SoundScrub

TrustMRR

SoundScrub is a desktop app that cleans up audio in videos and audio files. Describe a sound in plain language and it removes, isolates, or lowers it with AI. Remove background noise from a video, remove wind noise, remove background music but keep the voice, or strip copyrighted music to avoid Content ID claims and demonetization. Pay-per-use at $0.25 per 30 seconds, no subscription. Works alongside Premiere Pro, DaVinci Resolve, and Final Cut Pro. Available on Mac, Windows, and Linux.

Visit product View on TrustMRRMar 5, 2026Content Creation Software B2C

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating2

MRR growth 30d+745.0%

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, plain · Missing: agents, macos, agent

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

63%63% predicted probability of success on Indie Hackers, 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

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

23%23% 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

22%22% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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