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TanzeelAhmad
TanzeelAhmad

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# I Built a Local AI Pet That Yells at You to Go Outside — No Cloud, No API Keys

Hacktoberfest Week 1 • Touch Grass Challenge


This weekend I submitted Miso — a memory-efficient desktop companion that runs a real open-weight LLM locally and literally tells you to touch grass when your focus timer hits zero.

Miso — Touch Grass Desktop Companion

🤖 Built with open-source AI at its core.

What is Miso?

No cloud. Locally optimized. Memory efficient. Secure.

Miso is a cute, interactive desktop pet that sits in the corner of your screen. It works with you, not against you. The pet tracks how long you have been focused, and when your timer runs out, it gets angry (in a cute way) and literally tells you to go outside and touch grass using an open-weight language model running entirely on your laptop.

This is a Progressive Desktop Widget — fully offline, open-source, local-only, and designed specifically for low-end hardware (i7-6600U / 8GB RAM). Zero cloud APIs. Zero subscriptions. Zero data leaving your computer.

Why Open Source AI Matters Here

This project was built specifically for people with older, lower-spec hardware (like an i7-6600U with 8GB RAM). Rather than relying on expensive cloud APIs or…

What it does

Miso is a cute, interactive desktop pet (Pikachu-style) that tracks how long you've been focused. When the timer runs out, it gets angry — in its cute way — and uses a local LLM (SmolLM2 / Qwen2.5) to generate a unique scolding via TTS. No cloud APIs. Zero subscriptions. Zero data leaving your machine.

Features:

  • Hover interaction: dance and emotions
  • Drag-to-position widget
  • Idle tracking: calms down if you actually leave
  • Universal customization (MISO_CHARACTER=my_pet)
  • Character hub: drop GIFs + config.json to build your own

The real work this week (Hacktoberfest fixes)

The PRs I submitted cover:

  • Deprecated param fixed
  • Download script repaired
  • Memory-efficient local AI configured
  • Memory file added (local/secure tracking)
  • .gitignore, docs, tests, TTS fallback, install errors cleaned up
  • User guide + emphasis on local/secure/memory-efficient
  • Qt6 / MSVC runtime requirement documented + Linux/Mac alternative

Stack

  • Python 3.10+ with PyQt6 (Qt6 runtime)
  • transformers + llama-cpp-python for open-weight .gguf inference on CPU
  • pyttsx3 for offline TTS
  • pynput for idle detection
  • Designed for low-end hardware (i7-6600U / 8GB RAM)

Why open-source AI matters here

A cloud-based version would fail this challenge: it needs internet, sends your screen-time data to a server, costs per API call, and won't run on aging hardware. Miso proves you can have a powerful, private, auditable AI companion that runs fully offline — and swap the brain with any compatible .gguf model.

Quick start


bash
git clone https://github.com/CodewithTanzeel/touch-grasschallenge-miso.git
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python scripts/download_models.py
python main.py
Windows: .\install.bat


Link:https://github.com/CodewithTanzeel/touch-grasschallenge-miso
Built with open-weight AI • Runs locally • Works offline • Costs nothing 🤖

What’s the worst productivity trap you've coded your way out of? Or, if you run Miso with a different .gguf model, let me know which one works best on your hardware in the comments below!


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`Hacktoberfest` `OpenSource` `LocalAI` `Python`
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