JukeGPT – Inspiring Musicians with Original Melodies

Hacker News

Hey HN! We are excited to introduce JukeGPT a tool we're building for musicians to quickly generate original songs with catchy and aesthetically pleasing melodies and chord progressions. We use our library of carefully annotated songs to do few-shot prompting on the GPT-Turbo-16k model and have built a robust evaluation process that ensures the results are original and don't copy the source material. Our goal is to provide both beginners and professionals with great musical ideas they can build on. We envision our platform as a tool that musicians and studios will use to create memorable hits, soundtrack compositions, and everything in between. We invite you to check out our landing page where you can listen to some of the generated results and sign up for our waitlist. Happy composing!

Visit product View on Hacker NewsJun 24, 2023

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2points

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

86%86% 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: model · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

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

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

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% 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

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

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

Correct prediction on native model

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