We built a native macOS screen recorder focused on speed and quality

Hacker News

We built a fully native Swift macOS screen recorder, with a big focus on simplicity, speed and high quality output. We record the camera, screen and audio separately and composite them into your chosen layout on the fly as you record, we get you a shareable URL super quick and you can change your mind on the layout post recording. We designed our online video pipeline around Mac Mini's, processing chunks of video in real-time and the code is shared with the macOS app so we are able to fully render both online and offline. Now that we have built the baseline product we are finally ready to really start innovating. Happy to answer any questions, appreciate any feedback.

Visit product View on Hacker NewsMar 25, 2025

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

93%93% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

79%79% 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, pipe, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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

16%16% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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