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Kernel-wide Insight

Leveraging BPF to deliver full-stack observability and deep performance insights into memory, scheduling, networking, and block I/O, with 1% overhead.

Instant Observability

Event-driven context snapshot with instrumentation across kernel slow paths. Automatic reports for system-wide events, including scheduling, networking, and interrupts.

AutoTracing

Pinpoint performance degradation in cloud-native environments with tracing-based intelligence. Automated tracing for CPU idle drops, I/O latency, and Loadavg spikes.

Continuous Profiling

Continuous profiling of OS kernel and multi-language (e.g. Java, Python, Go, C/C++.) applications across CPU, memory, I/O, and Lock. Driving business innovation.

Heterogeneous Hardware

Encompassing core system components such as CPUs, memory, PCIe interconnects, network adapters, and storage, alongside specialized AI accelerators including GPUs and NPUs.

Ecosystem Integration

Integrate open‑source observability stacks. Support bare‑metal and cloud‑native deployments. Aware of K8s containers / labels / annotations. Support mainstream Linux distributions.

Best Practice

Blog

TCP Retransmissions, Explained: Inside the Kernel with tcpshark

2026-09-11 ,

Analyze TCP retransmissions with eBPF tracing and kernel packet drop correlation.

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Linux Kernel Observability for Agent Sandbox

2026-08-31

Building full-stack kernel observability for AI agent sandbox.

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Why We Revived go-pcap for AI Infrastructure

2026-08-04 ,

go-pcap compiles tcpdump-style filters in pure Go for real-time packet filtering across skb, mbuf, …

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Where Packets Get Dropped: Tracing the Linux Network Stack with eBPF

2026-07-01 ,

Trace Linux packet drops with eBPF, kernel stack traces, and targeted traffic filtering.

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Ecosystem

Huatuo Ecosystem

Users

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Contributors

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