PairPods – Share audio on macOS (pairpods.app)

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I built PairPods to solve a personal frustration: wanting to watch movies with a friend on my MacBook without having to share earbuds or disturb others with speakers. PairPods is a tiny (1.5MB) menubar app that lets you share audio between two Bluetooth devices simultaneously with a single click. It works with any macOS-compatible Bluetooth audio devices. The app is built with Swift and SwiftUI, and the entire source code is available on GitHub under an MIT license. It requires macOS Sonoma (14.0) or later. Technical implementation details: - Uses macOS's built-in CoreAudio framework to create an aggregate device - SwiftUI for the menubar interface - Automatic device detection and configuration - No background processes or persistent connections I'd appreciate any feedback on the UI, functionality, or code architecture! This is currently in beta, and I'm particularly interested in edge cases I might have missed. GitHub: https://github.com/wozniakpawel/PairPods Website: https://pairpods.app

Visit product View on Hacker NewsFeb 27, 2025

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

86%86% 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: compatible · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% 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: interface · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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

12%12% 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

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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