CriticalPath – Advanced Profiler for Android

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I am excited to announce the project I am working on: an advanced mobile app profiler for Android. Its primary purpose is to optimize app performance and make it faster. Additionally, it serves as an excellent debugging tool. The main distinction between existing tools, such as Perfetto and the Android Studio Profiler, is the ability to synchronize the trace with a video recording of your app. This allows you to see the app's UX alongside what's occurring within the app to identify what's causing slowdowns. Another key feature is the ability to click on any slice and view the cross-thread execution path of function calls, tracing from a user action to the selected slice. This makes it easier to identify performance regressions: slow functions, suboptimal network calls, or functions clogging the main thread. Additionally, we have implemented smart code instrumentation that has a minimal impact on performance. I am one of the developers of this tool. The project is in active development and are eager for feedback from Android developers. You can try it out at https://crpath.app

Visit product View on Hacker NewsFeb 28, 2024

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

91%91% 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: user, using, code · Missing: mac, agents, macos

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · Strong signals: smart · Missing: web3, chat, crypto

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

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