SceenYou.art-Your Personal AI Visual Studio

Hacker News

To be honest: I absolutely hate taking photos. Whenever a camera points at me, I freeze. I don't know where to put my hands, my smile looks fake, and I definitely don't have the time (or patience) to study "10 best poses for men/women" on YouTube. But as an entrepreneur, I know I need good photos—for my LinkedIn profile, for social media content, and just to document life. I realized I wasn't alone. Most of us want great photos but hate the awkward process and the expensive price tag of professional shoots. So, I built SceneYou.art to fix this. It’s designed for people like me who want the result (amazing photos) without the struggle (posing/makeup/travel). What makes it different? - Zero Skill Required: You don't need to learn prompting or complex AI tools. - Just ONE Photo: Upload 1 casual selfie, and that's it. - 1,000+ Templates: We have a massive library. Whether you need a corporate headshot, a gym pic, or a cyberpunk avatar, we have a template for it. - Plus, we are constantly adding new templates and scenes to keep your feed fresh! Give it a try!

Visit product View on Hacker NewsNov 27, 2025

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2points

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Launch Intel predictions

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

93%93% 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: new, visual · Missing: mac, agents, macos

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

AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

Correct prediction on native model

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