Bookmark Bar – Browser Hub (Open Any Bookmark in Any Browser on macOS)

Hacker News

Hi HN, I built a small macOS menu bar utility after running into the same daily annoyance for years: bookmarks are still tied to the browser you're using. As a developer I often switch between Safari, Chrome, Arc, Brave and Edge depending on testing, debugging extensions, or isolating workflows. Every browser keeps its own independent bookmark list, and syncing them (or importing/exporting) never really works well. I kept losing structure or duplicating folders. Why I built it I needed a simple way to access the same set of bookmarks no matter which browser I was in. Instead of syncing browser-to-browser or relying on cloud extensions, I thought a menu bar layer on top of all browsers would be more reliable and reduce cognitive load. Platform & Tech - macOS menu bar app - Built with SwiftUI + SwiftData - Uses system APIs to open URLs in specific browsers - All data is stored locally - No accounts, no network usage If you're curious Here’s the App Store link: https://apps.apple.com/us/app/bookmark-bar-browser-hub/id675... Happy to answer technical questions or hear ideas from others who work with multiple browsers daily (profiling, testing setups, automation, etc.).

Visit product View on Hacker NewsNov 29, 2025

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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

89%89% 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

73%73% 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

45%45% 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: platform · Missing: plus, intuitive, reviews

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% 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

22%22% 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.

Correct prediction on native model

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