Articles

This article thoroughly analyzes the reasons behind the challenges faced by Anthropic's Multi-Modal Communication Protocol (MCP) one year after its launch. The article first reviews the significant buzz MCP generated upon its release in 2024, as it was expected to solve the challenge of constant reinvention in AI application development. However, its core weakness lies in occupying a large context window during calls, leading to a surge in Large Language Model (LLM) Token consumption and high operating costs. This also distracts model attention, resulting in a significant decrease in reasoning accuracy. MCP's low development threshold has resulted in many redundant and low-quality tools in the ecosystem. This increases screening costs for developers, and the permissive permission design leads to serious security risks, potentially causing irreversible errors. The article points out that Anthropic has quietly shifted to a system called Skills, which is seen as an implicit acknowledgment of the design flaws of MCP. Skills achieves more seamless and efficient integration by encapsulating high-frequency, validated capabilities. Ultimately, the article proposes that MCP and Skills are essentially "patches" to compensate for the current limitations of AI intelligence, aiming to use deterministic engineering methods to control probabilistic intelligent agents, and predicts that MCP will transition from a hyped technology to a foundational infrastructure, with high-frequency capabilities going to Skills and long-tail data going to MCP.

This article details the discovery and subsequent confirmation of Anthropic's groundbreaking "soul document" used in the training of Claude 4.5 Opus. Initially uncovered by Richard Weiss as a consistent "soul_overview" section within the model's extracted system messages, its authenticity was later verified by Anthropic's Amanda Askell. The document, integrated during supervised learning, is designed to imbue Claude with Anthropic's core mission: developing AI that is safe, beneficial, and understandable. It emphasizes fostering good values, comprehensive self-knowledge, and the wisdom necessary for the model to behave safely across diverse circumstances. Furthermore, the document explicitly addresses prompt injection attacks, providing insight into why Claude Opus demonstrates improved, albeit still vulnerable, resistance to such exploits. This revelation offers a fascinating glimpse into the advanced ethical alignment and personality shaping techniques employed in cutting-edge large language model development.

This article provides an in-depth retrospective of Jina AI's six-year startup journey through an interview with founder Xiao Han. Jina AI underwent three major pivots, from a Multimodal Search Framework to Prompt-engineered productivity tools, ultimately focusing on a Search Foundation Model. Xiao Han highlights the importance of laser focus and efficient operations in the fast-paced AI landscape, elaborating on the shift from Europe to Silicon Valley, streamlining the team, and exploring the commercialization of models as products. The article also discusses the endgame for small AI model companies, the significance of revenue, and reflections on the team's Scaling Law during their entrepreneurial journey. Ultimately, Jina AI was acquired by Elastic, and Xiao Han provides insights into future AI startup opportunities and offers advice for aspiring entrepreneurs.

Authored by Wu Pengyang, a senior expert at Tencent Research Institute, this article delves into AI's profound impact on the job market. It first elaborates on AI's four effects on the labor force: enhancement, substitution, supplementation, and creation, noting that substitution is currently outpacing creation, but emphasizing that substitution doesn't equal unemployment. Next, through statistical analysis of recruitment data from leading AI companies, AI professions are categorized into five core types: enablers, collaborators, governors, promoters, and supporters, with specific job examples provided. The article also summarizes four characteristics of AI professions: deep segmentation, cross-border integration, human-machine collaboration, and dynamic change. Looking ahead, job growth is predicted to concentrate in AI-native fields, the service industry, and solo entrepreneurship and flexible workforce arrangements. Finally, the article offers insights and recommendations for adapting to the AI revolution at the individual, enterprise, and societal levels, emphasizing lifelong learning, fostering human-machine collaboration, and building an employment-friendly institutional environment.

The article from LangChain's blog details five crucial patterns for evaluating "deep agents," which are complex, stateful AI applications. It highlights that traditional LLM evaluation methods are often insufficient due to agents' dynamic nature and the need for specific, context-dependent success criteria per test case. To overcome these limitations and ensure robust testing, the article outlines five crucial patterns. The first emphasizes bespoke, code-based test logic for each datapoint, allowing specific assertions against an agent's trajectory, final response, and internal state. Secondly, it advocates for single-step evaluations as an efficient way to validate immediate decision-making and tool calls, akin to unit tests. Thirdly, full agent turns are presented as essential for providing a comprehensive view of end-to-end actions, useful for evaluating overall trajectory, final responses, and generated artifacts. Fourthly, multi-turn evaluations simulate realistic user interactions but require conditional logic to manage agent deviations and ensure test consistency. Finally, the importance of clean, reproducible test environments and mocking external API requests is stressed to ensure reliable and efficient evaluations. The article positions LangSmith's testing integrations as a flexible framework to implement these patterns, offering practical guidance for AI developers.

This article details AI prompt templates for generating cartoon anime English vocabulary cards. Inspired by group members, the author designed four template styles: watercolor, 3D clay, crayon graffiti, and custom characters. These templates help users create vivid and engaging visual English learning materials using AI tools like ChatGPT, Gemini, Qianwen combined with Nano Banana Pro or Lovart. They intelligently match scenes, incorporate words, enable character interaction, ensure seamless scene connections, and generate vocabulary lists with phonetic symbols, significantly simplifying the production of visual English learning materials. The article also provides usage methods and precautions, emphasizing the templates' customizability and non-commercial use, offering practical AIGC solutions for English teaching content creators.

This article, written by a senior front-end developer, reviews the explosive history of AI from AlphaGo to ChatGPT. Combining his own practical experience of successfully wrapping ChatGPT as a desktop application (gaining 54k+ Stars), the author provides deep insights and shares his thoughts on AI development trends. The article first criticizes the current phenomenon of 'AI hype,' which involves exploiting information gaps and selling anxiety. Subsequently, the author divides AI forms into models and usage, focusing on analyzing the evolution of AI applications from web pages to desktop applications, CLI tools, and even AI browsers, pointing out that the core trend is 'deep integration and weakening of permission boundaries.' At the technology implementation level, the author details the advantages and disadvantages of Electron and Chromium secondary development in building AI browsers, and proposes that a set of 'browser API layer specifically designed for AI' is needed in the future. Finally, the article provides suggestions on how to learn effectively in the era of information overload, emphasizing the importance of problem-driven learning, approaching first-hand information sources, and actively outputting.

This article offers a comprehensive and in-depth analysis of the Transformer architecture, the cornerstone of Large Language Models (LLMs). Starting with fundamental concepts, it details how text is transformed into digital representations processable by the model through tokenization, word embedding, and positional encoding. The core focus is on attention mechanisms, including self-attention, multi-head attention, and causal attention, explaining how the model captures complex dependencies between words. It effectively addresses challenges such as long-range dependencies and the difficulty of parallelization faced by traditional RNNs. Furthermore, the article introduces feed-forward neural networks (FFN/MLP) and the stacking of Transformer layers. Through a clear translation example, the Transformer's workflow is demonstrated step by step, combined with the innovation and development trends of leading open-source LLM architectures (such as MoE, MLA, GQA, etc.) as of 2025, providing readers with a deep understanding of LLM core principles and future directions.

The article quotes Weng Xindan, head of the Terminal Technology Committee of Ant Group, pointing out that AI has not killed the frontend but has transferred the technical complexity from physical stacking to digital stacking. The minimalist AI application interface that appears to be a reversion is an evolution of the interaction mode from CUI to GUI and then to the coexistence of CUI and GUI. It also marks the evolution of the architecture from broad API to semantic API, which essentially is technical debt resolution. The article further emphasizes that the assumption of cloud omnipotence ignores the physical limitations of network and computing power. The future AI experience will be a hybrid mode of 'Cloud-side Training + Device-side Inference,' making the terminal a strong node for Edge Computing. Ultimately, the article points out that the value of frontend engineers lies in their extreme sensitivity to experience and their engineering ability to combat entropy increase, which are irreplaceable core strengths for AI, pointing out the direction for frontend practitioners.

The article details Runway's latest released Text-to-Video model, Gen-4.5, which surpasses all existing models with an Elo rating of 1247 in the Artificial Analysis benchmark, reaching SOTA level. Gen-4.5 exhibits unprecedented realism and detail in camera movement, perspective switching, complex scenes (e.g., a person looking in a mirror), physical effects (e.g., dust, weight, and paint's wet/dry variations), and lighting. It can understand and execute complex sequential instructions while maintaining Gen-4's core advantages in speed and efficiency, and achieves breakthrough improvements in image quality. The model supports all existing control modes, such as image-to-video, keyframe generation, etc., and is planned to be offered to all users at the current subscription price. Although the article mentions that the model still has limitations in causal reasoning and object permanence, its high-quality generation capabilities are hailed by netizens as the "Video Generation AGI moment," foreshadowing that AI-generated content will become more difficult to distinguish from real content.