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Underground Music Discovery for DJs and Ravers

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Underground Music Discovery for DJs and Ravers

Over the years of being a DJ, playing and digging music, I’ve experienced a lot of sounds that create certain feelings. This process of discovering music always took a lot of time and recently I’ve found myself with a lot of material that I did not have time to get into, but wanted to discover in an aesthetically pleasing way. Which is - tracks perfectly mixed together. So, I’ve created a little software/music art project. I crawl the internet in search of new underground, (ro) minimal sounds to be mixed live by a custom, audio processing setup that was created to emulate normal Dj. The tracks are mixed live in an unattended way, chosen semi-randomly in sets (ordered by BPM). Ready to be discovered! The software stack is ffmpeg+python with some pre-trained ML models for beat detection. Track discovery happens via bot on the Telegram platform ( https://t.me/ROminimal_club ), where to get track ID, users are asked to rate the energy level (of whatever you see those ratings mean) of a currently playing track. This is saved as his library for his future reference via private msg. 1. Go to https://ROminimal.club website and listen to the radio stream (also live video with randomly mixed content and current dj actions -- https://io.rominimal.club/ 2. Start a chat with CrossFader bot https://t.me/xFadeBot 3. Rate the energy level of tracks in the main group to reveal ID. Enjoy! example: https://t.me/ROminimal_club/7426 ## What do our fans say? (Blaz) - I like how you organised your channel. You have implemented very clever gamification. And the music is top notch. My admiration. (Escobi) - some dope bangers here, i start listening 5 hours ago and still listening. Big up for this great project! The bombs are planted so great! (Lisa) - Best thing that happened on the internet in the past 10 years!

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
93%93% 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, user, new · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, users, way · Missing: mobile apps, ios, personal
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
47%47% 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 · Missing: https docs, excited, just released
47%47% 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
8%8% 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, audio · 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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