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Know when your network is down

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Know when your network is down

Panos here - cofounder of NetBeez. We started NetBeez because we were not able to detect who and where are the users experiencing network problems. When the Raspberry Pi came out we immediately saw the perfect platform to use for our monitoring agent. And we decided to build the tool that would capture the end-user experience on wide area networks. The low cost of the Raspberry Pi enabled us to build a tool that can scale to dozens and hundreds of locations without costing an arm and a leg. NetBeez does both wired and wireless monitoring. You can try it for free here: https://netbeez.net/free-instance-signup/

Visit productView on Hacker NewsJan 20, 2016
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
71%71% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
31%31% 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
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
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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