Daydream.fm - build and share your own world of soundscape and music.

Hacker News

Hi guys, I want to show you a site I built, which lets you build your own virtual world of soundscape. I work out of cafes all the time because the white noise and ambience helps me concentrate well. Then one day I wondered: what if I could have my own cafe on my laptop, so I can enjoy that state of mind anytime, anywhere? And what if I can share it, so other people can enjoy that emotion too? So I built Daydream. It lets you create your own world of soundscape, by combining visual, sound samples, and music. Once you set it up, you can just use it as an ambience player, or a music player with ambience, by adding more songs to their relevant contexts. Here's the link: http://daydream.fm I would really appreciate it if you guys shared what you think. Thanks!

Visit product View on Hacker NewsMay 16, 2012

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

81%81% 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 HackersFits the IH revenue-focused audience · Strong signals: songs · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

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

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% 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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