Audiopile – Audio collaboration multitool (ios, android, web)

Hacker News

What’s up HN! I’m a musician turned developer and this is the app of my dreams! Audiopile allows you to record and upload audio in various formats, transcodes everything server side to create a lower-res streamable version, and stores the original and it’s transcoded version in the cloud. Recordings can then be shared between Audiopile users in private groups or re-downloaded at full resolution. After years of playing in bands and struggling with large format audio, I’m finally starting to be satisfied with this solution. Plenty of tools exist to share large files, but Audiopile is designed to make sharing and previewing these files easy and fun. Transferring and previewing audio is clunky at best with current tools. You shouldn’t have to download giant files to your phone if you only want to give them a quick listen. A side benefit of creating this app has been the endless stream of lovely new music that my friends are sharing with me every day. Sometimes the coolest music is the stuff that isn’t out yet, and I’m part of a few great idea feeds and rehearsal groups. The secret music being created and sent only to you and a few others in real-time makes this app a whole lot of fun! Tech info: Client-side it’s React and React-Native. All core business logic for uploading and state management is fully isomorphic JS. for the heavier audio recording and playback functionality the app bridges out to native code on each platform (Swift & Java). Then there’s a whole lot of Python, Terraform and GCP behind the curtain. Audiopile is a totally indie, bootstrapped app with no outside funding, so please tell your friends! -Brian feedback@audiopile.cloud https://www.audiopile.cloud/ iOS: https://apps.apple.com/app/apple-store/id1463418804?pt=118611681&ct=SHOW%20HN&mt=8 Android: https://play.google.com/store/apps/details?id=com.audiopile Web App: https://app.audiopile.cloud/ Product Hunt: https://www.producthunt.com/posts/audiopile

Visit product View on Hacker NewsDec 21, 2020

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

91%91% 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: apple, google, apps · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

60%60% 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 · Strong signals: platform, users · Missing: plus, intuitive, reviews

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% 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.

Correct prediction on native model

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