xC

xCapture v3 for thread-level dimensional performance analysis with eBPF

Hacker News

xCapture v3 for thread-level dimensional performance analysis with eBPF

This is the first proper release of xcapture eBPF thread sampler and xtop frontend TUI tool. xCapture gives you an efficient, always-on observability signal for dimensional performance analysis of thread-level activity. In passive sampling mode on a 104 vCPU machine (~2000 threads), xcapture used 0.07% of a single CPU out of all CPU capacity. We just had a "launch party" at P99CONF and the 20 minute talk is available at: https://www.p99conf.io/session/xcapture-v3-efficient-always-...

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, single, activity · Missing: agents, macos, agent
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
62%62% 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
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
39%39% 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
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
10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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