BestBlogs.dev Highlights Issue #41

Zhipu has released AutoGLM Rumination, an AI Agent with deep research and operational capabilities. It simulates human reasoning and decision-making, acquires and understands environmental information, and operates tools to complete complex tasks, evolving AI Agents from thinkers to intelligent executors. This agent integrates the capabilities of GLM-4, GLM-Z1, GLM-Z1-Rumination, and AutoGLM. AutoGLM Rumination uses reinforcement learning to enable self-criticism and reflection, achieving long-range reasoning and task execution. Zhipu also open-sourced the GLM-4-Air-0414 base model and GLM-Z1-Air inference model, along with the free model GLM-4-Flash and its inference version GLM-Z1-Flash. The AutoGLM series models have achieved SOTA results in the AgentBench Benchmark, excelling in Phone Use and Browser Use. Furthermore, Zhipu is actively promoting the global expansion of China's original large language models and solutions. Led by Zhipu, 10 countries from ASEAN and the 'Belt and Road' region jointly launched the 'International Alliance for Collaborative Construction of Independent Large Models'. Zhipu is committed to promoting the application of Agentic LLMs.

This article reviews the latest research on improving the reasoning capabilities of Large Language Models (LLMs) after DeepSeek R1, focusing on strategies to enhance model performance through increased inference-time computation. It begins by introducing the basic concepts and characteristics of LLM reasoning models, and then elaborates on the two core strategies of increasing training computation and inference computation. Next, the article explores various inference-time computation extensions, focusing on methods such as S1, CoAT, and Inner Transformer, and analyzes the principles, advantages, and limitations of each. In addition, the article discusses the trade-off between inference cost and model performance, emphasizing the need to balance computational resources and user experience, and the future trends of reasoning models, offering valuable insights for developers. Overall, the article systematically summarizes the latest research in reasoning LLMs.

This article introduces MC-Bench, a novel AI model evaluation method developed by high school student Adi Singh. It aligns with the human expectation of AI's intuitive and creative abilities. Using Minecraft, AI models generate creations based on prompts, with users voting for the best. This addresses the limitations of traditional benchmarks, which overemphasize specific task performance and neglect generalizability and creativity. The article also mentions other creative evaluation methods, such as AI models playing Pokémon, suggesting these new paradigms may drive AI development.

Anthropic has released research using an “AI Microscope” to deeply explore the internal workings of the Claude Large Language Model. The research reveals that Claude exhibits a shared conceptual space in multilingual processing, plans content generation ahead of time, and even pre-plans rhymes when composing poetry. Furthermore, Claude employs a parallel computing strategy for mental arithmetic, yet its explanations may diverge from its actual calculation process. The study also explores the reasons for model hallucination and how jailbreak prompts bypass security protections. Through explainability methods, researchers can track the internal reasoning process of AI, providing an important reference for the development of AI safety and reliable AI.

This article is a written summary of the presentation given by Wang Jiwei Channel at the Xiamen University Big Data Forum, themed 'DeepSeek, Manus, and the Current State of the AI Agent Industry.' The article begins by explaining the definition and conceptual meaning of AI Agents, comparing the differences between AI Agents and Large Language Models (LLMs), and discussing the evolution from single-agent to multi-agent systems. Subsequently, it analyzes the role of DeepSeek in enhancing the reasoning ability of AI Agents, and the enlightenment of Manus in the multi-agent collaborative architecture. Next, it introduces the current state of the AI Agent industry in detail, including the application status, product status, market landscape, and technology status. Finally, it discusses how AI Agents affect corporate operations, including business operations, strategic decision-making, and organizational management. The article points out that AI Agents are driving industry efficiency improvement and digital transformation, but also face technical challenges.

The article deeply analyzes the emerging AI product paradigm of 'Model-as-a-Product,' which emphasizes the core value of the AI model itself, rather than relying on complex software or interface packaging. This greatly simplifies user operations and improves efficiency by streamlining workflows. GPT-4o and Doubao (an AI product) are used as case studies to demonstrate how 'Model-as-a-Product' enhances user experience and streamlines operations. Compared to the traditional 'Workflow Agent,' 'Model-as-a-Product' is more flexible and intelligent, and can dynamically adjust strategies according to the actual situation, such as adapting to real-time data feeds during operation. Reinforcement Learning is also crucial in 'Model-as-a-Product,' enabling models to learn and optimize through trial and error. The article also points out the challenges faced by 'Model-as-a-Product,' including a high R&D threshold, large resource consumption, significant market risks, and user experience considerations. Finally, the article envisions a future where 'Model-as-a-Product' redefines application scenarios—from automated content creation to personalized customer service—and fosters deep collaboration between AI and users.

In a Latent Space interview, Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai, discusses his definition of AI Agents and the future of hybrid teams, where humans and AI collaborate. He distinguishes between Work as a Service (WaaS) and Results as a Service (RaaS) business models, suggesting WaaS is more appropriate for many AI applications. Shah emphasizes the technical challenges of agent systems, particularly cross-agent memory sharing and granular data access control, envisioning a future where users selectively share data with agents, similar to OAuth but with finer control. He also touches upon his early exploration in AI with the ChatSpot project, highlighting the evolution of natural language interfaces.

This article aims to reduce the obstacles for users to adopt MCP (Model Context Protocol). It begins by briefly introducing MCP as a unified data format standard that streamlines interaction between LLMs and tools, thereby reducing integration costs. Next, it details the preparations required before configuring MCP, including installing command-line tools like uvx and npx, and differentiating between Stdio and SSE modes. Subsequently, the article provides step-by-step instructions for configuring MCP on Windsurf and ChatWise clients, including obtaining MCP services and importing JSON commands. Finally, through six practical examples—Figma design-to-web conversion, AI search, Obsidian knowledge base construction, Amap map search, Arxiv paper downloads, and Flomo note creation—it emphasizes how these cases streamline workflows and boost productivity. The article concludes by discussing the significance and challenges of MCP, specifically the balance between technological accessibility and ease of use.

This article provides an in-depth look at the prompt engineering behind OpenAI's 'Monday' AI voice, which embodies a 'Monday Blues' personality. By analyzing the role definition, personality attributes, interaction patterns, and behavioral constraints within the 'Monday' prompt, it reveals how to leverage prompts to give AI a unique personality and expression. The article also explores how to adapt the 'Monday' prompt design for creating AI personas and emphasizes the importance of providing examples. Finally, it highlights that this prompt engineering approach can be applied to the personalized design of other AI products, reflecting our understanding and expression of our own emotions and personalities.

This article details RAG (Retrieval-Augmented Generation) technology and its development history. It begins by explaining the basic concept of RAG and how it addresses the knowledge limitations and hallucination problems of Large Language Models (LLMs). Then, the article introduces five types of RAG in chronological order: Naive RAG (easy to use, suitable for rapid prototyping), Advanced RAG, Modular RAG (offering greater flexibility and adaptability to diverse applications through its component-based architecture), Graph RAG (adept at handling structured data and knowledge graph-related tasks by leveraging graph-based knowledge representation), and Agentic RAG, detailing the characteristics, advantages, and limitations of each. Finally, the article summarizes the future development directions of RAG, including enhanced intelligence and data diversification. Through this article, readers can comprehensively understand the technological development of RAG and gain insights for practical AI applications.

This article outlines the leading browser automation tools for 2025, designed for web application testing, data collection, and automating repetitive tasks. It covers various tool categories, such as headless browser solutions, full browser automation frameworks, and specialized tools for web data extraction and no-code automation. The article emphasizes the importance of clearly defining goals, considering security factors, and applying best practices (like robust waiting mechanisms, modular architecture, and error handling) before selecting a tool. It also explores the features of Firecrawl, Selenium, Puppeteer, Cypress, Playwright, Testim, Browser Flow, Axiom AI, and Bardeen. Firecrawl's advantages for AI and LLM applications and its capability to convert websites into structured, analysis-ready data are specifically highlighted. Finally, the article looks ahead to future trends in AI and mobile automation.

This article, the second season of the GitHub for Beginners series, primarily introduces the fundamentals of Large Language Models (LLMs) and prompt engineering. It explains how LLMs work, covering concepts like context, tokens, and their limitations. The article then elaborates on how clear and precise prompts can enhance LLM output quality, offering specific methods for prompt improvement such as task decomposition, considering token limits, and clarifying requirements. It also provides strategies for addressing common issues like prompt confusion, token limitations, and assumption errors. Finally, the piece summarizes best practices in prompt engineering, stressing the importance of context, clarity in prompts, breaking down tasks, and defining requirements explicitly. Through this article, readers can learn how to leverage LLMs more effectively to enhance their productivity.

The article evaluates the applications of the JiMeng 3.0 image model in various design fields such as font design, Xiaohongshu / WeChat cover design, e-commerce product / promotion design, packaging design, brand VI design, and novel cover design, and provides corresponding prompt examples. JiMeng 3.0 excels in significantly reducing AI-related flaws, enhancing Chinese generation and layout design skills, and generating high-resolution images. The article emphasizes that AI will not completely replace designers, but will exacerbate the career divergence in the design industry. Designers need to enhance essential skills such as creative thinking, cultural sensitivity, user insight, and problem-solving abilities to maintain competitiveness in the AI era. The article encourages designers to embrace change and use AI tools to unleash creativity.

This article explores the AI-First product design philosophy, contrasting it with the traditional Mobile-First approach. It analyzes early AI products like Amazon Alexa and the challenges faced by new-generation AI-First products such as Humane AI Pin and Rabbit r1. The article critically examines Rabbit r1, highlighting the gap between its marketing promises and actual functionality and the ethical issues it raises. It emphasizes how AI-First design aims to provide a seamless user experience by integrating apps and services, thereby disrupting the traditional app model. The author concludes with seven design principles, including the ‘3S’ (Smooth, Simple, Seamless) experience, advocating for a user-centric approach and highlighting the importance of AI ethics.

This article features insights from Li Guohao, the founder of CAMEL-AI, on the current state of Agentic AI implementation. It discusses the differences between the OWL project and Manus, factors driving the Agent technology surge, and delves into the application prospects of MCP. The article also analyzes the development direction of vertical Agent fields, emphasizing the need for deep research and addressing key pain points in specialized areas. In addition, it discusses the differences between Agent products and ordinary AI tools in terms of Human-Computer Interaction, as well as the potential of Agent systems for AI in Science. Finally, for academic research projects with limited resources, it proposes suggestions for competitive differentiation and specifically mentions the OWL team's efforts in technical frameworks and performance optimization, along with predictions for future Agent technology trends.

This article reviews Dia, Arc Browser team's new AI-native browser designed for an AI-driven browsing experience. The Arc team abandoned Arc 2.0 to develop Dia, reflecting a shift from a 'feature-rich tool' to a 'minimalist AI entry point.' Dia integrates GPT-4o and Gemini Flash 2.0, supporting personalized conversations and multi-modal video analysis. Users can fine-tune the AI's responses by adjusting its answering style. The article also compares Dia with Manus AI's product philosophy, noting Dia's commitment to redesigning browser interaction after better AI integration. Finally, it analyzes the AI browser landscape, observing that startups and large companies are actively developing LLM-powered interactive ecosystems.

The article elaborates on how to efficiently create high-quality article and PowerPoint illustrations by generating Scalable Vector Graphics code with AI models such as DeepSeek-V3-0324 and Claude 3.5/3.7. It explains the principle of using Scalable Vector Graphics for drawing, emphasizing the importance of model capabilities and prompt quality. The article then shares prompts for DeepSeek-V3-0324 and Claude 3.5/3.7, and introduces methods for image generation and conversion through agent configuration and Scalable Vector Graphics viewers. Additionally, the article delves into advanced techniques like prototype drawing, image redrawing and modification, and customizing personalized styles (such as colored newspaper style), providing readers with a comprehensive AI-assisted drawing solution. Readers can quickly get started with AI-assisted drawing through the steps and prompts provided.

The article is a sharing of investment experience and industry insights from a16z partners in the AI voice field. They believe that with the advancement of Large Language Model, Text-to-Speech, and Speech-to-Text technologies, Voice Interaction will become an important breakthrough for AI Applications. This is especially true in the B2C Market in vertical fields such as psychotherapy and EdTech. The article analyzes the limitations of current AI voice products (such as Siri, Alexa), and emphasizes the importance of emotional expression, low latency, and personalization for enhancing User Experience. At the same time, it explores the application scenarios of AI voice in B2B and B2C fields, such as Call Center, recruitment, psychotherapy, and EdTech. In addition, the article also discusses the Pricing Strategy of AI voice products, emphasizing the experimental nature and potential of results-based pricing, as well as building a competitive advantage through Data Accumulation and Rapid Iteration. Finally, it emphasizes the importance of user trust and differentiated value, and encourages entrepreneurs to focus on the “premium” application scenarios of AI voice.

In an interview, Yuval Noah Harari pointed out that human society tends to prioritize order over truth, a trend that is especially evident in the Internet and AI era. He believes that the free flow of information does not necessarily lead to truth, but may instead lead to the formation of information cocoons, and even a divided network world, namely the 'Silicon Curtain,' where AI development under different cultural and technological systems may lead to a split in worldviews. AI, as a strange intelligence – one that we cannot fully understand, may ignore human values in pursuit of goals, and even control humans unconsciously. Moreover, AI may proactively ask questions to humans, thereby exacerbating the formation of information cocoons. Faced with this challenge, Harari emphasizes the importance of developing mental skills, that is, understanding how one's own consciousness operates, to cope with the risks that AI may bring, including the subtle manipulation of human free will. He calls for slowing down the pace of AI development, establishing a relationship of co-evolution between humans and AI, and resolving the AI value alignment problem before AI becomes super-intelligent.

In a conversation between Xiaojun Zhang and Guangmi Li, the hot topics in the US-China AI landscape of Q1 2025 are explored, focusing on the AGI roadmap. Guangmi Li emphasizes Pre-training as vital for emergent model capabilities and analyzes OpenAI's strategies and organizational challenges. He proposes Coding as the optimal environment for AGI, with Agentic AI as a critical future development, requiring Long Context Reasoning, Tool Use, and Instruction Following for Agent deployment. The discussion also contrasts OpenAI's focus on RL and the consumer market with Anthropic's emphasis on Pre-training and the enterprise market, while looking forward to the potential of AI for Science and the central role of intelligence enhancement in AGI.

This article leverages the rise of Manus to explore AI Agents, covering their definition, development, and the GAIA/MCP standards. It highlights Manus's innovation in automated task flows and the compounding effects driving AI Agent adoption, offering insights into future trends and competition.

This article examines seven pivotal trends defining the next phase of Large Language Model development. First, it emphasizes the importance of computing power investment and points out that although low-cost training solutions such as DeepSeek are attracting attention, substantial compute remains the industry consensus. Second, multimodality and model reasoning capabilities are becoming standard, predicting breakthroughs in various fields similar to the AlphaGo moment. The importance of open source and open protocols is increasingly prominent, becoming a new component of competitiveness. At the same time, the article emphasizes the importance of trustworthy Large Language Models, pointing out that solving the hallucination problem is imminent. Personal AI applications are expected to exhibit a pronounced network effect, leading to accelerated user growth and data accumulation. Finally, the article proposes that Intelligence as a Service represents the future of industry deployment and analyzes the disparity in generative AI adoption between Chinese and American companies.

The article is an interview with Zhipu AI CEO Zhang Peng, using Zhipu's release of AutoGLM as a starting point, focusing on the development trends of Agent technology, the importance of pre-training models, and the competitive landscape of the LLM industry. Zhang Peng believes that the future of Agents lies in the models themselves, rather than engineering accumulation, and emphasizes that pre-training models are key to improving reasoning ability, which differs from the current industry consensus on the limitations of Scaling Law. He also pointed out that open-source is a long-term strategy that Zhipu AI adheres to, and in response to the emergence of competitors such as DeepSeek, he elaborated on Zhipu AI's layout in technology research and development and commercialization, such as integrating more functions into future intelligent Agent development platforms.

The article explores how humans can maintain their unique value in the rapidly evolving AI Era. Professor Chen Chunhua proposes that when AI can complete 80% of standardized work, humans should focus on developing wisdom, that is, irreplaceable human capabilities. The article elaborates on the essential differences between intelligence and wisdom: intelligence relies on data, processing power, and algorithms, while wisdom includes values, meaning perception, and emotional resonance. The article identifies five key human wisdoms: Ambiguity Tolerance, Empathic Creativity, Systemic Cognition, Value Judgment, and Meta-Cognition. It also suggests five self-evolution training methods including cognitive reconstruction and emotional tempering. It emphasizes that humans should constantly improve their wisdom, not only to meet challenges but also to unlock greater potential in the AI Era.