ClassicTunes – a from-scratch remake of iTunes 7-10 for Apple Silicon

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Hello everyone! This is a project I started working on in last October as I preferred to have the classic iTunes for my music stuff, but found out that the older versions are becoming increasingly difficult to run, and the regular Apple Music simply did not have that feel to it. Hence why I made this project. Currently it has: - Working Cover Flow - MiniPlayer - Playlists (with smart m3u support) - iTunes Store integration - Lyrics support (with lrclib or metadata) - iPod Syncing (1st-2nd gen Shuffles) - Discord Rich Presence (with MusicPresence) - Classic iTunes 7–10 aesthetics I plan to continue developing it until it reaches a level of functionality similar to iTunes 8 (this includes full iPod syncing). If you have any feedback, I would really appreciate it. GitHub: https://github.com/happysmaran/ClassicTunes (Also, this is my first HackerNews post. If I messed up something, please do let me know!)

Visit product View on Hacker NewsJun 28, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

43%43% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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