AppView 1.0.0 is released Instrument, Observe, Secure your deployments

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AppView is an open source, runtime-agnostic instrumentation utility for any Linux command or application. It helps users to explore, understand, and gain visibility with no code modification. With one instrumentation approach for all runtimes, AppView offers ubiquitous, unified instrumentation of any unmodified Linux executable. With AppView 1.0.0 comes the new threat detection logic that allows users to capture security-related events. Is your application accessing secure files? Is it making connections it shouldn't be? Is it exfiltrating data over DNS or is GOT poisoning in effect? Other features of the 1.0.0 release include the ability to: - Generate metrics on process and application performance. - Generate events, reporting on network, file, logs, console messages and http/s activity. - Capture (decrypted) payload data without the need for keys. - Generate a stack trace, and a core dump when an application crashes. - Generate network flow information. - Create a report on unique file and network activity. - Install AppView in a Kubernetes cluster. - Secure file and network access in an application. - Instrument both static and dynamic executables. - Attach to processes while they are running or start when the process does. - Normalize and forward metrics and events, in real time, to remote systems. - Summarize metrics and detect protocols. We are looking for users and contributors alike.

Visit product View on Hacker NewsApr 21, 2024

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

79%79% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% 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: users · Missing: plus, platform, intuitive

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

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

39%39% 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 · Strong signals: real time · 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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