Improve focus and sleep with mixable soundscapes for your Mac, and iPhone

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I began learning Swift by building something I wanted, a soundscape mixer that stayed out of the way on OS X and iOS. A few weeks ago I saw others were wanting the same thing based off the feedback in the Noisli thread, so now that the base features are done I wanted to share it. I'd love any feedback, and I hope it's useful to someone else: https://github.com/lukeheuer/soundescape-osx https://github.com/lukeheuer/soundescape-ios

Visit product View on Hacker NewsMar 2, 2015

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7points

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

66%66% 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: ios · Missing: supports, reddit linkedin, podcasting

65%65% 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

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs

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

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

39%39% 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

18%18% 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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