AvatarCraft – Showcase Your Best Self with AI-Generated Avatars

Hacker News

Hi HN Community! My name is Sosha and I'm excited to announce the launch of my startup, AvatarCraft.ai. For the past 6 months, I've been obsessively working to create the most realistic and highest quality AI avatars using Stable Diffusion technology. I was unsatisfied with the offerings from competitors, so I developed proprietary techniques to take avatar creation to the next level. AvatarCraft uses just 15-20 of your photos to create hundreds of customized profile pictures with our advanced AI. The avatars capture your likeness remarkably well. To showcase the difference, I've uploaded some example avatars generated by AvatarCraft along with avatars from our competitors created with the same datasets. I'd love for you to compare the quality side-by-side. https://ibb.co/q9g5PRK Original photo https://ibb.co/Mn7yV14 AvatarCraft's Competitors https://ibb.co/McF0wJF AvatarCraft's results from Hairstyle, Painting, Professional & Traditional Fashion Packs Whether you try out our service or just review the samples, I'd appreciate any honest feedback on the avatar quality.Are there any improvements I should make? Because we use proprietary techniques and extensive training to achieve maximum quality, it takes 6-10 hours to generate each set of avatars. This process costs us quite a bit, but I've set an introductory price of just $29.75 for now. I did create a 25% off discount code ( use code hnav) specifically for this community to make it even more affordable to test out. Even with the discount, this still doesn't cover our expenses at this stage - but your feedback will be invaluable in improving AvatarCraft. After we incorporate insights from early users, I will need to increase prices to be more sustainable. So take advantage of the launch discount while you can! I'd rather have redditors like yourselves shape AvatarCraft than anyone else. Let me know if you have any question.

Visit product View on Hacker NewsOct 19, 2023

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

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, lua, ide · Missing: https docs, just released, exist

38%38% 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 · Strong signals: training · Missing: arr, mrr, revenue

16%16% 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.

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