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Stock Photos Using Stable Diffusion

Hacker News

Stock Photos Using Stable Diffusion

Hi HN, this is an early version of what we’re imagining as a truly functional stock photo platform using Stable Diffusion. We’re doing our best to hide the customization prompts on the back end so users are able to quickly search for pre-existing generated photos, or create new ones that would ideally work as well. If we keep going with it, in future versions we’d like to add voting, better tags, and more varied prompts, or maybe whatever you recommend!

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Actual performance

190points
105comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
43%43% 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
17%17% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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