I recorded nature sounds for a year, then released an ambient sound app

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There are lots of ambient sound apps on the App Store, but they all seem to be paywalled. I thought I should make one where all the content was free. I bought a secondhand professional audio recorder, then took it to national parks in Australia and Germany, in case I heard anything interesting. It was a bit of a learning experience, but I now have a library of half a dozen passable recordings. The biggest problem was noise pollution: humans are noisy and everywhere; I never noticed until I started listening carefully for background noise. Traffic and building noise carries for huge distances. I wrapped all the sounds up in a swiftUI interface, built around a curved video carousel. I wanted to pay special attention to vision impaired users, so it all has VoiceOver support, and extra UI elements that can be turned on for vision impaired users. It's free on the App Store if you want to take a look.

Visit product View on Hacker NewsSep 26, 2023

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

84%84% 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: apps, user, recordings · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, video, users · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, 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

49%49% 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 · Strong signals: interface, users · Missing: plus, platform, intuitive

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

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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