与「Cloud」相关的搜索结果

Cloudflare 开源了 Kitesurf，一个专门为 AI Agent 设计的浏览器 这个项目完全不依赖 Chromium，也不需要启动传统的浏览器容器，直接运行在 Cloudflare Workers 上 它支持 HTML 解析、JavaScript 执行、页面截图，甚至兼容部分 CDP 接口，可以接入 Puppeteer Cloudflare 的思路很简单，AI Agent 需要的是读取和操作网页，很多时候根本没必要启动一个完整的 Chrome 但是有一个很有意思的地方 我们平时访问网站，应该经常会遇到 Cloudflare 的人机验证吧？ 现在，Cloudflare 自己开始给 Agent 造浏览器了，这不是盾和矛的关系嘛？ 其实，Kitesurf 的官方文档明确提到，目前无法完成某些依赖真实 TLS 指纹的机器人验证 也就是说，Cloudflare 自己开发的 AI 浏览器，也可能被自家的人机验证拦在门外 Cloudflare 并没有给自己的浏览器开后门，它已经提供了针对 AI Agent 的访问策略，网站可以自己决定允许哪些 Agent 访问 Cloudflare 现在还在推动 Web Bot Auth，让 Agent 可以证明自己的身份，而不必再假装自己是一个真人 以后不仅人要证明自己不是 Bot，Bot 也得开始证明自己是谁了 😂 项目开源地址：

Cloudflare 把给 AI 智能体用的浏览器开源了，叫 Kitesurf 它不跑 Chromium，直接跑在 Cloudflare Workers 上，解析 HTML、跑页面 JS、截屏都能干，还支持 Chrome DevTools 协议，Puppeteer 接上就能用 它把标签页、主题、扩展这些给人用的东西全砍了，只盯智能体关心的指标，截图、提 HTML 这类活儿 CPU 和内存都比 Chromium 省得多，并发能开更多会话 仓库是 cloudflare/kitesurf，Apache 2.0 协议，刚开源就已经 837 个 star 本地跑也不麻烦，pnpm run dev 就能起，跑在 8787 端口；不过部署到 Cloudflare 上要 Workers 付费套餐

Elon Musk just explained how agentic AI traffic will be 1,000 times human internet traffic within 5 years. He says that, per Cloudflare, agent traffic has already passed human traffic, and he thinks even the 1,000x forecast is too low. ---- Full video on "Baron Capital" YouTube channel, (link in comment)

Cloudflare 开发的实验性浏览器引擎 在没有 Chrome、没有浏览器容器的情况下解析 HTML、应用 CSS、执行页面 JavaScript 并输出截图，把整条渲染链放进 Cloudflare Workers。

AI Engineer Code Summit 2026 @aiDotEngineer 连续两天会议，合计超过 18 小时、31 场主题演讲。OpenAI、Anthropic、Google DeepMind、TypeSafe、Cloudflare、Cognition、Lovable、Ramp、Amp 等团队都带来了干货分享。咱们展开看看都讲了什么、对咱们有哪些启发？！

I am actually disgusted by the timing of this drop. And this will get a bit more technical. Let’s do a real technical teardown of this "1,385-sigma Blackwell weight fingerprint" report that OpenAI and Anthropic are breathlessly promoting today, because banking on nobody reading past the headline is how DC narratives get manufactured. ​First, look at the actual CUDA maths: when PyTorch initialises model weights (torch.nn.init) with a fixed PRNG seed, curand/Philox parallelises random number generation across the GPU’s Streaming Multiprocessors (SMs). Because Hopper has 132 SMs and Blackwell has 148 or 152 SMs, the exact same seed lays out initial weights in a different spatial pattern depending on the GPU’s SM count. In huge MoE models, certain low-gradient null-spaces or unrouted slices preserve faint directional traces of those step-0 weights even after training. ​So yes, brute-forcing the seed and SM grid can show that a model’s step_0 weight initialisation script ran on a 148/152-SM Blackwell GPU. Now read what the summary explicitly admits in the fine print: 1. Initialisation is not full training: The report literally states that these signatures show how the weights were created, and "support (but do not prove) the inference that Blackwell also participated in model training". Initialising a model checkpoint takes seconds on a single rented dev node before loading the safetensors onto a standard Hopper cluster. The "2,000 to 3,000 Blackwell GPUs" and "$500 million" cluster numbers do not come from the weight fingerprint and Depue admits in his own thread that he simply "estimated" cluster sizes from release schedules and model details. ​2. Zero geolocation and zero proof of smuggling: The final sentence of the executive summary explicitly admits that the findings "do not establish the physical location of the chips, how they were acquired, or whether export control regulations were violated". Depue himself rented a cloud GB200 instance just to run his verification test. Commercial labs that are not on the Entity List renting bare-metal Blackwell nodes in offshore clouds (Singapore, Japan, or the Middle East) is completely legal under BIS rules and leaves the exact same SM fingerprint. Now ask yourself why a former OpenAI researcher dropped this on November 12, the exact day Jensen Huang was pushed out, with OpenAI and Anthropic immediately stepping in to "independently verify" it. US closed-source labs are watching their paid API moats get eaten alive by free Chinese open-weights models. Conflating a step-0 PRNG initialisation seed with a "$500M smuggled Blackwell cluster" hands DC hawks the exact pretext OpenAI and Anthropic want to regulate global cloud compute and kneecap open-source AI.

Most of us open Google Sheets for budgets, trackers, and tables. With Gemini 4 Hydrogen, we wanted to see if it could paint a moving landscape instead 🍃 In Google @Antigravity, Hydrogen handled the coding to paint and animate thousands of cells — creating coastal trains, drifting clouds, and falling autumn leaves that recalculate frame by frame inside the spreadsheet.

Introducing Edge0-145B and Edge0-120B, @Edge0_AI’s first device-native 100B-class foundation models with open weights. Cloud-level intelligence on your laptop. Free, offline, and fast enough for real vibe coding. - Smart: beats Claude Opus 4.6 on Terminal-Bench 2.1 (86.5% vs. 78.2%) - Light: needs just 29G memory, 1/3 of a standard setup (100G+) - Fast: decodes 75+ toks/s, prefills 1050+ toks/s on RTX Spark and Mac. - Steady: handles 128K–256K tokens of context without slowing down, even on long tasks Frontier-class agentic coding. No cloud API. No token bill. Your code and data never leave your machine. Open weights are live on @huggingface. Run it locally or plug it into your favorite coding agent.

Self-hosted Claude Managed Agents is now in private beta. You can host the agent harness, sandboxes, and session history, in your own cloud or on-premises, and choose where requests to Claude are sent.

Today, we're introducing OpenOrange. Run Cloudflare Workers, Durable Objects, Workflows, etc on any cloud. 🍊 No-downtime migrations, no code changes 🍊 4.8× cheaper compute, 8.7× cheaper storage 🍊 No runtime limits, just Node/Bun 🍊 AWS, GCP, Azure, Rivet Cloud, your servers