Noise Destroyer

Hacker News

Noise Destroyer

I made noise destroyer to help people get rid of background noise from their audio or video files. I had to write code to split mp4 files into audio and video, then merge them once processed. What a load of effort! Who knew mp4 was so complicated. What's more, if there's one tiny bug, the whole thing fails to play with very little in the way of debugging! If anybody needs help with it shoot me an email at dane@turbobuilt.com and I can help. Please enjoy using Noise Destroyer whenever you have a file with background noise. And no, I'm not gonna say how I built it because I need money right now for various reasons and I don't want anybody to steal my idea! I hope you guys enjoy.

Visit product View on Hacker NewsMay 5, 2022

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

76%76% 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: new, email, tiny · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, 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

47%47% 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 · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

17%17% 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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