A macOS clock that stays visible when coding or binging in fullscreen

Hacker News

Have you ever wished you could see the time while coding or watching videos in fullscreen or when your menu bar is hidden? Corner Time is a minimalist macOS app designed for those who value a clean desktop but still want the clock to be always visible. Unlike other widgets or solutions, Corner Time sits unobtrusively in the corner of your screen as if it were a native part of macOS—even when you’re in fullscreen mode or using auto-hide for your menu bar. The app is highly customizable: you can choose your preferred date format, adjust the style, match your system language, and use it seamlessly across all your monitors. My personal use case is using it on my MacBook with TopNotch, along with always auto-hiding the menu bar and dock, to create a maximized, distraction-free workspace. Corner Time has quickly become a favorite among digital minimalists and power users. Many users describe it as a long-awaited solution, praising its simple but thoughtful design. Feedback and suggestions are welcome!

Visit product View on Hacker NewsJul 25, 2025

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

88%88% 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 · Strong signals: maximize · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% 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

11%11% 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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