Liangxu, a self‑taught IT professional now working as a Linux development engineer at a Fortune 500 multinational, shares extensive Linux knowledge—fundamentals, applications, tools, plus Git, databases, Raspberry Pi, etc. (Reply “Linux” to receive essential resources.)
The article explains Jev, a specialized AI model for rapid classification and decision-making in agent workflows, contrasting it with generative LLMs like ChatGPT and arguing that future AI systems will rely on modular, cost-efficient division of labor among models.
This article explains the differences between Skill, MCP, and Agent in AI systems using a restaurant analogy: Skills are specialized tools, MCP is a universal protocol for tool integration, and Agents are autonomous decision-makers that orchestrate Skills via MCP to achieve goals.
This article compares Claude Code, Codex, and Cursor, three leading AI coding tools, analyzing their distinct strengths—terminal-centric control, autonomous task execution, and editor-integrated ease of use—and recommends selecting based on individual development workflows rather than raw capability, as all three converge toward full-cycle AI-assisted development.
The article argues that seemingly incompetent programming mistakes often stem from exhausting work environments, unrealistic deadlines, and lack of learning time, not innate inability, urging empathy over mockery and highlighting how technical debt accumulates from systemic pressures.
The article explains why embedding a national anti‑fraud service into the Linux kernel is unnecessary and dangerous, violating the kernel’s principles of generality, stability, minimal privilege, and the open‑source community’s consensus.
The author examines how AI‑powered coding tools can automate repetitive tasks and boost productivity by about 20%, but also stresses that core architecture, performance tuning, and critical business logic still require human expertise, outlining which developers are most at risk and how to adapt.
The article explains why using Wine—a free, open‑source API‑compatibility layer that re‑implements Windows interfaces—to run licensed Windows programs on Linux does not constitute copyright infringement, illustrating the reasoning with real‑world examples and legal considerations.
Linux users can run WeChat through several community‑driven approaches—including deepin‑wine, virtual machines or Docker containers, third‑party Electron wrappers, and simply using the mobile app—each with its own trade‑offs in stability, isolation, and convenience.
The article argues that while AI tools like Cursor let product managers quickly prototype demos, building a production‑grade system still demands engineering tasks such as architecture design, reliability, performance testing, and ongoing maintenance, which AI alone cannot guarantee.
The article explains that programmers open‑source on GitHub not out of pure altruism but to gain influence, community contributions, and business opportunities, since the code itself rarely sells while support, stability, and maintenance are what customers actually pay for.
