陆三金
400 posts


CHINA CONSIDERS RESTRICTING OVERSEAS ACCESS TO CUTTING-EDGE AI MODELS China’s Ministry of Commerce has led meetings over the past month with major AI companies, including Alibaba, ByteDance, and Z.ai, to discuss measures that would restrict overseas access to cutting-edge AI models, including models that have not yet been released. The discussions reportedly include not only closed-source models but also open-weight models. However, the scope of application is still under debate, and the rules may ultimately apply only to future frontier models. Officials have also discussed designating the leakage or theft of proprietary AI technologies as a national security crime, with stronger penalties, as well as restricting the types of foreign capital that can invest in Chinese AI startups. The backdrop is the U.S. move to strengthen export controls on AI models, along with national security concerns over cutting-edge models that could possess advanced cyberattack capabilities. Chinese authorities are reportedly concerned that advanced U.S. cybersecurity AI models could be used to exploit vulnerabilities in Chinese software. Since the beginning of this year, China has continued to tighten measures to prevent AI technology from being transferred overseas. Authorities have investigated whether Chinese AI startups that relocated abroad violated export control laws, while also strengthening oversight of overseas transactions involving Chinese investors, technology, data, and national security concerns. Future regulations could take the form of a tiered framework based on technological capability. Basic open-source AI models may be managed through a filing system, high-performance models may be subject to security reviews, and the most sensitive frontier models may be banned from public release or restricted to use within China.





Here is the prompt method behind this AR try-on app. The trick is not a magic prompt. It is the architecture of the prompt, and it works across GLM-5.2 and other frontier models. Full prompt: chat.z.ai/space/k148m5py… The prompt has two parts. First, a task description. You write this fresh for each app to define the business logic. Second, a five-round polish process: Round 1 through Round 5. The structure is fixed and reusable across any app, but the specific content of each Round is tailored to the app at hand. The flow is simple. The task description builds the functional skeleton, then the five Rounds run in sequence to refine it into something that looks like a finished product. Why split it this way? Single-pass generation always prioritizes "it runs" over "it looks good." So it's better not to chase one perfect prompt. Divide the work. The business description makes it function. The five Rounds make it look like a real product. The polish is a reusable pipeline, not something you reinvent every time, even though you fill in app-specific details each time. How to write the task description: Treat it as a real PRD and engineering spec, not a user wishlist. Include the tech stack, information architecture, module specs, API integration, data model, and acceptance criteria. Declare autonomy at the top. State that the model should not ask questions, not stop early, and verify its own work. Otherwise it will pause to ask and break the long task. Write the fallback paths explicitly. Cover unsupported devices, older OS versions, and offline states. If you skip this, the model improvises at the edges and crashes. Number your acceptance criteria. Each should be independently verifiable, for example "tap a product and the look changes within 0.5 seconds." The principles behind the five Rounds: Quantify "good" into numbers. Models execute poorly on adjectives and precisely on constraints. Use spring response 0.3 to 0.4, button scale 0.95 to 1.0, at most 5 font sizes, and sound effects under 200ms. These principles stay constant, even as the exact targets shift per app, which is why the structure can stay fixed. List what is forbidden. Models cut corners in predictable ways, such as gray placeholders, solid color blocks, and spinners. Name them directly with "DO NOT" and provide an acceptable fallback. Inventory before fixing. Each Round follows the same loop: audit every asset, verify it is not a placeholder, replace, amplify, and re-screenshot to confirm. Strip the "tutorial" feel. AI output gives itself away with faker text, .test links, and emoji-only empty states. The final Round removes these.

小西天,看着像视频,但其实是我们在现场实地拍摄 3,811 张 206 GB 的照片后建模的。FUNES 把《黑神话:悟空》里「既见未来,为何不拜」满天神佛的原型,来自自山西临汾隰县的小西天,做成了一个可漫游的 3DGS 数字存档。 完全实地拍摄,每天清晨一开门就冲上山去,趁着没人的时候拍。然后通过 Gaussian Splatting 重建,没有手工建模,尽量保留真实悬塑和圆塑的极其密集的金色空间、细节和光感。不同的材质在这里交织成了无法分辨的一个天国世界。这种半空中的小塑像是「悬塑」,它们大多出现在十六世纪到十七世纪。 在现场如果要看清小西天的所有细节,我想大概需要三天时间。但是有了模型,我们可以在屏幕前慢慢看。在相当长的时间里,学术界并没有特别重视小西天这样的悬塑——因为在只有学术图录的年代,平面印刷无法展示出悬塑的震撼。而随着技术的进步,我们终于可以在远方一窥明代悬塑的璀璨。 重轻特意为这个模型做了配乐,大家可以打开慢慢欣赏。 推荐电脑访问:funes.world/apps/the-hangi…




其实只要 prompt 里的视觉逻辑对了,image2 很容易做出“反差感”。 这次我试的是: 严肃企业年报 × 儿童蜡笔涂鸦 外层是极其专业的年报版式: 留白、网格、标题层级、KPI、柱状图、增长曲线。 内核却全部换成儿童蜡笔。 关键不是“画得像小孩”, 而是让资本秩序的冷静排版和童真涂鸦的失控内容发生冲突。 画面一旦同时成立: 专业版式 + 幼稚内容 + 克制色彩 就会从普通趣味图,变成很有概念感的视觉实验。

看完了张小珺对罗福利和姚舜宇两个人的采访,特别有感触,很有意思。 我感觉罗福利的逻辑完全不在线,看不出来是能够带领大模型往前发展的人才。 但姚舜宇一看就是个非常聪明的人,而且也非常务实。虽然他工作经验才两年,但我觉得他对大模型的理解比罗福利要强很多。 所以说,这是一个很奇怪、有意思的现象。 过几年再回来看看吧





