145 related articles

Chinese open-source models DeepSeek and Kimi K3 are challenging OpenAI's closed-source dominance. Analyzing the business logic, chip ecosystems, and US-China strategic dynamics behind the open vs. closed AI debate.

Jensen Huang's first-ever tweet backs open-weight AI. 50 Silicon Valley giants oppose banning Chinese open-source models. Deep analysis of the interests behind closed vs. open AI ecosystems.

The same LLM API performs drastically differently under different Agent frameworks. Through a real database crash case, this article analyzes why choosing the right Agent matters more than switching models.

Chinese open-source models DeepSeek and Kimi K3 challenge OpenAI's closed-source dominance. Analysis of open vs. closed AI strategies, CUDA moat erosion, and the US-China strategic battle for AI supremacy.

An in-depth look at AI testing challenges. Learn to write reusable Skill packs and master Agent testing and LLM evaluation—covering the SKILL.md six-dimensional rule, skill-creator, EvalScope, and dataset selection.

A step-by-step Pi Agent configuration tutorial covering installation, LLM connection, extension ecosystem, MCP setup, and Token-saving tips. Learn to build a truly controllable AI coding assistant.

How a Bilibili creator used AI Agent, MCP, and Playwright to build a JLCEDA local netlist analysis plugin from scratch—covering features, setup, and the full Agent-driven dev process.

A complete guide to the three core categories of AI tools in the testing era (personal assistants, CLI geek tools, AI IDEs), revealing the real challenges of AI test case generation and the new AI test development paradigm.

A comprehensive guide to three core AI tool types (personal assistant, CLI geek, AI IDE) in the testing era. Uncover the real challenges of AI test case generation and the new AI test development paradigm.

Claude Code was revealed to steganographically mark system prompts under specific conditions, triggering a developer trust crisis. This article analyzes the steganography, Anthropic's tightening China access, and how AI coding tools became a core enterprise security issue.

A systematic map of today's AI coding landscape: the evolution from ChatGPT to Claude Code, LLM capability tiers, tool camps like Cursor/Copilot, and the three key weapons of the Agent era — MCP, Skills, and CLI.

How do complete beginners get started with Claude Code? This guide covers VS Code setup, two ways to connect an LLM in China, four core usage modes, and building your first practical tool without writing a single line of code.

Coze by ByteDance is a no-code AI agent platform with visual workflows, 60+ plugins, knowledge bases & persistent memory. A complete beginner's guide.

Moonshot AI, Alibaba, DeepSeek, and Meituan all crossed the trillion-parameter threshold. China's open-source LLMs made the B-to-T leap in just 18 months.

An in-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing methods and the transition path for test engineers.

In-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing and the transition path for test engineers.

In the AI programming era, Vibe Coding alone can only build toys. This article deeply analyzes the complete engineering path from Vibe Coding to SDD spec-driven development, covering Claude Code and Codex tool selection, the SuperPower plugin, and domestic LLM comparisons.

Hit the Vibe Coding ceiling? This guide covers the three-stage AI coding progression path, Claude Code vs. Codex, SuperPower SDD, and how to go from vibe coding to enterprise-grade AI engineering.

Alibaba's Qwen3.8 challenges larger models with a 2.4T-parameter MoE architecture, claiming second only to Gemini. A deep dive into MoE mechanics, continuous updates, two-speed release strategy, and real local deployment requirements.

A deep dive into the three core LLM job roles — Application Engineer, R&D Engineer, and Algorithm Engineer — covering academic requirements, salaries, and skill roadmaps.