, and reads it aloud as an MP3.

Then you switch on Pocket Mode: a full-screen timer, the phone goes into your pocket, and the guide plays. When the walk ends, you rate it. That rating goes back into the training data for the score model.

Who it is for: people who spend most of their day on screens and need a quick, clear reason to go out. Students, office workers and parents are the first audience I have in mind.

Honest current state: the TabPFN model is trained on synthetic seed data for now. Real ratings will improve it. The guide model is small (1B parameters), so the guides are simple. I would rather say that clearly than oversell it.

Demo

• Video (1–2 min): https://github.com/Himanshu-5680/WildWalk/blob/main/docs/demo.mp4

• Screenshots:
  

• Not deployed yet. It runs locally with setup.bat and start_all.bat.

Code

Himanshu-5680 / WildWalk

🌿 WildWalk

Touch grass, not screens.

WildWalk is a small, open-source tool that helps you decide whether to go outside today, and gives you a short audio guide for a screen-free walk. You enter your city. It checks the weather and air quality, gives the walk a score, suggests nearby places, and reads you a guide. Then you put your phone in your pocket and go.

Built for the Hacktoberfest 2026 Open-Source AI Challenge (Week 1: "Touch Grass").

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📸 Screenshots

Walk plan

Guide and audio

Pocket mode

Walk journal

Learn

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🎯 The problem

Many people spend most of their day on screens and rarely go outside. Deciding whether a walk is worth it is often guesswork: is it too hot, is the air bad, where should I go? WildWalk gives one clear answer in one place, and helps you stay off your phone once you leave.

💡 Goal

Make going…

View on GitHub

How I Built It

Everything in the main path is open-source or open-weight:

• Gemma 3 (1B) via Ollama. An open-weight model running on my own machine. It writes the walk guide from a prompt that includes the score, nearby spots, nature facts and past walks.
• TabPFN. A tabular foundation model that predicts a walk score from temperature, humidity, AQI, month and hour. Each rated walk is appended to a CSV, and the model retrains on it.
• Temporal. Every walk plan is a durable workflow: find spots, score, recall, write, voice, save. Each step has retries, so one failing step does not break the whole request.
• Tiger Data (Postgres + pgvector). Stores nature facts. The guide uses hybrid keyword and vector search to pick the facts that match the city and season.
• MongoDB Atlas. Vector memory of past walks, so the model can recall similar walks.
• SerpApi. Live web search for nearby parks.
• Edge TTS. A free neural voice for narration.
• Stack: FastAPI backend, plain HTML, CSS and JavaScript frontend, no build step.

Browser → FastAPI → Temporal workflow
                     ├─ SerpApi        → nearby spots
                     ├─ TabPFN         → walk score
                     ├─ Tiger Data     → nature facts (pgvector search)
                     ├─ MongoDB Atlas  → past walks (vector memory)
                     ├─ Gemma (Ollama) → walk guide
                     └─ Edge TTS       → audio

Fallback: if the backend is offline, the website computes a simple local score and guide, and shows a note saying so. The app never just breaks.

Why Does Open Innovation Matter?

• No per-request bill. Gemma and TabPFN run on my own machine. A closed API would charge for every guide and send every request to a company server.
• I can change it. I can swap the Gemma size, retrain TabPFN on real walk data, or rewrite the prompt. With a closed API I would be limited to what the provider allows.
• Open data. Open-Meteo and OpenStreetMap are free, so anyone in any city can run this without a paid data contract.
• Honest limits. Some parts are still cloud services (Atlas, Tiger Data, SerpApi, Edge TTS). The core decision and the guide run locally, but not everything stays on the device.

Prize Categories

• Best Use of TabPFN: walk scoring from tabular data, retrained on each rated walk.
• Best Use of Gemma: the guide writer, running locally.
• Best Use of Temporal: durable walk workflow with retries and resume.
• Best Use of Tiger Data: pgvector hybrid search over nature facts.
• Best Use of MongoDB Atlas: vector memory of past walks.
• Best Use of SerpApi: live search for nearby parks.

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