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Switchbar – Browser Picker for macOS and Windows

Hacker News

Switchbar – Browser Picker for macOS and Windows

Hi everyone, Switchbar is a lightweight link handler and browser picker for macOS and Windows. It lets you choose your preferred browser, browser profile, or email client every time you click a link. There are similar apps out there, but Switchbar stands out by supporting both Windows and macOS, along with a wide range of browsers. We welcome your feedback and are here to answer any questions.

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% 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
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
34%34% 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
14%14% 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
12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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