I’ve been working on a side project around Etsy digital products. The first version was pretty predictable: pick an idea → generate a product → make mockups → write SEO → upload it. But after using it, I realized I was automating the easiest part. Generating another printable is easy. Deciding whether that printable is worth making is the actual problem. So I rebuilt the whole thing around that. It’s called etsy-engine, and I just open-sourced it under MIT. The pipeline now looks roughly like this: research → score → validate → design → generate → QA → mockups → SEO → Etsy draft Before spending money generating anything, it checks live Etsy listings and tries to answer: Is there demand? How competitive is the niche? What are people charging? Can the current results realistically be beaten? Does the expected value justify generating the product at all? If not, it stops. If the idea passes, it also decides what kind of product makes sense instead of turning everything into the same generic template. For example: wall art → printable planner niche → cohesive multi-page kit party niche → printable party set editable niche → Canva-style template workflow One thing I cared about a lot was not creating fake listing photos. The generated product pages are passed back into the image model as references when creating the mockups. So the product shown in the listing image is actually the product inside the download. The pipeline also creates multiple candidates and uses vision QA to check typography, layout, print quality and accidental logos/IP before selecting one. Then it prepares: title 13 Etsy tags description alt text FAQ 300 DPI files combined PDF ZIP package And if you connect Etsy, it creates a draft only. I intentionally didn't let it auto-publish. I still want a human making the final decision. There’s also a feedback loop that can use real listing performance to adjust how future ideas are scored. You can test it without an Etsy account: etsy-engine demo "teacher appreciation week" GitHub: https://github.com/oguzhankayan/etsy-engine The image generation layer uses Raywake, which is another project I built, and reasoning/QA currently uses Anthropic: Would love feedback on the product direction. If you were continuing this project, would you focus next on better market research, better designs, or the post-sale learning loop?   submitted by   /u/oguzhankayan [link]   [comments]