ListingSmith — an open-weight AI listing writer for Etsy & eBay sellers

A developer built ListingSmith, an MIT-licensed open-source tool that turns rough product notes into marketplace-compliant Etsy and eBay listings, including titles, descriptions, tags, price suggestions and a rupee-denominated profit calculator. The app runs on open-weight models (Llama 3.3 70B, Mistral Nemo 12B, DeepSeek R1 Distill Llama 70B and Llama 3 8B) served through DigitalOcean GenAI Serverless Inference, and enforces platform rules like Etsy's 140-character titles and 13-tag limit in code after parsing so smaller models cannot overflow them. It was built with the Lovable coding agent for the Hacktoberfest Hack Day Pune side-hustle challenge.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle ListingSmith turns rough product notes into a sell-ready marketplace listing in seconds. Paste in a few lines about your product — materials, size, story, care instructions — and it produces a marketplace-compliant title, description, bullet points, tags, a price suggestion and an SEO tip, all tuned to the real rules of the platform you picked Etsy's 140-character titles and 13 tags, eBay's 80-character titles . It also shows a profit calculator next to every listing: platform listing fees, payment processing, shipping and your margin, in Indian rupees — so a seller knows whether the price actually makes money before they hit publish. Who would pay for it? Small makers and resellers. Writing one good Etsy listing by hand takes 20–40 minutes of fighting title limits and tag rules. A seller listing 50 products a month gets hours back, and the open-weight models it runs on cost a fraction of a rupee per listing. Live demo: https://my-listing-smith.lovable.app https://my-listing-smith.lovable.app Try it with the sample notes already filled in — pick Etsy or eBay, choose a tone, and hit "Write my listing". The full source is MIT-licensed: Rough notes in, sell-ready listing out. An open-weight AI listing writer for Etsy and eBay sellers, running on DigitalOcean GenAI Serverless Inference. Built for Hacktoberfest Hack Day Pune × AI Hackers Collective — Best Open-Source AI Project Side Hustle theme . Small makers spend 20–40 minutes per listing fighting title limits, 13-tag rules and SEO. ListingSmith produces a marketplace-compliant title, description, bullets, tags, price check and SEO tip in seconds, for well under a cent per listing. Highlights of the codebase: src/lib/listing-pipeline.ts — pure, unit-tested prompt building and output parsing. Marketplace rules title length, tag count, tag character caps live here as data, enforced after parsing so the model can never overflow them. src/lib/listing.functions.ts — the server function that validates input zod and routes to either the built-in AI gateway or any OpenAI-compatible endpoint. src/lib/listing-costs.ts — pure fee/shipping/profit math for Etsy and eBay, with tests. Dockerfile + .do/app.yaml — one-command deploy to DigitalOcean App Platform. The stack is TypeScript, React 19, TanStack Start, Tailwind CSS v4 and Vitest, containerised with Docker for DigitalOcean App Platform. The AI core is built around open-weight models served through DigitalOcean GenAI Serverless Inference an OpenAI-compatible API . The model picker offers: Llama 3.3 70B Instruct Meta Mistral Nemo 12B Apache 2.0 DeepSeek R1 Distill Llama 70B MIT Llama 3 8B Instruct fast/cheap tier The pipeline is: seller notes → buildMessages a system prompt that demands strict JSON with marketplace limits baked in → model → parseListing strips reasoning blocks and code fences, extracts the JSON, then hard-enforces title/tag caps and de-duplicates tags . Because the rules are enforced in code, not just requested in the prompt, the output is compliant even when a smaller model gets sloppy. Two environment variables configure it: DIGITALOCEAN GENAI API KEY and DIGITALOCEAN GENAI BASE URL. This project simply doesn't work as a closed-API product: I built this with an AI coding agent Lovable . The agent wrote the pipeline, UI, tests, Dockerfile and deploy spec; I steered the product decisions — the side-hustle angle, the rupee-denominated profit calculator, and the open-weight model lineup. Prize Categories Best Open-Source AI Project — the core mechanic is open-weight models Llama, Mistral, DeepSeek behind an OpenAI-compatible endpoint, the code is MIT-licensed, and it can run fully offline on local inference.