ShelfScribe: Local AI Listing Writer for Etsy Sellers (Open-Weight AI Side Hustle)

Rameez Shaikh — Sat, 17 Oct 2026 16:49:33 +0000

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.

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

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

DEV Community: Rameez Shaikh