Capshot – AI app that generates perfect captions for your photos

Hacker News

Hi HN, I’ve been working on a side project called *Capshot* – a mobile app that automatically generates short, catchy captions for your photos and videos using AI. The idea came from noticing how much time people spend thinking of a "perfect one-liner" before posting on Instagram, X, or TikTok. Most apps help with editing visuals, but captions are usually left up to the user. *Key features:* - AI-powered caption suggestions tailored to the content - Multiple styles (casual, witty, professional, etc.) - One-tap sharing to social platforms - Currently available on Android, iOS version coming soon I’d love to get feedback from the community on: - Potential use cases beyond social media (marketing, content creation, accessibility, etc.) - How to better evaluate caption quality - Concerns around dataset bias or ethical use of AI-generated text Play Store: https://play.google.com/store/apps/details?id=ai.capshot.app Website: https://capshot.ai Thanks for reading, and I’m happy to answer any questions!

Visit product View on Hacker NewsAug 20, 2025

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

93%93% 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.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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

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

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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