AppLaunchFlow: Create App Store screenshots in minutes

Hacker News

AppLaunchFlow uses AI to turn your app screenshots into fully editable App Store creatives. You upload screenshots → it extracts text, UI structure, and layout → converts everything into modular blocks inside a Figma-style editor. You can: - Edit copy across all screenshots instantly - Re-theme layouts in seconds - Regenerate designs with AI - Create promo videos from the same assets - Export ready-to-upload App Store creatives Built for indie devs and small teams shipping frequent updates. Since launch (Jan 5): - 700+ users - 60+ paying customers Looking for honest feedback: - Is AI-based screenshot extraction actually useful? - What would make this 10x better? Happy to answer any technical questions.

Visit product View on Hacker NewsFeb 26, 2026

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Actual performance

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Launch Intel predictions

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

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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

31%31% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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