Ayer – Relive your photo memories by date, 100% offline

Hacker News

Hi HN, I built a small mobile app that shows you all the photos and videos you took on this exact calendar day across past years. I started working on it after realizing that I almost never revisit my photo library. I take thousands of photos, but most of them just sit there. Existing “On This Day” features worked, but they always felt tied to cloud uploads, accounts, and algorithmic resurfacing — which didn’t sit well with me for something as personal as memories. So I wanted to explore a different approach. Ayer is fully local-first: • 100% on-device (no cloud, no sync) • No account, no login • No analytics, no tracking • Zero network calls — airplane mode works The idea is very simple: you pick a date (or open the app today) and you see what you captured on that same day in previous years. Sometimes it’s joyful, sometimes it’s heavy, sometimes it’s completely ordinary — but revisiting memories by date gives them context rather than ranking them by “importance”. A few details that might interest this crowd: • It handles large photo libraries (30k+ photos) smoothly • Navigation is date-first, not feed-based • Optional “then vs now” collages are generated locally • Built with React Native, using system photo APIs only This isn’t meant to replace Apple Photos, Google Photos, or Lightroom. It’s more like a quiet, focused space that sits alongside them, designed for reflection rather than optimization. If anyone is curious, it’s available on iOS and Android: https://www.chapiware.com/ayer/ I’d genuinely love feedback — especially from people who care about local-first software, privacy trade-offs, or performance with large datasets. Thanks for reading.

Visit product View on Hacker NewsDec 23, 2025

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

86%86% 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, google, context · Missing: mac, agents, macos

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

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, 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

38%38% 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

27%27% 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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