ShelfScribe: Local AI Listing Writer for Etsy Sellers
Turn product photos into ready-to-post Etsy listings — all running locally on open-weight AI models.
Built for Hacktoberfest 2026 Week 2: Side Hustle — something a real seller would actually pay for.
The Problem
Etsy sellers spend hours writing titles, descriptions, tags, and researching prices — time that could be spent making more products or fulfilling orders. Existing tools either require uploading your designs to the cloud (risking IP exposure) or come with recurring fees that eat into thin margins.
The Solution: ShelfScribe
ShelfScribe automates listing creation with a private, on-device AI that never uploads your product photos or designs to the cloud.
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Core model:
qwen3-vl:4b(open-weight vision + language model) running via Ollama - Zero cloud API keys: All inference happens on your laptop
- GDPR/CCPA friendly: Customer designs and product photos stay on-device
- Cost to run: $0 after download — the model is free and open-weight
How It Works
- Upload 1-5 photos of your product (clear, well-lit shots work best)
- Optional: Add keywords (e.g., "boho, ceramic, mug, gift for her")
- Optional: Specify product type (e.g., "jewelry, home decor, clothing")
- Click Generate Listing
- Copy the title, description, tags, and price range straight into Etsy
Why Open Innovation Matters
This project directly addresses the challenge theme:
“Build something with open-weight models or open-source AI that someone would actually pay for.”
Privacy
Product designs and customer data never leave your laptop — critical for makers selling unique, handmade goods.
Cost
Zero per-use fees; the open-weight model is free to run indefinitely.
Control
Fine-tune or swap models anytime; you're not locked into a vendor's pricing or API changes.
Transparency
You can inspect and modify the inference code — unlike closed APIs.
Tech Stack
| Layer | Technology |
|---|---|
| Backend | FastAPI + Python 3.13 + Ollama client |
| Frontend | React 18 + Vite + Tailwind CSS |
| AI Model |
qwen3-vl:4b (4B parameters, vision + text, open-weight) |
| Inference | Ollama (local model server) |
| License | MIT |
Get Started
See the README for full setup instructions.
Hacktoberfest Submission
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Required tag:
#hf26challenge - Partner categories targeting: Best Use of Gemma (via Ollama's open-weight model ecosystem), Best Use of Render (live demo)
- Submission: A new project built entirely during Oct 12–18, 2026 window
Try It Yourself
- Install Ollama
- Pull the model:
ollama pull qwen3-vl:4b - Clone the repo:
git clone https://github.com/RameezHiro/ShelfScribe - Follow the README to start backend and frontend
- Generate your first listing!
Built with 💙 by RameezHiro for Hacktoberfest 2026.
This post includes the required challenge tag: #hf26challenge
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