Group 1: AI Model Developments - HuggingFace's model rankings show that domestic companies dominate the top 10 positions [1] - GLM-4.5, with 358 billion parameters, has recently been updated and is gaining popularity, functioning as an efficient development assistant [2] - GLM-4.5 has demonstrated strong performance in generating flowcharts and code, ranking 3rd in a comprehensive evaluation across 12 benchmarks [3] - GLM-4.5 supports 128k context and native function calls, performing closely to Claude-4-Sonnet on the -bench and BFCL-v3 leaderboards [5] - The Qwen series, with multiple branches and strong inference capabilities, has also been recently updated [6] - Tencent's HunyuanWorld-1, a model for generating 3D worlds from images, has been updated and is open-source [8] - Following the open-sourcing of DeepSeek's R1 series model, several domestic large models have been rapidly open-sourced to avoid falling behind [10] Group 2: Firefox's Business Closure in China - Firefox China announced it will cease all operations in mainland China after September 29, 2025, including its Chinese website and community forums [11][12] - All related services will be terminated, although the Firefox browser will continue to receive updates [15] - Users are advised to back up their data before the closure, with specific instructions provided for data backup and recovery [21][23]

Core Viewpoint - The article discusses the capabilities of GLM-4.5, a new AI model developed by Zhipu AI, which can automate the process of generating diagrams and code, significantly improving the efficiency of software development. Group 1: GLM-4.5 Features - GLM-4.5 can automatically generate Draw.io or Mermaid diagram code from a textual description, streamlining the development process [3][12]. - The model supports 128k context and native function calls, performing closely to Claude-4-Sonnet on benchmark tests [7]. - It integrates various capabilities, aiming to excel in programming, mathematics, and reasoning tasks within a single model [6]. Group 2: Cost Efficiency - The API call pricing for GLM-4.5 is competitive, with input costs as low as 0.8 yuan per million tokens and output costs at 2 yuan per million tokens [3]. Group 3: Practical Applications - The model can be used to create a complete user login flowchart and generate corresponding code, demonstrating its practical utility in real-world projects [14][30]. - It can work alongside other tools like Claude Code, allowing for seamless project development from scratch or enhancing existing code tasks [9][22]. Group 4: User Experience - Users can easily test GLM-4.5 by logging into the platform and inputting their requirements, which the model processes quickly without server delays [15][39]. - The generated outputs can be previewed in real-time, enhancing user interaction and satisfaction with the development process [40].

Core Viewpoint - The concept of a "moat" for programmers is essential for survival in a rapidly changing job market, emphasizing the need for adaptability and continuous learning rather than just technical skills [4][10][15] Group 1: Importance of a Moat - A moat is not just a luxury but a necessity for survival in the workplace, especially as job security diminishes in various sectors [4] - The traditional skills that once provided a competitive edge, such as proficiency in specific programming languages or algorithms, are no longer sufficient due to the fast-paced evolution of technology [10][11] Group 2: Defensive Programming - Defensive programming has emerged as a new strategy for IT professionals, focusing on creating code that is difficult for others to modify or understand, thereby ensuring job security [5][8] - Key tactics include writing unreadable code, creating systems that only the programmer can fix, and keeping critical knowledge undocumented [8] Group 3: Evolving Skills - The ability to learn quickly, solve problems, and communicate effectively within teams is becoming more critical than traditional technical skills [11] - The industry is shifting towards valuing comprehensive skills that combine business understanding with technical expertise [11] Group 4: Success Stories - Small teams can achieve significant financial success, demonstrating that the ability to solve real-world problems is more valuable than merely writing code [12][13] - The rise of AI applications showcases the potential for high earnings through innovative solutions, highlighting the importance of adaptability in the tech landscape [12] Group 5: Future of Programmers - AI will not replace programmers but will eliminate those who only possess basic coding skills, emphasizing the need for continuous skill enhancement [14] - The true moat for programmers may lie in their resilience and ability to restart their careers after setbacks, rather than in specific technical skills [15]

Core Viewpoint - JetBrains has announced a unified distribution for IntelliJ IDEA, allowing users to access more features in the free version while maintaining a subscription model for advanced functionalities [1][3]. Group 1 - The new setup will replace the separate Community and Ultimate versions with a single installation program, where Ultimate features still require a subscription to unlock [1][3]. - Starting from version 2025.3, free users will gain access to many features previously exclusive to the Ultimate version, such as code highlighting for popular frameworks like Spring and Quarkus, and project creation wizards [3][4]. - The free version will now allow users to view database designs directly within IDEA, although data access and SQL execution will not be available [4]. Group 2 - Previously, if a subscription expired, users could not open projects in the Ultimate version, but now they will automatically revert to the free version, allowing project access and editing [4]. - JetBrains' strategy appears to balance the needs of free users while ensuring the sustainability of its business model, potentially attracting more developers to its ecosystem and converting them into paying customers [4][5]. - JetBrains has shown commitment to providing free versions of its products, including WebStorm, while also maintaining open-source versions [5].

Core Viewpoint - DeepSeek R1, initially a phenomenon in the AI sector, is now facing user attrition and declining traffic, raising questions about its market strategy and user experience [8][11]. Group 1: Market Performance - DeepSeek R1 achieved remarkable growth, with daily active users (DAU) reaching 22.15 million within 20 days of launch, topping the iOS App Store in over 140 countries [2]. - However, recent reports indicate a significant decline in web traffic, with DeepSeek's visits dropping from 614 million in February to 436 million in May, a decrease of 29% [9]. - In contrast, competitors like ChatGPT and Claude have seen increases in web traffic, with ChatGPT's visits rising by 40.6% [9]. Group 2: User Experience Issues - Users are migrating to third-party platforms, with third-party deployment usage of DeepSeek models increasing nearly 20 times since launch [16]. - Key user pain points include high token latency and a smaller context window of 64K, which limits its ability to handle large code or document analyses [21][23]. - DeepSeek's strategy of prioritizing low costs over user experience has led to longer wait times compared to third-party services [21]. Group 3: Strategic Choices - DeepSeek's approach reflects a focus on research and development rather than immediate profit, positioning itself more as a computational laboratory than a commercial entity [26]. - The company has chosen not to address user experience issues, indicating a deliberate strategy to maximize internal computational resources for AGI development [26]. Group 4: Competitive Landscape - The AI industry is witnessing intense competition, with new models like GPT-4.5, Gemini 2.5, and others being released, which has contributed to user migration from DeepSeek [38]. - Anthropic, facing similar challenges, has focused on optimizing its model and forming partnerships with cloud service providers to enhance computational resources [30]. Group 5: Public Perception - Domestic users have expressed mixed feelings about DeepSeek, citing slow speeds and server issues, while others remain supportive of its long-term vision [34][40]. - The competitive landscape is evolving rapidly, with new iterations of models being released, making it challenging for DeepSeek to retain users [38][47].

Core Viewpoint - The article introduces BrowserOS, an AI-powered browser designed to enhance productivity and automate web tasks while ensuring local privacy and compatibility with Chrome extensions [2][4][5]. Group 1: Product Features - BrowserOS is based on Chromium and aims to run AI agents locally, transforming browsing into automated task execution [4]. - It combines Chrome's compatibility with AI productivity tools and guarantees 100% local privacy [5]. - Key features include local AI agents, true privacy protection, open-source nature, and next-generation productivity tools [14]. Group 2: Productivity Enhancements - The browser enhances productivity by automating routine web management tasks, making it "smarter" [9]. - It includes a local AI agent inspired by the Manus project, providing a "you say, I do" automation experience [10]. - The Sidekick feature acts as an intelligent assistant, managing multiple tabs, summarizing web content, and extracting structured information [11]. Group 3: Installation and Compatibility - BrowserOS supports Windows, macOS, and Linux, with installation packages available for download [15]. - Users can import Chrome bookmarks and extensions seamlessly after installation [16]. - The browser allows simultaneous operation of multiple large AI models within the same window, enhancing efficiency [17]. Group 4: User Experience - BrowserOS aims to automate mundane tasks, allowing users to focus on more important activities while the AI handles the rest [15]. - It promises no data collection, no advertisements, and no hidden agendas, ensuring a user-friendly experience [15]. - The browser features built-in AI highlighting and ChatGPT smart bookmarks for semantic search of history and bookmarks [15].

Core Viewpoint - The article emphasizes the importance of integrating existing technical skills with large model applications to enhance career prospects in the AI era, rather than abandoning current expertise [2][3]. Summary by Sections Current Industry Trends - Many professionals in programming fields are feeling anxious about the rise of large models like GPT and DeepSeek, prompting a need to adapt and learn new skills [2]. - Despite layoffs and salary reductions, the trend towards AI application implementation is expected to continue, presenting opportunities for career advancement and salary increases [3]. Course Offerings - A course titled "Large Model Application Development Practical Training" is introduced, designed to help developers master the complete AI application development process through practical projects and live instruction [3][4]. - The course covers essential technologies such as RAG, AI Agent, and Transformer architecture, structured in five modules from basic to advanced levels [7]. Learning Outcomes - Participants will learn to fine-tune mainstream large models for specific scenarios, utilize domain data for model customization, and understand RAG technology for efficient knowledge retrieval and generation [9]. - The course aims to build skills for developing AI Agents capable of multi-task collaboration and complex problem-solving in various industry applications [9]. Success Metrics - The course has served over 20,000 students, receiving positive feedback for its learning methods and outcomes, with many participants securing high-paying job offers [11]. - The program offers opportunities for networking with product teams, building technical barriers, and avoiding job insecurity, particularly for those approaching career milestones [13]. Additional Benefits - Participants will receive access to real-world case studies and insights into high-demand AI applications, enhancing their practical experience and employability [14][16]. - The course includes direct referral opportunities to companies, increasing the chances of obtaining high-paying positions in the AI field [18].

Core Viewpoint - The article discusses the increasing importance and variety of API client tools in 2025, highlighting their efficiency and user-friendliness for developers. Group 1: Popular API Client Tools - Postman is a widely used API debugging tool known for its user-friendly interface and comprehensive features, including request building, documentation, and testing assertions [4][5]. - Insomnia is an open-source, lightweight API client that supports REST, GraphQL, and gRPC, designed for developers seeking a simple and efficient interface [8][9]. - Paw is a macOS-specific API client with a beautiful interface and full functionality, including request building and team collaboration features [11][12]. - Hoppscotch is a lightweight, open-source API client that operates in the browser, supporting modern protocols like WebSocket and GraphQL [14]. - Bruno is a new open-source API client that emphasizes offline functionality and project-based request management, suitable for developers concerned about privacy [16][18]. - Apifox is a Chinese API ecosystem tool that integrates API documentation, debugging, mocking, and testing, with a user-friendly design for local developers [19]. - Apipost is another Chinese API client focusing on debugging, documentation generation, and team collaboration, featuring a modern interface [21][22]. - Thunder Client is a lightweight API client plugin for VS Code, allowing developers to debug APIs directly within their coding environment [24][25]. - Advanced REST Client (ARC) is an open-source API debugging tool that has transitioned from a Chrome extension to a standalone desktop application, maintaining its comprehensive functionality [26][27]. - HTTPie is a user-friendly API client that emphasizes human readability, available in both desktop and web versions, supporting REST and GraphQL [28][29].

Core Viewpoint - DBeaver is an open-source, cross-platform database management tool that supports nearly all mainstream databases, making it a versatile choice for database management and development needs [2][3][10]. Group 1: Product Overview - DBeaver supports a wide range of databases including MySQL, PostgreSQL, Oracle, SQL Server, SQLite, MongoDB, and many others, totaling over 80 supported databases [3][15]. - The tool is available in two versions: DBeaver Community (free) and DBeaver PRO (paid), with the PRO version offering advanced features and support for more databases [8][10]. - DBeaver runs on Windows, Linux, and macOS, providing flexibility for users across different operating systems [11]. Group 2: Features and Functionalities - The Community version includes essential features such as data editing, SQL editing, basic ER diagrams, and data import/export capabilities [9]. - The PRO version enhances security, supports more databases via ODBC, includes NoSQL database support, and offers cloud database integration [12]. - Advanced functionalities in the PRO version include a visual query builder, SQL AI assistant, performance visualization tools, and task scheduling capabilities [18][19]. Group 3: Installation and Usage - DBeaver can be easily downloaded from its official website, with installation instructions provided for various operating systems [6][20]. - Users can connect to databases through a user-friendly interface that allows for easy navigation and management of database connections [28].

Core Viewpoint - Optimizing SQL queries is essential for improving application performance, resource efficiency, and user experience in the context of exploding data volumes [1]. Index Creation - Creating indexes is crucial as they help databases quickly locate data, significantly reducing the amount of data scanned [2][7]. Query Selection - Avoid using SELECT * as it retrieves all fields from a table, wasting memory and bandwidth; instead, query only the necessary fields [3][4]. Data Return Control - Use LIMIT to control the number of rows returned, preventing excessive data retrieval [5][6]. Efficient JOIN Usage - Understanding the differences between INNER JOIN, LEFT JOIN, and RIGHT JOIN is vital to avoid performance issues; proper indexing on join columns can enhance efficiency [6][9][17]. Query Execution Plan - Utilizing the EXPLAIN command allows for analysis of how queries are executed, helping to identify issues like full table scans [18]. WHERE Clause Optimization - Optimize WHERE clauses by avoiding functions on indexed fields, which can lead to index inefficiency [19][23]. Subquery Management - Simplifying or rewriting subqueries using JOIN or Common Table Expressions (CTE) can improve performance [20][21]. DISTINCT Usage - Use DISTINCT cautiously as it can slow down queries; consider using GROUP BY or window functions for better performance [23][25]. Database-Specific Features - Leverage unique database features, such as specifying indexes or join methods, to enhance query performance [26][31]. Keeping Statistics Updated - Maintaining up-to-date statistics is crucial for the database optimizer to select the best query strategies [27]. Stored Procedures - Implementing stored procedures for common SQL operations can lead to cleaner code and improved execution efficiency [28]. ORDER BY and GROUP BY Caution - Minimize the use of ORDER BY and GROUP BY as they are resource-intensive operations; only use them when necessary [29][30]. UNION vs UNION ALL - Prefer UNION ALL over UNION when deduplication is not required, as it is faster [34][35]. Complex Query Splitting - Breaking down complex queries into simpler, intermediate queries or using materialized views can enhance readability and performance [36][38].