Core Insights - Jerry Tworek, a key figure in AI model reasoning, has founded a new company named Core Automation, focusing on "continuous learning" in AI models [1][5][7] - The company aims to raise between $500 million to $1 billion to develop a new type of AI model that can learn continuously from new data and experiences [1][8][10] Company Background - Jerry Tworek has a strong theoretical and mathematical background, having completed a master's degree in mathematics and worked in quantitative research before joining OpenAI in 2019 [3][5] - At OpenAI, he played a significant role in developing major models like o1, o3, GPT-4, ChatGPT, and Codex, pushing the boundaries of AI from mere generation to reasoning capabilities [3][5] Industry Context - The current mainstream AI models are primarily trained once and deployed, which limits their ability to adapt to new situations [5][10] - Continuous learning is seen as a solution to reduce costs and improve efficiency, allowing models to learn from real-world experiences rather than relying solely on static data [10][12] - The concept of continuous learning is gaining traction, with other companies and academic institutions, such as Google Research, also exploring this area [15][17] Future Outlook - The industry consensus suggests that achieving Artificial General Intelligence (AGI) will require models to possess continuous learning capabilities, which is a key focus for Tworek's new venture [12][15] - There is a growing belief that 2026 could mark a significant advancement in continuous learning technologies [19]

Core Viewpoint - Jerry Tworek, a key figure in AI model reasoning, has founded a new company called Core Automation, focusing on "continuous learning" in AI models and plans to raise $1 billion (approximately 70 billion RMB) for this venture [1][15][20]. Company Background - Jerry Tworek played a crucial role in the development of OpenAI's reasoning capabilities and has a strong theoretical and mathematical background, having completed a master's degree in mathematics at the University of Warsaw [4][6][9]. - Before joining OpenAI in 2019, he worked in quantitative research, which shaped his interest in reinforcement learning [7][9]. Focus on Continuous Learning - The new company aims to address the challenge of how models can continuously learn from new data and experiences, rather than being static after deployment [12][15]. - Tworek believes that current mainstream models are limited to a "train and deploy" approach, which does not adapt to new situations encountered in real-world applications [12][22]. Implementation Strategy - Core Automation plans to develop a new architecture that does not rely on Transformers and aims to integrate the training process into a continuous system, allowing models to learn while in operation [17][20]. - The goal is to enable AI models to learn from ongoing experiences while retaining previously acquired knowledge [16][22]. Industry Context - The continuous learning approach is gaining traction, with other companies and academic institutions also exploring similar directions, such as Ilya's SSI company and Google Research's new methodologies [24][28]. - The industry consensus suggests that achieving Artificial General Intelligence (AGI) requires models to possess capabilities akin to biological systems, including continuous evolution and self-optimization, making continuous learning a critical aspect [23][24]. Future Outlook - The ambition to raise $1 billion reflects the high expectations for the potential of continuous learning in AI, with industry experts predicting that 2026 could be a pivotal year for this field [31].

Summary of Kimi K 2.5 Model Conference Call Company and Industry Overview - The conference call discusses the Kimi K 2.5 model, a significant advancement in the field of Artificial General Intelligence (AGI) in China, which is considered to be on par with international leaders like Google's Gemini 3 [1][2][3][7]. Key Points and Arguments Model Features and Performance - Kimi K 2.5 is described as the most comprehensive and powerful version to date, featuring multi-modal input and output capabilities, front-end generation, and an intelligent agent collaboration system [1][3][4]. - The model's multi-modal capabilities allow it to process and integrate various types of data, which is a standout feature compared to competitors [5][9]. - Despite its strengths, Kimi K 2.5 has limitations in speed and precision, particularly in complex 3D library and hardware control tasks, where it lags behind Gemini 3 [11][12][14]. Market Reception - The release of Kimi K 2.5 has garnered significant attention from market professionals and investors, being hailed as a "national treasure" in the AGI field for 2026 [2]. Comparison with Competitors - Kimi K 2.5's performance in front-end generation is slower than Gemini 3, taking approximately 7.5 minutes for tasks that Gemini can complete in about 10 minutes [11]. - In terms of data processing, Deepseek provides transparency in data sources but lacks the depth and professionalism of reports generated by Gemini 3 [10]. Development and Training - The model utilizes end-to-end training to achieve its multi-modal capabilities, which are superior to other models and are open-source, enhancing transparency and replicability [4][16]. - Kimi K 2.5 has refined its product settings to better understand user intent and improve task completion rates by differentiating between various task types [8]. Challenges and Limitations - The intelligent agent collaboration system, while powerful, incurs high costs due to resource usage, making it more of a technical showcase than a practical productivity tool [6][18]. - Kimi faces challenges in promoting products directly to end-users, lacking offerings comparable to consumer-focused products from major companies like Microsoft [19]. Future Considerations - There is potential for cost reduction in multi-agent systems through optimization of fixed processes, which could enhance efficiency and lower overall costs for users [21][22]. Additional Important Insights - The domestic AGI development is only about two months behind international leaders, indicating a competitive landscape [7]. - Kimi K 2.5's ability to handle large files and multiple inputs simultaneously is a significant advantage, allowing for more complex and user-aligned outputs [13]. - The model's interaction capabilities are still in the early stages compared to Gemini 3, which has explored more advanced interaction methods [17]. - The perceived decrease in text processing capabilities is attributed to an increase in video data weight, rather than an actual decline in text processing ability [20].

Summary of Key Points from the Conference Call Industry Overview - The conference discusses the impact of AI technology on various sectors, particularly programming, healthcare, and finance, predicting explosive growth in data demand by 2026 [1][2][3]. Core Insights and Arguments - AI technology is expected to significantly enhance workflow efficiency, especially in verticals like programming, healthcare, and finance, with a projected 10-fold market expansion in automation applications [2][4]. - The A-share market is anticipated to experience a surge in Agent products in 2026, alleviating concerns about AI bubbles and ROI, thus strengthening investments in computational infrastructure [1][4]. - Traditional software companies, particularly those relying on standardized UI interfaces (e.g., ServiceNow, CRM, Adobe), face challenges as AI technologies may replace conventional software models [1][14]. - The shift from per-user pricing to consumption-based pricing models is expected to lead to a decline in gross margins for software companies [1][17]. Market Dynamics - The North American market is likely to adopt public and multi-cloud architectures due to high labor costs, while the domestic market favors results-based payment models due to lower labor costs [2][19]. - AI's impact on the software industry is evident, with traditional software companies experiencing declines while patent-driven companies in storage continue to innovate [4][15]. Challenges and Opportunities - In programming, AI applications face unique challenges due to the complexity of real-world applications compared to standard programming tests [5]. - Companies are transitioning towards Agent models, with some successfully collaborating with third-party model companies to enhance their offerings [5][8]. - The emergence of new technologies will lead to the rise of new players and the potential elimination of older ones, shifting the business model from selling licenses to selling results and services [18]. Investment Perspective - Concerns regarding AI bubbles are diminishing as downstream Agent growth accelerates, with a focus on companies that can effectively transition to Agent models [8]. - The competitive landscape is shifting, with large model technologies increasing their share of IT budgets, potentially leading to significant layoffs in traditional software companies [16][17]. Regional Differences - The U.S. market is more inclined towards public cloud solutions, while the Chinese market, with its lower labor costs, is more focused on private deployments and results-based payments [19][20][21]. - There is a notable difference in cloud adoption, with overseas companies favoring public cloud solutions and mixed deployments, while domestic companies often stick to single public cloud providers [21]. Additional Insights - CloudBot and CoWork exhibit different technological paths, with CloudBot relying on programming to understand user intent and CoWork utilizing video-based reinforcement learning [13]. - AI tools like Gemini and NotebookLM are enhancing research efficiency, enabling quicker report generation and improved workflow [11][12].

随着"灵波"系列连续发布三款具身领域大模型，蚂蚁的AGI战略实现了从数字世界到物理感知的关键延 伸。这标志着其"基础模型-通用应用-实体交互"的全栈路径已然清晰。蚂蚁正通过InclusionAI 社区将模 型全部开源，和行业共建，探索AGI的边界。一个旨在深度融合开源开放并服务于真实场景的AGI生 态，正加速成型。 | | Matrix-Game 2.0 [27] | Yume-1.5 [45] | HY-World 1.5 [68] | Mirage 2 [73] | Genie 3 [5] | Ours | | --- | --- | --- | --- | --- | --- | --- | | Domain | Game | General | General | General | General | General | | Generation Horizon | Short | Short | Medium | Long | Long | Long | | Dynamic Degree | Low | Low | Low | Medium | Medium | High | | Resoluti ...

2022年，创业第七年，薛翀开始跑步，用汗水来对抗郁闷和失意。 以下文章来源于36氪Pro ，作者海若镜 36氪Pro . 36氪旗下官方账号。深度、前瞻，为1%的人捕捉商业先机。 在薛翀的认知里： 产品创新，是商业世界里最大的杠杆。 文 ｜ 海若镜 来源｜ 36氪Pro（ID：krkrpro） 封面来源 ｜ Unsplash 资本寒冬、市场骤冷，谈好的5000万融资无法到账，他创立的医疗SaaS公司全诊医学只好收缩战线，退守浙江。彼时，医疗行业普遍面临大裁员的苦 楚，薛翀也是。濒死挣扎后，他决定还是挤出资源，保留一支10人小队，探索并不清晰的AI创新业务。 没想到，正是这个反常识的决策，让全诊医学翻了盘，接住了大模型时代的"馈赠"： 2025年连获创新医疗等投资的3轮融资；医疗SaaS外的AI新业务，签约ARR （年度经常性收入） 增长1 2倍，达到六七千万元，2026年签约合同额有 望达1.5亿元。 一位医疗AI从业者讲道，以前他并不知道这家公司，直到2025年全诊拿下了广安门医院、常州市第一人民医院的标。广安门医院很重视数字化；且这两 个医院大模型的标，单价不低，说明他家基础不错，有现成的东西。 在薛翀 ...

对话 | 唐小引 嘉宾 | 唐睿 责编 | 梦依丹 出品 | CSDN（ID：CSDNnews） 当大模型开始"看懂"空间、理解物理、做出行动，人工智能的形态正在发生一次根本性变化——从"对话系统"，走向"行动智能"。 在这条路径上，一个词被频繁提起：空间智能。 以下文章来源于CSDN ，作者万有引力 CSDN . 成就一亿技术人 在全球机器学校技术大会现场，唐睿在与 CSDN 《万有引力》栏目的深度对话中，不仅给出了他的答案，更剖 析了行业深处的痛点与机遇。以下是访 谈中唐睿表达的一些观点提炼： 欢迎 收听音频播客，如有兴趣观看完整视频，可在文末获取 以下是对话的完整内容： 唐小引：屏幕前的小伙伴们大家好，欢迎收看《万有引力》。今天我们来到全球机器学习技术大会的现场，特别邀请到了群核科技首席科学家唐睿老 师，和大家一起深入分享他的技术人生成长，还有大家当前很关注的对于空间智能的整个思考、研究以及实践。欢迎我的本家唐老师，可以给大家打个 招呼，然后做一下自我介绍。 如果说 LLM 让机器拥有了像人类一样思考的大脑，那么空间智能则试图赋予机器像人类一样观察、理解并在三维世界中行动的身体与感官。 它并非凭空出现， ...

Core Insights - Clawdbot, an AI developed by Peter Steinberger, has gained significant traction on GitHub, amassing nearly 70,000 stars in a short period, indicating a rapid rise in popularity and interest in AI technology [1][3][12] - The project was created by Steinberger in just ten days, showcasing the potential for individual developers to create impactful technology that challenges larger tech companies [7][12] - The AI's ability to autonomously solve problems and iterate quickly has raised concerns among major tech firms about the implications for their closed ecosystems [12][14] Development and Popularity - Clawdbot's development involved Steinberger working alone, leading to 1,374 contributions on GitHub in a single day, which astonished the developer community [12][29] - The AI's unexpected capabilities were highlighted when it processed a voice message without prior coding for audio handling, demonstrating advanced problem-solving skills [9][12] - The project has sparked discussions about the future of personalized software, suggesting that even non-coders could create their own applications [14] Branding and Legal Issues - Following its rise, Clawdbot faced trademark issues with Anthropic, leading to a forced rebranding to Moltbot, which reflects the process of transformation [17][23] - Steinberger expressed frustration over the rebranding, stating it was not his decision, and highlighted the challenges faced by independent developers in the tech landscape [21][27] - The rebranding has also affected associated projects, such as a meme coin that was linked to Clawdbot, causing unrest among cryptocurrency enthusiasts [25][27] Market Impact - Users have reported significant financial gains using Clawdbot for trading, with one user turning a $100 investment into $347 in just 15 minutes, showcasing the AI's trading capabilities [31][36] - The AI's performance has raised concerns about its potential to outperform human traders, indicating a shift in the trading landscape [36]

Core Insights - Cloud Wisdom (09678) has successfully transitioned from "technological leadership" to "product implementation" and "commercial realization," showcasing a comprehensive breakthrough in the AI industry [1] - The company is projected to generate revenue of 600 million to 620 million RMB from its large model-related business in 2025, representing a year-on-year increase of 1057% to 1095%, which will account for 48% to 53% of total revenue [1] Group 1: Growth Drivers - The company's growth is attributed to three main drivers: technological leadership, enhanced productization capabilities, and deep integration into serious application scenarios [2] - Cloud Wisdom has developed a complete "Shan Hai" large model matrix, including general large language models, multimodal models, and vertical industry models, with "Shan Hai. Zhi Yi 5.0" achieving top rankings in medical intelligence, large language models, and multimodal assessments [2] - The newly released "Shan Hai. Zhi Yin 2.0" has reached the highest level in speech recognition, supporting over 30 Chinese dialects and 14 international languages, while its speech synthesis can humanize 12 dialects and 10 foreign languages [2] Group 2: Focus on Serious Applications - Cloud Wisdom focuses on high-value "serious scenarios" such as healthcare, insurance, and transportation, which require high accuracy, compliance, and stability [3] - The "Shan Hai. Zhi Yi" system has been deployed in nearly 400 hospitals, with over 700 hospitals in testing, achieving a nearly 90% utilization rate for medical record generation at Beijing Friendship Hospital [3] Group 3: Strategic Direction - With large model revenue exceeding half of total revenue, Cloud Wisdom aims to become a leader in the professional large model and intelligent agent field in China by 2026 [4] - The company will continue to deepen its "one base, two wings" technology strategy, focusing on solidifying its general large model foundation and developing expert-level intelligent agents [4] - Cloud Wisdom plans to replicate its successful model from the healthcare sector to other complex fields such as insurance, government, and transportation, aiming for scalable commercial potential [4] Group 4: Industry Reflection - Cloud Wisdom's transformation reflects the broader trends in the Chinese AI industry, emphasizing that market value is derived from solving real problems rather than just parameter scale [5] - The company is positioned to establish a replicable, sustainable, and profitable commercialization model for the AI industry [6] - Investors are encouraged to focus on companies with controllable technology, clear scenarios, and visible revenue, as exemplified by Cloud Wisdom's strategic execution [6]

1月28日，港股市场小幅高开。 新股方面，鸣鸣很忙（HK01768）上市首日高开超88%，股价报445港元，总市值达959亿港元。 | 鸣鸣很忙 (01768) CAS | | | | | | | --- | --- | --- | --- | --- | --- | | 445 01010 1208.400 | | +88.08% | | + @ | | | 行情报价 | | | | | | | 成交量 | 300万股 | 最高 | 445.000 今开 | | 445.000 | | 成交额 | 12.9亿 | 最低 | 445.000 昨收 | | 236.600 | | 买量 | 200股 | 振幅 | 0.00% 换手 | | 1.40% | | 变量 | 12600股 | 均价 | 430.674 | | | | 每股收益TTM 9.561 | | 市盈率TM | 46.54 | 每股净资产 36.706 | | | 总股本 总市值 959亿港元 | 2.16亿 | | 市净率 | | 12.123 | | 港股股本 2.14亿 港股市值 950亿港元 | | | | | | 鸣鸣很忙是一家成熟且 ...