BestBlogs.dev Highlights Issue #41

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๐Ÿ‘‹ Dear friends, welcome to this issue of AI Field Highlights!

This week, we've curated 24 insightful articles from the field of artificial intelligence, offering a panoramic view of the latest breakthroughs and trends. Stay ahead of the curve and grasp the pulse of AI development! This week saw major model updates focusing on multimodality, enhanced reasoning, and openness; AI development tools continue to evolve, with Agents, MCP, and low-code/no-code development gaining traction; AI applications are accelerating in programming, creativity, recruitment, gaming, and education, while debates around AGI, startup strategies, and AI's impact on work and learning deepen.

This Week's Highlights:

  1. AI Agent Development Accelerates, Moving from "Thinking" to "Doing" : Zhipu AI released "AutoGLM Rumination," an AI Agent with deep research and operational capabilities, designed to simulate human reasoning, reflection, and execute complex tasks, aiming to evolve AI from thinkers to executors. Concurrently, the industry is deeply discussing Agent definitions, technological status, and implementation challenges (insights from Manus, OWL team), while focusing on the core drivers (Zhipu AI's CEO believes the model itself, not just engineering, is key).
  2. Exploring the Model "Brain" & the "Model-as-a-Product" Paradigm : Anthropic utilized its "AI microscope" technique to reveal the internal workings of the Claude large model during tasks like multilingual processing, content planning (e.g., poetry rhyming), and mental arithmetic, while also investigating the root causes of hallucinations and jailbreaking. Meanwhile, the emerging "Model-as-a-Product" paradigm was proposed, emphasizing the core value of the AI model itself and suggesting future AI products might increasingly focus on inherent model capabilities, simplifying interaction.
  3. Innovating AI Evaluation and Boosting Reasoning Capabilities : A novel AI evaluation method, MC-Bench, uses the game Minecraft to assess models on intuitive, creative tasks, aiming to complement traditional benchmarks that may fall short in evaluating generality and creativity. The research community also continues to focus on enhancing LLM reasoning abilities, with particular attention on strategies post-DeepSeek R1, such as increasing inference-time computation to improve performance.
  4. Developer Protocols and Agent Frameworks Gain Traction : The Model Context Protocol (MCP), aimed at standardizing data formats for LLM-tool interactions, received attention, with articles providing user-friendly getting-started guides and practical use cases spanning design and knowledge management. Technical challenges facing AI Agent systems, such as cross-agent memory sharing and fine-grained data access control (discussed in an interview with HubSpot founder Dharmesh Shah), are also prompting industry reflection.
  5. Popularizing Practical Dev Tech: RAG, Prompt Engineering & LLM Tips : The development history of RAG (Retrieval-Augmented Generation)โ€”a key technology for addressing LLM knowledge limitations and hallucinationsโ€”was systematically reviewed, from Naive RAG to Agentic RAG. The value of prompt engineering was also highlighted, not just for shaping an AI's unique "personality" (like OpenAI's "Monday" voice) but also for guiding AI to generate specific code (like SVG for illustrations), thereby enhancing interaction and creation efficiency. GitHub also shared practical tips for effectively utilizing LLMs.
  6. Automation Tools Empowering Web Interaction and Data Processing : A range of browser automation tools (such as Firecrawl, Selenium, Puppeteer, Playwright) designed for web application testing, data collection, and automating repetitive tasks were spotlighted. The growing importance of these tools in improving development/testing efficiency and supporting AI applications (e.g., converting websites into structured data for LLM consumption) is increasingly evident.
  7. AI Driving New Product Forms: Browser and Voice Interaction Revolution : The AI-first design philosophy is spawning new products. For instance, the Arc browser team launched Dia, a new product centered around AI, aiming to reconstruct browser interaction logic. Concurrently, partners at a16z expressed optimism about the potential of AI voice interaction, viewing it as a significant potential breakthrough for AI applications, particularly in B2C vertical sectors like mental health therapy and edtech, emphasizing that emotional expression, low latency, and personalization are key to enhancing user experience.
  8. AI Empowering Creative Design, Lowering Professional Barriers : AI image models like Jimeng 3.0 demonstrated strong generation capabilities and improved handling of elements like Chinese characters across various scenarios (typography, commercial covers, e-commerce materials, packaging design), effectively lowering the barrier to entry for professional design. Combined with prompt engineering, using AI (like DeepSeek V3, Claude 3.5) to generate SVG code allows for the efficient creation of illustrations for articles and PPTs, boosting content creation efficiency and quality.
  9. Industry Trend Foresight and Strategic Viewpoint Collisions : As large model development enters its "second half," sustained compute investment, multimodality and reasoning capabilities becoming standard, the prominence of open source and open protocols, the pressing need for trustworthy AI, and "Intelligence-as-a-Service" are identified as key trends. Simultaneously, differing viewpoints emerged within the industry on critical issues like the path to AGI (e.g., Pre-training vs. RL), the core of Agent technology (model vs. engineering), and open-source strategies (featuring perspectives from figures like Li Guangmi and Zhipu AI's CEO).
  10. Deep Reflections in the AI Era: Human Wisdom and Societal Direction : Facing AI's rapid advancement, Yuval Noah Harari discussed potential risks, such as exacerbating information cocoons, forming a "Silicon Curtain," and subtly influencing human free will, calling for the cultivation of mental skills to navigate these challenges. Chen Chunhua, meanwhile, clearly distinguished between intelligence and wisdom, emphasizing that humans should focus on developing five core wisdoms that AI cannot replace (such as fuzzy decision-making, empathetic creativity, and value judgment) to maintain unique value and achieve greater creativity in the age of AI.

๐Ÿ” This week, the AI field saw rapid technological iteration, expanding application boundaries, and accelerating business exploration. Simultaneously, long-term reflections on technological paths, industry structure, and human-machine relationships are deepening. We invite you to click on the article links to delve deeper into these developments and collectively embrace the opportunities and challenges brought by AI.

Zhipu Releases AutoGLM: First AI Agent with Deep Research and Operational Capabilities

ยท03-31ยท3143 words (13 minutes)ยทAI score: 92 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Zhipu Releases AutoGLM: First AI Agent with Deep Research and Operational Capabilities

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.

How Have Reasoning Models Developed After DeepSeek-R1? Raschka Summarizes 14 Key Papers Since the R1 Release

ยท04-01ยท8116 words (33 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
How Have Reasoning Models Developed After DeepSeek-R1? Raschka Summarizes 14 Key Papers Since the R1 Release

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.

High School Student Uses "Minecraft" to Evaluate State-of-the-Art Models! Claude is Currently Leading, DeepSeek Trailing Closely Behind

ยท03-29ยท2286 words (10 minutes)ยทAI score: 90 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
High School Student Uses "Minecraft" to Evaluate State-of-the-Art Models! Claude is Currently Leading, DeepSeek Trailing Closely Behind

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 Pokรฉmon, suggesting these new paradigms may drive AI development.

Anthropic Unveils Claude's Inner Workings!

ยท03-28ยท3394 words (14 minutes)ยทAI score: 93 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Anthropic Unveils Claude's Inner Workings!

Anthropic has released research using an โ€œAI Microscopeโ€ 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.

AI Agent Theme Sharing: DeepSeek, Manus, and the Current State of the AI Agent Industry, with 51-Page PPT Available for Download

ยท04-02ยท12155 words (49 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
AI Agent Theme Sharing: DeepSeek, Manus, and the Current State of the AI Agent Industry, with 51-Page PPT Available for Download

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.

Model-as-a-Product: A New Paradigm for AI Product Evolution

ยท03-31ยท2034 words (9 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Model-as-a-Product: A New Paradigm for AI Product Evolution

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 scenariosโ€”from automated content creation to personalized customer serviceโ€”and fosters deep collaboration between AI and users.

The Agent Network โ€” Dharmesh Shah

ยท03-28ยท23815 words (96 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
The Agent Network โ€” Dharmesh Shah

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.

6000 Words + 6 Cases: A Beginner's Guide to MCP for Everyone

ยท04-02ยท5388 words (22 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
6000 Words + 6 Cases: A Beginner's Guide to MCP for Everyone

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 examplesโ€”Figma design-to-web conversion, AI search, Obsidian knowledge base construction, Amap map search, Arxiv paper downloads, and Flomo note creationโ€”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.

Unlocking the AI Personality: The Magic of Prompt Engineering with the 'Monday' Voice

ยท04-02ยท4024 words (17 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Unlocking the AI Personality: The Magic of Prompt Engineering with the 'Monday' Voice

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.

An Introduction to RAG: Beyond Simple Document Retrieval in Dify

ยท04-02ยท2624 words (11 minutes)ยทAI score: 90 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
An Introduction to RAG: Beyond Simple Document Retrieval in Dify

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.

Top 9 Browser Automation Tools for Web Testing and Scraping in 2025

ยท04-01ยท2126 words (9 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Top 9 Browser Automation Tools for Web Testing and Scraping in 2025

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.

GitHub for Beginners: How to get LLMs to do what you want

ยท03-31ยท1687 words (7 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
GitHub for Beginners: How to get LLMs to do what you want

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.

JiMeng 3.0 Image Generation Handbook: A Turning Point for Design Professionals | Full Industry Prompt Collection

ยท04-03ยท5769 words (24 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
JiMeng 3.0 Image Generation Handbook: A Turning Point for Design Professionals | Full Industry Prompt Collection

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.

The Rise of AI-First Products

ยท04-01ยท2598 words (11 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
The Rise of AI-First Products

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 โ€˜3Sโ€™ (Smooth, Simple, Seamless) experience, advocating for a user-centric approach and highlighting the importance of AI ethics.

Insights from OWL Team: Agentic AI Implementation and Replicating Manus

ยท03-28ยท9118 words (37 minutes)ยทAI score: 92 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Insights from OWL Team: Agentic AI Implementation and Replicating Manus

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.

Dia: A Hands-On with Arc Team's Vision for AI Browsers

ยท04-03ยท3800 words (16 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Dia: A Hands-On with Arc Team's Vision for AI Browsers

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.

Here's the Solution for Article and PowerPoint Illustrations! The Ultimate Guide to Scalable Vector Graphics Drawing Expert Agent

ยท04-02ยท4647 words (19 minutes)ยทAI score: 90 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Here's the Solution for Article and PowerPoint Illustrations! The Ultimate Guide to Scalable Vector Graphics Drawing Expert Agent

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.

VC Insights | a16z Partner: Voice Interaction as a Key Breakthrough for AI Applications; Giants Face Challenges in the B2C Market

ยท04-01ยท12770 words (52 minutes)ยทAI score: 92 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
VC Insights | a16z Partner: Voice Interaction as a Key Breakthrough for AI Applications; Giants Face Challenges in the B2C Market

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 โ€œpremiumโ€ application scenarios of AI voice.

Yuval Noah Harari: How the Human Desire for Order Drives Internet and AI Control

ยท04-01ยท10503 words (43 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Yuval Noah Harari: How the Human Desire for Order Drives Internet and AI Control

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 โ€“ 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.

Guangmi Li on the Future of AGI: Pre-training and Coding as Key Enablers

ยท04-04ยท23522 words (95 minutes)ยทAI score: 92 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Guangmi Li on the Future of AGI: Pre-training and Coding as Key Enablers

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.

The Rise of Manus: 20 Questions to Understand AI Agents

ยท03-29ยท18154 words (73 minutes)ยทAI score: 92 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
The Rise of Manus: 20 Questions to Understand AI Agents

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.

7 Key Trends in the LLM Landscape

ยท04-01ยท5788 words (24 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
7 Key Trends in the LLM Landscape

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.

Zhipu AI CEO Zhang Peng on the Overlooked Aspects of Large Language Models

ยท04-01ยท4674 words (19 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Zhipu AI CEO Zhang Peng on the Overlooked Aspects of Large Language Models

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.

Chen Chunhua: Wisdom - The Lighthouse in the Age of AI

ยท04-01ยท4813 words (20 minutes)ยทAI score: 91 ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ๐ŸŒŸ
Chen Chunhua: Wisdom - The Lighthouse in the Age of AI

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.