BestBlogs.dev Weekly Selection Issue #2

This article revolves around the core topic of JIT and interpreted execution in programming languages. It first elucidates the underlying mechanisms of JIT and interpreted execution, pointing out that JIT is not always faster and has limitations such as memory consumption, system constraints, and startup overhead. The article deeply analyzes the essence of slow interpreted execution in dynamically typed languages, which lies in CPU pipeline stalls, and details the complex conditions required to implement high-performance JIT engines, such as static type information, optimization analysis, and runtime data collection, using V8's null pointer handling as an example. Next, the article reviews V8's 'detours' and introduces the advantages of WASM in achieving efficient JIT through statically typed IR, emphasizing its core value in expanding the functional boundaries of browsers rather than absolute speed. Finally, the article discusses the JIT implementation methods of different language engines, the feasibility of multi-language engines, and the pros and cons of assembly-optimized interpreters and the potential of tail call optimization through examples such as CPython, LuaJIT, and GraalVM, providing a multi-dimensional perspective for efficient code execution.

Written by the Meituan Tech Team, this article details the application of Domain-Driven Design (DDD) in building a marketing system for merchants, tackling the challenges of high business complexity, frequent requirement changes, and high maintenance costs. The article begins by explaining the core concepts of DDD, including Strategic Design and Tactical Design, and demonstrates its practical application using a marketing system example. It then delves into DDD practices in B-side marketing systems, covering the establishment of Ubiquitous Language and Conceptual Models, system decomposition methods, context mapping, and the iterative process of strategic and tactical design. The article also discusses Object Models, Aggregate Root Design, code architecture practices, and common pitfalls, emphasizing the importance of Ubiquitous Language and business understanding. Finally, the article lists several classic books and articles on Domain-Driven Design and Enterprise Architecture, providing further learning resources for readers.

The article chronicles LinkedIn's remarkable scaling journey from its humble beginnings as a monolithic application named Leo to a sophisticated, globally distributed service-oriented architecture. It elaborates on how LinkedIn addressed critical scaling challenges driven by exponential user growth and increasing platform complexity. Key strategies included the early extraction of specialized services like the Member Graph and Search, followed by horizontal scaling of the monolith and database replication to handle read/write loads. As complexity mounted, LinkedIn transitioned to a Service-Oriented Architecture (SOA), breaking down Leo into hundreds of independent, stateless services. The article also highlights the crucial role of caching, the development of Apache Kafka for universal data pipelines, and the "Inversion" initiative to boost developer productivity through tools like Rest.li and Super Blocks. Furthermore, it covers the implementation of multi-data center strategies for high availability and global reach, leveraging Azure Front Door. More recent innovations like Pinot for real-time analytics and a scalable authorization system are also discussed, providing a comprehensive overview of LinkedIn's engineering evolution and the invaluable lessons learned for building large-scale systems.

This article selects and introduces 13 JavaScript libraries that significantly improve development efficiency for frontend developers. It starts with the UI component library Ant Design, detailing its rich components and design philosophy. It then delves into Axios as an HTTP request library and its encapsulation practices, as well as Day.js's lightweight and powerful date processing capabilities. Subsequently, the article introduces Lodash's practical functions in data collections, function utilities, type checking, and highlights the importance of the XSS library in preventing cross-site scripting (XSS) attacks. In addition, it also covers Classnames in CSS class name management, Copy-text-to-clipboard in text copying, UUID in unique identifier generation, Quill in rich text editing, Crypto-JS in data encryption, ViewerJS in image preview, LocalForage in browser local storage, and VConsole in mobile debugging, including their applications and advantages. Each library is equipped with core feature descriptions and practical code examples, offering developers quick start guides and practical references.

The article thoroughly explains the React Context API as a solution to 'prop drilling,' a common issue in React applications where state is passed through many layers of components that don't directly use it. It begins by illustrating prop drilling with a practical counter example, then details how the Context API works, including createContext, Provider, and Consumer (or useContext hook). Step-by-step instructions and code snippets demonstrate how to set up and consume context. The article also covers common use cases like global state, authentication, and theme management, and compares Context API with other state management solutions such as Redux, Zustand, and MobX. Finally, it outlines best practices for effective Context API usage, including providing default values, avoiding overuse, managing frequent updates, using custom hooks, and memoizing context values.

The Elastic InfoSec team integrates with Tines to automate SIEM alert investigations, effectively reducing false positives and improving the efficiency of security analysts. The article details how to leverage Elastic's alerting capabilities to send alert data to Tines, and use Tines' automation workflows to perform initial analysis and processing of alerts. By querying other index patterns in Elasticsearch, it determines whether alerts originate from trusted devices or IP addresses, automatically closing false positives and escalating unresolved alerts to the Security Operations Center (SOC) team for further investigation. This automation not only reduces analyst fatigue but also addresses visibility gaps caused by excessive alert noise. The article demonstrates how to configure Elastic Security's alert actions and build automation workflows in Tines, including using Webhooks to receive alerts, parsing data, routing alerts to different processing paths based on tags, and utilizing Elasticsearch APIs for querying and updating alert statuses. Tines plays a key role in alert routing, event processing, and interaction with Elasticsearch. The automated workflow processes and closes over 3,000 alerts daily, significantly saving manual analysis time and costs.

The NetEase Cloud Music Desktop 3.0 version faced several severe performance challenges after being refactored from the NEJ+CEF architecture to React, including page transition lag, scrolling white screen, and high memory usage. This article provides an in-depth analysis of these issues and offers systematic solutions. Regarding playback startup time, interface preloading and rendering optimization of core components (such as decoupling TableIndex and replacing CSS-in-JS) significantly reduced the startup time. For common interaction lag, the declarative UI event registration scheme was refactored into JavaScript event invocation, greatly improving the application's responsiveness. The scrolling lag of the playlist was resolved by refactoring the virtualized list (introducing react-virtualized) and separating component responsibilities. In terms of system resource usage, listening to the application window state to implement on-demand execution of CSS animation, optimizing the use of the backdrop-filter property, and reasonably managing DOM elements outside the viewport effectively reduced CPU and GPU consumption. Finally, the Performance Monitor, Detached Elements, and Memory tool helped discover and resolve the global memory leak caused by the Analytics SDK failing to clean up DOM references in time. The article concludes with a look at future optimization strategies, including performance monitoring, self-drawing UI, CEF container updates, and playback process orchestration.

The official MongoDB blog has introduced the Atlas Search Playground, a new sandbox environment designed for developers to rapidly experiment with, iterate on, and collaborate on search indexes and queries. The platform is characterized by its ability to allow developers to instantly try creating indexes and formulating data search queries without the need to fully set up Atlas collections or wait for index construction. It offers a seamless user experience, enabling users to complete all operations within a single user-friendly interface without any prior experience or account setup.

This article delves into how ByteDance's Cloud Native Observability team built the next-generation universal high-performance OneAgent to address challenges such as multiple coexisting agents, resource wastage, difficult operation and maintenance, and unified internal and external environment access faced by the company's massive hosts and microservices instances. Built upon open-source iLogtail, OneAgent adheres to the core principle of unification and reuse , aiming to achieve unified collection of Metrics, Logs, Trace, and Event, and support Client/Server reuse and internal/external environment reuse, thereby simplifying the complexity of integrating with the observability system. The team has deeply modified iLogtail, including introducing a more flexible PipelineEvent data model to improve general processing capabilities. They also designed a new construction scheme to support the combined construction of internal private plugins and open-source community plugins, and actively contribute general capabilities back to the community. Through practical cases, such as replacing the storage base with Telegraf, optimizing log file collection, and connecting to the open-source ecosystem, the article demonstrates OneAgent's significant results in reducing CPU usage, solving data loss and delay issues, Agent consolidation, and improving system stability. In the future, OneAgent will continue to enhance its capabilities and deepen its collaboration with the open-source community.

This article records Jiazi Guangnian's (a Chinese tech media) interview with Dr. Yuandong Tian, a researcher and senior manager at Meta FAIR. Dr. Tian raises profound questions about Scaling Law, which is generally regarded as the gold standard in the current AI field. He believes that its essence is to obtain limited benefits by relying on exponential data growth. It cannot solve the problems of long-tail demand and data silos and is not the ultimate answer to AGI (Artificial General Intelligence), emphasizing that it is still 2-3 breakthroughs away from true AGI. Drawing on his experience at Google's self-driving car project, he pointed out the limitations of the data-driven approach. At the same time, he highly agrees with Generative AI as the mainstream trend of future Human-Computer Interaction and content creation, defining it as the other end of the 'continuous spectrum' that can cope with the infinite diversity of needs. In terms of Multimodal AI, he believes that application is the mainstream, but breakthroughs in basic research still require structured data. Dr. Tian firmly bets on the basic research of Model Interpretability, believing that Neural Network AI models are interpretable. He hopes to understand their learning dynamics mechanism and find more efficient training methods. At the end of the article, he also discussed the final state of human-AI integration, believing that self-awareness originates from the brain's modeling of itself. In the future, humans will combine with AI to form a composite to explore the world together, urging researchers to have their own conviction and dare to explore unconventional paths.

This Stack Overflow survey analyzes how professional developers are using AI code assistants. Based on over 1,700 responses, the findings indicate that 76% of developers are using or planning to use these tools, with academic researchers, AI developers, and frontend developers being the most frequent users. ChatGPT and GitHub Copilot dominate the market. While developers find these tools satisfying and easy to use, and report increased productivity, significant challenges remain, including difficulties with context, complexity, obscure problems, and accuracy (38% report inaccuracy half the time or more). Adoption barriers include lack of trust, inability to handle high complexity, and absence of clear company AI policies. Despite these hurdles, AI assistants are perceived to enhance the quality of work time and foster creativity, potentially redefining the developer experience.

Authored by AI experts, this article provides actionable tactical guidance for LLM developers based on a year of building LLM products. It delves into prompt engineering, RAG optimization, workflow optimization, and LLM application evaluation and monitoring. As the first part of a series, it emphasizes practicality and aims to solve real-world product development challenges, providing a tactical foundation for building robust LLM applications.

The article details several core feature enhancements in Dify Workflow version v0.6.9. Firstly, it supports publishing Workflows as reusable tools, enhancing modularity and reusability by allowing calls in Agents or other Workflows. Secondly, the addition of iteration nodes allows performing the same steps on each item in a list, addressing the processing needs of multi-step, repetitive tasks. Furthermore, the parameter extraction node utilizes LLM capabilities to extract structured parameters from natural language, significantly lowering the barrier to tool invocation and streamlining the integration of external tools and HTTP requests. In addition, the article mentions improvements to the variable aggregator (formerly variable assignment) and optimizations to the tool management interface. Finally, a practical example of automatically processing emails demonstrates how these new nodes work together, aiming to help users build more complex, tailored automated AI applications.

The author of ControlNet, Lvmin Zhang, has launched a new project named Omost, which aims to simplify the process of writing prompts for AI-generated images. Users can now generate detailed compositions with just a simple sentence prompt. Key features include breaking down prompts into sub-prompts, defining numerous positions and offsets for elements in an image, and using a baseline renderer based on attention manipulation. The project is designed to make image generation intuitive and user-friendly, with tools for modifying images with minimal effort.

Hugging Face announced the release of Transformers Agents 2.0 on their blog, marking a significant upgrade to the existing agent framework. The new version introduces two new agent types that can solve complex tasks based on historical observations, enhancing the adaptability and problem-solving capabilities of agents. The article delves into the core design principles of the agents, including code clarity, modular design, and tool transparency, all aimed at improving the maintainability and scalability of agents. Additionally, the new version includes a sharing feature designed to promote agent development and sharing within the community, further advancing agent technology. The article also explores the working principles of the agents, including how tools enhance agent capabilities and how agents interact with Large Language Models (LLMs) through the agent.run() method. Special emphasis is placed on the agents' performance in handling complex tasks, such as their outstanding results on the GAIA Leaderboard, showing that the Llama-3-70B-Instruct agent outperforms agents based on GPT-4. Furthermore, the article provides several practical application cases, including self-correcting Retrieval-Augmented Generation (RAG) systems and multi-agent collaboration web browsing tasks, demonstrating the potential and flexibility of agents in real-world applications. Finally, the article outlines the future development roadmap, including more agent sharing options, better tools, long-term memory management, and multi-agent collaboration, aimed at further enhancing agent performance and application scope.

This article begins by introducing the rise of large Transformer language models (LLMs) in the field of open-domain language generation, highlighting the significance of sampling strategies in enhancing generation quality. It then delves into five primary sampling strategies: Greedy Search, Beam Search, Top-K Sampling, Top-p (Nucleus) Sampling, and Temperature Parameter. Each strategy is explained through theoretical explanations and practical code examples, showcasing their application within Hugging Face's Transformers library. Greedy Search simply selects the word with the highest probability at each time step, while Beam Search mitigates the risk of missing high-probability sequences by retaining multiple candidate words. Top-K and Top-p Sampling enhance the diversity and quality of generated text by dynamically adjusting the size of the sampling pool. The Temperature Parameter influences the model's 'randomness' by adjusting the output distribution of the softmax function. The article concludes by summarizing the advantages and disadvantages of these strategies and emphasizes their importance in practical applications.

This article starts by highlighting the limitations of Large Language Models (LLMs) and introduces the concept of Agents. Agents, equipped with memory, planning, and tool use capabilities, can interact with the real world. The article details the three key components of Agents: Planning, Memory, and Tool Use. Through LLM Prompt Engineering, it demonstrates how Agents can perform complex reasoning and task completion. Additionally, the article explores the mechanism of Function Calling in large language models, which allows LLMs to connect with external tools. It also discusses the convenience and diversity of Agent development frameworks. Finally, the article looks forward to the potential of Agent technology based on large models in future AI applications, believing it will drive a rapid and comprehensive restructuring of AI applications, enhancing human productivity.

The article, a presentation transcript by Jay Alammar (Cohere, author of "The Illustrated Transformer"), begins by framing generative AI as a transformative technological paradigm while also emphasizing the need to cut through the hype and focus on practical applications. It urges developers to perceive Large Language Models (LLMs) as versatile tools for both language understanding and generation, extending beyond simple chat interfaces. A significant segment details semantic search, introducing dense retrieval (using embeddings and vector databases) and reranking as effective strategies to enhance search accuracy. This robust search capability is presented as critical for building reliable LLM applications, primarily by mitigating hallucinations and enabling access to up-to-date, external information with explainable sources. The core focus then shifts to Retrieval-Augmented Generation (RAG), defined as the synergy between search and generation. Alammar explores advanced RAG patterns, including query rewriting to refine user prompts, multi-query RAG for complex information synthesis, multi-hop RAG for iterative information gathering, and the futuristic concept of LLM-backed agents capable of tool use and API interactions. He strongly advocates for incorporating citations to ensure transparency and user trust. The discussion also touches upon evaluation metrics for search systems, like mean average precision, and concludes by emphasizing LLMs as adaptable problem-solving tools, with Cohere's Command R models offered as practical implementations.

LangChain v0.2 has introduced versioned docs, and LangSmith has added off-the-shelf evaluator prompts, plus dataset splits and repetitions. The article also highlights a new contest with NVIDIA and upcoming meetups in New York City and San Francisco.

The article explores the application of 'Cognitive Bias' in design from a professional designer's perspective. It defines 'good design' as being based on professional theory, user needs, and business results. It elaborates on Cognitive Bias and its importance in guiding user decisions and driving business value. Through a mobile poster case study, it analyzes design schemes and demonstrates how to integrate psychological biases like perceived value, availability heuristic, Anchoring Effect, decoy effect, Bandwagon Effect, present bias, Scarcity Effect, and ambiguity effect. It emphasizes that design is not deception, but a way to promote user behavior effectively through psychological principles, ultimately empowering business.

This article analyzes how Webflow, a no-code website building platform, overcame early challenges to achieve a $4 billion valuation, driven by its unique product-led SEO strategy. At its core is building valuable products that solve user pain points and precisely align product value with user search intent. The article details how Webflow uses CTAs to guide users to experience the product for free and shortens the B2B SaaS sales cycle with rich templates. Furthermore, it proposes four key steps for building a similar strategy: creating detailed user personas, adopting a freemium model instead of a free trial, defining the product's core theme, and continuously showcasing product use cases at each stage of the user journey. By combining Webflow's data and examples, this article provides valuable and actionable insights for products, marketers, and entrepreneurs.

This article provides a detailed overview of multi-channel customer acquisition strategies for B2B tech products. It begins by distinguishing the key distinctions between B2B and C-end customer acquisition, emphasizing the importance of continuously exploring new channels. In terms of online customer acquisition, the author regards SEO as the cornerstone of B2B products, highlighting its long-term value; points out that social media is about 'generating interest' rather than 'saturation attacks'; and analyzes the advantages and disadvantages of content platforms, customer referrals, app stores, and email marketing, while questioning the overall ROI of advertising. The offline channels section details various methods such as industry events, associations, partner recommendations, offline on-the-ground marketing, and project bidding. In particular, the article emphasizes that on-the-ground marketing is the most efficient customer acquisition method for B2B e-commerce platforms. Finally, the article emphasizes the critical role of the website and dedicated landing pages in conversion and provides successful cases. Overall, the article provides a comprehensive and actionable customer acquisition system for B2B products.

The article explores the limitations of traditional marketing in today's fragmented information landscape. It introduces a framework centered on user understanding and content strategy, highlighting the importance of 'tasks,' 'scenarios,' and 'specific people.' The article also differentiates between 'needs,' 'pain points,' 'itch points,' and 'delight points,' advocating for a needs-first approach. This framework enables marketers to refine their strategies, better understand their audience, and unlock growth opportunities, especially for high-value products.

The article systematically elaborates on how Huawei has built a robust project management system since the late 1990s based on the 'Project-Centric' management philosophy. This system has undergone four stages of development: specialization, systematization, digitalization, and value creation. It summarizes eight key 'Ways': development, culture, delivery, talent utilization, governance, digital intelligence, value, and the future. The article details Huawei's 'Eighteen Skills' in project delivery, the H5M Model for talent development, the application of the ISDP digital platform, the governance approach built through the 'War of the Platoon Leader' (打赢班长的战争, a metaphor for empowering frontline teams), the cultural essence of 'Making Victory a Belief,' the practice of 'Value Delivery Three-Stage Six-Step,' and reflections on future trends in project management. These practices collectively constitute Huawei's project management approach, which combines theory and practice, providing valuable references for enterprises to address complex challenges and enhance strategic execution.

The article delves into the core role of User Feedback in Product Optimization and proposes a comprehensive framework for evaluating feedback quality. The author argues that feedback extends beyond explicit suggestions to encompass all user behaviors and judgments related to the product or service. It further explains the breadth of feedback, emphasizing the diversity of content, users, product services (including competitors), and scenarios, providing guidance for comprehensive data collection. The article then analyzes the importance of feedback speed, breaking it down into user perception, transmission, and enterprise response speeds, and provides strategies for efficiency improvement in each area. Regarding accuracy, the article highlights the importance of scientific research methods, user honesty, information clarity, and verification mechanisms. Finally, the article emphasizes that feedback is a two-way process. It suggests that enterprises should proactively consider what users 'should know' and 'want to know' and design effective feedback mechanisms to enhance User Experience and build trust.

This article offers UX designers a comprehensive guide to interaction scenario design. The author categorizes product interaction scenarios into nine groups: Role, Network, Content, Device, Loading, Interruption, Special, Operation, and Limitation, further detailing them into 38 specific scenario elements. Emphasizing the importance of thoroughly predicting and designing these critical interaction states, the article highlights their crucial role as an essential safeguard in ensuring product usability, ease of use, and enhancing the overall user experience. For each scenario, the author presents specific design considerations and practical examples, aiming to assist designers in delivering more complete solutions and swiftly identifying and resolving potential issues during the product review stage.

Paul Graham's essay delves into the strategies and mindset shifts that enable ordinary individuals to achieve extraordinary success. He begins by emphasizing the importance of choosing fields that resonate with one's talents and interests, suggesting that individuals should strive to reach the frontier of knowledge through practice and learning, identifying and exploring gaps within their chosen field. Graham highlights that curiosity, happiness, and a sense of accomplishment serve as powerful intrinsic motivators for achieving remarkable results, encouraging individuals to boldly pursue unconventional ideas. The essay further explores the process of finding and committing to work that ignites passion, emphasizing the crucial roles of curiosity, courage, and even a degree of self-deception in the pursuit of greatness. Graham critiques the shortcomings of the education system in guiding career choices, pointing out that its often-oversimplified approach can lead young people to make decisions without a comprehensive understanding of their options. The essay also delves into the methods for achieving remarkable success through sustained effort and a positive mindset, emphasizing the dangers of procrastination, the cumulative effect of consistent work, the significance of unconscious thinking, and the importance of avoiding pretense. Graham further explores the role of qualities like sincerity, intellectual honesty, optimism, originality, and the determination to abandon unsuitable pursuits in achieving exceptional work. He emphasizes the cultivation of creative thinking and the importance of choosing problems that are truly original, arguing that the originality of the problem itself is often more significant than the originality of the solution. The essay concludes by discussing how to achieve great things through experimentation and continuous work, starting with small steps and gradually building towards larger goals. Graham also highlights the advantages that young people possess in entrepreneurship and learning, emphasizing the fresh perspective and critical thinking that often accompany inexperience. He acknowledges the potential distorting effects of traditional education on learning and thinking, offering suggestions for overcoming these influences.

At the 20th Global China Summit held in Shanghai, Alibaba Group Chairman Daniel Zhang discussed with Morgan Stanley's North Asia Chairman and Vice Chairman of Investment Banking for Greater China, Kam Shing Kwang. Zhang shared his insights on artificial intelligence, likening the training of AI models to the education of children, and suggested that AI could potentially surpass the academic level of a human PhD in just three to four years. He emphasized the importance of the deep integration of cloud computing and AI for Alibaba, and mentioned the company's two core business areas: e-commerce and cloud computing. Zhang also talked about Alibaba's reorganization, which empowered business unit managers with greater autonomy, and introduced the new CEO, Wu Yongming. In the field of AI, Zhang believes that machine intelligence will continue to advance and highlighted Alibaba's large language model "Tongyi Qianwen," as well as the company's contributions to the open-source AI community through ModelScope. He also discussed examples of AI applications in vertical domains, Alibaba's growth targets over the next decade, aiming to achieve double-digit growth by March 2027, and addressed challenges such as regulatory environment, competitive pressure, and geopolitics, as well as how the company is tackling these issues. Finally, Zhang shared his leadership style and the importance of self-discipline and adequate sleep for maintaining health and peak performance.

The article provides a detailed interpretation of the recent aggressive price war initiated by domestic Large Language Model vendors, especially cloud providers, pointing out that their intention is to attract developers by lowering trial costs and strengthen the sales of cloud products. However, contrary to market expectations, startups in the model layer have shown a less concerned attitude, believing that the price cuts by major companies are more like introductory offers with many limitations, potentially resulting in high actual costs. Additionally, they suggest that major companies' open-source small models may attract shady activities, while price reductions strengthen market education, benefiting the entire industry. The article delves into the unspoken points behind the price cut trend, such as the impact of TPM, RPM, and other limitations on actual costs, as well as developers' concerns about performance and business risks. At the same time, the article proposes response strategies for startups in the model layer, including self-developed applications, continuous improvement of model intelligence, and focusing on technology to reduce costs and increase efficiency, emphasizing that cost is not the only concern for customers. Ultimately, the article points out that the trend of Token costs approaching zero is a long-term trend, which will profoundly reshape the business model of Large Language Models, prompting the industry to shift towards application monetization and making developers a scarce resource, but also facing the challenge of short-term ROI recovery for Large Language Models.

This article provides a detailed record of a historic dialogue between AI Godfather Geoffrey Hinton and AI Godmother Fei-Fei Li in late 2023. The conversation begins with the "big bang" moment of ImageNet and AlexNet, reviewing the ten-year journey of deep learning technology from initial setbacks to breakthrough and widespread adoption by tech giants. The two scholars delve into the rise of the Transformer architecture and the profound impact of AI technology on society after the emergence of ChatGPT. They share their initial motivations for entering the field of AI and express different but complementary views on the future development of AI, potential risks (such as unemployment, fake news, weaponization, and existential risks), and humanity's collective responsibility: Hinton is pessimistic about the existential risks that AI may bring and AI's potential to surpass humans in knowledge sharing, while Fei-Fei Li emphasizes the urgency of catastrophic risks and maintains a pragmatic optimism about humanity's collective will and resilience. The article also calls for public sector investment and cross-border collaboration to achieve the healthy development of AI and encourages the younger generation to combine technical expertise with humanistic care to address the greatest challenges posed by AI.

The article delves into the product strategy and thinking behind the launch of Tencent's AI assistant "Tencent Yuanbao" through an interview with Liu Yuhong, head of Tencent Hunyuan Large Model. Liu Yuhong pointed out that despite the fierce competition in the AI industry, the penetration rate of AI products is extremely low. Tencent chose not to prioritize being first to market, but instead focused on understanding the needs of 99% of users. He introduced in detail the design concept of the "Tencent Yuanbao" consumer-facing product, focusing on key efficiency scenarios like AI Search, AI Summarization, and AI Writing, while also exploring engaging entertainment applications with its multi-modal capabilities. The article emphasizes Tencent empowers developers to build agents using the "Yuanqi" open platform, and reveals that Hunyuan Large Model has been widely used in more than 600 internal business scenarios of Tencent, with a daily call volume of 200 million times, demonstrating its strong technical foundation and the advantages of its end-to-end, in-house developed Angel Machine Learning Platform. Regarding commercialization, Liu Yuhong believes that it is too early to talk about commercialization at the stage where the penetration rate is less than 1%. The current focus is on optimizing user experience and integrating Tencent's resources. In the future, the integration of agents with the WeChat ecosystem will bring more exposure and traffic to creators.

This article summarizes the speech by Silicon Valley investor Zhang Lu at the China AIGC Industry Summit. She noted that AI is a powerful tool driving digital transformation across industries, with opportunities potentially ten times greater than those of the Internet era, though startups capture only one-third. Zhang Lu emphasized that in Silicon Valley, AI's theme is enablement rather than disruption. The Silicon Valley Large Language Model market is well-defined, divided into platform-based models (like OpenAI), self-use models (like Apple), and open-source platforms, which she favors. Zhang Lu suggests startups adopt a 'hybrid approach,' leveraging Large Language Model APIs and integrating them with fine-tuned open-source models to build domain-specific models. She stressed data quality over quantity and the advantages of domain-specific small models in cost-effectiveness. Healthcare, financial services, and robotics are rapidly iterating application areas. The article highlights infrastructure as key, requiring solutions to high computing costs, energy consumption, data privacy, and latency. Industry expectations for AI have become more realistic, shifting from model performance to large-scale industrial applications and cost control.

This article introduces a cognitive training system. It comprises five 'Mental Exercises' designed to comprehensively enhance personal cognitive abilities. First, the 'Serial Recall Method' is used to exercise memory, suggesting recalling key points in order after a period of time following information intake. Second, the 'Feynman Learning Technique' deepens comprehension, emphasizing explaining new knowledge points to an audience with no prior knowledge, breaking them down from the three dimensions of 'what, why, and how.' Next, the 'Structured Thinking Method' enhances expressiveness by constructing a rigorous argumentation system of 'Claim-Reason-Evidence.' Then, the 'Sensory Immersion Method' is introduced to enhance attention, suggesting a silent outdoor experience focusing on sensory reception. Finally, the 'Bedtime Priming Method' optimizes information organization by consciously reviewing positive and valuable events of the day, guiding the brain to strengthen memory during sleep. These methods are highly practical and operational, aimed at helping readers develop good thinking habits and enhance cognitive function.