Maintained by a like‑minded team, covering both operations and development. Topics span Linux ops, DevOps toolchain, Kubernetes containerization, monitoring, log collection, network security, and Python or Go development. Team members: Qiao Ke, wanger, Dong Ge, Su Xin, Hua Zai, Zheng Ge, Teacher Xia.
This guide shows how to create a fully local Retrieval‑Augmented Generation (RAG) system using n8n, Docker, Ollama and the free Qwen3 embedding model, enabling secure, up‑to‑date AI assistants that answer enterprise questions without exposing any proprietary data.
This article explains how to design and implement an automated inspection platform that leverages Prometheus and Grafana for metric collection, splits inspection tasks, schedules them with cron, generates reports, sends WeChat notifications, and exports results to PDF, all using Go and the gin‑vue‑admin framework.
This article walks through deploying the high‑performance vLLM LLM inference framework, covering GPU and CPU backend installation, environment setup, offline and online serving, API usage, and a performance comparison that highlights the ten‑fold speed advantage of GPU over CPU.
This tutorial walks you through using a one‑click script to create a GPU‑enabled Kind Kubernetes cluster, evenly distribute GPU resources across nodes with nvkind, install necessary drivers and toolkits, deploy a vLLM‑served large language model, and verify its operation, all on a local or cloud environment.
This comprehensive tutorial shows how to use the Model Context Protocol (MCP) to transform AI assistants like Claude, ChatWise, or Cherry Studio into powerful tools that can access your computer’s files, fetch real‑time information, interact with software, and even control smart devices, with clear step‑by‑step instructions, code examples, and troubleshooting tips.
This article introduces k8m, a lightweight AI‑enhanced console for Kubernetes that simplifies cluster management, installation, configuration, and daily operations, while offering features such as YAML auto‑translation, AI‑driven event and log diagnostics, command generation, multi‑cluster support, and role‑based access control.
This article explores the Model Context Protocol (MCP), the emerging “USB‑C” for AI, detailing its core advantages, implementation with Kubernetes, a six‑layer cloud‑native architecture, practical code examples, and developer guidelines for building AI‑powered, secure, and scalable services.
Operations leaders and CTOs often confront the question of the true value of their teams, and this article explores who asks it, why it matters, typical challenges, and practical ways to define and protect the operational role through unified platforms, processes, and strategic collaboration with development.
KubeDoor 1.0 introduces a new architecture for unified multi‑Kubernetes monitoring, offering components for master and agent, flexible deployment options, Helm‑based installation, configurable storage and alerting settings, and detailed guidance on integrating with existing Prometheus/VictoriaMetrics setups while providing automatic peak‑usage data collection.
This article provides a comprehensive overview of Kubernetes fundamentals, covering core control‑plane and node components, key object differences such as Pod vs Deployment, Service types, ConfigMap vs Secret, scheduling, health checks, scaling, security, storage, and troubleshooting techniques.
