# Snaplist: A Local AI Listing Writer for Etsy & eBay Sellers (Gemma 3 + Ollama) > Source: > Published: 2026-10-18 20:45:37+00:00 *This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle](https://dev.to/challenges/hacktoberfest-week2-2026-10-12)* **Snaplist** is a listing writer for solo sellers on Etsy and eBay. You drop in a few photos of an item, pick a marketplace and a tone, add any facts the photos can't show (condition, size, backstory), and it drafts a title, a description, and up to 13 search tags. Every word is editable before you copy it out. Who would pay for it? Anyone who lists secondhand or small-batch items and keeps rewriting the same kind of description. The idea is a one-time desktop package rather than a monthly subscription. My working guess is around ₹999, but that is a pricing hypothesis I haven't tested, not revenue. Video walkthrough, generating a listing from photos of a kitchen timer: [https://youtu.be/j0H6ZO6grRg](https://youtu.be/j0H6ZO6grRg) Public repo, MIT licensed: [https://github.com/anshraj3060/snaplist](https://github.com/anshraj3060/snaplist) The core is **Gemma 3 (4B)**, an open-weight vision model, served by **Ollama** on my own MacBook Air. The app is a small Node.js server plus a browser UI. When you press Generate, the server sends your photos (base64) to Ollama's `/api/chat` endpoint and asks for structured JSON back, which the UI turns into an editable title, description, and tags. I built the app with an AI coding agent (Arena) from a prompt, then ran it myself: installed Ollama, pulled `gemma3:4b`, switched Snaplist from its labeled demo mode to "Use my local model", and tested it with my own photos. A few details I like: `OLLAMA_MODEL=gemma3:12b npm start` is all it takes to try a bigger one. Where it did *not* beat a closed model: a 4B model is small. In my first test I uploaded photos of four unrelated items, and it merged them into one confused listing. It did read a brand name off a bottle, which was a nice surprise. Descriptions come out short and need a human edit. CPU inference can be slow, and there is a 180-second timeout. Hardware and electricity still cost something, and Gemma is open-weight under its own terms, not OSI-approved open source. I also deliberately did not build price suggestions. Snaplist has no comparable-sales data, and I'd rather ship nothing than invent numbers. I haven't had anyone else try it yet, so there is no first-user story to tell. Next step: put it in front of a real seller and ask what they'd pay, and what they'd want it to do next. **Best Use of Gemma**: the whole project runs on Gemma 3 vision through Ollama.