AI DJ for Apple Music

Hacker News

Hi! I made a generative radio app that features AI radio hosts providing commentary, news, and traffic updates throughout your day. Features: - Custom setlists based on their background and personality preferences - Live commentary and updates between songs - Local news, stories, and traffic updates tailored to your area - Unique AI DJ personalities, making every station feel different All feedback is welcome! If you want to help shape the future of this app, join the Discord community we're building. Join here: https://discord.gg/TkdWznAP Demo. Check out a demo from an earlier post: https://www.youtube.com/watch?v=-tYp66kFfQU More demos coming soon! Try the App. You can try the app through TestFlight here: https://testflight.apple.com/join/DxnbA3pw

Visit product View on Hacker NewsFeb 23, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

70%70% 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

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, soon · Missing: plus, platform, intuitive

48%48% 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

43%43% 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

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

17%17% 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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