TextaVibe, Receive music link at specific time and day, based on moods

Hacker News

My thought why I'm building this: https://textavibe.com/blog/adding-music-to-daily-routine I'm looking for people who is interested in my product, TextaVibe. It's a service that sends email at specific time and day. I was building this for my own use, however I thought it could help someone who needs it. Now I've opened a waitlist to find people who are interested! Feel free to ask me anything. Cheers!

Visit product View on Hacker NewsMar 23, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Product HuntUnlikely to reach the leaderboard · Strong signals: email, open · Missing: mac, agents, macos

48%48% 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 · 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 · Missing: mobile apps, ios, personal

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

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

36%36% 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

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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