Bayān: Touch Grass with open-source AI and local Arabic reflection

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

What I Built

Bayān is a local-first Arabic rhetoric companion that gets people off the screen and back into the world.

It turns a short outdoor walk into a focused reflective writing task. The user picks a rhetorical exercise — simile, metaphor, or sensory description — then steps away from the screen, notices a detail in the environment, and returns to turn that observation into a stronger Arabic phrase.

The experience is intentionally minimal. The app does not try to keep the user glued to a dashboard. It creates a brief, calm moment of attention, then gets out of the way.

The project is built for anyone who wants a low-friction way to practice Arabic imagery, sensory language, and thoughtful observation without needing a cloud AI service or an overly complicated interface.

Demo

• GitHub repo: https://github.com/FreemanBoss/bayaan
• Local demo: http://127.0.0.1:8000
• Project repository and usage details: https://github.com/FreemanBoss/bayaan

The project is designed to work as a real local experience: choose a prompt, step outside, notice something, come back, and write. The experience is meant to feel grounded, not like a synthetic AI showcase.

Code

• Repository: https://github.com/FreemanBoss/bayaan

The codebase includes the backend, frontend, local model integration, validation logic, fallback behavior, and optional local speech/transcription support. Everything is open and easy to inspect in the public repository.

How I Built It

I built Bayān around open-weight local AI, not closed cloud APIs.

The project uses:

• Ollama with Gemma 3 4B for mission generation, translation, and chat
• FastAPI for the backend service layer
• a lightweight static web frontend for the game flow
• browser-local storage for lightweight game state
• optional local Piper speech synthesis for Arabic playback
• optional local Whisper transcription for voice-note input

The important design choice was that the app stays local-first. The user experience is built around the assumption that the walk happens outside the app, not inside it. The model is there to support reflection and language, not to dominate the interaction.

The app also includes validation and fallback behavior so that if the local model is unavailable or returns weak output, the user still receives a coherent experience instead of a broken UI.

Why Does Open Innovation Matter?

Open innovation matters here because the app is meant to work in the real world, not just in a cloud lab.

A closed API would not fit the spirit of the project. The app is designed for:

• offline or low-connectivity outdoor use
• privacy by default
• local model control
• transparent behavior and editable prompts
• a system that can be understood, run, and improved without vendor lock-in

This project is especially relevant to the challenge because it uses open models to create a useful, grounded experience in a real-world setting. The model does not replace the user’s attention; it supports their attention. That is the meaningful difference.

My Agent Session

This project was built with a local AI-assisted workflow grounded in real runtime checks, not a fake “it works” claim.

I validated the app by:

• running the backend locally
• checking the live frontend
• testing the API routes
• validating the local speech and transcription flow
• confirming the fallback behavior under real runtime conditions

The build process emphasized verification over hype: if the app did not run, it was not treated as finished.

Prize Categories

• Touch Grass
• Best Use of Gemma

This project fits the core Touch Grass theme directly: it is a local-first, real-world, attention-returning app built around stepping away from the screen and noticing the world.

It also uses Gemma as a core part of the product, which makes Best Use of Gemma a natural fit.