Break out of your Spotify bubble with my tastemaker service

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I love the Spotify recommendations, but they mostly recommend me the same music all over again. To fight (what a harsh word) this issue I did build 𝘁𝗮𝘀𝘁𝗲𝗺𝗮𝗸𝗲𝗿™[0] a web service that auto creates unique playlists for your taste, but tries to not recommend you music that you already know. At the moment there are three available playlists you can subscribe to which are then recalculated every night (4 am - CET, sorry Americans for you it is in the late evening): - 𝗡𝗲𝘄𝗰𝗼𝗺𝗲𝗿𝘀™[1] Analyses your taste and recommends you unknown musicians. 𝐼 𝑑𝑖𝑑 𝑓𝑖𝑛𝑑 𝑞𝑢𝑖𝑡𝑒 𝑠𝑜𝑚𝑒 𝑛𝑒𝑤 𝐺𝑒𝑟𝑚𝑎𝑛 𝐼𝑛𝑑𝑖𝑒 𝑎𝑟𝑡𝑖𝑠𝑡𝑠 𝑤𝑖𝑡ℎ 𝑙𝑒𝑠𝑠 𝑡ℎ𝑎𝑛 100 𝑙𝑖𝑠𝑡𝑒𝑛𝑒𝑟𝑠 𝑝𝑒𝑟 𝑚𝑜𝑛𝑡ℎ 𝑤𝑖𝑡ℎ 𝑖𝑡 - 𝗣𝗿𝗼𝗱𝘂𝗰𝗲𝗿𝗣𝗼𝘄𝗲𝗿™[2] Finds out the producers of your favorite songs and recommends you other songs they worked on. 𝑇ℎ𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑟𝑠 𝑓𝑜𝑟𝑚 𝑡ℎ𝑒 𝑠𝑜𝑢𝑛𝑑 𝑞𝑢𝑖𝑡𝑒 𝑎 𝑙𝑜𝑡 𝑜𝑓 𝑎 𝑠𝑜𝑛𝑔 - 𝗧𝗮𝘀𝘁𝗲𝗯𝗿𝗲𝗮𝗸𝗲𝗿™[3] Checks your taste and recommends you songs from genres you normally don't listen to 𝑇ℎ𝑖𝑠 𝑝𝑙𝑎𝑦𝑙𝑖𝑠𝑡𝑠 𝑠ℎ𝑜𝑤𝑒𝑑 𝑚𝑒 𝑠𝑜𝑚𝑒 𝑎𝑤𝑒𝑠𝑜𝑚𝑒 𝑅𝑢𝑠𝑠𝑖𝑎𝑛 𝑃𝑜𝑝 𝑠𝑜𝑛𝑔𝑠 Thank you folks for reading until here and maybe also trying my service. Please be patient as it is still in an early phase and I have now clue if the server is strong enough for a lot of users from HN. (Or better: I am quite sure it is not) All the best from Berlin! p.S. Yes I claim the ™ symbol to be a symbol for 𝗧aste 𝗠aker p.p.S. To subscribe to a playlist click the *"Get it on Spotify"-Button, login to your Spotify and allow the access. (Yes I know it requires quite some privileges, but without that it can not gather your taste) [0] https://tastemaker.cc [1] https://tastemaker.cc/newcomers [2] https://tastemaker.cc/producerpower [3] https://tastemaker.cc/tastebreaker

Visit product View on Hacker NewsFeb 17, 2021

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

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Strong signals: songs · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

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: io · Missing: https docs, excited, just released

36%36% 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

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

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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