Articles

GPT-5-Codex, OpenAI's latest breakthrough, is a GPT-5 model revolutionizing agent programming. It excels in real-world software engineering tasks, responding quickly to interactive sessions and independently completing complex tasks for up to 7 hours, including project construction, feature development, test writing, debugging, and large-scale refactoring. GPT-5-Codex excels in code review, proactively identifying critical vulnerabilities. It has already reviewed most of OpenAI's internal PRs. The model outperforms GPT-5 in SWE-bench Verified and Code refactoring tasks (key software engineering benchmarks), and dynamically adjusts thinking time based on task complexity. The article also introduces a series of upgrades to the Codex platform, including a newly designed open-source Codex CLI (supporting image input, to-do lists, tool calls, and permission management), plugins for IDEs such as VS Code (providing context awareness and seamless cloud-local switching), and deep integration with GitHub. OpenAI also emphasizes Codex's security measures, such as the default sandbox environment, permission mechanisms, and configurable security settings. Codex is included in various ChatGPT subscriptions, with API access coming soon, opening up new possibilities for developers.

This article features an exclusive interview with OpenAI researcher Yao Shunyu. We delve into the evolution and future trends of AI Agents. Drawing from his personal experience, Yao Shunyu explains his path from computer vision to language models, ultimately focusing on Language Agent research. He emphasizes language as the most essential tool for achieving generalized systems, and points out that GPT models outperform BERT in decision-making within open action spaces. The article details the three waves of Agent development: from symbolic AI to deep reinforcement learning, and then to Agents driven by Large Language Models, proposing that the core of current Agent research lies in defining tasks and environments, rather than the modeling approach itself. He believes that code is AI's most important 'affordance' (the potential uses of the environment for an actor), similar to the human hand, and is the cornerstone for Agents to achieve universal capabilities. The interview also discusses the internal logic of OpenAI's model capability grading (L1-L5) and points out two key directions for Agent development: Intrinsic Reward and Multi-Agent collaboration, corresponding to the future forms of individual Innovators and Organizers. Yao Shunyu delves into the essence of 'generalization,' believing that language models achieve cross-task generalization through reasoning abilities. Finally, he predicts that startups have a huge opportunity to design novel interaction methods that surpass existing models, creating a landscape ripe for disruptive innovation, and expresses optimism about breakthroughs in Agent's long-term memory and intrinsic reward mechanisms.

The article aims to correct the common misunderstanding of Model Context Protocol (MCP) among AI engineers, which is to simply regard it as "a more advanced Function Calling." The author, through rigorous "hypothesis-validation" logic, analyzes MCP from three perspectives—architecture analysis, SDK source code inspection, and Host dissection of the open-source project CherryStudio—arguing that MCP is essentially a model-agnostic engineering protocol for building interoperable AI applications. The article clearly distinguishes the responsibilities of MCP's Client-Host-Server (CHS) three components, emphasizing that the Host is the only component that carries AI intelligence (Prompt construction, LLM invocation), while the Server and Client are purely RPC middleware. Subsequently, the article deeply distinguishes the hierarchical relationship between MCP (infrastructure protocol) and Function Calling (model decision-making capability), and demonstrates the engineering advantages of MCP in decoupling, standardization, and interoperability through pseudo-code comparison. Finally, the article discusses the key factors that determine the application effect of MCP (tool quality, Prompt Engineering, LLM capability) and its inherent challenges (high Token cost, stability of intent recognition), offering AI engineers a comprehensive understanding and practical guidance on MCP.

Gregor Hohpe's article, "Thinking Like an Architect," redefines the architect's role from a mere decision-maker to an "IQ booster" who empowers teams to make smarter, more informed choices. He outlines three core practices: "connecting levels" (the architect elevator), which involves spanning technical details and business strategy to ensure alignment and prevent disconnects; "using metaphors" to communicate complex technical concepts to diverse audiences, fostering shared understanding and parallel thinking; and "seeing more than other people" by revealing additional dimensions to problems, such as viewing "lock-in" as a nuanced cost-benefit analysis rather than a binary issue. Hohpe advocates for expanding the solution space, challenging linear thinking (e.g., speed vs. quality) to achieve better outcomes, and introduces the 'architect boomerang' concept, emphasizing the importance of deeply understanding problems before presupposing solutions. This approach highlights that true architectural value lies in facilitating understanding and enabling collective intelligence, rather than dictating solutions.

Alibaba Tongyi team open-sourced the next-generation Large Language Model architecture, Qwen3-Next. The model has a total of 80B parameters but activates only 3B parameters, achieving a breakthrough in reducing training costs by 90% and increasing inference throughput by more than 10 times. Its core innovations include: a Hybrid Attention Mechanism combining Gated DeltaNet and Gated Attention, designed to optimize Long Context processing; an extremely Sparse MoE Structure with 512 experts, 10 routing experts, and 1 shared expert, activating only 3.7% of parameters; designs that enhance training stability (such as Zero-Centered Root Mean Square Layer Normalization); and a native Multi-Token Prediction (MTP) mechanism to improve inference efficiency. The Qwen3-Next-80B-A3B model rivals the Qwen3 flagship version in performance and outperforms State-of-the-Art (SOTA) Dense Models in multiple evaluations, demonstrating extremely high training and inference cost-effectiveness. The model has been open-sourced and launched on platforms such as Hugging Face, providing an efficient solution for future trends in Large Language Models (Context Length (上下文长度) and Parameter Scaling (参数量扩展)).

The article explores how to build effective tools for AI agents to address their non-deterministic characteristics. First, the author emphasizes a shift in thinking from traditional software development to AI agent tool design. They argue that tools should be tailored for agents, rather than written as APIs for deterministic systems. Next, the article details the iterative process of building and improving tools. This includes rapid prototyping and local testing, establishing a comprehensive AI agent evaluation system (covering real-world task generation, programmatic execution, and results analysis), and leveraging AI like Claude Code for collaborative optimization. Finally, the article distills five core principles: judiciously selecting tools (rather than simply encapsulating APIs), clearly dividing tool functions through namespaces, ensuring tools return meaningful and high-value contextual information, optimizing tool responses to improve Token efficiency, and optimizing tool descriptions and specifications through Prompt Engineering. These principles are designed to help developers create more intuitive, efficient, and adaptable tools for AI agent workflows. This significantly improves the performance of AI agents in real-world tasks.

This article is for data professionals without specialized data engineering backgrounds, providing a practical guide to ODPS SQL query optimization. It starts with an analogy of "express station message transmission" to explain the "divide and conquer" thinking of MapReduce and its four stages: Split, Map, Shuffle, and Reduce, analyzing the execution logic of SQL in MaxCompute's underlying MapReduce with real-world SQL cases. It then proposes a "pre-flight checklist" before submitting SQL, emphasizing vigilance about common performance pitfalls like full table scans, missing column pruning, and data skew. Next, it guides on using Logview, a "dashboard," to monitor SQL tasks and identify MapTask resource issues, Reduce data skew, and ReduceTask resource bottlenecks. Finally, it offers practical tuning strategies and parameter configurations for the Map stage (like small file merging, Split Size adjustment) and GROUP BY data skew (like Map-side pre-aggregation, skewed Key splitting and secondary aggregation), helping readers improve SQL execution efficiency and save cluster resources.

The article thoroughly explains why front-end developers need to understand UI (User Interface) and UX (User Experience) design beyond just writing code. It differentiates UI (product's look and feel) from UX (user interaction and satisfaction), then details the critical importance of this knowledge. Key reasons include bridging the gap between design mockups and functional applications, improving usability, ensuring consistent user experiences, enhancing collaboration with designers, and ultimately boosting user satisfaction and achieving business goals. The author outlines core UI/UX principles such as consistency, visual hierarchy, accessibility, responsive design, clear feedback, and user-centered design, providing clear examples for each. Furthermore, the article highlights the significant professional benefits, including building for true usability, facilitating faster collaboration, strengthening problem-solving skills, and gaining a competitive advantage in the job market. Finally, it offers practical advice on how developers can acquire UI/UX skills through observation, understanding fundamental principles, and practicing with side projects.

This article delves into the application of the AI-assisted programming tool Cursor in enhancing development efficiency, with a particular focus on its impact on legacy projects like WebX. The article first elaborates on the 'efficient usage' philosophy of AI-assisted programming, which involves letting AI handle the main programming tasks while developers act as code reviewers or solution architects. It then details the product features of Cursor, including core functions such as the AI chat area, Composer, and Bug Finder, and emphasizes the importance of introducing contextual information through Notepad and Rules to improve the accuracy of AI code generation. In the practical demonstration section, the article showcases how Cursor intelligently generates code skeletons adhering to complex specifications based on project design documents and existing coding styles, through two specific scenarios: building new features in existing projects (such as generating SQL, Mapper, Bean, Controller, and HSF services) and code refactoring optimization. Finally, it provides tips for using Cursor and envisions its potential integration with MCP (Multi-Cloud Management Platform), highlighting the importance of continuous practice and context accumulation for maximizing AI-assisted programming effectiveness.

This article analyzes the latest AI usage reports from OpenAI and Anthropic. The OpenAI report indicates that ChatGPT has surpassed 700 million weekly active users with 18 billion weekly messages as of July 2025. Its core uses are practical advice, information retrieval, and writing, with non-work-related messages growing significantly while technical uses like programming decline. The report also reveals higher ChatGPT usage among individuals with higher education and income, with a narrowing gender gap. Anthropic's Economic Index Report highlights Claude's advantages in code writing and automation, with automated task delivery rising to 39%. Enterprise-level API customers, in particular, show a strong inclination towards automation, with up to 77% of tasks being fully automated. The article further explores the relationship between AI usage and regional economic structure and income levels, raising concerns about unequal distribution of AI benefits and potential widening of the wealth gap.