如今国内使用开源软件、贡献开源社区已经蔚然成风，不过很少有企业像华为一样迅速从使用开源、参与开源到成为诸多大型开源项目的主要贡献者。华为 计算开源业务总经理李永乐在近日接受采访时表示，华为从2010年开始有组织地使用开源软件，2012年起逐步参与OpenStack等开源项目，2019年和2020年 开源openEuler、openGauss等项目，与业界主流基金会都建立了密切合作关系，是300多个开源社区的核心贡献者，有六千多名员工参与开源项目开发。"华 为既是开源的受益者，也是开源的坚定支持者和开源精神的践行者。"李永乐说。 在通用计算领域，由于国外企业起步早、生态完善，国内自主技术和产品的发展举步维艰。2019年华为推出了基于ARM架构自主研发的高性能计算芯片鲲 鹏处理器，由于当时市场上X86架构占据主流，大量开源软件都面向X86开发，因此鲲鹏处理器首先需要解决对软件的支持能力问题。经过多年努力，截至 2025年上半年，鲲鹏已经可以支持大数据、数据库等领域的多款主要开源软件。随后华为又推出了面向软件开发人员的鲲鹏DevKit开发套件和鲲鹏应用使能 套件BoostKit，通过软硬协同提高计算性能，极大加强了 ...

2月25日，OpenAI在其官方视频栏目中发布了对知名开源项目OpenClaw创建者Peter Steinberger的深度访谈，由OpenAI高管Romain Huet主 持。 访谈里，Steinberger复盘了OpenClaw的爆红、自己如何用Codex等"代理式工具"写软件，以及开源与安全的真实矛盾。 几周前还需要主持人"介绍一下"，如今Steinberger在旧金山被上千名用户线下"围观"。他形容自己"各方面都有点超载"，但也直言这正是他想 看到的结果："我一开始就是想激励别人——现在这就是最有趣的形式。" 涌现的智能："它自己找到了解决问题的路径" 很多人以为OpenClaw是一夜成名，但其背后是长达十个月的疯狂试错。真正让Peter确认这款产品具备极高市场契合度（PMF）的，是AI智能 体展现出的"涌现能力"。 Peter把未成熟的AI智能体接入了日常通信软件。某天，有人发来一条语音信息。按照原本的程序逻辑，AI不具备处理这种未知音频文件的能 力。然而，令人毛骨悚然又无比兴奋的一幕发生了：AI开始显示"正在输入"。 "我当时就在想，我根本没写过这个功能，这怎么可能奏效？"Peter回忆道。 当他 ...

在与其他网友的互动中，@xydotdot 对"开源能保障独立性"的观点进行了反驳。 他认为，开源只会让基础层变成大宗商品，无法让算力、专有模型、用户数据、托管服务等上层层级商品化。 这些层级才是价值捕获的关键，而开源无法分叉生态系统，因此开源项目最终往往会被运营最大技术栈的主体 在经济上主导。即使架构开源，控制权仍会落入运行主导运行时环境的人手中，聘请核心人员正是为了让技术 与自身生态兼容。 2 月 16 日， OpenAI 宣布聘请知名开发者 Peter Steinberger（OpenClaw 项目核心人物），引发业内热议 ， 双方都在推特发文了。 网友 @xydotdot 在转发 Sam Altman 推文时指出这一举措并非为了保护独立技术架构，而是一场精心策划的 公关噱头，背后是 OpenAI 对技术控制权与商业变现的深层野心。 他认为，在 Sam Altman 主导下的 OpenAI 已从非营利组织转向商业化，并在 GPTs 中率先引入广告，这与他 2017 年"只对全人类负责，不为股东利益决策"的表态形成鲜明反差。因此，聘请 Peter Steinberger 的真实意 图并非扶持独立架构，而是为 ...

Core Insights - Chinese technology companies are launching new AI models, with Alibaba's Qwen3.5-Plus being a significant highlight, featuring 397 billion parameters and a cost-effective API pricing of 0.8 yuan per million tokens, which is 1/18 of Gemini3 Pro's price [1][2] - The trend in AI model development is shifting from sheer size to efficiency and intelligence, as demonstrated by Qwen3.5's ability to perform well with a smaller model while maintaining high performance [2][3] Group 1: Model Launches and Features - Alibaba launched the Qwen3.5-Plus model on New Year's Eve, which has 397 billion total parameters and 170 billion activated parameters, achieving a 60% reduction in deployment memory usage [1] - Other companies like Zhiyun, iFlytek, and MiniMax have also introduced new models, including GLM-5, Xinghuo X2, and M2.5, showcasing advancements in decision-making capabilities [1][2] Group 2: Performance and Efficiency - Qwen3.5 has shown exceptional performance in various benchmark tests, achieving results comparable to Gemini3 Pro and excelling in visual understanding assessments [1][2] - The new models are designed to be more practical and efficient, with a focus on multi-modal capabilities and reduced resource requirements, leading to increased deployment efficiency [2][3] Group 3: Open Source and Global Impact - The number of open-source models related to Qwen has exceeded 400, with over 200,000 derivative models and downloads surpassing 1 billion, indicating a strong global presence [3] - China is emerging as a leading provider of open-source large models, with significant contributions from companies like Qwen and DeepSeek, which rank highly on AI model evaluation platforms [3]

Core Insights - OpenAI CEO Sam Altman announced the addition of Peter Steinberger, founder of OpenClaw, to OpenAI, focusing on the development of next-generation personal intelligent agents, which are expected to become a core part of their products [1] - Altman emphasized that OpenClaw will exist as an open-source project within the foundation, with continued support from OpenAI, highlighting the importance of open-source in achieving a future characterized by highly multi-agent systems [1]

（来源：千龙网） 马年春节前夕，国产大模型集体"炸场"。 继豆包Seedance2.0刷屏后，2月11日，位于北京海淀的大模型企业智谱，发布了新一代旗舰模型GLM-5。同时证实，此前在海外引爆技术圈的匿名模 型"Pony Alpha"，正是GLM-5的内测版。 DeepSeek-OCR 2模型、Kimi K2.5模型、MiniMax Music2.5音乐生成模型……半个月，近十款模型落地，从通用对话到编程、音乐生成、经营决策，各有 各的"杀手锏"。这些亮眼成绩，也让北京的AI创新实力得到国际市场与科研领域的双重认可。 两年前，我们还在为文心一言画出一只像样的猫而惊叹。而今天，AI大模型已经在加速迈过从"秀肌肉"到"真干活"的临界点，突破代码和工程之间的鸿 沟。 比如，在智谱AI编程平台Z Code上，用户只需把要求说清楚，模型就能自动拆解任务，做出复杂应用；有人用手机，就可远程指挥电脑桌面的智能体， 完成了过去需要几天耗时的工程任务；在Vending Bench 2测试里，GLM-5独自经营的模拟自动售货机，一年后账户余额4432美元，这个表现已经接近了国 际顶尖模型。 必须承认，美国在算力上的优势仍是客观现 ...

Core Viewpoint - GOSIM Paris 2026 is a significant global open-source and AI technology event aimed at fostering collaboration and innovation within the open-source community, focusing on the development of Agentic AI systems and applications [4][11][46]. Group 1: Event Overview - GOSIM Paris 2026 will take place from May 5 to 6, 2026, at Station F in Paris, inviting global technology leaders, contributors, researchers, and innovative companies to explore open-source methodologies for building models and systems in the Agent era [4][8]. - The event is positioned as a continuation of the successful GOSIM conference held in May 2025, which featured 89 speakers from top organizations like Meta, NVIDIA, and Oxford University, leaving a lasting impact on the global tech community [7][8]. Group 2: Venue Significance - Station F, the largest startup campus in the world, serves as the venue for GOSIM Paris 2026, known for its innovation and collaboration in AI, housing over 1,000 startups and numerous cutting-edge tech projects [8][10]. - The venue is recognized for nurturing AI innovation, with major tech companies like Microsoft and Meta establishing incubation projects there, making it a hub for technological advancement [8][10]. Group 3: Focus Areas of the Conference - The conference will concentrate on seven core technology areas, including Agentic AI, Open Source Robotics, Open Source Models, AI Generative Applications, Edge AI, AI Protocols, and AI Hardware, each addressing critical topics relevant to the future of AI and open-source collaboration [12][14][17][18][19][21][23]. - Specific topics of interest include communication protocols for agent systems, the development of autonomous robotic systems, and the optimization of open-source models for cognitive capabilities in the Agent era [14][17][21]. Group 4: Engagement and Participation - GOSIM Paris 2026 will feature hands-on workshops, hackathons, and competitions, encouraging participants to engage actively in coding, building robots, and debugging, rather than passively attending presentations [24][25]. - The event aims to create a vibrant international community by attracting over 970 technical experts and contributors from 28 countries, fostering deep organizational links with various renowned tech communities [10][11]. Group 5: Call for Contributions - The conference is actively seeking proposals for topics and speakers, inviting individuals with unique insights or practical experiences in Agentic AI, open-source models, robotics, or edge computing to participate [28][30]. - Opportunities for showcasing innovative open-source projects and engaging in collaborative coding challenges will be available, enhancing the interactive experience of the event [30][31]. Group 6: Sponsorship Opportunities - GOSIM Paris 2026 offers sponsorship opportunities for organizations looking to enhance their brand visibility, connect with top developers, and engage with startups and investment institutions globally [34][36][38]. - Sponsors will benefit from high-frequency brand exposure, direct access to a network of over 1,000 developers, and opportunities to promote their technologies through workshops and hackathons [36][38].

Core Insights - The article discusses the challenges faced by Peter Steinberger, founder of OpenClaw, after the project's sudden rise to fame, including name changes and harassment from the crypto community [1][2] - OpenClaw is currently operating at a loss, relying on donations and limited corporate support, and is considering acquisition offers from major companies like OpenAI and Meta, with a focus on maintaining open-source status [1][2] Name Change Challenges - The project initially named Wa-Relay faced pressure from Anthropic to change its name, leading to a stressful and chaotic renaming process [4][6] - The renaming involved securing various domain names and social media handles, which proved to be a complex and time-consuming task [6][10] - The crypto community's aggressive behavior added to the pressure, resulting in account hijacking and the spread of malicious software [7][8] Technical Insights - Steinberger expressed concerns about the AI industry's exaggerated safety fears, suggesting that incidents like MoltBot are more about entertainment than real privacy threats [2][17] - He highlighted the importance of efficient collaboration in AI development, warning against overly complex agent orchestration [2][21] Security Concerns - The article addresses the security challenges faced by AI systems, emphasizing that many reported vulnerabilities stem from user misconfigurations rather than inherent system flaws [22][23] - Collaboration with VirusTotal aims to enhance security by scanning skills before deployment, although no solution can guarantee complete safety [22][23] Development Philosophy - Steinberger advocates for a shift in mindset when working with AI agents, suggesting that developers should design projects to be easily understood by agents rather than solely based on personal preferences [32][35] - The article emphasizes the importance of iterative development, where learning and adaptation occur through hands-on experience with AI tools [36][37] Future Directions - The future of AI interaction is expected to evolve towards more integrated systems that combine personal assistance with development capabilities, moving beyond current chat-based interfaces [54][56] - The article suggests that the current state of AI development is still in its early stages, with significant potential for improvement in user interaction and system integration [56][57]

Core Insights - The article highlights the significant advancements in China's AI models, particularly the DeepSeek-R1, which has achieved performance comparable to top US models at a much lower cost and computational power, marking a pivotal moment in the AI industry referred to as the "DeepSeek moment" [1][6] - Chinese tech companies continue to innovate and release powerful new AI models, maintaining momentum in the industry despite global challenges [6][10] Group 1: AI Model Developments - ByteDance has launched the Seedance 2.0 video generation model, capable of understanding complex natural language instructions and producing high-quality video content, representing a significant leap from earlier AI video tools [3][9] - The Seedance 2.0's groundbreaking feature is its ability to generate coherent multi-scene narratives from a single instruction, maintaining consistency in characters, lighting, style, and atmosphere, achieving near "zero post-production" results [3][9] - Other companies, such as Zhiyuan and Alibaba, are also making strides with new models like GLM-5 and the upcoming Qwen 3.5, respectively, indicating a competitive landscape in AI development [4][10] Group 2: Market Perception and Trends - Analysts note that the reasoning capabilities of Chinese AI models are on par with their US counterparts, but at significantly lower deployment costs, drawing global attention [6][10] - The success of DeepSeek has shifted perceptions regarding the necessity of massive investments in computational infrastructure for developing advanced AI, challenging the notion that only companies spending billions can create cutting-edge AI [5][10] - The trend towards open-source AI models, exemplified by DeepSeek, is lowering barriers for developers and businesses to access advanced AI technologies, making "open-source and low-cost" the new norm in the industry [5][10]

Core Insights - Alibaba's Chairman, Joe Tsai, acknowledged that the company started working on Transformer models in 2019 but failed to allocate sufficient resources for their development until the launch of Tongyi Qianwen in 2023, marking a significant entry into the AI race [1][5][24] Group 1: Adoption - The first key point emphasized by Tsai is that AI must be used in practical scenarios to generate real value, not just developed as models [6][7] - The Tongyi App is crucial in Alibaba's AI strategy, serving not only as a user interface but also as a test for the AI's capabilities in real-world applications [8][11] - The unique characteristics of the Chinese market, such as the lower acceptance of enterprise software payment models compared to the U.S., necessitate alternative paths for AI adoption, making the Tongyi App a vital attempt to ensure real usage of models [9][10] Group 2: Scale - Tsai pointed out that AI investment is shifting focus from training to inference, with major tech companies increasing their capital expenditures from $60-80 billion to $120-150 billion annually [12][12] - Inference is identified as the main battleground for AI costs, as it is a daily requirement for users and businesses, unlike training which occurs less frequently [13][14] - The ability to handle high concurrency and maintain stability under load is crucial for scaling AI models, with Alibaba opting to deploy models on its own cloud infrastructure to control performance and throughput [15][16] Group 3: Open Source - Tsai advocates for open source as a practical choice rather than an idealistic one, driven by the commercial landscape and market conditions in China [17][18] - The primary value of open source is not cost but sovereignty, allowing companies and developers to have full control over their models [18][20] - Alibaba's strategy involves making Tongyi Qianwen open source while encouraging users to utilize Alibaba Cloud for training and inference, creating a commercial loop where infrastructure usage generates revenue [22][23]