iOS Provisioning profile hell begone

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I'm sure I'm not the only iOS developer who has torn out my hair dealing with provisioning profiles. Manually managing them in the iPhoneOS 2.x, 3.x days or Xcode's "automatic" management have always been painful, especially when doing contract development for multiple clients. I made this app + quicklook plugin for my own use, but realised it may be of use to the wider community. It allows you to introspect .mobileprovision files to investigate which app they are for, which UDIDs are provisioned and so on. Does anyone like it / have advice on how I can improve it? This is my first foray into "product" development. (I hope this post abides by the rules of Show HN etiquette, please delete if it does not) Some redemption codes: W6WHWAYXEMF6 LL4W9HFMNFNE WJE99T73XTXF TN93RTWLYPJR 4AFRNYFMEP9H

Visit product View on Hacker NewsNov 27, 2013

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

73%73% 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.

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs

64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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.

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

31%31% 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 · Missing: arr, mrr, revenue

23%23% 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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