Bezel, my macOS app to mirror an iPhone onto your Mac

Hacker News

Every product demo with an iOS app, I wanted a better way to present my iPhone to the audience. I used QuickTime a lot over the years, but it’s such a hassle to setup. It also looks confusing to non-techies, being just a square. My friend Tom and I finally took the time to create a better alternative. Our Mac app Bezel mirrors your iPhone as soon as you plug it in. (Once the device is trusted.) If it isn’t trusted yet, the app helps you pair the iPhone and Mac. It also shows a highly accurate bezel of the connected device. It’s ready for more people to give it a try now, so we’re putting it out in the world. Really looking forward to feedback from the HN community. What would you use Bezel for? Any ideas for further improvements? Love to hear from you!

Visit product View on Hacker NewsSep 26, 2023

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

95%95% 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: ios · Missing: supports, reddit linkedin, podcasting

91%91% predicted probability of success on Indie Hackers, 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

49%49% 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 · Strong signals: soon · 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: ios, way · Missing: mobile apps, personal, entrepreneurs

39%39% 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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