于开发者而言，开源一个项目很简单，一个命令足矣，但维护一个项目，却意味着： 一边扛着本职工作，一边独自修复 Bug、优化文档； 深夜改着无人问津的 PR，独自面对着扎堆涌来的 Issue…… 看着冷清的仓库，一个问题在深夜里反复叩问你的内心： "开源容易，让项目活起来，怎么这么难？" 你肯定也曾仰望过那些 GitHub 上数万 Star 的项目，心中既有无限敬佩，又感到一丝遥远。你也渴 望自己的项目 Star 能从 1 增长到 Star 10000 …… 那么，如何才能穿越这段"至暗时刻"？ 为了回答这个问题，在 12 月 21 日（ 本周日 ）的 GOBI 2025 全球开源商业创新大会上，组委会 把那些真正戴上桂冠，并走得更远的人请到了现场。他们不是 理论家，而是从枪林弹雨中杀出来的 实战派。 万人 Star 的开源项目是如何炼成的？ 郑耀威 LLaMA Factory 作者 在「聚力·开源社区的进化与未来 聚拢微光，可成星河」Panel 上，来自 GitHub 60,000+ Star 的 LLaMA Factory 郑耀威、顶级推理框架 vLLM 社区核心贡献者张家驹、企业级 RAG 引擎新星 RAG ...

责编 | Echo 大模型进入下半场言犹在耳，姚顺雨（Vincesyao）花落谁家尘埃落定。 12 月 17 日，CSDN 获悉，腾讯大模型研发架构的重大升级，著名的华人 AI 学者、前 OpenAI 科学家姚 顺雨（Vincesyao）加盟腾讯，出任"CEO/总裁办公室"首席 AI 科学家，直接向腾讯总裁刘炽平汇报。同 时，他将兼任新成立的 AI Infra 部负责人及大语言模型部负责人，向技术工程事业群（TEG）总裁卢山 汇报。 对于 AI 社区，特别是关注大模型前沿研究的开发者来说， 姚顺雨 这个名字绝不陌生。 作为普林斯顿大学博士、前 OpenAI 研究科学家，姚顺雨是全球公认的 AI Agent（智能体）与大模型推 理领域的领军人物 。他的研究不仅停留在理论层面，更直接定义了当前大模型应用的主流范式： 姚顺雨的加入，被业界视为腾讯在 AI 领域的一次"精准且重量级"的补强。他不仅带来了 OpenAI 级别 的研究视野，更关键的是，他在 "如何让模型更聪明地解决问题" 这一核心命题上拥有世界顶级的技术 直觉。这与腾讯当前强调的"系统化工程建设"及 AI Infra 战略不谋而合。 这一人事任命与架构调 ...

AI 时代，开源如何不再"为爱发电"？商业如何借助"源力"飞跃？ 当开源的理想主义之光，照进商业化的现实土壤，一个长久以来的问题摆在了所有技术人的面前：我 们该如何平衡开放协作的精神与价值变现的渴望？ 12 月 21 日，由 Upstream Labs、AI 原点社区、CSDN 联合主办的 GOBI 2025 全球开源商业 创新大会将在北京海淀东升万丽酒店盛大启幕。我们把" 开源、商业、AI "三股关键力量汇聚于同一 场域，旨在与 500+ 开源领袖、独角兽创始人、顶级 VC 及一线开发者，共同探寻未来三年的确定 性机遇。 官网： https://gobi.upstreamlabs.org/cn/ 这不仅是一场会议，更是一份通往未来的完整行动指南。现在，终极议程正式揭晓！ 开源年终之战，GOBI 2025 全日程发布 GOBI 2025 的议程设计并非简单的演讲堆砌，而是一场精心编排的价值闭环。我们用一天时间，为 你呈现从顶层战略洞察、到一线战术拆解、再到未来新星诞生的全过程。 大会将由 三场高屋建瓴的 Keynote 演讲拉开序幕，共同构成上午的核心思想板块。下午场则设置 了 4 场高密度的主题圆桌 （Pa ...

Core Insights - The article introduces a "Digital R&D System" aimed at addressing common pain points in the R&D process, such as lengthy development cycles and communication inefficiencies [3][7][16] - The system is designed to enhance collaboration across departments and streamline the transition from market needs to product design [4][6][10] Digital Transformation in R&D Management - The "Digital Brain" concept is introduced, which integrates marketing, R&D, and collaboration to create a seamless workflow [4][5][6] - It aims to solve five major issues: shortening R&D cycles, eliminating design version confusion, breaking down departmental silos, managing change impacts, and improving standardization [7] Empowering the Manufacturing Process - The introduction of the "AI Expert" system, which automates repetitive tasks and enhances efficiency in the manufacturing process [8][10] - The AI system can reduce design verification time by over 50% and improve review efficiency by 80% [9][11] Practical Applications and Benefits - Specific scenarios illustrate the system's impact, such as automated design checks that halve inspection time and AI-generated work instructions that enhance accuracy and efficiency [14][15] - The overall value proposition includes significant reductions in product development time, early detection of design issues, and the liberation of talent for innovation [16] Future Outlook - The article emphasizes the potential of the "Digital R&D System" to serve as a foundational element for innovation in manufacturing, advocating for a shift away from inefficient practices [16]

Core Insights - The article discusses the challenges of handling million-token inputs in large language models (LLMs) and introduces DeepSeekOCR's "Context Cascade Compression" (C3) technology, which achieves a 10x token compression rate [1][2]. Group 1: Compression Technology - DeepSeekOCR's success has led to misconceptions that "visual encoding" is the key to compression, while the research team identifies that the core of high compression rates lies in Latent Tokens, which are more efficient than discrete text tokens [1][2]. - C3 proposes a new approach that directly compresses text without visual intermediaries, utilizing a dual LLM architecture for encoding and decoding [6][9]. Group 2: Performance Metrics - C3 demonstrates superior performance with a 20x compression ratio achieving 98% decoding accuracy, compared to DeepSeekOCR's 60% accuracy [4][14]. - Even at a 40x compression ratio, C3 maintains over 93% reconstruction accuracy, showcasing its effectiveness in context compression [4][14]. Group 3: Unique Features - C3 exhibits a unique "forgetting pattern," where information loss tends to occur at the end of the text, resembling human memory's gradual forgetting process, which differs from the global blurriness seen in optical compression methods [12][13]. - This characteristic allows for more predictable applications, ensuring that critical information can be prioritized at the beginning of the text [13]. Group 4: Applications - C3 can serve as a front-end compressor for existing LLMs, enabling the processing of large token inputs, such as entire books or large codebases, while reducing computational costs [16]. - The architecture of C3 can be applied to next-generation models, facilitating the conversion of variable-length text into fixed-length latent representations [18].

Core Insights - The "2025 Global C++ and System Software Technology Summit" will be held on December 12-13, 2025, in Beijing, featuring prominent figures in the field, including Bjarne Stroustrup, the father of C++ [1][5][30] - The summit aims to explore the evolution of C++ and system software in the AI-native era, focusing on engineering practices and future paradigms [1][5] Event Overview - The summit will cover twelve major themes, including modern C++ best practices, software development driven by large models, AI computing and optimization, heterogeneous computing, high performance and low latency, and software quality construction [4][17] - Over 40 technical experts from leading companies such as Baidu, Alibaba, Tencent, and Xiaomi will share insights and experiences [4] Agenda Highlights - The first day will feature keynotes from Bjarne Stroustrup and other industry leaders discussing language design philosophy and AI-native infrastructure evolution [5][6] - A high-end roundtable discussion titled "System Software in the AI Native Era" will be held, featuring dialogues among top technical experts [8] Technical Sessions - The afternoon sessions will include topics such as "Modern C++ Best Practices," "AI Computing and Optimization," and "Concurrency and Parallelism," with contributions from industry leaders and academic researchers [10][17] - The second day will focus on challenges and practices in software engineering in the AI-native era, with sessions on large model-driven software development and system-level software [17][19] Registration and Participation - Registration for the summit is currently open, with incentives for early registrants, including a chance to receive a commemorative edition of the "AI Native Software Development Maturity Model" white paper [30][32]

Core Insights - Linus Torvalds emphasizes that he has transitioned from being a programmer to a system maintainer, focusing on overseeing the development of Linux rather than directly coding [4][6][7] - The introduction of AI in software development is viewed as a tool that enhances productivity without replacing programmers, similar to how compilers transformed programming practices [21][25] - The rise of Nvidia and AMD has shifted the hardware focus away from traditional CPUs to accelerated processing units, yet Torvalds believes that general-purpose CPUs remain crucial for Linux [17][18][19] Group 1: Role Transition and Development - Torvalds states that for nearly 20 years, he has not been a programmer but rather a technical leader and maintainer of Linux [4][6] - He notes that the core work of long-standing projects like Linux is maintenance and continuous support, rather than reaching a final completion point [8][9] - The development model of the Linux kernel has remained stable over the past 15 years, although Torvalds has shifted from primarily saying "no" to sometimes saying "yes" to new ideas [10][11] Group 2: AI and Software Development - Torvalds has not personally used AI to assist in coding but acknowledges that others are exploring its application in kernel development [21][23] - He highlights that AI's impact on the Linux community has been largely experimental, with some disruptions caused by AI crawlers affecting kernel.org [20][21] - The potential for AI to enhance productivity is recognized, but it is believed that the need for skilled programmers will persist as new development areas emerge [25][26] Group 3: Hardware Evolution - The conversation notes a significant shift in hardware focus from CPUs to GPUs and APUs, driven by companies like Nvidia and AMD [17][18] - Torvalds argues that while AI and accelerated processors are gaining attention, the role of Linux in managing systems and user interfaces remains vital [18][19] - The involvement of Nvidia in the Linux kernel development is seen as a positive outcome of the AI boom, indicating a growing interest in Linux from hardware manufacturers [19][20]

Core Viewpoint - The article discusses the concept of "AI wrappers," which are products that leverage existing AI models and APIs to provide specific functionalities without developing core technologies. The debate centers around whether these wrappers are merely temporary solutions or can evolve into sustainable products that thrive in competitive markets [2][4][20]. Group 1: Definition and Characteristics of AI Wrappers - AI wrappers are often seen as products that do not involve complex underlying technology, instead relying on existing APIs to create user-friendly interfaces [2][4]. - A key distinction is made between "functionality" and "product," where some applications may only serve as temporary tools, while others can establish a strong market presence [4][21]. Group 2: Market Examples and Financial Performance - Applications that allow users to interact with PDFs exemplify the AI wrapper concept, providing immediate solutions to specific problems without creating new content [3][5]. - Financial data indicates significant monthly recurring revenues for various AI wrapper applications, such as PDF.ai at $500,000 and Jenni AI growing from $2,000 to $333,000 in 18 months, highlighting the lucrative nature of this business model [6]. Group 3: Challenges and Competitive Landscape - AI wrappers face challenges from major tech companies that can integrate similar functionalities into their ecosystems, posing a threat to the survival of independent applications [7][11]. - The reliance on external models for functionality creates vulnerabilities, as companies like Cursor depend on access to APIs from larger firms like OpenAI and Google [9][10]. Group 4: Strategies for Survival and Success - Successful AI wrapper applications must establish a foothold in user workflows and capture proprietary data to maintain a competitive edge [17][19]. - Speed and execution can provide opportunities for smaller companies to thrive, as seen with Cursor and other rapidly growing tools that attract acquisition interest [12][13]. Group 5: Niche Markets and Long-Term Viability - There are niche markets that may not attract the attention of larger tech firms, allowing smaller developers to create profitable businesses without direct competition [14][16]. - Applications that can integrate deeply into user workflows and continuously learn from user interactions are more likely to survive and thrive in the long term [21].

Core Insights - Google has recently launched multiple products, including Gemini 3 and Nano Banana Pro, while OpenAI has been relatively quiet [1] - The focus of Google is not only on showcasing advanced models but also on improving efficiency, which is crucial for commercial viability [4][22] - Google has utilized advanced distillation techniques to significantly reduce the operational costs of its top models, making them more accessible for widespread use [4][22] Efficiency and Performance - Google aims to maintain a leading position on the Pareto frontier of cost and performance, ensuring that its models are both powerful and cost-effective [5][22] - The new Gemini 3 model is designed to be smarter and cheaper than its competitors, while also being more efficient than previous models [6][22] Model Characteristics - Gemini 3 has shifted away from a "people-pleasing" persona to a more straightforward, efficient information processor, focusing on delivering concise and relevant answers [7][9][10] - The model is designed to understand the context better, enhancing its programming capabilities and making it more useful for developers [10][17] Future of AGI - The timeline for achieving Artificial General Intelligence (AGI) is estimated to be 5 to 10 years, requiring significant breakthroughs in reasoning, memory, and world models [11][18] - Current models still lack a true understanding of the physical world's causal relationships, which is essential for reaching AGI [11] Competitive Landscape - Google is transitioning from a defensive posture to a more aggressive stance in the AI market, indicating a shift in competitive dynamics [12][20] - The company is focused on integrating AI advancements into its existing products, enhancing user experience and satisfaction [20][26] User Experience and Interaction - The Gemini 3 model is expected to improve user interaction by presenting information in a more understandable and engaging manner [16][17] - The emphasis is on making AI a powerful tool for users, assisting with various tasks rather than mimicking human-like interactions [19] Safety and Testing - Extensive testing has been conducted to ensure the safety and reliability of the new model, addressing potential risks associated with its advanced capabilities [24] - The company is aware of the dual-use nature of its technology and is taking precautions to prevent misuse [24] Market Outlook - There are indications of a potential bubble in certain areas of the AI industry, but Google remains optimistic about its position and future opportunities [25][26] - The company is focused on leveraging AI to enhance existing products and explore new markets, which could lead to significant revenue growth [26]

Core Viewpoint - The "2025 Global C++ and System Software Technology Conference" will be held in Beijing, focusing on the new paradigms and future directions of system software in the AI-native era, featuring prominent figures like Bjarne Stroustrup, the father of C++ [1][2]. Group 1: Conference Overview - The conference will gather top compiler experts, system software architects, and engineers to discuss the intersection of AI and system software [1][2]. - It aims to redefine the computing foundation and engineering paradigms in the intelligent era [2]. Group 2: Key Topics and Speakers - The conference will cover various core topics, including modern C++ best practices, AI-driven software development, AI computing and optimization, and system-level software challenges [4][5][11][22]. - Notable speakers include Bjarne Stroustrup, John Lakos, and experts from companies like Xiaomi, Bloomberg, and Adobe, who will share insights on software architecture, AI integration, and modern C++ applications [7][11][12]. Group 3: AI and Software Development - The shift from "automation" to "intelligence" in software development will be a key focus, emphasizing the role of large models as collaborative partners for developers [11][12]. - Discussions will include the transition of software development processes to AI-native paradigms, ensuring sustainable evolution and quality assurance [11][12]. Group 4: AI Computing and Optimization - The "AI Computing and Optimization" topic will explore the foundational paradigms of intelligent computing, addressing challenges in heterogeneous computing and system-level optimization [18][20]. - Experts will present innovative solutions for optimizing AI model inference and managing diverse hardware architectures [20][22]. Group 5: System-Level Software Challenges - System-level software is crucial for the stable and efficient operation of intelligent applications, facing challenges in performance, reliability, and scalability [22][24]. - Experts will share practices and insights on compiler optimization and edge deployment challenges in AI software stacks [22][24]. Group 6: Software Quality and Development Efficiency - The conference will highlight the importance of development efficiency and software quality as competitive advantages in the AI and large model technology landscape [25]. - Topics will include intelligent testing, quality visualization, and the integration of AI in software engineering processes [25][30]. Group 7: High Performance and Low Latency - High performance and low latency are critical for system software innovation, with discussions on optimizing database kernels, operating systems, and execution paths [31][30]. - Experts will share practical experiences and technical insights on achieving performance breakthroughs through code optimization [31][30]. Group 8: Concurrency and Parallelism - The conference will address the significance of concurrency and parallelism in enhancing system performance, featuring discussions on the latest trends and breakthroughs in parallel computing [36][37]. - Experts will explore the design and implementation of efficient data transmission and task scheduling in heterogeneous computing environments [37]. Group 9: Invitation to Participate - The conference invites technology experts, corporate representatives, developers, and open-source contributors to join in exploring the future of C++ and system software [43][45]. - It serves as a platform for showcasing cutting-edge achievements, fostering technical collaboration, and expanding industry partnerships [44][45].