Easy picture in picture final cut pro automation

Hacker News

Take this hammerspoon lua script: https://github.com/andrewarrow/cutlass/blob/main/scripts/ham... And you can now press one buttons to start a movie recording and a screen recording at the same time. After you have talked and demo'd your screen save the files and run: ./cutlass fcp add-video ~/Documents/test1.mov -o demo.fcpxml ./cutlass fcp add-pip-video ~/Desktop/test1.mov --input demo.fcpxml -o demo.fcpxml And it will make you a final cut pro xml file you can import to FCP and have a video like: https://www.youtube.com/watch?v=dxQkM0Sszeo

Visit product View on Hacker NewsJun 18, 2025

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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

66%66% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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 · Strong signals: arr · Missing: mrr, revenue, profit

19%19% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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