Releese – Music business management software

Hacker News

After working in the music industry myself as an artist (Midsplit) and as a label manager / employee (ChillYourMind, Strange Fruits) I noticed that the music industry has very antiquated tools that are often expensive and rarely updated. I had the idea to build a platform that could streamline the entire releasing process of music, we nailed it down to metadata management, royalty splits, landing pages generation, distribution to major stores and royalty collection (publishing + neighboring) rights. After building this with a small team for 18 months, we finally managed to create our vision with a surprisingly functional product. Our cloud infrastructure is based on Google Cloud and our app is 98% Typescript (Thank you Github) I would love to know how someone in or outside the music industry feels about the platform. Hopefully we will help shape a better music industry!

Visit product View on Hacker NewsAug 10, 2022

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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

71%71% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

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

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

49%49% 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

26%26% 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 · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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