Listing Writer is a single-page web app that turns a 30-second item description into a polished eBay / Facebook Marketplace listing. You add a photo, the brand, the condition, and any flaws — a 270M-parameter Gemma 3 running entirely in your browser (Transformers.js, no server, no API key) writes you back:
Each section has a copy button. Paste straight into eBay.
**Live demo:** [https://qife422.github.io/listing-writer/](https://qife422.github.io/listing-writer/)
**Code:** [https://github.com/qife422/listing-writer](https://github.com/qife422/listing-writer)
Resellers. I did a cross-border e-commerce internship this summer (AI solutions for product selection and listing SOPs), and I'm gearing up to sell secondhand goods on eBay and Facebook Marketplace myself. Listing writing is the most tedious, most skipped step in resale: bad titles bury your item in search, vague descriptions invite returns.
A tool like this earns its keep two ways: it saves ~10 minutes per listing, and better titles measurably get more views. For a seller moving 20 items a week, that's an afternoon back — or the difference between listing and not listing.
My first user is me. I fed the app the spec for a test item — a Sony WH-1000XM4 wireless headset, black, like new, light scratches on the earcup, with the original case and cable — and it wrote back, in one pass:
Title (52 characters) Sony WH-1000XM4 Wireless Headphones Black - Like New
Description
Like-new Sony WH-1000XM4 in black with light scratches on the earcup. Includes the original case and cable.
Item specifics
Brand: Sony, Model: WH-1000XM4, Color: Black, Condition: Like new Pricing guidance
Check 3 sold listings for this model in like-new condition and price slightly below their average for a faster sale.
It didn't invent a single brand, model number, or price, and hitting Rewrite gave the same four-section format instead of echoing my input back. (Honest footnote: the first prompt version just paraphrased what I typed and blew past the 80-char title limit, so I taught the prompt with a full format example and re-ran. The pipeline held; the prompt needed the lesson.)
Three reasons, all economic:
onnx-community/gemma-3-270m-it-ONNX via Transformers.js 4.2.0, fp32 (the fp16 WebGPU path overflows on this model — learned that the hard way in Week 1).