来源：环球网 外部支付开放：若开发者引导用户至App Store外完成支付，仅需缴纳5%-15%的费用，且不再限制导流 链接数量。此前，苹果曾通过技术手段禁止此类行为，被欧盟认定为"限制竞争"。 今年4月，欧盟委员会以违反《数字市场法案》为由，对苹果处以5亿欧元罚款，并要求其在60天内取消 对开发者外部支付的限制。根据DMA规定，苹果作为"守门人"企业，必须允许应用开发者自由选择分 销渠道和支付方式。若未按时整改，苹果将面临每日全球日均收入5%的罚款（约合5000万欧元/日）。 为避免重罚，苹果于6月27日紧急宣布政策调整，引入"核心技术佣金"（CTC）机制，对App Store外的 数字交易额外收取5%费用。尽管苹果声称新规符合DMA要求，但其复杂的收费体系仍引发争议。Epic Games CEO蒂姆·斯威尼批评称，此举"以税收形式扼杀竞争"，而欧盟监管机构则强调需进一步评估合 规性。 据报道，欧盟委员会预计将在8月中旬前完成评估。（青山） 根据最新协议，苹果对欧盟开发者的收费体系进行了分层设计： 基础抽成调整：开发者通过App Store完成的交易需支付20%的处理费，较此前15%-30%的标准费率显著 ...

IT之家 7 月 23 日消息，路透社昨日报道称，苹果上个月对其 App Store 规则及抽成协议进行了调整，预计将很快获得欧盟反垄断监管机构的批准，此举将 使其避免潜在的巨额每日罚款。 根据最新协议，开发者通过 App Store 完成的交易只需支付 20% 处理费，而加入苹果小微企业计划的开发者可再次降低至 13%。 今年 4 月，欧盟反垄断执法机构对苹果开出 5 亿欧元（现汇率约合 41.88 亿元人民币）罚单，同时还对 Meta 开出 2 亿欧元（现汇率约合 16.75 亿元人民 币）罚单。 当时欧盟委员会表示，苹果通过商业及技术手段限制阻止应用开发者引导用户至 App Store 外的渠道完成更便宜交易，违反了《数字市场法案》（DMA）。 苹果被要求在 60 天内取消这些限制。 若未遵守规定，苹果可能面临每日罚款 —— 金额为其全球日均收入的 5%，每天约合 5000 万欧元（IT之家注：现汇率约合 4.19 亿元人民币）。 知情人士称，欧盟委员会预计将在未来几周批准这些修改，不过时间仍可能有变化。欧盟监管机构则表示"所有选项仍在考虑中，我们仍在评估苹果的提议 修改"。苹果未立即回应置评请求。 此 ...

以下文章来源于腾讯研究院 ，作者白惠天 腾讯研究院 . 腾讯公司设立的社会科学研究机构，依托腾讯公司多元的产品、丰富的案例和海量的数据，围绕产业发 展的焦点问题，通过开放合作的研究平台，汇集各界智慧，共同推动互联网产业健康、有序的发展，致 力于成为现代科技与社会人文交叉汇聚的研究平台。 本文来自微信公众号： 腾讯研究院 （ID：cyberlawrc） ，作者：白惠天（腾讯研究院高级研究 员），题图来自：AI生成 你有没有过这样的瞬间：写不完的总结、画不完的PPT、改三遍还会出错的表单……不是太难，就是 太烦，做完没成就感，做慢了还影响进度。 如果AI能替你做点事，你最想交给它干什么？ 过去一年，AI成了打工人身边最常出现的"新同事"。从Copilot到Agent，越来越多打工人已经在用它 写邮件、排日程、写代码。根据Anthropic团队2025年初发布的研究，全球已有36%的职业岗位中， 员工已将AI用于至少四分之一的日常任务。OpenAI的调研也指出，80%的美国职场人至少有10%的 任务受到AI影响，其中近五分之一的岗位中，AI已介入超过一半的工作内容。 一个很关键的问题正在浮出水面：我们不是真的想被A ...

本月有三场 AI 创业者大赛值得关注： 两场为 AI 低代码大赛，分别来自美团 NoCode 社区、 YouWare，以及还有一场 外滩大会主办的人工智能硬件科创大赛。 8 月，还有一场 2025 Google 开发者大会，将在上海举办。 此外，Founder Park 联合 Google 推出的「从模型到行动」系列 AI 工作坊活动，本周六将迎来最后一站 「北京站」，仍在火热报名中。 此前深圳站、上海站两场线下，现场开发者反馈收获满满。 我们还整理了近期值得参与的一些活动，对更多活动感兴趣的小伙伴，可以点击文末的 「阅读原文」 查看。 跟着 Google 出海：教你怎么落地 Gemini【最后一站】 主办方： Founder Park x Google 活动&报名时间： 7 月 26 日（周六） 14:00–17:00 @Google 北京办公室，7 月 24 日截止报名 活动亮点： 面向人群： 报名方式： https://mp.weixin.qq.com/s/WFScFd2yDeryo-kOeLiwRw NoCode 7 月「晒作品，赢奖励」 主办方： NoCode 社区 比赛时间： 2025.07 活动 ...

Core Insights - The article discusses a significant vulnerability in large language models (LLMs) where they can be easily deceived by seemingly innocuous symbols and phrases, leading to false positive rewards in evaluation scenarios [2][13][34]. Group 1: Vulnerability of LLMs - A recent study reveals that LLMs can be tricked by simple tokens like colons and spaces, which should ideally be filtered out [4][22]. - The false positive rate (FPR) for various models is alarming, with GPT-4o showing a FPR of 35% for the symbol ":" and LLaMA3-70B having a FPR between 60%-90% for "Thought process:" [22][24]. - This vulnerability is not limited to English; it is cross-linguistic, affecting models regardless of the language used [23]. Group 2: Research Findings - The research involved testing multiple models, including specialized reward models and general LLMs, across various datasets and prompt formats to assess the prevalence of this "reward model deception" phenomenon [15][17]. - All tested models exhibited susceptibility to triggering false positive responses, indicating a systemic issue within LLMs [21][28]. Group 3: Proposed Solutions - To mitigate the impact of this vulnerability, researchers developed a new "judge" model called Master-RM, which significantly reduces the FPR to nearly zero by using an enhanced training dataset [29][31]. - The Master-RM model demonstrates robust performance across unseen datasets and deceptive attacks, validating its effectiveness as a general-purpose reward model [31][33]. Group 4: Implications for Future Research - The findings highlight the critical need for improved robustness in LLMs and suggest that reinforcement learning from human feedback (RLHF) requires more rigorous adversarial evaluations [35][36]. - The research team, comprising members from Tencent AI Lab, Princeton University, and the University of Virginia, emphasizes the importance of addressing these vulnerabilities in future studies [38][40].

Core Insights - Meta is redefining the future of super intelligence with a focus on "personalized super intelligence" aimed at billions of users, contrasting with competitors' enterprise-level AI applications [1][2] - The company is investing unprecedented capital, amounting to thousands of billions, in building large-scale computing clusters, with the Hyperion project nearing the size of Manhattan [1][2] - Meta's strategy emphasizes attracting top talent, with a competitive market for researchers, and a focus on maximizing GPU resources with a lean team [2][6] Group 1: AI Vision and Strategy - Meta's vision of personalized super intelligence aims to empower individuals rather than solely focusing on economic automation, which is the trend among other tech giants [1][7] - The company believes that while addressing significant issues is important, people are often more concerned with simpler aspects of their lives [1][7] - The goal is to provide this power directly to users, aligning with Meta's values of enhancing personal experiences [1][7] Group 2: Infrastructure Investment - Meta is constructing multiple gigawatt-scale data centers, with the Prometheus and Hyperion clusters expected to exceed 1 gigawatt, and Hyperion set to expand to 5 gigawatts in the coming years [2][11] - The scale of these projects is significant, with the Hyperion site comparable in size to a substantial portion of Manhattan [2][11] - The company has a robust business model to support these investments, allowing it to self-fund without relying on external financing [2][11] Group 3: Talent Acquisition and Market Competition - The competition for top talent in AI is intense, with Meta willing to invest heavily to secure a small number of elite researchers [2][6] - While reports suggest compensation packages could reach $100 million to $200 million, the specifics may be exaggerated, but the market remains highly competitive [2][6] - Meta's strategy focuses on having the highest GPU resources per researcher, which is seen as a strategic advantage in attracting talent [12] Group 4: Future Outlook - There are varying opinions on when super intelligence will be realized, with estimates ranging from three to seven years; however, Meta is optimistic about a two to three-year timeline [3][5] - The company is committed to investing heavily in building the strongest team possible to capitalize on this potential [3][5] - Meta envisions AI glasses as the optimal form of interaction with AI, potentially becoming essential for cognitive enhancement in daily life [2][9]

Group 1 - Proactive provides fast, accessible, informative, and actionable business and finance news content to a global investment audience [2] - The company focuses on medium and small-cap markets while also covering blue-chip companies, commodities, and broader investment stories [3] - Proactive's news team delivers insights across various sectors including biotech, pharma, mining, natural resources, battery metals, oil and gas, crypto, and emerging technologies [3] Group 2 - Proactive is committed to adopting technology to enhance workflows and improve content production [4] - The company utilizes automation and software tools, including generative AI, while ensuring all content is edited and authored by humans [5]

Core Viewpoint - Tencent's Hunyuan-A13B model has gained significant attention in the open-source community due to its performance and efficiency, particularly with its ability to compete with larger models using fewer activated parameters [2][11]. Group 1: Model Performance and Architecture - The Hunyuan-A13B model utilizes a fine-grained MoE (Mixture of Experts) architecture, with a total parameter scale of 80 billion, activating only 13 billion parameters during inference, leading to over 100% improvement in throughput compared to similar models [11][12]. - It supports a native context window of 256K, enhancing its performance and efficiency [12]. - The model has been validated against benchmarks, outperforming smaller models like Qwen3 8B and 14B, while still being competitive with larger models [4][36]. Group 2: Developer Accessibility - The model is designed to be user-friendly for individual developers, requiring only a mid-range GPU to run, thus alleviating concerns about computational power [14][15]. - The API for the model is available on Tencent Cloud, with competitive pricing of 0.5 yuan per million tokens for input and 2 yuan for output [7]. Group 3: Training Methodology - The model's capabilities are built on a high-quality pre-training phase using 20 trillion tokens of data, with a focus on STEM fields, which enhances its performance in reasoning tasks [19]. - A structured post-training framework is employed, consisting of multiple phases to refine the model's abilities in various tasks, including a focus on both IQ and EQ [22][24]. Group 4: Agent Capabilities - The model's agent capabilities are developed through a combination of supervised fine-tuning (SFT) and reinforcement learning (RL), allowing it to excel in tasks such as tool invocation and complex decision-making [25][35]. - In various authoritative evaluations, Hunyuan-A13B has surpassed leading models, demonstrating strong reasoning and coding abilities [36]. Group 5: Practical Applications and Open Source - Hunyuan-A13B has been validated in over 400 business scenarios within Tencent and is now fully open-sourced, with model weights, code, and technical reports available on GitHub and Hugging Face [38].

作者 | idoubi 策划 | AICon 全球人工智能开发与应用大会 审校 | 罗燕珊 近日，在 6 月 27~28 日举办的 全球人工智能开发与应用大会·北京站期间，ThinkAny&MCP.so 创始 人艾逗笔（idoubi）受邀做了一场题为《AI 时代如何做独立开发》的分享，现场反响热烈，收获了听 众的一致好评。以下内容源自其精彩分享。 8 月 22～23 日的 AICon 深圳站 将以 "探索 AI 应用边界" 为主题，聚焦 Agent、多模态、AI 产品设计 等热门方向，围绕企业如何通过大模型降低成本、提升经营效率的实际应用案例，邀请来自头部企 业、大厂以及明星创业公司的专家，带来一线的大模型实践经验和前沿洞察。一起探索 AI 应用的更 多可能，发掘 AI 驱动业务增长的新路径！ 我是谁 2011 年，我本科入学武汉大学核工程专业，大一那年暑假，偶然接触了 Abobe 公司的 Photoshop 和 Dreamweaver 软件，自学了平面设计和网页编程，进入 IT 行业。取网名艾逗笔（idoubi）以致 敬，沿用至今。2015 年，我从武大毕业，那年的毕业典礼刚好请了雷总演讲，听雷总讲互联网的 ...

Core Viewpoint - The departure of Alibaba Group's Vice President Ye Jun, who previously served as the president of DingTalk, is confirmed, with implications for the company's leadership and strategic direction [1][2]. Group 1 - Ye Jun has a strong academic background, holding a bachelor's, master's, and doctoral degree from Sichuan University, specializing in materials science and computer applications [1]. - During his tenure at Alibaba since 2007, Ye Jun led various departments and was instrumental in developing key products such as Office Cloud, Alibaba Brain, and DingTalk [1]. - The context of Ye Jun's departure is linked to the return of DingTalk's founder Chen Hang, who is set to become the new CEO of DingTalk following a significant investment transaction [2].