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UGCReal – AI influencers so realistic, people are trying to date them

Hacker News

UGCReal – AI influencers so realistic, people are trying to date them

Hi HN! We built AI-generated influencers that are fooling everyone – including us sometimes. The breakthrough everyone said was impossible: Not "AI-looking" people. Not "almost realistic." Actually indistinguishable from real humans. We're the first to achieve true photorealism in AI-generated people. How real are we talking? Brands report customers asking for the @ of our AI models to follow them Professional photographers studying our images can't find the tells We've had people try to book our AI influencers for real photoshoots A/B tests show audiences engage MORE with our AI content than real UGC What everyone else gets wrong: Other AI tools give you plastic skin, symmetric features, that "perfect" CGI look. We generate humans with natural flaws – uneven skin tone, asymmetric features, stray hairs, authentic body language. The imperfections that make people real. See it to believe it: Upload any product. Get back content so authentic you'll double-check it's not a real photoshoot. While others are still fighting uncanny valley, we've moved on to building the future of marketing. The wild part: This isn't our endgame. If we can generate undetectable humans today, imagine what's possible tomorrow. No waitlist. No hype videos. Just try it yourself and tell us if you can spot the AI. What does HN think happens to influencer marketing when anyone can generate perfect UGC instantly?

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
87%87% 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: model, models · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
27%27% 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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