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Tone – a pure C# cross platform auto tagger without dependencies

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Tone – a pure C# cross platform auto tagger without dependencies

Hey HN, just wanted to show my latest project called tone to get your feedback. It is a cross platform command line audio tagger. Currently in an early state of development it already comes with a neat set of features: - read and write support for a decent amount of fields - supports id3, mp4, flac and most other formats - chapters, covers and even custom tag fields - install one single binary - no dependencies (M1 macOS does not work atm - maybe someone can help out here) Source code and release download: <https://github.com/sandreas/tone> Feedback is really appreciated. Thanks :-)

Visit productView on Hacker NewsJun 2, 2022
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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
80%80% 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: mac, macos, single · Missing: agents, agent, cursor
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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