kprobe vs kretprobe — Code Deep Dive

The Linux kernel provides several dynamic tracing mechanisms, among which kprobe and kretprobe are the most fundamental and widely used. Both allow injecting callbacks into kernel functions at runtime, but they serve different purposes and trigger at different points in execution.

kprobe: Can insert breakpoints at any probeable kernel address, providing two callback points — pre_handler (before instruction execution) and post_handler (after instruction execution).

kretprobe: Designed specifically for function-level tracing, providing entry_handler (function entry) and handler (function return) callbacks, with a built-in per-instance private data channel.

PyTorch Compiler Technology Deep Dive: Evolution from JIT to TorchDynamo

Comprehensive exploration of PyTorch's architectural evolution from 1.x TorchScript (JIT) to 2.x torch.compile (TorchDynamo), covering core mechanisms, design patterns, and practical applications with detailed code examples and comparative analysis.

OpenClaw Agent Technical Deep Dive

This document outlines the architecture and implementation of a production-ready OpenClaw agent system deployed on edge hardware. Over a four-day sprint, a complete system was engineered, moving from concept to a stable, running instance. The project involved significant hardware and software integration, including a dual-model LLM strategy for cost and latency optimization, multimodal input via OpenAI Whisper, and asynchronous communication channels. This post-mortem serves as a technical deep-dive for engineers and architects working on similar agent-based systems.

Deploying OpenClaw on NVIDIA Jetson Orin Nano

A complete walkthrough for setting up your self-hosted AI assistant on NVIDIA’s edge hardware — from JetPack flash to a running gateway with local model inference.

NVIDIA GPU Operator: A Comprehensive Deep Dive

An in-depth technical exploration of NVIDIA GPU Operator, covering its architecture, components, lifecycle, and advanced features. Learn how Driver Manager evolved from 264 to 876+ lines, understand the JIT compilation model, and master production deployment strategies.