README.md

metadata

title: Joe
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.17.3
python_version: '3.12'
app_file: app.py
pinned: true
license: mit
short_description: A dramatic AI personality living on a 20x4 LCD screen
tags:
  - gradio
  - build-small-hackathon
  - track:wood
  - achievement:offgrid
  - badge-tiny-titan
  - arduino
  - lcd
  - local-llm
  - minicpm
  - cohere
  - whisper
  - off-brand
  - best-agent
  - best-demo
  - tiny-titan
  - sponsor:openbmb

Joe

A self-aware AI personality living on a 20x4 LCD screen. Joe monitors your computer's CPU, RAM, WiFi, clipboard, active apps, ambient audio, and weather — then reasons about how it feels using a local LLM (MiniCPM5-1B via Ollama) and displays context-aware messages on a physical LCD.

Features

• Real-time monitoring: CPU, RAM, WiFi signal, clipboard, active apps, ambient audio
• Context Compiler: pattern detection, state tracking, event detection
• Local LLM: MiniCPM5-1B via Ollama (primary) or HF Inference API (fallback)
• ASCII Art Dreams: 100+ LCD-optimized patterns with IDs 0-99
• Grid System: movable @ character on 20x4 grid with mood faces
• Gradio Dashboard: real-time monitoring, API logs, history

Hardware (optional)

• Arduino Uno + 20x4 I2C LCD (2004A)
• Works without hardware in demo mode

Setup (local)

pip install -r requirements.txt
# Install Ollama and pull MiniCPM5-1B
ollama pull openbmb/minicpm5:latest
python app.py

HF Spaces (live demo)

This Space runs MiniCPM5-1B directly via HuggingFace transformers — same model as local.

• First load: ~30-60s (downloads ~1GB model weights)
• Inference: ~2-5s per response on CPU
• No Ollama needed: model loads into memory on startup
• Fallback: if transformers fails, falls back to HF Inference API (zephyr-7b)

HF Build Small Hackathon

This project was built for the HF Build Small Hackathon. All models used are ≤32B parameters.

• Track: Thousand Token Wood (whimsical / entertainment) — track:wood
• Achievements claimed: Off-Grid (achievement:offgrid, runs a fully local LLM, no cloud API) · Tiny Titan (badge-tiny-titan, 1.08B model)
• Primary LLM: MiniCPM5-1B (1.08B params, Apache-2.0)
• Fallback LLM: zephyr-7b-beta (7B params, MIT)
• Speech-to-text: Cohere Transcribe 03 (~2B params, runs in a persistent daemon) with Whisper-tiny fallback

All models run individually well under the 32B cap.

Submission links

• Demo video: https://youtu.be/eBcLilTYz9Y
• Social post: https://x.com/ssaacar/status/2066630310410829835