I made yet another AI headshot app because the world needed one more

Hacker News

Look, I know. There are like 47 of these apps already. I'm aware. But here's the thing - I kept trying them and they all took forever (30+ min processing), need to be a prompt master or charged $200 for 5 photos. So I built one that does it in 60 seconds from any selfie. The pitch is simple: LinkedIn, Tinder, Bumble, Hinge, your mom's WhatsApp profile pic - whatever. Upload a selfie, pick a style, done. Let people go wild with it. Tech stuff: Swift app, nano-banana, no face training required. It's style transfer that keeps you looking like yourself, just better lit. Free trial, no credit card. Built it solo from Braga, Portugal. Happy to talk about the tech or explain why entering the most saturated App Store category seemed like a good idea at the time.

Visit product View on Hacker NewsNov 22, 2025

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

83%83% 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: apps, plain · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% 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

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

19%19% 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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