现象与趋势

Cursor's new Composer 2.5 takes third on the Artificial Analysis Coding Agent Index and is ~10-60x lower cost than the higher-effort Opus 4.7 and GPT-5.5 variants above it. This release puts Composer among the leading coding agent models, something that wasn’t clear for past releases @cursor_ai has released Composer 2.5, the latest model in its Composer line. Composer 2.5 scored 62 on our Coding Agent Index, a 14 point gain over Composer 2 (48). This puts it in third place of our tested agents, behind only Claude Opus 4.7 (max) in Claude Code (66) and GPT-5.5 (xhigh reasoning) in Codex (65). These cost $4.10 and $4.82 per task respectively, ~10x the cost of Composer 2.5 Fast ($0.44) and ~60x the cost of Composer 2.5 standard ($0.07). Key results for Composer 2.5 in Cursor CLI: ➤ Cost-quality Pareto frontier: At $0.07 (standard) and $0.44 (Fast) per task, Composer 2.5 is cheaper than every other agent scoring above 60 on the Index. Medium-effort peers cost $1.24–$2.21 per task; higher-effort variants land 3-4 points above at $4.10–$4.82 ➤ Per-benchmark gains vs Composer 2: +35 points on SWE-Bench-Pro-Hard-AA (12% → 47%), +2 points on Terminal-Bench v2 (64% → 66%), and +3 points on SWE-Atlas-QnA (69% → 72%). At 47%, Composer 2.5's score on SWE-Bench-Pro-Hard-AA is comparable to Claude Opus 4.7 (max) in Claude Code ➤ Among the fastest coding agents: Composer 2.5 Fast runs at an average wall time of 6.7 minutes per task, the third-fastest agent on the Artificial Analysis Coding Agent Index, behind only Claude Opus 4.7 (medium) in Claude Code (5.8m) and GPT-5.5 (medium) in Cursor CLI (6.2m) ➤ Fast mode enables better responsiveness at 6x pricing: Fast runs 30% faster than standard Composer 2.5, but is ~6x the cost per task ($0.44 vs $0.07). Token pricing is 6x higher for Fast: $3.00/$15.00 vs $0.50/$2.50 per million input/output tokens Model details: ➤ Base model: Continued training on @Kimi_Moonshot's open weights Kimi K2.5 as with Composer 2, with Cursor reporting ~85% of total compute from its own additional training and reinforcement learning ➤ Pricing: $0.50/$2.50 per million input/output tokens for the standard variant; $3.00/$15.00 for the Fast variant (the default in Cursor) ➤ Available exclusively in Cursor: both Cursor IDE and Cursor CLI, an externally accessible API is not available Congratulations @cursor_ai and @mntruell on the impressive release!

LEAKED AUDIO: In an all-hands meeting on April 30, Mark Zuckerberg tells employees that he's training AI on them ahead of mass layoffs. "The AI models learn from watching really smart people do things... The average intelligence of the people who are at this company is significantly higher than the average set of people that you can get to do tasks. So if we're trying to teach the models coding, for example, then having people internally build tools or solve tasks that help teach the model how to code, we think is going to dramatically increase our model's coding ability faster than what others in the industry have the capability to do, who don't have thousands and thousands of extremely strong engineers at their company." Video

兄弟们！ 今天已经可以在ZenMux上免费体验Gemini 3.5 Flash 了！ 我第一时间用它跑了那个经典的「AI模型递归二叉树生长测试」. 同一个 Prompt ，不同模型画出的树形态完全不一样。（见视频-Prompt见评论区） Gemini 3.5 Flash 从输入提示词到生成完整 HTML 动画网页（树干慢慢长出、分支递归展开、最后随风摇摆），全程只用了 77.56 秒！ 整体效果非常惊艳：树形态自然优雅、生长动画丝滑、视频和内容呈现都顶级！ 熟悉的老朋友都知道，ZenMux 每次新模型都是 ZeroDelay 首发. Google I/O 2026 今天刚发布，现在立刻就能通过 API 调用！ 还有免费额度可以白嫖～ 速度是真的没话说，还完美保留了旗舰级模型的能力。 专为 Agent 设计，在 MCP Atlas、Toolathlon、Finance Agent 等多项榜单直接拿下第一！ 多模态理解也极强：MMMU-Pro 83.6%、CharXiv Reasoning 84.2%，全面超越上一代 Gemini 3.1 Pro。 完全兼容主流 API 格式，无需改动现有工具链。 支持按量计费 + Builder 套餐。 👇 直接体验 正式版 → zenmux.ai/google/gemini-3.5-… 免费试用 → zenmux.ai/google/gemini-3.5-… Video

Just off stage at #GoogleIO, some highlights from this morning 🧵 Gemini 3.5 Flash is available today for everyone in @antigravity and across our products and APIs. Compared to 3.1 Pro, 3.5 Flash is better across almost all benchmarks with huge progress in coding. It’s also comparable to the best models but very fast (4x faster tokens/ second than other frontier models). And when looking at the intelligence versus output speed, it’s in a league of its own in the top right quadrant.

Anthropic is acquiring @stainlessapi, an SDK and MCP server platform that has powered every Anthropic SDK since the earliest days of our API. Read more: anthropic.com/news/anthropic…

飞书 CLI 牛皮啊，发布一个月多点就达到 10000 Star 了！ 说明用户和市场相当认可这个动作 最近我们可以发现，越来越多的传统办公产品开始发布 CLI 和 Agent。 AI 时代的 SaaS 软件可能得换个做法了：UI 只是最基本的，接下来还要竞争对 Agent 的适配程度以及覆盖率。在这块，我觉得飞书走得相当靠前。 作为一个 IM 软件，飞书在 AI 时代去做这种开放自己所有能力的 CLI 工具，其实是一种非常不传统互联网的尝试。 这对于之前的互联网产品逻辑和经验来说，是一个非常不应该做的决定。 因为他们这个 CLI 几乎可以控制飞书的所有能力：你可以完全不跟飞书的传统 UI 去交互。只跟 CLI 交互，也可以完成飞书上所有的工作。 传统的 IM 办公软件通常非常复杂，入门门槛相对较高。无论从产品逻辑、UI 设计还是交互设计的角度来看，都没有办法太好地消解这种复杂性。 但是 CLI 工具交付给 Agent 以后，就可以快速消解这种复杂性。用户只需要进行对话，这是非常本能的行为，不需要在繁杂的层级列表 UI 里去寻找功能入口。 我拉了一下数据，他们迭代效率也非常恐怖，它们是 3 月 28 号开源的，一个多月发了 32 个版本、385 个提交。 这说明飞书对这块是非常重视的，投入的人力和精力也非常大。 他们在 CLI 本身的设计上也考虑得非常多，下了很多功夫。主要分为三层： 面向日常任务的快捷命令、开放平台对应的标准 API、兜底的 API 调用。 因为人和 Agent 都不喜欢从 2500 个 API 里去寻找参数，但又需要把这些能力暴露出来，所以他们采用了这种分层的形式。 即使做了分层设计，CLI 本身的内容和 API 依然非常多。所以他们把 CLI 作为工具本身，同时做了很多 Skills 用来充当 CLI 的说明书。 Agent 可以分层、分类型地了解应该如何调用这些 CLI 及其命令。 此外，他们在对 Agent 友好的命令包装上做了很多工作，例如： (a) 内置了 Dry Run (b) 结构化输出 (c) 身份选择、权限检查与风险等级评估 (d) 允许 Agent 在发消息前预览请求 (e) 建立了输出格式的“契约”：将成功或失败的结果、原因以及风险提示都放在结构化数据里。 这样如果出错了，AI 可以非常清楚地进行调试和修改，而不是盲目猜测。 其实现在你如果要创业或者做自己的 Agent，就不需要非得写一个界面。 飞书 CLI 加上 Agent 框架可以完成所有的 Agent 产品常见的操作： 你的聊天界面就是你的 Agent 聊天界面； 你的数据库就是飞书多维表格和文档； 你的用户就是把你拉到组织里的群成员；

“We did the hardest thing in the history of American health care. We got the majority of American doctors to all voluntarily adopt a single technology platform.” NBC News on how that happened, what U.S. physicians actually do with OpenEvidence, and how partnerships with NEJM, JAMA, NCCN, and Wiley make it possible.

Krishna Rao is the CFO of Anthropic, and this is his first podcast appearance. He joined the company two years ago when run-rate revenue was about $250M. Today it is $30B. He has helped raise ~$75B and is responsible for the procurement and allocation of compute. I feel lucky we get to hear what it is like to sit inside a company this consequential at a moment this pivotal. We discuss: - The cone of uncertainty - How he allocates compute across Trainium, TPUs, and GPUs - What investors misunderstand about model companies - Why the returns to frontier intelligence keep rising - Platform vs application and where Anthropic builds its own products - How Anthropic uses Claude internally I have asked my closing question about the kindest thing more than 500 times. Krishna's answer is one I have never heard before. Enjoy! Timestamps: 0:00 Intro 2:38 The Compute Canvas 6:51 The "Cone of Uncertainty" 11:58 Why the Returns to Frontier Intelligence Are So High 16:45 Recursive Self-Improvement 20:20 Scaling Laws 23:30 Sourcing $100 Billion in Compute 28:05 Platform vs. Application Strategy 32:52 Pricing Dynamics 38:48 How Anthropic’s Finance Team Uses Claude 43:24 Raising Capital & Overcoming Investor Skepticism 52:32 Public Perception, Risks, and Government Regulation 57:25 Mythos Release 1:12:33 What Could Derail the AI Revolution? 1:13:47 Biotech and Healthcare 1:15:31 The Kindest Thing Video

Starting June 15, paid Claude plans can claim a dedicated monthly credit for programmatic usage. The credit covers usage of: - Claude Agent SDK - claude -p - Claude Code GitHub Actions - Third-party apps built on the Agent SDK

The Google Threat Intelligence Group has detected the first known instance of a threat actor using an AI-developed zero-day exploit in the wild. While the attackers planned a wide-scale strike, our proactive counter-discovery may have prevented that from happening. This finding is part of our new report on AI-powered threats.

I hope you guys understand that this is going to keep getting worse