Live Streaming Performance Analyzer
Analyze real-time latency for entertainment platforms.
What it does
"How do global leaders like Evolution Casino maintain 99.9% uptime with sub-second latency?" EvoTrack is a specialized benchmarking tool designed for developers and analysts in the iGaming and live-streaming sectors. We’ve deconstructed the architectural patterns of world-class services like Evolution Casino (에볼루션카지노) to provide you with a comprehensive performance dashboard.
Does the same job
all alternatives →- SASping – An HTTP/TCP latency tool that's easy on the eye2025 · dseltzer.gitlab.io · ▲178
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…
- AOAn open source performance monitoring tool2024 · github.com · ▲135
Hey HN. We’re Jay and Vadim from Highlight.io (https://highlight.io). We’re building an open source [1] monitoring platform for web applications. Today we’re excited to be sharing a performance tool we’ve been working on, which helps you inspect the latency of code execution from the client to the server. As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had…
- UAUtilyze – an open source GPU monitoring tool more accurate than nvtopApr 2026 · systalyze.com · ▲128
The standard GPU utilization metric reported by nvidia-smi, nvtop, Weights & Biases, Amazon CloudWatch, Google Cloud Monitoring, and Azure Monitor is highly misleading. It reports the fraction of time that any kernel is running on the GPU, which means a GPU can report 100% utilization even if only a small portion of its compute capacity is actually being used. In practice, we've seen workloads with ~1–10% real compute throughput while dashboards show 100%. This becomes a problem when teams rely on that metric for capacity planning or optimization decisions, it can make underutilized systems…
- NGNeurox – GPU Observability for AI Infra2025 · github.com · ▲25
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