Wachy – A UI for eBPF-based performance debugging

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eBPF is an amazing technology that allows safely running user-supplied functions at pretty much arbitrary probe points in a kernel/user space context. Much has been written about how amazing this feature is for kernel observability. But as someone who writes user space code, what I find even more amazing is the support for tracing arbitrary user space programs, with no code changes and low overhead. However, doing in-depth analysis can get complicated and time-consuming. My goal with wachy was to make this debugging significantly easier/faster to use, by displaying traces in a TUI next to the source code and allowing for interactive drilldown analysis. If you get a chance, check out the start of the demo video since (AFAIK) it's quite unique and gives a much clearer idea than I can provide with just text.

Visit product View on Hacker NewsMar 26, 2022

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

91%91% 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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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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