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FlipClock – fullscreen flip clock with alarms, Pomodoro, timer

Hacker News

FlipClock – fullscreen flip clock with alarms, Pomodoro, timer

I’m a CS student who enjoys building distraction‑free tools. I wanted a screen‑filling, old‑school flip clock that could also handle everyday timing needs, so I built FlipClock – https://aflipclock.com . What it does Alarms that ring even if the tab’s in the background (Service Worker + Web Audio) Pomodoro sessions with automatic breaks Countdown timer and lap‑enabled stopwatch Works offline and is installable as a PWA Dark/light themes, 12 / 24‑hour toggle, mobile vibration feedback Totally free, no ads or tracking

Visit productView on Hacker NewsApr 20, 2025
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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
68%68% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
39%39% 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 · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
21%21% predicted probability of success on Product Hunt, 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 · Strong signals: audio · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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