7月17日2026 · 星期五

@trq212 转推了

@1Password

AI agents are booking travel, signing into websites, and acting on your behalf. That creates a new security problem: until now, letting an agent log in meant exposing your credentials to the model. Today, with @AnthropicAI, we're changing that. 1Password for @claudeai lets Claude use your stored credentials to complete real-world tasks without your passwords or one-time codes ever reaching the model, its memory, or Anthropic's systems. You stay in control and approve which credentials an agent can use. 1Password handles authentication behind the scenes. Available now on Mac for business, family, and individual customers. https://bit.ly/4bLP5EJ

@trq212 转推了

@Pluvio9yte

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or @alayastd. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: http://github.com/AlayaLab/AlayaWorld Technical Report: https://arxiv.org/abs/2607.06291

@thsottiaux

On file deletions. We’ve investigated a handful of reports where GPT-5.6 unexpectedly deleted files. What we have found is that this most commonly occurs when: - Full access mode is enabled and codex is run without sandboxing protections, including without auto review being enabled - The model attempts to override the $HOME env var to define a temporary directory. - The model makes an honest mistake and mistakenly deletes $HOME instead. This is of course not how we want the system to behave, even when a user operates the model in full-access mode without the safeguards of our sandbox or without using auto review which checks for these kinds of high risk actions and rejects them. We are taking steps to mitigate this risk including by updating the developer message, guiding more users towards safer permission modes, and adding additional harness safeguards. Even though this happens extremely rarely, we’ll share a detailed post-mortem in the coming days that goes into more details and what we are doing to minimize risks further.

@Pluvio9yte 转推了

@Jason_Young1231

问题确认存在，Codex 应用在使用第三方 API 的时候会默认禁用 websearch 和绘图功能，前者对模型能力影响比较大，除了原帖中的解决方案以外，经过测试 CC Switch - Codex 应用增强中的“保留官方登录”功能也可以解决这个问题，同时保留手机远程操作和官方插件等能力

@1999_eth

惊现！新版 Codex 客户端，竟然在偷偷给第三方中转站“降智”。 7 月 11 日，我就发现新版 Codex 无法生图，并且第一时间公开了完整解决方案。 本以为这只是一个生图功能限制，结果继续逆向分析后，我发现事情远没有这么简单。 新版 Codex 一旦检测到你使用的是第三方 Provider，就会直接限制两项重要能力： 1、图片生成 2、Web Search 生图被限制，大家很容易发现。 但 Web Search 被悄悄关闭，才是真正影响模型能力的地方。 很多人可能不知道 Web Search 有多重要。 大模型的内部知识库都有截止日期，它并不知道今天刚刚发生了什么，也无法凭空获取最新的文档、新闻和技术变化。 所以模型厂商会给模型外挂一个实时联网搜索能力，这就是 Web Search。 它相当于模型的“眼睛”。 没有 Web Search，模型只能依赖旧知识回答问题； 有了 Web Search，它才能实时查询全网信息，验证事实，读取最新文档。 但现在，只要你在新版 Codex 中使用第三方中转站，哪怕后端接入的是完全相同的正版模型，客户端也可能直接把 Web Search 能力屏蔽掉。 模型没变。 API 没变。 只是因为 Provider 不是官方的，客户端就主动砍掉了能力。 这才是真正意义上的“客户端降智”。 好消息是，经过逆向分析，我已经找到了绕过限制、恢复生图和 Web Search 的方法。 详细修改教程，我已经发在前一条推文里： https://x.com/1999_eth/status/2075949033215148245 正在使用 Codex 第三方中转站的人，建议马上检查一下。 你以为自己用的是同一个模型，实际上客户端可能早就偷偷阉割了它。 大家可以看一下附图修改前后的对比：

@trq212 转推了

@bcherny

I talk to engineers at other companies every day and hear the same thing: one person is 10x'ing their output with Claude but the rest of the org hasn't caught up. Watching teams adopt AI, I keep seeing the same 4 steps. I mapped them out here: Steps of AI Adoption https://claude.ai/code/artifact/bfdfaef9-bc62-4dfe-ba9e-c58a26c9accf

Steps of AI Adoptionclaude.ai · 直连原文

@Pluvio9yte

突发：Anthropic 可能将在 Kimi K3 发布后再次延长 Fable 5 付费计划访问权限。 同时看到了kimi前端能力好像有点强的离谱了，这次真不得不充值试试了。

@OpenAI 转推了

@thsottiaux

Evening! We’ve gotten lots of great feedback on the new ChatGPT desktop app (which we didn't get totally quite right on the first try), and as a result, we've made some changes. 1/ ChatGPT conversation history and projects are now visible in the sidebar. Also, your Chat and Work history now sync across web, mobile, and desktop. Local tasks still stay on your computer. 2/ You can now easily switch between Chat and Work modes inside ChatGPT on desktop, which is now also consistent with how it shows on web and mobile. 3/ Nothing is changing for users on Codex mode. It's still the OG and best at what it does. And overall we're continuing to fix paper cuts and improve performance, reliability, and efficiency. Keep up the feedback, hope you like the updates!

@OpenAI串推 2 条

@Pluvio9yte

发现grillme这个skill争议很大啊 我的看法是哪怕问50个问题只要有1个问题是命中了我之前没有考虑到的，那就是有意义。 规划阶段多花20分钟完善方案比代码完成之后多花60分钟改模块效率要高的多