Packara – Native macOS GUI for signing, notarizing, and stapling DMGs

Hacker News

I kept copying the same shell script between projects every time I needed to ship a Mac app. It wrapped codesign, hdiutil, notarytool, and stapler into one flow. Eventually I turned it into a proper app. Packara runs the full pipeline in sequence with live log output at every stage. Notarization credentials stay in Keychain and are never passed as CLI arguments. There's a 7-day trial, $9.99 one-time after that. Site: https://packara.techfixpro.net

Visit product View on Hacker NewsMar 25, 2026

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5points

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Launch Intel predictions

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

84%84% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% 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 · Missing: mobile apps, ios, personal

40%40% 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

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

22%22% predicted probability of success on AppSumo, 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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