Free, open-source native macOS client for di.fm

Hacker News

I built a menu bar app for streaming DI.FM internet radio on macOS. Swift/SwiftUI, no Electron. The existing options for DI.FM on desktop are either the web player (yet another browser tab) or unofficial Electron wrappers that idle at 200+ MB of RAM to play an audio stream. This sits in the menu bar at ~35 MB RAM and 0% CPU. The .app is about 1 MB. What it does: browse and search stations, play/pause, volume, see what's playing (artwork, artist, track, time), pick stream quality (320k MP3, 128k AAC, 64k AAC). Media keys work. It remembers your last station. Built with AVPlayer for streaming, MenuBarExtra for the UI, MPRemoteCommandCenter for media key integration. The trickiest part was getting accurate elapsed time. DI.FM's API and the ICY stream metadata don't always agree, so there's a small state machine that reconciles the two sources. macOS 14+ required. You need a DI.FM premium account for the high-quality streams. Source and binary: https://github.com/drmikexo2/DIBar-macOS

Visit product View on Hacker NewsFeb 28, 2026

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

91%91% 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

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% 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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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