Sping – An HTTP/TCP latency tool that's easy on the eye

Hacker News

I've frequently found myself using [nvitop]( https://github.com/XuehaiPan/nvitop ) to diagnose GPU/CPU contention issues. The two best things about it are: - It's easy to install if I can access pip in the container - It makes a compelling screenshot (which helps me communicate with coworkers.) With those two lessons in mind: Here is Sping! Purpose: Help observe and diagnose latency issues at layer 4+ (TCP/HTTP/HTTPS) Two good things about it: - It's easy to install if you have pip. (Available at [service-ping-sping]( https://pypi.org/project/service-ping-sping/ ) on PyPi) - It makes a compelling screenshot. Not sure if this is the kind of thing that anyone else would be interested in. But I've enjoyed making it and intend to keep using it.

Visit product View on Hacker NewsAug 24, 2025

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Hacker NewsStrong engagement from HN community · Strong signals: 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

11%11% 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.

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