BestBlogs.dev Weekly Selection Issue #2

With the proliferation of scripting languages and increasing performance demands, interpreted execution and Just-In-Time (JIT) compilation have become two common methods of code execution. This article explores both technologies through detailed examples and in-depth analysis, revealing their working principles, performance differences, and respective advantages and disadvantages.

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.

This article provides a detailed look into how LinkedIn handled the challenges of scaling its platform to accommodate exponential growth. It covers the transition from a monolithic architecture to a distributed system, the creation of specialized services like the Member Graph Service and Search Service, and the use of various tools and techniques to manage the increasing demand.

The Ctrip technical team has progressively refined its data foundation platform over the past few years, evolving from the 1.0 architecture to the 2.0 version in 2023. This platform primarily includes the HDFS distributed storage cluster, the YARN computing cluster, and the Spark and Hive computing engines. Faced with the rapid growth of data and computing tasks, the team has taken measures such as multi-data center architecture upgrades, tiered storage strategies, transparent migration technology, priority-based scheduling, NodeManager node mixing, and the integration of offline and online nodes, as well as the introduction of Celeborn as a new Shuffle service. These steps address the pain points in storage, scheduling, and computing engines. Additionally, the team has achieved a smooth upgrade from Spark2 to Spark3, optimized the partition filtering function in Spark3, tackled the issue of data skew, and introduced the Apache Kyuubi project as the Thrift Server for Spark3, providing enhanced multi-tenancy and resource isolation support. Through these improvements, the Ctrip technical team has not only enhanced the scalability, resilience, and performance of the data platform but also ensured the stable operation of the group's data.

For Java programmers without a Go background, the speed of developing a usable program in Go is visibly slow. The main difficulty lies not in the Go language itself but in setting up the entire project pipeline, or 'environment configuration.' This article describes how to configure an environment suitable for Go development to avoid common pitfalls.

Uber has migrated all its payment transaction data from DynamoDB and Blob storage to a new long-term solution called LedgerStore. The company aimed to reduce costs and previously minimized DynamoDB usage, only using it for hot data. The new system offers immutable storage with data integrity guarantees, saving Uber around $6 million annually.

Experts from around the world gather at Xiaohongshu to discuss new trends in web technology. The article delves into the potential of WebGPU as a high-performance API standard for 3D graphics and data parallel computing, its applications in various industries like gaming, VR, and machine learning, and how Xiaohongshu leverages it for business growth. Key points include: 1) The advantages of combining WebCodecs, Streams, and WebGPU for real-time media processing; 2) Use cases of interactive rendering technology in Xiaohongshu; 3) Comparative advantages of WebGL over Lottie and optimization strategies for WebGL; 4) Future prospects of WebGPU in enabling richer and more dynamic web experiences.

This article, based on a lecture by Bilibili's senior SRE engineer Hong Peng, details the construction of an emergency response system at Bilibili. The system aims to detect issues within 1 minute, respond within 3 minutes, pinpoint and identify within 5 minutes, and recover within 10 minutes. The article covers three main areas: 1. Stability assurance challenges, 2. Emergency Response Center (ERC) construction strategy, and 3. Platform capabilities. Key points include the technical means of monitoring and handling failures, the role of customer feedback, and the automation of the emergency response process.

Kubernetes provides a robust solution for managing high-availability large-scale applications, but it may not be suitable for all SaaS businesses, especially for independent developers or smaller projects.

The article introduces the 1.0.0 Milestone 1 release of Spring AI, highlighting its new features and improvements. Key points include: 1. ChatClient Fluent API for handling prompts and AI model calls. 2. Usage examples with @RestController, returning AI-generated content. 3. Integration with WebClient for reactive calls. 4. Configuration options for default values in ChatClient. 5. Advisor model for contextual data and conversational history.

The article's author shared 13 front-end libraries that he frequently uses in his work to help developers improve their efficiency. First, he introduced Ant Design, a React component library that provides a variety of commonly used components, supporting internationalization and custom theme colors. He then discussed Axios, an HTTP request library based on Promises, which supports request and response interceptors as well as the ability to cancel requests. Day.js is a lightweight date processing library with an API design that supports chained calls. Lodash is a JavaScript utility library that offers a range of functionalities including collection processing, functional tools, type checking, deep cloning, string manipulation, and mathematical operations. The XSS library is used to process HTML and prevent XSS attacks, with support for whitelist configuration. Classnames is used for dynamically adding or removing CSS class names. Copy-text-to-clipboard is a lightweight library for copying text to the clipboard. UUID is used for generating globally unique identifiers. Quill is a rich text editor suitable for edit box requirements in mid to back-end products. Crypto-js provides various encryption algorithms and common encryption functions. Viewerjs is an image preview library that supports interactive features such as zooming, dragging, and rotating. Localforage is a library that encapsulates the browser's storage engine, allowing for the selection of appropriate storage engines for data storage. Vconsole enables real-time viewing of debugging information in mobile browsers.

The article first discusses the complexity of managing state in React applications using prop drilling, which involves passing props down through the component hierarchy. This approach becomes challenging to maintain and understand as the complexity of the application increases. To address this issue, React offers the Context API, which enables state sharing across the component tree without the need for manual prop passing.

Next, the article details the creation and use of the Context API through a counter example. It begins by creating a context named CounterContext and defining a CounterProvider component to supply the state and setState function. It then demonstrates how to consume these states within the GrandChildComponent using the useContext hook, instead of via prop passing.

The article also outlines several common use cases for the Context API, including global state management, authentication management, theme management, and more. Additionally, it compares the Context API to other state management solutions such as Redux, Zustand, and MobX, highlighting their respective features and appropriate scenarios for use.

Finally, the article offers some best practices for using the Context API effectively, which include providing default values, avoiding overuse of Context, minimizing frequently updated states, and utilizing custom hooks and memoization of context values to enhance performance.

The Elastic InfoSec team faces a major challenge in SIEM management: analysts are overwhelmed by a large number of false positives, leading to fatigue and visibility gaps. To address this issue, the team has implemented the Tines automation tool to reduce the manual investigation workload of SIEM alerts. By integrating Tines with Elastic's SOAR system, the team has established automated investigation workflows that leverage Elasticsearch's _searchAPI and Signals API to automatically close false positive alerts and escalate real threats when necessary. This automation enables Elastic to automatically process over 3,000 alerts daily, equivalent to saving the workload of 94 full-time employees.

The Yun Music desktop version was released in May 2014 and had been using the NEJ + CEF based Hybrid APP architecture until the 3.0 update. Despite trying to adopt React in conjunction with NEJ during 2021-2022, it proved inefficient due to the heavy reliance on NEJ. The 3.0 version brought significant interaction and visual updates, necessitating a complete React-based overhaul. This article discusses the main performance challenges encountered and the optimization strategies implemented, covering areas such as playback initiation time, UI component rendering, handling vast amounts of playlist data, managing diverse events, and complex state subscriptions.

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 explores the development of OneAgent by ByteDance's Cloud Native Observability team, focusing on its data model, pipeline, orchestration, and build system. It highlights the challenges faced due to the vast scale of ByteDance's infrastructure and the need for a unified observability solution. OneAgent aims to simplify observability system integration and enhance data collection efficiency, resource consumption, and system stability. The article also discusses the collaboration with the iLogtail community and the architectural details of OneAgent, including its core and plugin systems.

Cloudflare has extended its Local Traffic Management (LTM) load balancing capabilities to support all TCP and UDP traffic, which previously only supported HTTP(S) traffic. The integration of Cloudflare Spectrum, Tunnels, and load balancers now allows enterprise customers to manage a broader range of network protocols like SSH, FTP, NTP, and SMTP. Key benefits include eliminating the need for on-premise load balancers, increased security through IP concealment, and enhanced scalability via Cloudflare's global Anycast network.

In this article, Tian Yuandong provides profound insights into several key issues in current AI research, particularly his skepticism towards the Scaling Law and his advocacy for generative AI.

Tian first discusses the limitations of the Scaling Law. Proposed by OpenAI in 2020, the Scaling Law suggests that the ultimate performance of large models is primarily determined by computational power, model parameter size, and the amount of training data, rather than the specific structure of the models. However, Tian points out that as model performance approaches human levels, acquiring new data becomes increasingly difficult, and further improvements become harder to achieve. Additionally, he highlights that many real-world long-tail needs involve scenarios with very little data, which cannot be addressed by relying on the Scaling Law alone. This could eventually lead to a situation where everyone is isolated on their own "data islands," unable to share and utilize each other's data.

Tian then emphasizes the advantages of generative AI. He believes that generative AI can generate large amounts of content from minimal prompts, reducing the need for manual input and repetitive labor. Generative AI can work similarly to teaching a child, where minimal guidance allows it to extrapolate and create more, significantly boosting productivity. This is because generative AI can work around the clock, has low replication costs, and replicating engineers is very difficult.

Furthermore, Tian presents his views on breakthroughs in data efficiency. He argues that achieving truly data-efficient artificial general intelligence (AGI) requires 2-3 major breakthroughs. While the Scaling Law may be effective in some aspects, it is not the complete solution, as it represents a very pessimistic future.

Regarding the interpretability of AI, Tian believes that AI models based on neural networks are interpretable, and eventually, humans will understand how these models are trained. Despite many currently inexplicable aspects, he argues that this should not be a reason to abandon exploration.

Lastly, Tian discusses the diversity of technology in Silicon Valley, noting that everyone has their own methods, and technological progress does not necessarily rely on current mainstream approaches. Non-mainstream explorations could potentially drive the next technological revolution. He also suggests abandoning the notion that "the brain is the controller of humans," asserting that every part of the body has a vote in behavioral expressions, and future integrated AI will have a vote as well.

Through Tian Yuandong's perspective, this article offers readers unique insights into the development of AI, the limitations of data-driven models, and the prospects of generative AI, making it a valuable read for deeper understanding and contemplation.

This article explores the use of generative AI tools among professional developers and their impact on productivity. Based on a survey of over 1,700 Stack Overflow community members, it reveals varying usage rates and experiences among different roles. Academic researchers and AI developers have higher usage rates, while data analysts and desktop developers use these tools less, reflecting differences in training data and application contexts.

Despite challenges in accuracy and handling complex problems, AI tools are found to improve work quality and developer satisfaction. ChatGPT and GitHub Copilot are the most popular tools, with distinct preferences among professional developers and learners.

While productivity gains are hard to quantify, most users report improved productivity thanks to these tools. However, low trust and adoption rates within teams hinder broader utilization.

Dify Workflow has been updated with new capabilities to enhance the flexibility of building production-level AI applications. The update includes the ability to publish Workflow as a tool, add iteration nodes for multi-step generation, extract structured parameters from unstructured information, and optimize node capabilities. The article provides examples of how these features can be applied in real business scenarios.

Baidu Comate is an intelligent coding assistant based on the Wenxin large model, which supports multiple programming languages and can be deeply integrated into mainstream IDEs. It provides features such as real-time code continuation and comment-based code generation, significantly enhancing the efficiency of code writing. Wang Rongsheng, a postgraduate student at the Macau University of Science and Technology, along with his laboratory colleagues, used Baidu Comate to process 150GB of medical imaging data, reducing the work that originally required three people for a week to just one person in two days, increasing efficiency by more than ninefold. Baidu Comate is capable of intelligently generating code blocks by analyzing contextual logical relationships and supports outputting code through natural language commands, thus improving the response speed to new requirements. Additionally, Baidu Comate's "code generation comments" and "private domain knowledge enhancement" functions, as well as the recently released "Comate Open Platform" feature, have further facilitated team collaboration and efficiency. Wang Rongsheng believes that Baidu Comate has not only improved the quality and speed of code generation but also helped their team achieve their own customized capabilities, enhancing the efficiency of research and development.

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.

Suno V3.5 has expanded the minimum segment length to 4 minutes, allowing creators to generate complete songs more easily. The new version also analyzes and constructs music structures more effectively, resulting in smoother and more natural music. The article discusses the practical experience of using Suno V3.5 and its impact on the music industry.

This article summarizes the experiences gained from a year of building products with Large Language Models (LLMs). It highlights the advancements in LLMs, their application in real-world scenarios, and the challenges in creating robust AI products. The article also discusses the importance of prompt design, retrieval-augmented generation, and structured input/output in developing effective LLM applications.

Perplexity AI has recently introduced Perplexity Pages, a tool designed to assist users in creating visually appealing reports, articles, or guides. Users simply input a prompt, such as "information about the Sahara Desert," and the system generates customized content based on their input. Users can select different audience types to adjust the tone of the generated text. Perplexity's algorithm is capable of creating detailed articles with various sections and allows users to rewrite, reformat, or delete parts of the text. Additionally, users can draft sections about specific subtopics through prompts and assist in finding and inserting relevant media items, such as images and videos. The pages created can be published and searched through Google, and users can share the page links, enabling others to ask follow-up questions on the topic. Henry Modisett, the design lead for Perplexity, stated that the company aims to leverage its core technology to use Perplexity as a research tool but in a more shareable format. He emphasized that although the AI engine can quickly answer questions and form pages, completing a page takes a few minutes. Perplexity views this tool as a means of information filtering rather than complete content generation by AI, as users have decision-making power over the content and organization of the pages. The Perplexity Pages feature will initially be rolled out to a limited number of users, with plans to eventually offer it to all users.

Tencent has launched its new AI application 'Yuanbao', which integrates AI search, AI summary, and AI writing features. Unlike other AI assistants, Yuanbao combines multiple functionalities, such as real-time news push, and uniquely leverages Tencent's vast content resources from platforms like WeChat. Yuanbao aims to enhance user experience in content creation, multilingual translation, and even creative tasks like generating AI images. The app benefits from Tencent's advanced Hunyuan AI model, positioning it at the forefront of AI applications.

The article details a case study on how to transform oneself into an anime character from "My Neighbor Totoro" using AI tools within half an hour. It begins with generating background and bus images using AI tools like Midjourney, followed by adjustments with an AI image editor. Next, the article guides on recording one's own motion video and processing the background using an AI green screen removal tool. Then, it describes using tools like DomoAI to convert the video into an anime style and compositing all materials, including the background image, processed video, and bus image, in a video editing software. Finally, the article concludes with adding green screen elements and sound effects to complete the video production.

OpenAI has announced that ChatGPT is now free for all users, allowing access to customized GPTs, chart analysis, photo-related questions, and other features added to GPT-4o in early May. Free users can browse, use visual and data analysis tools, upload files, and access GPTs, but they cannot create their own GPTs, which is reserved for paid users. Paid users have higher message limits and access to the 'income sharing program' for creators of custom GPTs. The article also introduces four recommended GPTs available in the GPT Store.

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 explores the Hugging Face Text Generation Inference (TGI) Benchmarking tool, designed to profile LLM deployments more effectively. It discusses the inefficiencies of LLMs, advancements in optimization techniques, and the importance of configuration based on use cases. Key concepts like latency and throughput are explained to help users optimize their deployments.

JLama emerges as the first pure Java inference engine available in Maven Central, following the widespread adoption of Andrej Karpathy's open-source llama.c inference interface. This library leverages the Vector API and PanamaTensorOperations class with native fallback, promising faster inference using Java 21. JLama supports various models including Gemma, Llama, Mistral, GPT-2, BERT, and offers features like distributed inference, flash attention, and Hugging Face SafeTensors model compatibility. Developers can easily download models, interact with them through prompts or chat functionalities, and even utilize a simple web UI provided by JLama. The emergence of such tools signifies a growing trend towards smaller, more accessible LLMs, making their integration into Java applications increasingly feasible.

Google Cloud Run is a container platform that accelerates the development and deployment process of AI applications by providing a set of key features. These features include a quick transition from prototyping in Vertex AI Studio to containerized deployment; built-in Service Level Objective (SLO) monitoring and observability solutions; parallel version testing through traffic splitting; ensuring relevance and factuality by securely connecting to cloud databases; and achieving multi-regional deployment and high availability through global load balancers.

The article details how to migrate from prototype creation in Vertex AI Studio to containerized deployment on Cloud Run, as well as how to use Cloud Run's code generation feature to transform experiments into deployable code. Additionally, the article explains how to monitor application performance using Cloud Run's SLO monitoring and Google Cloud's observability tools, and how to accelerate innovation with parallel versions and Cloud Deploy.

This article provides a detailed guide on how to train and finetune embedding models using Sentence Transformers v3. It explains the importance of finetuning for specific tasks, the components involved in the training process such as datasets, loss functions, and the new trainer, and how to use them effectively. It also discusses the significance of dataset format matching the chosen loss function.

This article explores how synthetic data technology can balance the need for innovation and privacy protection in a data-driven world. It highlights that synthetic data allows AI models to be trained and adapted without using real user data, thus reducing privacy risks and complying with data privacy regulations. Differential Privacy (DP) is introduced as a key technique to generate statistically representative synthetic data while protecting the privacy of data contributors.

The research showcases recent advancements, including the application of DP in fine-tuning generative language models (LLMs) to ensure the generated text is both representative and privacy-preserving. Additionally, methods for generating synthetic data via APIs and privacy-preserving techniques in few-shot learning are discussed. These findings provide organizations with new ways to generate useful and privacy-safe data, fostering responsible AI development.

By leveraging these technologies and methods, synthetic data and differential privacy effectively support AI model training and application while ensuring data privacy, laying a solid foundation for innovation across various fields.

SiliconCloud, launched by Silicon-based Flow, is a cloud service platform that integrates various open-source large language models and image generation models, including DeepSeek V2, Mistral, LLaMA 3, and more. It allows users to freely switch between these models to adapt to different application scenarios. The platform provides ready-to-use large model inference acceleration services, significantly enhancing the user experience of generative AI applications. For example, it accelerates the token output speed of DeepSeek V2 and the image generation speed of Stable Diffusion XL. Through extreme compute optimization, SiliconCloud achieves up to 10 times inference acceleration, greatly reducing compute costs and addressing the shortage of high-end chips. During the 6.18 Mid-Year Shopping Festival in China, SiliconCloud offered developers a free benefit of 3 billion tokens per person, aiming to lower the barriers to large model application development and promote the democratization of large models.

Jay Alammar discusses the burgeoning field of Retrieval-Augmented Generation (RAG) systems and their impact within the broader scope of AI and language models. Highlighting both historical context and practical insights from industry experiences, he offers key perspectives on the capabilities of language AI. Key points include: 1. The historical evolution of generative AI and its comparison to past technological shifts. 2. The conceptual foundation and utility of RAG systems. 3. Practical insights from industry experience, particularly from Cohere. 4. Useful recommendations on viewing language models beyond mere black boxes. 5. Future directions and potential impact of language AI technologies.

The article announces the introduction of the Property Graph Index in LlamaIndex, a new feature designed to enhance the flexibility, extensibility, and robustness of knowledge graph capabilities. Traditional knowledge graph representations, such as knowledge triples, are limited in their expressiveness, lacking the ability to assign labels and properties to nodes and relationships, represent text nodes as vector embeddings, and perform both vector and symbolic retrieval. The existing KnowledgeGraphIndex in LlamaIndex faced these limitations, as well as general architectural constraints. The Property Graph Index addresses these issues by adopting a labeled property graph representation, enabling richer modeling, storage, and querying of knowledge graphs. This new index allows users to categorize nodes and relationships into types with associated metadata, treat the graph as a superset of a vector database for hybrid search, and express complex queries using the Cypher graph query language. The article provides detailed instructions on constructing a knowledge graph using the Property Graph Index, including schema-guided extraction, implicit extraction, and free-form extraction. It also covers various querying techniques supported by the index, such as keyword/synonym-based retrieval, vector similarity, Cypher queries, and custom graph traversal. Additionally, the article discusses the use of the PropertyGraphStore abstraction for lower-level control over graph data storage and retrieval. The Property Graph Index is supported by several backing stores, including in-memory, disk-based, and Neo4j. The article concludes with thanks to Neo4j for their collaboration and invites the community to share their projects and seek support on Discord.

The article introduces how to utilize the grounding feature with Google Search in Vertex AI to enhance the accuracy and reliability of large language models (LLMs). The article first highlights the issues LLMs may face, such as generating incorrect, outdated information, lacking citations, and not being able to access private data. By enabling grounding with Google Search, the model can generate more reliable responses based on the latest public knowledge and provide sources for the information.

The article demonstrates the practical effects of this feature by comparing the model's responses before and after enabling grounding. For example, when asked about Arsenal FC's game results and the weather in London, the model with grounding enabled can provide accurate and cited answers. The process to enable this feature is straightforward, requiring just a few settings in the Vertex AI console.

Additionally, the article provides code examples in Python and C#, showing how to integrate Google Search grounding into applications. These examples allow readers to easily implement this feature in their projects.

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.

Anthropic’s Claude 3 Opus, tool use, and provisioned throughput are now available on Google Cloud's Vertex AI. The announcement marks a significant milestone as the Claude 3 model family becomes generally available, offering capabilities ideal for complex tasks across various industries. Key features include assured performance with provisioned throughput, and enhanced flexibility and control with tool use, enabling models like Claude 3 to interact autonomously with external tools and data sources.

The article discusses the concept of "cognitive bias" and its application in design to drive business results. It explains what constitutes "good design" from both aesthetic and functional perspectives and provides a detailed analysis of how cognitive biases can be utilized to enhance design effectiveness. Specific design cases and their psychological underpinnings are explored in depth.

• Defines "good design" and its business impact
• Describes cognitive bias and its role in perception and decision-making
• Case studies of design applications using cognitive bias
• Analysis of design strategies and optimization methods

This article delves into key issues in marketing strategies, emphasizing the correct approach to understanding user needs. It points out that marketers often mistakenly believe they can understand customer needs from an office setting, whereas true understanding and satisfaction of user needs come from immersing oneself in the customer's actual environment and observing consumer behavior. This is the foundation of all marketing promotions and brand building.

The article outlines the five stages of the user purchase journey: tasks, information gathering, comparison and evaluation, purchase, and sharing. It emphasizes that task-driven marketing is crucial because tasks stem from users' specific needs, desires, and self-awareness. In the marketing process, brands need to find touchpoints at each stage of the user's purchase journey.

Furthermore, the article stresses the importance of avoiding market noise and focusing on specific user scenarios. Only in specific scenarios can marketers accurately identify users' concrete needs and propose suitable solutions rather than striving for perfection.

The article also elaborates on the distinctions between needs, pain points, itching points, and pleasure points. Needs are the problems and goals users have in specific contexts; pain points are needs that current solutions cannot fully satisfy; itching and pleasure points fulfill deep-seated user needs and provide immediate gratification.

Finally, the article highlights the importance of focusing on specific individuals, including direct and indirect beneficiaries, and analyzes the needs and decision-making processes of different types of users. In summary, the article underscores the significance of tasks, scenarios, pain points, and specific individuals in marketing strategies, proposing a method that bases effective marketing strategies on tasks and scenarios, combined with the needs of specific groups.

This article analyzes Webflow's success through its product-driven SEO strategy. Key takeaways include:

• Understanding User Intent: Webflow aligns its content with the four types of user search intent: informational, commercial investigational, navigational, and transactional.
• Value-Driven Content: Webflow's blog content, particularly articles like "8 best cheap domain registrars compared and reviewed," targets long-tail keywords related to user needs and subtly promotes its product as a solution.
• Strategic CTAs: Webflow utilizes clear CTAs, tailored to different user personas, to drive product trials and highlight the value proposition of a freemium model.
• Templates for Faster Sales: By offering free, customizable templates, Webflow shortens the traditional B2B SaaS sales cycle by allowing users to experience the product's value before committing.
• Freemium Model for Acquisition: Webflow's freemium model, with no credit card required and no trial period, encourages widespread adoption, turning free users into paying customers over time.

"Projects are the foundation and cells of company management. If you understand project management, you are qualified to be a 'commander'." This article deeply analyzes the strengths of Huawei's project management, systematically showcasing its core advantages and successful experiences. The main points are as follows:

1. Practical Foundation: Huawei systematically summarizes and refines successful experiences through project practices across multiple clients and industries. This practical knowledge base makes project management methods more practical and operable.

2. Systematic Development: Huawei's project management has gone through four modernization stages: specialization, systematization, digitalization, and value orientation. Each stage is optimized for different challenges, ensuring continuous evolution and improvement of the project management system.

3. Customer-Centric Approach: From individual projects to project groups and portfolios, Huawei gradually shifts the focus of project management from contractual delivery to value delivery centered on the customer, enhancing customer satisfaction and the ultimate value of projects.

4. Talent Development: Huawei cultivates and selects project management talent through practical experience, using the HEROS and BEST models to ensure that project managers possess professional skills and practical experience. This talent development mechanism is key to project success.

5. Intelligent Digital Platform: Huawei has built the ISDP digital platform, achieving visualization and efficiency in project management, thereby enhancing management efficiency and project transparency.

6. Project Management Culture: Emphasizing teamwork, open innovation, and contractual spirit, Huawei forms a "project-centered" corporate culture. This culture is an important support for project success.

7. Forward-Looking Thinking: Huawei continuously observes the latest changes in project management, proposing "seven development trends" and "eight responses," making its project management system forward-looking and flexible enough to handle future challenges.

Through systematic practice summarization, customer-centric value delivery, talent development mechanisms, digital platforms, and project management culture, Huawei's project management system provides a solid foundation for enterprises to tackle complex and changing challenges. The article not only reveals Huawei's successful experiences in project management but also offers valuable insights for other companies.

User feedback is an essential way to gather product requirements. However, not all feedback is valid and the quality of feedback directly impacts product improvement and strategy formulation. The article outlines four dimensions for evaluating the quality of user feedback: dimensions of feedback, breadth of feedback, speed of feedback, and accuracy of feedback. These dimensions ensure that feedback is multidimensional, diverse, quickly obtainable, and accurate, allowing businesses to optimize and iterate their products effectively.

Key Points:

1. Dimensions of Feedback: Incorporate all user behaviors and judgments, both active and passive.
2. Breadth of Feedback: Diverse feedback covering different user demographics and product usage scenarios.
3. Speed of Feedback: Fast feedback loops from user perception to company response.
4. Accuracy of Feedback: Ensuring feedback is genuine, clear, and verifiable.
5. Two-Way Feedback: Companies should also provide feedback to users to improve the experience.

This article comprehensively discusses the challenges and strategies for customer acquisition in B-end internet products. It emphasizes the differences between B-end and C-end customer acquisition methods and provides a detailed analysis of various online and offline channels, including SEO/SEM and social media. The article also highlights the importance of understanding where the customers are and the need for a diversified and sustainable approach to customer acquisition.

The author discusses their experience pricing a design tool SAAS product, considering factors like competition, user base, and subscription models.

Understanding the various interactive states users may face in different scenarios is an important task for UX designers. A comprehensive and clear anticipation of these states benefits designers, particularly newcomers, in two ways. It ensures the completeness of the solution delivery, making the product more usable and easy to use, and helps quickly identify and fix potential issues during the walk-through phase after product development. The article explores nine categories of interactive states subdivided into 38 detailed states, with design considerations provided for each.

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.

AI Grant, known as the AI version of YC, is an early-stage investment firm specializing in AI technologies. With a focus on practical technical products, AI Grant has invested in several successful projects, including AI search engines, AI-driven image products, and AI-powered video editing tools. The founders, Nat Friedman and Daniel Gross, are recognized for their deep understanding of both technology and investment, making AI Grant a significant trendsetter in the AI investment landscape.

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.

This article provides an in-depth analysis of the background, impact, and future trends of the current large model price war. Since DeepSeek’s price cut, major companies such as ByteDance, Zhipu, Alibaba, and others have followed suit, triggering a wave of price reductions, even offering models for free under certain conditions. The primary goal of these price cuts is to attract developers by lowering trial costs and promoting cloud and other product sales. The article points out that despite the widespread attention from the big companies' price cuts, model startups are not panicking because these cuts have many conditions, and the actual usage costs have not significantly decreased.

Performance remains a key factor for developers, and the price war has limited actual impact on them. What really matters is the model's performance and business risk. The article also mentions, "Regardless of whether it’s for large companies or model startups, reducing Token costs is an inevitable trend," indicating that future Token prices may become negligible, fostering the prosperity of the large model ecosystem and benefiting companies along the industry chain.

Future business models may evolve in two paths: serving customers for free to achieve large scale or charging users directly to achieve high profitability. As the article states, "The aggressive cloud vendors' intentions are obvious: to attract a large number of trial developers by lowering trial costs; to enhance cloud and other product sales by reducing model costs."

In summary, while the price war brings challenges, it also offers more opportunities and market education for the industry. Model vendors need to continuously innovate in technology and business models, reduce costs, and improve performance to remain competitive.

Apple's latest update to iPadOS 18 introduces eye-tracking technology, suggesting a new direction for iPad interaction. This feature, initially seen in the Vision Pro, allows users to control applications and navigate menus through eye movements, enhancing convenience and efficiency. The technology leverages machine learning and existing hardware to provide a seamless experience across all devices running iPadOS 18. This advancement not only expands the iPad's capabilities but also raises questions about privacy and the future integration of AI in user interfaces.

This article discusses the launch of Tencent's AI assistant, Yuanbao, and the strategic thinking behind its development.

1. The AI product market is still in its early stages: Despite the high enthusiasm in the AI industry, the penetration rate of AI products is less than 1%, indicating a vast potential for market growth.

2. The design philosophy of Tencent Yuanbao emphasizes a simple interface with powerful functions, focusing on efficiency and entertainment scenarios, offering capabilities such as AI search, AI summarization, and AI writing.

3. Technical advantage: Tencent's self-developed Angel machine learning platform has significantly improved training and inference speeds, supporting the efficient operation of large models.

4. Ecosystem integration: Tencent Yuanbao integrates various internal ecosystem resources, such as WeChat Official Accounts content, as well as external search engine resources.

5. Open platform and resource sharing: Tencent's intelligent agent open platform "Yuanqi" provides developers with free model resources and distribution channels.

6. Commercialization and promotion strategy: Tencent Yuanbao is currently not considering charging users, with a focus on enhancing user experience and empowering other mature products.

7. Combating homogenization: Tencent leverages its advantages in product capabilities, engineering capabilities, and technological innovation, as well as deep integration with its ecosystem, to address the issue of homogenization among AI assistant products.

8. Content visibility and copyright protection: Tencent Yuanbao considers the copyright of content creators when integrating content and uses intelligent agents to enhance content visibility and the ability to discern authenticity.

This article delves into the current state and future development of the large model market in Silicon Valley. At the China AIGC Industry Summit, Silicon Valley investor Lu Zhang highlighted that startups can optimize industry-specific models by combining large model APIs with open-source models in a "cocktail" approach. She emphasized that AI is a super tool driving the digital transformation of entire industries, though only one-third of the opportunities are available to startups despite the enormous potential.

The article outlines several challenges AI faces at the infrastructure level, including high computing costs, high energy consumption, data privacy, and latency issues. Lu Zhang pointed out, "In Silicon Valley, the theme of AI is empowerment rather than disruption or transformation," and stressed that data quality is more important than quantity. She also mentioned that healthcare, financial insurance, and robotics are the fastest-evolving application fields.

Key quotes:

1. "AI is an efficient super tool representing the trend of digital transformation across entire industries."
2. "Empowerment means not only startups but also large tech companies can be empowered."
3. "In specific application scenarios and industries, training small models specific to the industry can perform as well as general large models."

In summary, the article emphasizes the critical role of infrastructure and the importance of high-quality data, offering valuable advice for startups in AI applications.

This article documents a historic dialogue between AI pioneers Li Feifei and Geoffrey Hinton, discussing the development of AI, particularly in the field of computer vision. The conversation, spanning 110 minutes, covers the creation of ImageNet, a pivotal dataset in the advancement of deep learning, and its impact on the AI community. The article also highlights the challenges faced by both researchers in their pursuit to revolutionize the field of AI.

This article provides a detailed introduction to five simple and effective methods to enhance thinking abilities. Here are the specific methods:

1. Sequence Recall Method: This method involves recalling the content you have read or watched after a certain interval to train memory and information retention. For example, after reading several articles or watching several videos, take a break and then try to recall the order and key points of these contents. The key points of this method are to recall after a certain interval, avoid looking at the answers, and try to recall in order.

2. Feynman Learning Technique: This technique involves deepening understanding by simulating explaining new knowledge to others. The specific steps include explaining the core content of the knowledge point (what it is), describing its background and purpose (why it is), and summarizing its application (how to do it). This method helps to reinforce understanding of the knowledge, making it better absorbed and internalized.

3. Structured Thinking Method: This method helps in organizing thoughts and expressing viewpoints more clearly by clarifying points, reasons, and evidence. Specific training methods include briefly summarizing the viewpoint, listing the reasons supporting the viewpoint, finding evidence for each reason, and integrating them into a complete argument. This can make expressions clearer and logic more rigorous.

4. Sensory Immersion Method: This method involves focusing attention on sensory experiences to enhance concentration. It is suggested to choose a quiet outdoor environment and concentrate on sensory experiences such as sight, hearing, and smell, avoiding analysis or thinking. This can effectively improve perception of the current environment and enhance the focus of attention.

5. Pre-Sleep Guidance Method: This method suggests reviewing interesting and valuable experiences of the day before sleep, avoiding the repeated recall of negative information to optimize memory. The specific approach is to review and record positive and meaningful events that happened during the day before going to bed every night, strengthening the brain’s memory of these positive information while avoiding recalling negative events to maintain a positive mindset.

The article not only provides detailed steps and usage tips for each method but also encourages readers to integrate these methods into their daily lives gradually forming habits, thereby achieving comprehensive enhancement of thinking abilities.

1. The key to becoming a good employee lies in proactively taking on responsibilities and accumulating scarce skills to enhance one's value.

2. To become a good manager, one must govern the organization through system design, achieving self-management and efficient operation.

3. Becoming a good boss requires adhering to core values, centering on the user, and providing genuine products and services without resorting to deceptive practices.