Clover - The LEAF Hunt

What I Built✨

Leaf Hunt is an offline, audio-first plant scavenger hunt. Each week it generates five seasonal clues based on your local last spring frost date, like “find a leaf with five leaflets” or “find something still flowering.” You walk, point your phone at a plant, a local open-weight vision model identifies it, and you describe what you found by voice. Finds go into a local journal that you can export as a styled HTML page. The screen is only needed at the start and end.

It’s for anyone who wants a reason to go outside: families, hikers, students, and people who enjoy birding or nature walks without needing a signal.

Demo
Drive link🍀 -

GitHub link🍀 - https://github.com/parnita-Singh/leaf-hunt

How I Built It
Vision: a small quantized open-weight vision-language model, served by Ollama or llama.cpp
Speech-to-text: whisper.cpp
Text-to-speech: Piper
Storage: SQLite
Interface: Typer CLI, running on a laptop or Raspberry Pi

Clue selection is rule-based, using a bundled seasonal plant dataset and the frost date. The LLM only phrases the clues. Every model sits behind one adapter interface, so you can swap backends in config.toml without touching app code.

Why Does Open Innovation Matter?
It works offline. Trails often have no signal, and nothing calls a remote API.
Your data stays with you. Photos of places and voice notes never leave the device.
It costs nothing to run. There are no per-request fees, so you can play all season.
The models are swappable. I can try different vision models without rewriting the app.

My Agent Session

I built Leaf Hunt in Cursor’s Agent mode, one step at a time, reviewing each stage before moving on. The session is saved with DevRelay and embedded below.