Create custom macOS native live wallpapers by injecting SVC MOV atoms

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Hi HN, from South Korea. I’ve been fascinated by how macOS native live wallpapers work, so I decided to dig into the MOV atoms and the Scalable Video Coding (SVC) mechanism behind them. I’m excited to share that I’ve successfully figured out how to inject custom atoms into these MOV files to make them compatible with the system's native wallpaper engine. I’ve open-sourced the core tool so anyone can try building their own: GitHub: https://github.com/aground5/livid-community How to use it: 1. Clone livid-community 2. Run make run to clone, build, and run. 3. For more details, please check the README.md. I haven't seen much documentation on how the system handles these SVC files, so I’m happy to share my findings. The project is under the MIT license, and I want to keep the community-driven core open for everyone. Would love to hear your feedback!

Visit product View on Hacker NewsFeb 27, 2026

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

88%88% 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: compatible · Missing: supports, reddit linkedin, podcasting

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

53%53% 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

37%37% 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

27%27% 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

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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