Real-time Network IN/OUT and Syscall monitoring using eBPF

Hacker News

Hi HN, I just released bpfsnitch, an open-source, real-time monitoring tool for Linux systems and Kubernetes clusters. Inspired by the need for deeper security insights without adding unnecessary layers, bpfsnitch leverages eBPF technology to monitor system calls and network activities at the kernel level. The aim is to make security more accessible by providing a tool that's easy to deploy and offers immediate value. Whether you're a DevOps engineer, a security professional, or someone interested in learning more about eBPF and kernel-level observability, I believe bpfsnitch can be a valuable resource. Feel free to check out the repository, give it a star if you find it useful, and join the discussion. I will be here to answer any questions and would love your feedback! Looking forward to your thoughts and contributions! Swan

Visit product View on Hacker NewsSep 30, 2024

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

76%76% 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: just released, lua, io · Missing: https docs, excited, exist

65%65% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

45%45% 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

9%9% 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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