Z-

Z-nettool – a cross-platform network troubleshooting CLI

Hacker News

I work at an MSP, and we often deal with poorly labelled cables and limited documentation. A common question during troubleshooting is: which switch and port is this computer connected to? Is the link negotiating at Gigabit, or has it fallen back to Fast Ethernet (100 Mbps) or even 10 Mbps due to a bad cable or port? To make basic network troubleshooting faster, I built a small cross-platform CLI tool that bundles common checks in one place. It supports: - LLDP discovery (useful when switch ports aren’t labelled) - DHCP checks (including detecting multiple replies, e.g. rogue DHCP servers) - Ping, DNS, and a basic connectivity test The goal is to help techs remote or onsite to troubleshoot faster using a single, portable tool that works across platforms. Built mostly for our own use, but I’ve found it handy enough to share. Feedback welcome.

Visit product View on Hacker NewsJan 31, 2026

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

81%81% 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: computer, single, using · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

43%43% 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

42%42% 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

13%13% 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.

Correct prediction on native model

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