Easy Audio Sync: audio library syncing and conversion utility

Hacker News

I have a lot of lossless audio files in my music library, and I needed a way of mass-transcoding them to a lossy format for my mobile device. I came up with Easy Audio Sync as a solution. It's a desktop application that performs full-library conversions between a source and a destination folder. MP3, AAC, Ogg Vorbis, and Opus are supported as output formats when transcoding. An option exists to copy files that are already in a lossy format, to avoid lossy-to-lossy conversions. The program can skip existing files to perform incremental sync, and also there is a "clean destination" feature that deletes files in the destination if they have been deleted in the source. The program handles metadata in a robust manner, so that tags and cover art are properly transferred when converting between different formats. Multithreading is supported while transcoding to enable fast conversions.

Visit product View on Hacker NewsMay 19, 2023

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

73%73% 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.

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

48%48% 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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

14%14% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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