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I built an AI-powered professional headshot generator

Hacker News

I built an AI-powered professional headshot generator

Hello HN, I've created QuickHeadshots ( https://www.quickheadshots.com ), an AI-powered service that generates high-quality professional headshots from a few selfies. As a developer, I found existing solutions lacking in consistency and quality, so I decided to build my own. The app uses fine-tuned stable diffusion models to create realistic, professional-looking headshots suitable for LinkedIn profiles, company websites, or any other professional use. This is the result of a lot of experimentation around this to get to a decent quality. The goal has been quality and faithfulness to uploaded images. I'd love to get your feedback on the user experience, technical suggestions or any other aspects of the project. What do you think about the current state of AI-generated images for professional use? Are there any features you'd like to see added? I'm also happy to answer any questions. If you're interested in trying it out, drop a line at drop a line to support-at-quickheadshots.com for a discount code. Looking forward to your thoughts and suggestions!

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · 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, user, models · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
56%56% 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
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
42%42% 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
11%11% 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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