Zeitgeist – Stories for You

Hacker News

Hi HN! Recently, I looked at my old Instagram Stories and thought: Wow, Stories feel much more immersive than normal photos in my Gallery! The catch: I always have to publish them. *Zeitgeist* fixes that. It’s a story camera for yourself. No followers, no posting. Just private stories you can look back on. *What it does* * Shoot in the familiar story format (photo/video), then add text, GIFs, Memos and more. * View your stories on a Grid / Map * Everything stays on your device/iCloud. No account. No feed. *That's it* The app is completely free; in the future I may add some paid cosmetic IAPs but nothing major like ads or feature blockers. If you want to try it out: https://apps.apple.com/de/app/zeitgeist-stories-for-you/id67...

Visit product View on Hacker NewsOct 21, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

78%78% 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: apple, apps · Missing: mac, agents, macos

75%75% 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, video, way · Missing: mobile apps, ios, personal

39%39% 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 · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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