Rapidmg – Automatically detect & install apps from DMG files

Hacker News

Hi HN! I've built this little macOS app to automate app installations from DMG files. Opening DMG files with Rapidmg is basically equivalent to: 1. Mount 2. Drag the app to the "Applications" folder 3. Unmount 4. Delete the dmg (optional) 5. Locate the app in "Applications" Here are some technical details that make Rapidmg possible inside the macOS app sandbox: - DMG files are containers of raw filesystem data, so you'll need userspace filesystem implementations to extract files from them. Thankfully, 7zip has both HFS and APFS implementated. Rapidmg makes use of that with some additional tweaks[0] about extended attributes (xattr). - "Security-scoped bookmark"[1] is used to keep persistent access to "/Applications" - If no app is detected in the DMG file, Rapidmg can extract all content to the same folder with the same basename as the dmg file, without the need to present the save panel. This is allowed by "Related Items"[2] Let me know if you have any questions or feedback! [0] https://github.com/branchseer/rapidmg_7z/tree/2301 [1] https://developer.apple.com/documentation/professional_video... [2] https://developer.apple.com/documentation/security/app_sandb...

Visit product View on Hacker NewsAug 5, 2023

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

87%87% 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

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

60%60% 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 · Strong signals: users · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

15%15% 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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