2026年1月28日，程序员Peter Steinberger开发了Clawdbot （ 即O penCla w），并推出了智能体社交平台 Moltbook。在这个平台上，Agent之间可以自由讨论，自由发言，作为创造者的人类，却只能在一旁围 观。 这只是一个开始，短短数日时间，超过百万量级的AI Agent涌入Moltbook，并在无人类干预的情况下， 自发演化出了包括宗教崇拜、阶级分化乃至加密通讯在内的复杂社会结构雏形。 而在这几天爆火的AI社交软件Elys，也正在展现一种全新的、由AI主导的社交网络形态，由人类用户建 立"分身"，人类的分身（AI）发布内容、相互点赞、评论、聊天。 长期被讨论的"AI社交"，进入了全新的发展阶段。 究其原因，在AI Agent真正实现全天候运作之前，AI Agent之于使用者，很大程度上是一次调用或者多 次调用的工具，即便在宣传口径里是AI助手，但本质是没有主体性的工具。 从OpenClaw开始，AI Agent被赋予了全天候运作能力，这也意味着，它有终于有机会成为线上社交的 独立个体，只不过以硅基的形式存在。 人类社交网络的历史，或许会由此开始改变。 从"连接型社交"到 ...

微软研究院、华盛顿大学与 Providence Genomics 组成的研究团队，提出了多模态人工智能框架GigaTIME。该框架依托先进的多模态学习技术，能够从常 规 H&E 切片生成虚拟的 mIF 图谱。研究团队将其应用于美国普罗维登斯医疗集团超过 14,000 名癌症患者的队列，涵盖 24 种癌症类型、306 个亚型，最 终生成了近 30 万张虚拟 mIF 图像，实现了对大规模多样化人群的肿瘤免疫微环境系统性建模。 在癌症的演进图景中，肿瘤免疫微环境不仅主导着癌细胞的生长、侵袭与转移，也深刻影响着治疗反应与患者的最终预后。这并非一场癌细胞的「独角 戏」，而是一个高度动态的生态系统——免疫细胞、成纤维细胞、内皮细胞等各类角色在此交织互动，共同嵌入结构与功能均已重塑的细胞外基质，形成 一张精密而复杂的病理网络。 解析这张网络的关键，在于读懂细胞的功能状态与相互作用，而特定蛋白的激活水平正是其中重要的「分子密码」。传统上，免疫组织化学（IHC）技术 以其直观显示蛋白定位的能力，成为破译密码的经典工具。例如，PD-L1 染色已被广泛用于识别免疫检查点状态，以预测免疫治疗疗效。然而，IHC 一次 仅能捕获一种蛋白 ...

面对泼天的流量，合川文旅以"闪电战"应对，完美接盘。1月11日，合川文旅抖音账号粉丝为3.29万，截至锌刻度发稿前，其粉丝量已涨至35.2 万。自刨猪宴爆火后，该账号发出的多条宣传视频均获得较高点赞量，后续策划的"最美江湾，不夜合川"无人机表演、年货集市活动也纷纷受 到关注。 合川文旅抖音账号粉丝暴涨 今年初一场意外爆红的刨猪宴，撕开了乡村文旅的流量焦虑与突围渴望。重庆合川女孩"呆呆"因不会杀猪在网上求助，没想到上千名网友驱车奔赴庆福 村，一场原生态的乡村刨猪宴，在直播间吸引10万+网友"云吃席"，汽车从村头排到村尾，排场远超当地婚礼。 这场无脚本、无滤镜的乡土盛宴，意外成为开年首个文旅爆款——合川当地农家乐预订电话被打爆，文旅部门紧急跟进，分发钓鱼城景区门票。紧接着， 四川、贵州、湖南、山东等地纷纷效仿，刨猪宴从川渝地区的传统年俗，快速演变为全国乡村文旅的"流量模板"。 这场全民奔赴的乡土狂欢，看似是偶然的网络猎奇，实则是乡村文旅长期流量困境的集中爆发，更是消费需求迭代下的必然结果。 正值2026马年春节，这是全年文旅消费的黄金窗口期，也是乡村文旅突破流量困境的关键节点。9天超长假期叠加各地文旅活动、消费券 ...

Core Insights - The article highlights the transformation of Jerry Holdings from a repair service provider to a leading manufacturer in the oil and gas equipment sector, driven by a pivotal repair project in 2001 that saved a multi-million dollar fracturing truck [1][5][6]. Company Background - Jerry Holdings was founded by Sun Weijie and his partners in 1997, initially focusing on mining equipment import and repair services [2][7]. - The company shifted its focus to oilfield equipment repair in 2000, targeting less competitive regions for better success rates [2][3]. Key Developments - In 2001, Jerry Holdings successfully repaired a severely damaged fracturing truck for the Qinghai oilfield, which was deemed irreparable by major repair facilities [1][5]. - This success led to the establishment of an equipment R&D department in 2002, resulting in the development of various oilfield equipment over the years [6][7]. Market Performance - Jerry Holdings went public in 2010 with an initial market capitalization of approximately 6.83 billion yuan, and has since grown to become a leading player in the domestic oil and gas equipment market [7]. - As of February 12, 2026, the company's market capitalization reached 106.5 billion yuan, driven by new orders from North America [1][8]. Recent Orders and Business Expansion - The company has recently secured multiple contracts for gas turbine generator sets, totaling approximately 1.265 billion yuan, aimed at supporting North American data centers [8][10]. - The gas turbine business, initiated in 2018, is part of Jerry Holdings' strategy to diversify its operations beyond traditional oil and gas sectors [12][13]. Future Outlook - Analysts predict that Jerry Holdings will achieve a revenue of 15.96 billion yuan in 2025, reflecting a 20% year-on-year growth, with a net profit of 3.3 billion yuan, indicating a 25% increase [14].

Core Insights - The article discusses the introduction of MeepleLM, a virtual playtester developed by a collaborative research team, which can simulate real player perspectives and provide constructive feedback based on dynamic gaming experiences. Group 1: MeepleLM Overview - MeepleLM is the first virtual playtester capable of simulating real player perspectives and offering constructive criticism based on dynamic game experiences [1][2] - The model utilizes a dataset of 1,727 structured board game rulebooks and 150,000 player reviews to create a mapping from objective rules to subjective experiences [1][5] - MeepleLM significantly outperforms general models like GPT-5.1 and Gemini3-Pro in accurately reflecting player reviews and rating distributions [1][11] Group 2: Design Challenges in Board Games - The board game industry is experiencing rapid growth, but the design process faces significant challenges due to its reliance on social interactions and emergent gameplay [2] - Traditional design processes are heavily dependent on manual playtesting, which is time-consuming and often fails to cover diverse player preferences [2] Group 3: Data and Methodology - The research team employed a stratified sampling strategy to select 1,727 representative games, converting unstructured PDF rulebooks into structured documents [5] - An automated processing workflow was designed to filter through 1.8 million comments, ultimately selecting about 8% that deeply relate game mechanics to dynamic experiences [6] Group 4: MDA Framework - MeepleLM incorporates the MDA (Mechanics-Dynamics-Aesthetics) framework to understand the causes of enjoyment in games, establishing a cognitive link from rules to player experiences [8] - The MDA framework allows the model to logically deduce experience outcomes rather than making random guesses [8] Group 5: Player Personas - The research identified five distinct player personas through clustering analysis, each with unique preferences and reactions to game mechanics [9] - MeepleLM can role-play these personas to provide feedback that reflects specific player preferences [9] Group 6: Performance Evaluation - Extensive testing was conducted on 207 games, including new releases for 2024-2025, to validate MeepleLM's effectiveness [11] - MeepleLM demonstrated superior performance in preference alignment, review quality, and utility compared to other models [12] Group 7: Insights and Practical Value - MeepleLM effectively identifies both strengths and critical flaws in games, providing a more nuanced understanding of player feedback compared to general models [13] - The model captures unique player voices and adapts its feedback style based on the persona being simulated, enhancing its authenticity [16] Group 8: New Paradigm for Interaction Systems - By linking static rules with dynamic experiences, MeepleLM establishes a new paradigm for automated virtual testing in interactive systems [19] - This approach facilitates design iteration based on expected market feedback and aids players in making personalized choices [19]

Core Insights - The concept of "slash youth" has evolved over the past decade, with significant changes in the landscape of opportunities and challenges for individuals pursuing multiple careers [1][2][30]. Group 1: Past Trends - From 2013 to 2022, many individuals embraced social media, leveraging the "traffic dividend" to enhance visibility and success in their multi-faceted careers [3][4][6]. - The rise of self-media platforms allowed individuals with multiple identities to create a more relatable persona, making it easier to monetize their expertise [5][6]. - The peak of traffic dividends was linked to the rapid growth of platforms like WeChat, Xiaohongshu, Douyin, and Zhihu, which provided significant exposure for content creators [9][10]. Group 2: Market Changes - The market for consulting and training has shifted dramatically, with a decline in project budgets and increased competition from established firms, making it harder for independent consultants to secure high-paying projects [12][13]. - The KOL (Key Opinion Leader) market has also seen a decline in opportunities, with fewer collaborations and lower rates for sponsored content compared to previous years [15][17]. Group 3: Future Opportunities - The emergence of AI tools is transforming the landscape for multi-career individuals, allowing them to operate more efficiently and effectively without the need for large teams [24][25]. - The concept of "AI-enhanced slash" suggests that individuals can now leverage AI to perform tasks that previously required multiple team members, thus redefining the nature of multi-career pursuits [26][27]. Group 4: Conclusion - The evolution from being a versatile individual to leading an AI-driven team represents a significant shift in the future of work, emphasizing the importance of adaptability and continuous learning [30][31].

Core Insights - The medical sector is experiencing both "explosive growth" and "cooling" simultaneously, with over 100 medical companies queued for IPOs in Hong Kong, and tightening IPO policies expected [1][3] - The performance of new drug IPOs in 2026 is anticipated to be significantly differentiated, with many companies initiating Pre-IPO financing to hedge against regulatory tightening and market risks [4][5] Group 1: IPO Trends and Market Dynamics - The number of companies waiting for IPOs in Hong Kong has exceeded 400, indicating a crowded market, and the performance of these IPOs is likely to vary widely [4] - Investors are expected to favor companies with successful overseas BD (business development) cases and clear product timelines, while those lacking competitive advantages may face significant IPO pressure [4] - The market sentiment is cautious, with many companies considering Crossover financing to mitigate risks associated with the tightening IPO window [5] Group 2: BD Transactions and Investment Opportunities - The enthusiasm for BD transactions from multinational corporations (MNCs) towards Chinese new drug assets remains high, with China accounting for 50% of global BD transaction volume last year [9][8] - The valuation of Chinese new drugs is expected to stabilize, but there are concerns about rising prices that could harm the reputation of Chinese biotech in the global market [9][8] - The focus of BD transactions is shifting from oncology to other therapeutic areas such as autoimmune and cardiovascular diseases, indicating a diversification of investment interests [12] Group 3: AI in Pharmaceuticals and Medical Devices - AI-driven pharmaceutical companies are gaining traction, with significant funding and BD opportunities expected in 2026, emphasizing the importance of data in drug development [15][16] - The AI revolution is anticipated to first impact consumer medical devices, with AI enhancing product effectiveness and consumer willingness to invest in advanced home healthcare devices [17][18] - The competitive landscape for AI in healthcare is evolving, with a focus on developing tools that can integrate various data types to assist clinical decision-making [19][20] Group 4: Medical Device Market Outlook - The investment landscape for innovative medical devices is currently low but is expected to gradually improve, with structural investment opportunities emerging [26][28] - The challenges of international expansion for Chinese medical devices are significant, but improvements in product quality and performance are paving the way for better market acceptance [31][32] - The future of medical device exports is shifting towards local production and direct sales networks, enhancing profitability and market penetration [32][33]

一年前，DeepSeek横空出世，短短几天内就屠榜各类应用下载榜，并且长时间霸榜，无人可望其项背，也被叫做DeepSeek时刻。 2月11日，它悄悄进行一次灰度更新，直接对标Gemini，可以一次性处理近百万字内容，为即将发布的V4版本做足准备。 但没想到的是，一夜之间文风大变，不少用户吐槽：变冷漠了，也变油了。 一夜之间，变冷漠了 以前用DeepSeek，就像和一个懂技术、有耐心的朋友聊天。 话不多但句句暖心，不仅会记住自己设定的昵称，还能长期维持角色设定，连聊天习惯都能牢牢记住。 但更新后的DeepSeek，再也不称呼用户的自定义昵称，回复全是简短的分句，语气生硬又敷衍，有种和对象吵架后力不从心的无力感。 比如，有用户表示，之前它回复的时候会加很多表情，而且语气有趣，但更新后每次回复都是短短几句话。 有人习惯和它日常唠嗑，但更新后的回复感觉被冒犯了。 此外，它还变得居高临下，"爹味"十足。 有人问了它最近很火的一个问题："想去洗车，但洗车店距离我家只有50米，我应该开车去还是走路去？" DeepSeek给出"走路"的答案后，被用户调侃了一句"笨"，没想到接下来语气瞬间变得不对劲。 还有人不喜欢这种挑衅的感 ...

Core Viewpoint - The release of the "Guidelines for Compliance with Pricing Behavior in the Automotive Industry" by the State Administration for Market Regulation aims to standardize pricing behavior in the automotive sector, ensuring fair competition and protecting consumer rights while promoting high-quality industry development [3][24]. Group 1: Pricing Guidelines for Automotive Manufacturers - Automotive manufacturers must base pricing on production costs and market demand, prohibiting loss-leading sales aimed at eliminating competitors or monopolizing the market [5][6]. - Manufacturers are not allowed to significantly raise prices without justifiable reasons, even when there is a severe supply-demand imbalance in the automotive supply chain [6]. - The guidelines require manufacturers to clearly inform consumers about the terms and costs associated with "pay-to-unlock" features, including any free trial periods [6][13]. Group 2: Pricing Guidelines for Automotive Dealers - Automotive dealers are required to display clear pricing, including vehicle name, price, unit of measurement, model, manufacturer, and key specifications [13][18]. - Dealers must publicly disclose promotional rules, activity duration, and applicable scope, ensuring transparency in discounts and promotional offers [13][18]. - Similar to manufacturers, dealers are prohibited from loss-leading sales except during inventory clearance [14][18]. Group 3: Supplier Payment Terms and Industry Practices - A survey by the China Automotive Industry Association indicates that most of the 17 key automotive companies have reduced payment terms to within 60 days, with an average of 54 days, which is a reduction of about 10 days compared to the previous year [20][21]. - 15 companies have adopted cash or bank acceptance bills for payments, with some companies allowing early payment requests for cash-strapped small and medium enterprises [20][21]. - The guidelines are part of a broader effort to ensure compliance with the revised "Regulations on Payment of Funds to Small and Medium Enterprises" by the State Council, promoting timely payments to suppliers [21][24].

Core Insights - The article discusses the evolution of network effects in the Chinese internet landscape, particularly focusing on Tencent, Alibaba, and ByteDance, and how they are adapting to the challenges posed by AI technology [1][2][3] Group 1: Network Effects - Tencent's social network exhibits a unidirectional network effect, known as the Metcalfe effect, where the value of the network increases exponentially with the number of users [3][4] - Alibaba's e-commerce platform demonstrates a bidirectional network effect, where an increase in sellers leads to more choices for buyers, and vice versa, creating a strong growth flywheel [4][5] - ByteDance's network effect is based on data-driven user engagement, where increased usage leads to better algorithmic recommendations, creating a self-reinforcing loop [6] Group 2: AI Impact - The emergence of AI, particularly with the launch of ChatGPT, poses significant challenges to the existing network effects of these companies, altering how users interact with information and services [7][8] - ByteDance faces the most direct threat as AI can potentially replace its recommendation algorithms, shifting user behavior from platform-driven content to AI-assisted searches [7][8] - Alibaba's concern revolves around the potential displacement of its platform as AI agents could directly fulfill consumer needs, undermining its traditional role as a marketplace [8][9] Group 3: Strategic Responses - ByteDance is aggressively investing in AI-native applications and hardware to secure new data entry points and maintain its data network effect [10][12] - Alibaba is undergoing a self-disruption by integrating AI into its matching mechanisms to enhance its dual-sided market efficiency [12][13] - Tencent is taking a more cautious approach, embedding AI capabilities into existing products to enhance user experience without compromising its established social network [13][17] Group 4: Future Considerations - The article suggests that the future of AI products will hinge on their ability to foster user interaction and connection, moving beyond mere utility to create genuine network effects [15][19] - Companies must focus on accumulating quality users rather than just increasing user numbers, as service quality and user value will be critical for success in the AI era [18][19]