Edit videos faster by automatically removing silences

Hacker News

Our team is filled with technologists and creators, and when we record and edit videos, 80% of the time is spent chopping up the video, removing silences, and picking the right takes. So we decided to build a tool that did that for you — or at least get you there most of the way! Our initial implementation is somewhat naïve and uses a user configurable silence threshold that just reads in volume levels. In the future, we’d like to use a frequency-based approach that focuses on the human voice. We’re also open to ideas, so let us know if you have any!

Visit product View on Hacker NewsFeb 3, 2022

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

92%92% 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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% 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

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

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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · 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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