I built a HUD-like timer for macOS that stays visible in full-screen

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Hi HN, I’ve just released my first Mac App Store app, Buoyant Timer (paid, no in-app purchases) It’s a lightweight HUD-like timer that floats above everything else on macOS, even full-screen apps. I always wanted something like a configurable HUD for macOS. An app, or set of apps that stay visible at all times: a timer, runtime notes and scriptable alert/notification display. This app is the first item on that roadmap. Something simple for deep focus where I switch between full-screen apps a lot, and often lose track of time. What's under the hood? This will probably be no news to seasoned Swift developers, but I was surprised by how much can be done with NSPanel. So, the main view is a non-activating, borderless NSPanel: NSPanel( level = .floating, collectionBehavior = [.canJoinAllSpaces, .fullScreenAuxiliary] ) orderFrontRegardless() This keeps it always on top. There’s also a pass-through mode (ignoresMouseEvents = true) so clicks go to the app underneath uninterrupted. Features - Stopwatch and adjustable countdowns - Pass-through clicks so it won't interrupt your workflow - Global hotkeys - Auto-start stopwatch when countdown ends (proved helpful on occasion) - Customizable colors, opacity, and high-contrast mode - Minimal interactive panel when you don’t want pass-through I’d love to hear feedback, whether on the app, feature requests or anything else :)

Visit product View on Hacker NewsSep 18, 2025

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

82%82% 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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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

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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