做交互做视频时，跟 AI Agent 描述半天动画效果也说不明白？-- 1 分钟认识 13 类常见 Motion 动效名称 “从右边快速滑入，停下时稍微回弹一下……” 为了让 Agent 做出想要的动画，你是否写了很长一段描述，结果它还是理解错了？ 很多时候，不是描述得不够详细，而是缺少双方都能准确理解的动效名称。与其反复解释动画长什么样，不如直接说出它的名字。 这段不到 1 分钟的视频整理并演示了 13 类常见 Motion 动画： 1. 页面与视图切换：Push Slide 2. 元素进入与退出：Staggered Entrance 3. 状态变化：State Morph 4. 操作反馈：Press + Ripple 5. 滚动动画：Scroll-driven Transform 6. 导航动画：Sliding Indicator + Drawer 7. 加载与等待：Spinner + Shimmer 8. 数据变化：Bar Growth 9. 布局重排：FLIP Reorder 10. 手势与物理运动：Drag + Spring 11. 引导与提示：Pulse + Popover 12. 品牌与装饰动画：Orbit + Assemble 13. 3D 与空间运动：3D Card Flip 每个片段都包含醒目的中英文名称和直观示例，帮助你快速理解动画的视觉表现与适用方向。 知道名字之后，你不仅能更快找到参考，也能更准确地向 Agent、设计师或开发者表达需求。少写一大段模糊描述，用一个明确的名称，让所有人更快理解你想要的动画。

Karpathy 发布了一个github开源项目，狠狠让我惊艳到了 这个项目叫 andrej-karpathy-skills,GitHub 13 万+ star,我愿称之为2026 最有用的 AI 工程项目 它解决的问题极其精准：让 Claude Code 不再瞎写代码 这个项目到底有多厉害？ 先说结论：一个 4KB 的文本文件，让 AI 写代码的错误率暴降 90% Karpathy 自己说，他现在 80% 的代码都让 Claude 写，但 AI 经常犯几个典型错误： 不问就瞎猜需求 过度设计，写一堆用不上的抽象 改 A 顺手把 B、C、D 也重构了 代码能跑就行，不管目标达成没有 这个项目就是专门给 Claude Code 戴上guardrails，用 4 条行为准则约束 AI 的编码行为 核心亮点：4 条准则改变一切 整个项目就是一个 CLAUDE.md 文件，里面只有 4 条规则，但每一条都直击 AI 编码的痛点 1. Think Before Coding - 先思考再动手 AI 最大的问题是“太听话”，你说啥它就写啥，从不质疑 这条准则要求：明确说明假设、权衡 tradeoffs，不确定就直接问 不再是“我猜你想要这个”，而是“我理解你的需求是 A，但 B 方案可能更合适，你要哪个？” 2. Simplicity First - 极简实现优先 AI 天生爱炫技，你要一个登录功能，它给你写个完整的 OAuth 2.0 + JWT + 刷新令牌 + 权限系统 这条准则强制：只写刚好能解决当前问题的最小代码 不搞 speculative abstractions，不写未来功能，不过度工程 一个用户反馈：用了这条规则后，代码 diff 从动辄几百行缩减到几十行，review 轻松太多 3. Surgical Changes - 手术式精准修改 这是我最爱的一条 AI 有个恶习：你让它改个 bug，它顺手把整个文件的命名风格、缩进、注释全优化了 这条准则要求：只改用户要求的部分，严格匹配原有代码风格 不碰无关文件，不顺手 refactor，不加“看起来更好”的改动 有开发者实测：启用这条后，git diff 从“满屏红绿”变成“3 行精准修改” 4. Goal-Driven Execution - 目标驱动执行 AI 经常写完代码就交差，但代码能跑 ≠ 任务完成 这条准则要求：把任务转化为可验证的目标/测试/成功标准，然后 loop 执行、验证、迭代 直到真正达成目标才停止 这让 AI 从“代码生成器”变成“问题解决者” 真实效果：社区反馈炸裂 X 上这个项目刷屏了，开发者反馈高度一致： 代码质量飞跃：diff 更紧凑、干净，overbuild 和 side changes 大幅减少 错误率暴降：有人实测从 41% 掉到 11%，继续优化后低至 3% 中文社区评价：“必备 skills”“Claude/Cursor 实用技能 Top1”“直接扔项目里就完事了” 很多人直接 @ 朋友推荐：“把这个 CLAUDE.md 放进去，Claude 立刻像换了个人，写代码更靠谱、不乱改、不瞎猜“ 使用方式：简单到离谱 这是我见过最简单的 AI 工程优化方案： 方法一：直接 curl 把 CLAUDE.md 下载到项目根目录 curl -o CLAUDE.md 方法二：用 Claude Code / Cursor 的 plugin 一键安装 支持 Claude Code、Cursor 等主流 AI coding 工具 完全开源（MIT 协议），拿来就用 作为产品经理出身的开发者，我看到的不只是 4 条规则，而是对 AI 编码行为的深刻洞察 Karpathy 做的事情本质上是：给 AI 建立编码的第一性原理，他把他对于AI编程的理解写入了文件中 不是教 AI 怎么写代码（它已经会了），而是教 AI 什么时候该问、什么时候该停、什么时候该简化 这 4 条准则就像产品经理给开发团队定的 PRD 原则： 需求不清楚？ 先问 功能够用就行？ 别过度设计 改需求？ 只改需求相关的 做完了？ 先验证目标达成没有 它能帮到我们什么？ 如果你是独立开发者或小团队，这个项目能直接提升你的 AI 协作效率： 减少返工：AI 不再瞎猜需求，写出来的代码更符合预期 降低 review 成本：改动精准，不用在一堆无关修改里找真正的变更 提升代码质量：极简实现意味着更少的 bug、更好的可维护性 加速迭代：目标驱动让 AI 真正解决问题，而不是生成代码 对于中大型项目，这是让 Claude Code 真正“生产可用”的关键一步 我的使用体会 我在自己的几个项目里部署了这个 CLAUDE.md，最直观的感受是： AI 变聪明了 以前它是个听话的实习生，你说啥它做啥，经常做错 现在它像个有经验的同事，会主动问“你确定要这么做吗？ 我有个更简单的方案” 代码 diff 变干净了 以前一个小需求能改几十个文件，现在精准到只改 3-5 行 我的工作重心变了 以前 60% 时间在 review AI 的代码、修 bug 现在 80% 时间在思考产品逻辑，AI 真正成了生产力工具 最后 这个项目被誉为 2026 年 AI coding 领域的“现象级”黑魔法工具 小文件，大作用 如果你在用 Claude Code / Cursor 写代码，强烈建议直接把这个 CLAUDE.md 扔进项目根目录 GitHub 地址： 试过的人基本都是“已全项目部署”的状态 作为一个天天和 AI 协作的开发者，我的建议是：别犹豫，直接用

#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#

虽然越来越多的代码由 AI 生成，但一个好的程序员仍然需要对计算机底层有所理解，程序员能力的差距往往也体现在这里。 1. 故障排查 线上 P99 偶发毛刺，到底是 GC、缺页中断、NUMA 跨节点访问，还是容器 CFS quota 被限流？多线程偶现的脏数据，是锁写漏了，还是内存序没对齐？这类问题不懂底层就只能靠猜，或者重启大法。 2. 性能优化 现实中八成以上的性能问题在算法、IO、数据库和网络层，轮不到底层出场。 但剩下小部分就是不同程序员拉开差距的地方——CPU 侧是缓存局部性、分支预测、指令级并行；GPU 侧则是另一套模型：合并访存、warp divergence、shared memory bank conflict。 3. 代码审查 AI 写出来的代码往往「看着对」，但藏着数据竞争、意外的内存拷贝、O(n²) 的隐式行为。看不出来，你就只能全盘接受 AI 的输出。 现代编译器已经非常高级、非常复杂，作为写代码的程序员，我们完全不需要懂编译器的底层细节。 但如果需要做性能优化，理解一些基本的编译原理也是非常有帮助的，例如： (a) switch 是否永远比 if-else 性能更好？ (b) 函数调用的开销到底有多少？ (c) 为什么遍历二维数组时，行优先比列优先快几倍？ (d) 指针是否比数组 index 性能更好？ (e) 为什么循环里用本地变量累加，比用 argument passed by reference 强得多？ (f) 为什么调整运算的结合方式，就能提高一个函数的性能？ （g）为什么 if 分支的顺序会影响性能？ 当 AI 编程普及、写代码不再是门槛的时候，对计算机底层的理解——能写出更高性能的代码，能解决更底层的 bug，能做更底层的性能优化——就更加重要了。

D奶人妻母狗，奉主人命令侍奉黑人大屌 先被强行按头深喉口交，操得她不断干呕、眼泪直流 随后骚穴被暴操，从跪操干到床上窒息掐脖抬腿猛干、奶子狂甩 操完，主人冷令一下，母狗人妻立刻乖乖跪趴在地上，亲吻黑人大脚，以表感谢 完整版联系机器人24小时自助下单进超级资源群

D奶人妻母狗，奉主人命令侍奉黑人大屌 先被强行按头深喉口交，操得她不断干呕、眼泪直流 随后骚穴被暴操，从跪操干到床上窒息掐脖抬腿猛干、奶子狂甩 操完，主人冷令一下，母狗人妻立刻乖乖跪趴在地上，亲吻黑人大脚，以表感谢 完整版联系机器人24小时自助下单进超级资源群