I've Created an AI Monster

Hacker News

Last week, I came across sunoAI on X. I noticed many people were having fun with it, creating quirky tracks to share on Twitter. Intrigued, I tried it out and was impressed by the quality of the results. As part of my journey in building 10 startups, I am always on the lookout for new ideas and sources of inspiration. Could I establish a music label using this technology? The seed of an idea was planted last week, and now, just one week later, we are officially live on all major streaming platforms. Lars, AI-driven music label, you ask? Well, let’s say 90%. The songs are entirely AI-generated, including the sound mastering, with artwork created using Midjourney and artist names and website content crafted by chatGPT. Music videos were produced using tuneform, and even certain web components were developed with GitHub Copilot. Despite all this, 10% of human interaction was still necessary to bring it all together. What comes next? I have a plethora of songs lined up. My plan is to release a single every day, culminating in the launch of our first EP with five tracks over the weekend. Let's see if this strategy can attract a dedicated audience of listeners. If successful, my next step will be to introduce another artist specializing in a different genre. I’m curious to hear your thoughts on this experiment. Your feedback on the music would be greatly appreciated. Lars

Visit product View on Hacker NewsApr 11, 2024

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

90%90% 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: new, chatgpt, single · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, way · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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