AI-Powered Product Photography

Hacker News

Hey folks, We're excited to announce the launch of Stylized, a photo editor for e-commerce sellers that leverages computer vision, 3D rendering, and some generative AI to transform any product photo into a professional asset in seconds. We make it easy for e-commerce sellers to create clean, realistic, high-quality product images quickly and easily. We started working on Stylized 3 months ago when sellers at craft fairs kept mentioning that simple white background photos (or silo photos) were a huge roadblock to running an online store. Our backgrounds are in computer vision, e-commerce, and VR, so it was only natural for this to be the solution we felt most excited about. Silo photos look simple, but the details have to be right, the product has to be accurately represented, the shadows have to look realistic. The goal is to first create professional product images, and from there start enabling editorials, brand marketing, and more - without having to sink time and money into traditional commercial photography. We're excited to share our project with the community and hear your thoughts. We're passionate about using technology to solve problems and improve people's lives, and we believe Stylized is a great starting point for most e-commerce sellers. Thanks for checking out our project! We love feedback, so have at it!

Visit product View on Hacker NewsJan 23, 2023

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: computer, using · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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