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Snaplist: A Local AI Listing Writer for Etsy & eBay Sellers (Gemma 3 + Ollama)

A developer built Snaplist, an MIT-licensed desktop listing writer for Etsy and eBay sellers that runs Gemma 3 (4B) vision locally through Ollama on a MacBook Air, generating editable titles, descriptions and up to 13 search tags from item photos. In testing, the 4B model merged photos of four unrelated items into one confused listing and produced short descriptions needing human edits, with CPU inference slow enough to require a 180-second timeout. The developer deliberately omitted price suggestions for lack of comparable-sales data and has not yet tested the tool with outside sellers.

by read2 min views12 publishedOct 18, 2026

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle 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.

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