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Inspect iOS traffic without messing Proxy and Certificate

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Inspect iOS traffic without messing Proxy and Certificate

Hi HN. I've recently built a Swift OSS - Atlantis[1] - which uses Method Swizzling[2] to automatically capture all traffic then transmitting it to the Proxyman [3] macOS app via Bonjour Server[4] for a later inspector. Therefore, no need to mess up with HTTP Proxy or Certificate anymore. It just works. Hopefully, it's easier to help folks debug your iOS app. [1]: https://github.com/ProxymanApp/atlantis [2]: https://nshipster.com/method-swizzling/ [3]: https://github.com/ProxymanApp/Proxyman [4]: https://developer.apple.com/bonjour/

Visit productView on Hacker NewsDec 29, 2020
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor
72%72% 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: ios · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
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.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
48%48% 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
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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