Articles

This article deeply analyzes the current dilemma of "spaghetti code" faced by large language model (LLM) system prompts, namely engineering problems such as rule collisions caused by the unordered accumulation of rules, difficulty in maintenance, and dilution of core values. The author highlights that significant technical debt may underlie seemingly 'god-level' prompts. To solve this problem, the article proposes introducing system architecture thinking and essentially regarding the prompt as a blueprint for a virtual intelligent system. The article elaborates on the four-layer architecture model composed of Core Definition, Interaction Interface, Internal Processing, and Global Constraints, providing a clear and structured framework for prompt design. In addition, the article summarizes six compilation principles to guide how to effectively transform this rigorous architecture blueprint into prompt text that LLMs can understand and execute stably, thereby upgrading prompt engineering from a craft to software engineering and realizing a fundamental shift from managers of rules to designers of intelligent systems.

The article details Microsoft Edge's "Copilot Mode," which deeply integrates AI capabilities, evolving it from a display tool to an AI Assistant capable of proactive task execution. Its core strength lies in cross-tab context awareness, enabling simultaneous analysis of all open tabs for complex summaries and comparisons. The AI Browser offers intelligent navigation, information extraction, and tab grouping via a unified input box, and previews upcoming "Themed Journeys" with automated booking and shopping features. It also addresses user privacy and authorization, contrasting Microsoft's strategy with Google Chrome and emerging AI browsers. Finally, the article explores potential business model changes, suggesting a potential shift from free to subscription-based browser services, and highlighting a fundamental transformation in internet usage.

The article provides an in-depth look at Cursor, an AI-first code editor built as a fork of VS Code, which has gained rapid adoption due to its seamless integration of advanced AI models. It explains how Cursor's core features, such as AI code autocomplete, a powerful AI chat assistant, inline edit mode, and the BugBot code review tool, are engineered for high performance and user privacy. The article delves into the technical mechanisms behind these features, including ultra-low latency inference for autocomplete, project-wide understanding via codebase indexing and semantic search, and the unique persistent knowledge features (Rules and Memories). Furthermore, it outlines Cursor's sophisticated cloud infrastructure, detailing the roles of various providers like AWS, Azure, GCP, OpenAI, Anthropic, and specialized services like Turbopuffer for vector embeddings, all designed to handle immense scale while prioritizing data security and user privacy.

The article details the newly released open-source flagship model GLM-4.5 series by Zhipu. Designed for agent applications, this series of models (including GLM-4.5 and GLM-4.5-Air) adopts a Mixture of Experts (MoE) architecture. The article emphasizes that GLM-4.5 has reached the SOTA level among open-source models in reasoning, code, and agent comprehensive capabilities, and performs excellently in multiple benchmark tests, especially demonstrating the best performance among domestic models in human evaluations of real-world code agents. In addition, the model has achieved significant optimization in parameter efficiency, API call costs, and generation speed, providing an input price as low as 0.8 yuan/million tokens and a generation speed of up to 100 tokens/second. The article also showcases the application effects of GLM-4.5 in real-world scenarios such as full-stack development, Artifact generation, and PPT creation, and provides API, open-source repository, and online experience addresses for developers and users to test and integrate.

The article introduces GitHub's managed Model Context Protocol (MCP) endpoint as a superior alternative to running the MCP server locally. It highlights key benefits such as automated updates, simplified OAuth authentication instead of manual PAT management, and broader accessibility from any IDE or remote-dev box. The managed service eliminates infrastructure headaches like Docker maintenance, allowing developers to focus on coding and leveraging richer AI workflows. The guide provides a step-by-step tutorial for installation on VS Code and other clients, demonstrating how to configure access controls like read-only modes and dynamic toolsets for enhanced safety and efficiency. It showcases practical examples of GitHub Copilot Agent's capabilities with the managed server, including automating CODEOWNERS file creation, debugging failed CI/CD workflows, and triaging security alerts. The article also previews future developments like secret scanning and agent-to-agent collaboration, emphasizing the open-source nature of the GitHub MCP project. Ultimately, it positions the managed MCP server as a foundational tool for advanced, automated developer workflows.

This article presents an interview with Saoud Rizwan and Nik Pash, the founders of Cline, an open-source AI coding agent distributed as a VS Code extension, following their recent $32 million funding round. Cline differentiates itself in the crowded AI coding space by introducing a 'Plan & Act' paradigm, where the AI first formulates a comprehensive plan before executing tasks, moving beyond simple sequential chat. A key technical innovation highlighted is its shift from traditional RAG (Retrieval-Augmented Generation) for codebase indexing to 'agentic search,' underpinned by advanced 'context engineering' practices. These practices include dynamic context management, AST-based analysis for precise code extraction, maintaining narrative integrity across tasks, and developing a memory bank for persistent knowledge. The discussion also emphasizes Cline's modular architecture via 'MCPs' (Modular Code Providers), which enable seamless integration with various tools like file systems, browsers, Git, and third-party services. Surprisingly, MCPs have expanded Cline's utility to non-technical users for workflow automation, such as social media content generation and presentation creation. The founders explain their strategic decision to build on VS Code as an extension rather than a fork, citing benefits in distribution, onboarding friction reduction, and maintenance. The interview concludes by reinforcing Cline's commitment to the agentic programming paradigm as the future, simplifying complex development tasks through natural language interaction.

This article introduces LangExtract, a new open-source Python library by Google designed to programmatically extract structured information from unstructured text using large language models (LLMs) like Gemini. It addresses challenges such as manual data sifting, bespoke code development, and LLM hallucinations by providing a flexible and traceable solution. Key features include precise source grounding, ensuring every extracted entity is mapped back to its source, and reliable structured outputs enforced by few-shot examples and Controlled Generation. LangExtract is optimized for long-context information extraction through chunking and parallel processing, making it effective for large documents. It also offers interactive visualizations, supports various LLM backends, and is flexible across different domains without requiring model fine-tuning. The article provides a quick-start guide with Python code examples, demonstrating its application from literary analysis to specialized fields like medical information extraction and structured radiology reporting. LangExtract aims to empower developers to unlock valuable insights from data-rich text efficiently.

This article records a roundtable discussion from Tencent Research Institute's 'Midsummer Six Days Talk,' exploring the information ecosystem shift from 'Information Cocoon' to 'Information Beehive.' The discussion traces the connection between the 'My Daily' concept and the Information Cocoon and analyzes the existence, types, and causes of Information Cocoons, highlighting the combined effects of user cognitive bias, inertia, and algorithms. The guests (Hu Yong, Yang Jian, Huang Chenxia) emphasized that 'Information Cocoon' should be regarded as a hypothesis in academia, rather than an established theory, and mentioned the potential 'Hallucination Cocoon' trap from Large Language Models. The article critically examines the limitations of solely relying on technology to solve the Information Cocoon, calls for the reconstruction of the 'Information Gatekeeper' role, and re-evaluates the value of 'Legacy Internet' mechanisms such as RSS Subscription, Search Engine, and BBS, believing they offer potential paths to resist Information Cocoons. Ultimately, the article envisions the 'Information Beehive' as a diverse, transparent, and collaborative information ecosystem, emphasizing the need to jointly construct it from multiple dimensions of audience, algorithms, content, and social environment.

This article serves as an extensive handbook for React developers on effectively managing shared state, a common challenge in growing applications. It begins by explaining the fundamental concepts of props and the issues arising from prop drilling, illustrating how data is unnecessarily passed through intermediate components. The guide then delves into various solutions, including a detailed exploration of the React Context API (with `useContext` and `useReducer` for complex logic) and popular state management libraries like Redux (with Redux Toolkit) and Zustand. Crucially, it provides strategies for performance optimization in shared state scenarios, covers testing approaches for Context and Redux, and offers a decision framework to help developers choose the most suitable approach based on application complexity. The article concludes with insights into common pitfalls and essential best practices for building maintainable React applications.