得益于电影《穿普拉达的女王2》上映次周末在全球斩获1.188亿美元票房，迪士尼影业在2026年前五个月的全球总票房已突破20亿美元，成为本年度唯一达到此里程碑的电影公司。根据Box Office Mojo数据，全球影史上票房超过10亿美元的60部影片中，有35部归属于迪士尼旗下，并且在7部票房超过20亿美元的影片中，迪士尼拥有其中6部的版权。 来源：Sherwood

这段时间看了不少 AI 新模型，我发现很多人的用法其实还停留在聊天。 写个文案、翻译一段文字、总结一篇文章，这些当然都很好用，但如果把 AI 只当成聊天机器人，我觉得有点浪费了。 我以前也一直觉得，模型越聪明越重要，但最近接触越来越多 Agent 和自动化工作流之后，我发现真正影响体验的，很多时候不是模型会不会回答，而是它能不能稳定把一整件事情做完。 因为工作流里只要有一步出错，前面几十步可能都要重来。 现在越来越多团队开始讨论的，其实不是 AI 能回答什么问题，而是 AI 能不能真的帮你把事情做完。 举个很简单的例子： 以前老板把一份 Excel 丢给员工，员工要先整理数据、做分类、生成图表，再复制到 Word 或 PPT，最后检查格式，一整套流程下来，重复又花时间。 现在如果接上 Office MCP，这些动作已经可以交给模型去完成。 你只要告诉它要整理什么，它就能读取表格、分类数据、生成图表，再放进文档里完成排版。 整个过程不是靠模拟鼠标点击，而是直接调用工具完成，所以稳定性会高很多。 我觉得这里最大的变化，不是 AI 更聪明，而是它开始真正参与工作。 还有一个官方展示的案例，我觉得也很有意思。 很多团队每天都在 Slack、飞书或者 Discord 里聊天，消息一多，最容易发生的就是事情讲完了，但没人记得是谁负责，也没人知道什么时候要交。 如果让 Ling-3.0-flash 挂在这样的工作流里，它不用一直和大家聊天，而是在后台默默整理信息。 有人说这个需求我来跟，有人说下周三上线，它就自动提炼重点，生成待办事项，再分发给对应的人。 写这篇的时候，我也顺手拿一段团队聊天记录试了一下，会议摘要、负责人、待办事项和风险点基本一次就整理出来了（看图） 这种能力放到团队每天的沟通里，比单纯聊天更有价值，因为大家不用再花时间翻聊天记录找重点。 所以我越来越觉得，不要用一个擅长规划的大模型去做所有事情，毕竟规划是规划，执行是执行。 如果只是要稳定调用工具、整理资料、批量处理数据，那更重要的是响应快、执行稳，而不是每一步都花很多时间去思考。 Ling-3.0-flash 给我的感觉，更像团队里那个执行力很强的同事。 你把规则和目标告诉它，它不会一直发散，也不会想着重新设计整个方案，而是按照要求把事情一步一步完成。 这也是我觉得它和很多人理解中的 AI 最大的不同。 未来的 AI，可能不会一直停留在聊天窗口里，而是越来越多地出现在我们的工作流里。 它不一定是最会思考的那个，但可以是那个一直把事情做完的人。 如果有兴趣体验，我觉得可以自己试试看👉 拿一段会议记录、聊天记录或者日常工作内容丢进去，实际跑一遍，会比单纯看介绍更容易理解它为什么更适合作为 Agent 工作流里的执行模型。

#为什么是中国# #WhyChina# The Global Logic of China's Economic Growth in the First Half of 2026: A 4.7% GDP Increase 2026年上半年GDP增长4.7%：中国经济增长的全球逻辑 According to the latest semi-annual report, China's gross domestic product (GDP) reached 69.6 trillion yuan, representing a year-on-year increase of 4.7% at constant prices, in line with the annual growth target. Against a backdrop of intertwined international complexities and volatilities, China's economic performance is commendable. Yet, a closer look at the data reveals that the logic underpinning China's economic growth is being reshaped. (I) At the Industry Level, Notable Highlights Emerge: First, new quality productive forces are being cultivated and strengthened at an accelerated pace.** In the first half of the year, industrial production grew robustly. The value-added output of the equipment manufacturing sector increased by 9.3% year-on-year, and that of high-tech manufacturing grew by 13.3%, both outpacing the overall growth rate of industrial output above a designated scale. This underscores a clear trend toward a high-end, intelligent, green, and integrated industrial structure. Looking at specific products, the output of 3D printing equipment, lithium-ion batteries, industrial robots, and other products emblematic of new quality productive forces surged. The average daily token call volume has reached hundreds of trillions, showcasing the vitality and potential of the digital and intelligent economies. Second, new growth drivers are accelerating to take on a leading role. Preliminary estimates suggest that new growth drivers—encompassing high-end manufacturing, the digital economy, and modern services—contributed over 40% to economic growth in the first half of the year. The economy's distinct shift toward a higher quality and more optimized structure is evident, and this overall trend is accelerating. For instance, industries related to artificial intelligence, such as integrated circuit manufacturing and intelligent vehicle equipment manufacturing, have all maintained high growth rates exceeding 30%, vividly illustrating the pace of China's industrial upgrading. Third, confidence on the investment front is on the rise. In the first half of the year, investment in high-tech industries grew by 4.6% year-on-year. Notably, investment in the manufacturing of aircraft, spacecraft, and equipment, computer and office equipment manufacturing, and information services grew by 23.3%, 8.1%, and 15.5%, respectively. Investment structure best reflects market expectations. The increasing "new economy content" in investments signals an acceleration in the replacement of old growth drivers with new ones. Investment in intellectual property products grew by 9.4%, indicating that enterprises are placing greater emphasis on R&D and innovation. This suggests that technological advancement is not simply about capacity expansion but about qualitative change driven by innovation. (II) Observing a Major Economy Requires Looking Beyond the Immediate Figures to the Long-term Trajectory. Behind the "new economy content" of the semi-annual report lies China's ongoing transformation from a global manufacturing hub to a global center of innovation. At the 17th Annual Meeting of the New Champions (Summer Davos), observers noted a new phenomenon: a host of unicorn companies are heading to China. They are establishing R&D centers, regional headquarters, and deeply integrating into China's innovation and industrial chains—shifting from "produced in China" to "created in China." So, why China? Economist Justin Yifu Lin, in his book *Demystifying the Chinese Economy, touched upon the theory of the "speed of technological change." He argues that the essence of the industrial revolution is not just the application of new technologies, but more fundamentally, the ever-accelerating pace of technological change. Since the mid-18th century, starting with the steam engine reshaping the textile industry, the snowball of technological change has grown, rapidly sweeping through industries like chemicals and automobiles, ultimately redrawing the geographical map of great power competition. Looking at China today, the trajectory of accelerating technological change is equally clear. A leading enterprise can drive an entire industry, which in turn can boost a whole region. These burgeoning industrial clusters, growing from saplings to forests, not only enhance production efficiency and invigorate market vitality but also effectively improve development quality and resilience. For example, specialized and sophisticated "little giant" enterprises above a designated scale in Beijing, through deep cultivation of innovation chains, supply chain collaboration, and international expansion, have become "connecting points" and "accelerators" for the dual circulation strategy. More importantly, emerging industrial clusters possess powerful spillover effects. The rapid rise of new energy vehicles is not only reshaping the automotive industry but also driving transformations in chips, software, and energy networks, allowing more sectors to gain value from efficiency improvements. The swift advancements in AI and biomedicine are sparking a "gentle qualitative change" in people's livelihoods, significantly enhancing the sense of fulfillment and well-being through smarter, more affordable products and more livable environments. (III) Looking from the First Half to the Full Year, China's Development Momentum Remains Positive. Of course, during this critical period of transitioning between old and new growth drivers, China's economy still faces lingering issues and new challenges. Some core areas are still grappling with "bottleneck" technologies, certain high-tech industries face external risks of "decoupling" and supply chain disruptions, and "involution"-style competition affects the new energy market ecosystem. However, most of these are issues arising from development and transition, and they can be addressed with effort. The supporting conditions and fundamental trends for long-term economic improvement remain unchanged. China's economic journey toward a newer, higher-quality model is itself a process of encountering new problems and solving them along the way. By maintaining confidence, proceeding steadily, and balancing both qualitative improvements and quantitative growth, China's industries are poised to be brimming with dynamism, and the Chinese economy will continue to advance steadily and sustainably. #China# #Jiangxi# #JiangxiEconomy# #世界经济看中国# #赣出新精彩#

国产办公软件金山 @WPS_Office 宣布支持 #Markdown，用户可以直接在# WPS 客户端或网页版中打开和编辑 MD 文件，可以实时渲染可视化格式。 例如用户可以屏幕左侧编写文件，右侧实时显示渲染出来的可视化格式，也可以将 MD 文件转换为其他文档格式方便进行协作。 查看详情：

打工人 学生党 生产力玩家必备的AI？！✅ Misa发现了一款微软亲自开源的神器markitdown，直接把 Word PDF Excel PPT 变成了AI友好的干净 Markdown～ 以前复制粘贴报告 合同 演示文稿到 ChatGPT或者Claude 里，排版全乱，表格丢掉，图片看不懂 现在全解决了！ markitdown 能把几乎所有办公文件转成结构清晰的 Markdown，让 AI 完美吃进去。 总结 提取数据 分析报告非常的快。 普通人 10 分钟上手，零基础也能用～ 而且GitHub 最近星标暴涨，Daily Work 系列都在推。 超详细新手教程！快来试试💚 第1步：准备环境（一次就好） 1确保电脑有 Python 3.10 或更高版本（没装的去 下载最新版，安装时勾选“Add to PATH”） 2打开终端/命令提示符： ◦Windows：按 Win + R 输入 cmd 回车 ◦Mac：Spotlight 搜 “Terminal” 3（推荐）创建虚拟环境（避免冲突）：
python -m venv markitdown-env 4markitdown-env\Scripts\activate # Windows 5# source markitdown-env/bin/activate # Mac/Linux 第2步：安装神器（一行命令） pip install 'markitdown[all]' （[all] 会自动装 PDF、Office 文件所有依赖，懒人首选） 第3步：一键转换文件（最常用方式） 把文件拖到桌面或记下来路径，然后在终端输入： # 示例1：把 PDF 转成 Markdown（输出到文件） markitdown "我的报告.pdf" -o 报告总结.md # 示例2：Word 文档 markitdown "合同.docx" -o 合同.md # 示例3：Excel 表格（表格会自动转成超级干净的 Markdown 表格！） markitdown "数据.xlsx" -o 数据.md # 示例4：PPT 演示文稿 markitdown "演示.pptx" -o 幻灯片.md 转换完直接打开 .md 文件，用记事本、Typora、VS Code 都能看，内容干净到可以直接丢给 AI！ 第4步：更懒的用法（不用输出文件） markitdown "报告.pdf" # 直接在终端显示内容，复制粘贴给 AI 就行 第5步：进阶玩法（想让 AI 帮你描述图片/幻灯片） 安装完后可以用 Python 简单脚本（复制下面代码保存为 运行）： from markitdown import MarkItDown md = MarkItDown() # 想让 AI 看图就加 LLM 参数 result = md.convert("带图片的报告.pdf") print(result.text_content) # 复制这个结果给 AI 立即冲 GitHub： #AI工具# #效率神器# #打工人# #Office# #生产力# #Markdown# #微软开源# #AI教程#

Microsoft Office 2021 (含 LTSC 长期服务版) 将在 10 月结束支持，结束支持后仍然能用，但将失去安全更新。微软始终建议用户包括企业用户升级 Microsoft 365 云订阅版，不过如果用户实在不愿意使用云版本也可以继续采用 Office 2024 系列买断版。查看详情：

据 Front Office Sports，Kalshi 联创兼 CEO Tarek Mansour 表示，他并不将 Polymarket 视为最主要竞争对手，反而更关注 CME、Robinhood 等平台。他称，Kalshi 拥有“一整套竞争对手”，并认为竞争有助于扩大预测市场整体规模。并表示，希望 Polymarket “进入受监管的框架”。他称，Polymarket 国际平台近期涉及内幕信息交易等争议，可能损害整个预测市场行业声誉。

悬吊耐受测试 Suspension tolerance test Model by @AC_ac003 Rope by @CK11230970 Office by @WANIMAL912 Photo by @whiteplace_1 #shibari# #kinbaku# #BDSMArt# #RopeBondage# #FetishPhotography#

微软正在用自研 MAI 模型替换 Excel 和 Outlook 中的 OpenAI 及 Anthropic 调用。 这一动作直接推动微软股价上涨了 0.5% 至 1.75%。过去几年，微软通过 Azure 与 OpenAI 深度绑定，将 GPT 系列嵌入 Microsoft 365 Copilot 等核心产品；现在，这种“外包”模式正在转向自研模型驱动。 对于开发者和企业用户而言，这意味着： 1. 算力成本结构发生变化：微软正试图降低对第三方模型的依赖，提升毛利空间。 2. 产品体验趋于闭环：MAI 模型与 Office 系列产品的原生集成，将带来更低的延迟和更高的安全性。 3. 生态格局重塑：Copilot 的底层逻辑正在从“接入第三方”转向“自研全栈”。