Desk to Dirt: Breaking Screen Inertia with Open-Weight AI (Pace-Bud)

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Touch Grass

What I Built

It’s 4:30 PM. Your eyes burn from staring at VS Code. You know you should step outside, but the mental friction of deciding where to go and what to do keeps you glued to your chair. Standard fitness apps expect you to already have your running shoes on. I wanted to build an app that actively kicks you out the door.

Enter Pace-Bud: an offline-resilient, "Desk-to-Dirt" micro-expedition generator.

Instead of passive tracking, Pace-Bud uses a single "Break My Screen" button. When clicked, the app calculates your exact local daylight remaining and generates a highly specific, time-sensitive outdoor micro-quest. It beams the dispatch to your earbuds, and the screen UI enters a minimalist "Outdoor Mode." If you switch tabs, the timer pauses—enforcing total screen detachment. While outside, you can record offline field notes that automatically sync and transcribe when you return to Wi-Fi.

Demo

Note on the Demo: To stay true to the core philosophy of absolute data privacy and zero runtime costs for runners, the Gemma 2 inference engine does not run in the cloud. It is hard-routed to localhost to run exclusively on the user's local GPU via Ollama. Clone the repo and spin it up to experience the full AI concierge!

Code

Surendra-T / Pace-Bud

Pace-Bud

An offline-resilient, voice-activated micro-expedition generator designed to break screen inertia using open-weight AI.

Pace-Bud is a fully open-source React/FastAPI application built for the Hacktoberfest 2026 "Touch Grass" challenge. It actively targets desk fatigue by calculating local daylight windows and generating spontaneous, time-sensitive outdoor routing dispatches. It features a decentralized, zero-signal audio queue, allowing runners to log field observations entirely offline, syncing only when network connectivity is restored.

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System Architecture & Features

1. Astronomical Daylight Calculation: Computes exact local sunset and remaining daylight minutes via ephemeris (astral), triggering time-sensitive dispatch windows.
2. Open-Weight AI Dispatches: Leverages Google's Gemma 2 9B (via local Ollama) to generate concise, location-aware outdoor objectives without relying on proprietary cloud LLM providers.
3. Decentralized Audio Field Queue (Offline Mode): Captures voice logs in zero-signal environments. Audio blobs are saved locally via browser IndexedDB (idb-keyval) and synced asynchronously when the device regains connectivity.
4. Resilient Run Telemetry:…

View on GitHub

How I Built It

My goal was to prove that you can build highly resilient, context-aware outdoor workflows without relying on fragile cloud APIs that crash the moment you enter a forest dead-zone.

• The Local AI Engine: I used Ollama to run Google's Gemma 2 9B on my local RTX 4070. It acts as the "Continental Concierge," generating the stoic outdoor objectives and tagging the natural elements found in my field notes.
• The Offline Audio Queue: I bypassed standard speech-to-text APIs (which fail without Wi-Fi) by using the browser's native HTML5 MediaRecorder and IndexedDB. When you record a voice note on the trail, the audio blob is saved locally to your device.
• The Sync Engine: A FastAPI backend wrapped in a Temporal workflow automatically detects when network connectivity is restored, flushes the queue, transcribes the audio, and saves the field log.
• The Voice: To ensure the screen stays in your pocket, I hooked up the ElevenLabs API to stream Gemma's dispatches in a crystal-clear, stoic "John Wick" voice straight to your earbuds.
• The Telemetry & Memory: I integrated MongoDB Atlas as a time-series database. When you return from a run, the app charts your "Dopamine Delta" (pre- vs. post-run mood) against your Apple Health biometric data and remaining daylight.
• The Frontend: Built with React (Vite) and Tailwind CSS, featuring bespoke "old-money" luxury themes and a wall-clock delta timer that leverages the Page Visibility API to log "pocket time" versus "screen time."

Why Does Open Innovation Matter?

The biggest lie in outdoor fitness tech is the assumption of continuous 5G.

Closed-source wrapper apps fail spectacularly when you are deep in the woods. Furthermore, proprietary algorithms optimize for one thing: keeping your eyeballs glued to their feeds.

Open-Weight models like Gemma 2 change the paradigm. By running the inference entirely on local hardware, Pace-Bud operates at zero-runtime cost. More importantly, its sole optimization metric is getting you away from the computer. Logging exact GPS coordinates, biometric health data, and mental health mood-scores requires absolute data sovereignty. With local open-source inference, my physical and mental data never touches a corporate server.

Prize Categories

I am officially throwing Pace-Bud into the ring for the following Hacktoberfest partner categories:

• Best Use of Gemma: Best open-weight AI application using local Gemma 2 9B for location privacy.
• Best Use of MongoDB Atlas: Best time-series backend for biometric vector storage and mood-tracking.
• Best Use of ElevenLabs: Best Voice/Audio AI integration for screen-free outdoor interactions.
• Best Use of Temporal: Best robust retry workflow for the offline field-log queue.