A new approach in troubleshooting distributed systems

Hacker News

Hi HN, We'd love your feedback on our MVP, designed to help Operations and DevOps monitor and troubleshoot large distributed systems in general, and web applications in particular. http://packetbeat.com It works by sniffing protocols like HTTP and MySQL (for now, more to come) and showing the transactions in a web interface. You can also use it to create metrics on pretty much anything by using our filtering language. We are especially interested in: * What do you think about the copyright and the way we present the benefits. Is it clear enough what the product does? * What do you think about the website design (us being primarily backend people). * What do you think about the idea? Many thanks!

Visit product View on Hacker NewsSep 20, 2013

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Launch Intel predictions

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

87%87% 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.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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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