DI

DIY Wedding Portraits – Create Art, Save Money

Hacker News

DIY Wedding Portraits – Create Art, Save Money

We’ve built a web app that lets couples create beautiful, artistic wedding portraits without breaking the bank. Key features: * Upload your favorite wedding photo * Choose from 15+ artistic styles (e.g. oil painting, watercolor, pop art, caricature, line drawing, …) * High-resolution output suitable for printing * Fraction of the cost of traditional portrait artists Try it out: https://portraitart.app/wedding-portrait We built this because professional wedding portraits can cost thousands, but everyone deserves a special memento of their big day. Would love feedback from the HN community! Note: The service requires email sign-up to use. I know this might not be popular with the HN community, but it helps us manage user accounts and provide better service, including server-side processing status notification.

Visit product View on Hacker NewsAug 18, 2024

Actual performance

2points

Did not reach leaderboard

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

90%90% 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 HuntUnlikely to reach the leaderboard · Strong signals: user, email · Missing: mac, agents, macos

47%47% 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

39%39% 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

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

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

37%37% 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

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.

Correct prediction on native model

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