I Reinvented the iPod but for Desktop

Hacker News

Hey HN, I'm a student shipping projects on my nights & weekends (today is no other) Music is a huge aspect of my life. I appreciate tons of artists and constantly search for a new way to experience it. Then something catches my eye... An iPod. This seemed like a perfect way to have fun while listening to music + the added mindfulness of intentionally selecting an album. Sadly, it's a no go in 2025. Lack of Bluetooth, Spotify sync and high prices was a deal-breaker for me. That's why I decided to build an iPod player for your desktop. Yup, you heard it right. From the prototype as I was just starting with Swift until now, I've been in love with it and even have a small community of people who like it too! Right now it works with local audio files, and today I'm launching internet radio support! A couple of folks are demanding Spotify syncing, which is an ongoing process, but should be here very soon (Windows version is also coming up). I hope you'll enjoy as much as I do. Would love some feedback! - Iskren

Visit product View on Hacker NewsFeb 27, 2025

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

83%83% 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

64%64% 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

48%48% 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

47%47% 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

30%30% 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

11%11% 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.

Correct prediction on native model

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