Cross-platform network analyser developed in Rust

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In the past months I developed an open source, cross-platform application to help people monitor their personal Internet traffic. The application is thought to be easy-to-use and therefore can be used even if you don't have a strong networking experience. I'm currently maintaining the project, implementing new features such as geolocation of the addresses, customisable notifications to alert the user when specific events occur and a deeper inspection page to retrieve more details about each connection. The whole application, including the GUI, is developed using the Rust language. If you have any consideration, I'd be glad to hear feedbacks about the app or suggestions for new features. Feel free to open an issue on GitHub or drop a comment here!

Visit product View on Hacker NewsJan 22, 2023

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

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.

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

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

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: including · Missing: supports, reddit linkedin, podcasting

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

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

12%12% 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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