I

I Built a Privacy First Clipboard History Manager for macOS

Hacker News

I Built a Privacy First Clipboard History Manager for macOS

Hello HN , I built Recall because I kept losing copied text while working. Existing clipboard managers felt bloated or unsafe so I designed Recall to be privacy first and simple: - History clears automatically when the app is killed. - All data is stored locally on your Mac — nothing leaves your machine. - Minimal UI, works instantly with ⌘ + V. It’s built with Electron + sqlite, lightweight enough for daily use, and packaged for macOS (both Intel + Apple Silicon). You can try it here its 100% free: https://ajmayafi.gumroad.com/l/recall-macOS Would love your feedback.

Visit product View on Hacker NewsAug 26, 2025

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

80%80% 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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing · Missing: https docs, excited, just released

54%54% 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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

25%25% 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

19%19% 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.

Incorrect prediction on native model

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