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I built an affordable alternative to Spotify and YouTube Music Premium

Hacker News

I built an affordable alternative to Spotify and YouTube Music Premium

Hey guys, I had to cancel my spotify premium subscription which was a terrible thing once I like to listen music in loop while coding or at the gym. So I decided to code myself something cool to solve my own problem that makes possible listen music in loop and without ads. I took a night off and started coding something.. so that's how I built Youtube Music Loop. I know many ppl like to listen music but not everyone can afford to pay, so it's a lite affordable version of spotify and youtube premium, where you can: - Keep listen music without ads. - Synch your playlist through all your devices. - Listen music in loops and without ads. - Search for your fav music or just copy and past the link of youtube song (when search api doesn't reach its limit). - Get it as pwa / app in your phone home screen. - Works on pc and laptops, tablets and mobile devices. - Easy and simple to use, no fluffy stuff. It's an affordable option and you pay once and use it forever, which is more than half of youtube and spotify premium subscription price. You check it here: https://ytb-music-loop.web.app/ Let me know what you think and improvements you like to see, I have made many improvements as you can see here and here. -Fausto

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3points
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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
83%83% 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: coding, code · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
57%57% 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
43%43% 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 · Missing: mobile apps, ios, personal
29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
16%16% 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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