@satyanadella · 5.9M 粉丝 · 1.9M 阅 · 3.5K 赞 · 541 转
06/14 23:33
I’ve been thinking a lot about the future of the firm in an AI-driven economy. This transition is different than any previous platform shift. In the past, we used digital systems to enhance human
中文介绍 讨论 AI 经济下「公司」形态如何变化:这次平台迁移不同于以往数字化提效,重点不再只是增强人,而是重组组织、流程与生态。角度偏战略判断,强调没有生态支撑的前沿能力并不稳固。
@eng_khairallah1 · 67.2K 粉丝 · 1.2M 阅 · 510 赞 · 73 转
06/15 16:26
You read maybe two hundred articles this year. A few dozen papers. Hundreds of threads. Save this Every second-brain method ever sold to you, Zettelkasten, PARA, the graph view, the daily note,
中文介绍 一套用 Obsidian 搭建「自动运转第二大脑」的完整方法,针对一年读数百篇文章、论文与帖子后的信息失控,反思 Zettelkasten、PARA、日记页等常见套路,强调更可执行的知识整理工作流。
@sairahul1 · 116.6K 粉丝 · 905.5K 阅 · 503 赞 · 102 转
06/14 16:35
Everyone talks about LLMs. Nobody explains how they actually work under the hood. GPT. Claude. Gemini. Llama. They all come from the same 5-stage pipeline. And once you understand it, you can build
中文介绍 把 GPT、Claude、Gemini、Llama 拆成同一条「5 阶段流水线」来讲清底层原理,定位是从零理解并尝试自建 LLM 的入门教程。价值在于把常被神秘化的大模型流程压缩成可学习框架。
@BradGroux · 5.9K 粉丝 · 714.6K 阅 · 1.0K 赞 · 638 转
06/14 07:24
Most people still use coding agents like fancy autocomplete or a one-shot chat box. That leaves a lot of value on the table. The better pattern is to treat Codex like a durable operating loop:
中文介绍 不是把 Codex 当高级自动补全或一次性聊天,而是当成可持续运行的「操作循环」:核心是让代理长期接手执行、反馈与迭代。属于 coding agent 使用范式升级,强调工作流设计胜过单次提问。
@lqiao · 95.4K 粉丝 · 501.6K 阅 · 509 赞 · 86 转
06/15 14:14
Mythos got shut down this week. Whether you agreed with the decision or not is almost beside the point. A company built on top of intelligence it didn't control suddenly found itself exposed to
中文介绍 借 Mythos 被关停讨论「拥有智能」与「租用智能」的差别:若公司建立在自己无法控制的模型能力上,策略、成本与生存都会受制于人。角度偏商业基础设施,提醒 AI 创业的底层依赖风险。
@Fintech03 · 34.9K 粉丝 · 438.8K 阅 · 501 赞 · 137 转
06/15 00:41
The critique that Indian IT services (the classic TCS, Infosys, Wipro, HCL cohort) are failing India in the current generative AI race is an incredibly popular talking point. It is easy to look at the
中文介绍 反驳「印度 IT 在生成式 AI 时代掉队」的流行说法,认为 TCS、Infosys、Wipro、HCL 这类服务商本就不是为打造 ChatGPT 而生。重点在重新定义其角色与竞争维度,而非追逐基础模型叙事。
@mikenevermiss · 10.8K 粉丝 · 261.4K 阅 · 568 赞 · 67 转
06/15 14:03
stop making prompts. start designing loops. a prompt gets you one response. a loop gets you a system that keeps working after you close the laptop. Boris Cherny, who runs Claude Code at Anthropic, put
中文介绍 从「写 prompt」转向「设计循环」:单个 prompt 只产出一次回答,loop 才能形成关机后仍持续工作的系统。结合 Anthropic 的 Claude Code 负责人观点,重点讲可持续代理系统而非提示词技巧。
@AnthonyNAguirre · 4.2K 粉丝 · 232.1K 阅 · 527 赞 · 38 转
06/14 21:48
I've been getting really bothered lately by something subtle but quite dangerous that I believe is currently going on and promises to get worse: Large numbers of very smart, capable, and important
中文介绍 一篇偏批判性观察,讨论 AI 语境中一种「微妙却危险」的观念塑形:大量聪明且有影响力的人正在被某种叙事潜移默化地影响。不是工具教程,而是对行业认知、权力与话语方向的警惕。
@athcanft · 19.1K 粉丝 · 205.6K 阅 · 514 赞 · 23 转
06/15 21:37
I've been mass-producing TikTok slideshows with AI and scheduling them weeks in advance. Zero filming. Zero editing. Zero daily posting grind. This article breaks down the exact system, step-by-step,
中文介绍 拆解用 AI 批量生产 TikTok 幻灯片内容的低成本流程:号称每月约 600 美元、成本仅 2 美元,且可提前数周排程,无需拍摄、剪辑和日更。属于典型自动化变现案例,卖点是极简内容工厂。
@matanSF · 20.2K 粉丝 · 123.1K 阅 · 529 赞 · 60 转
06/16 01:46
In 2023, we launched Factory with the mission to bring autonomy to software engineering. While others were using models to speed up coding, we set out to deploy autonomous Droids across the
中文介绍 发布或阐释「Factory 2.0」方向:从 coding agents 走向「软件工厂」,不只加速写代码,而是让 autonomous Droids 在更完整的软件工程流程中协作。核心概念是把代理从助手升级为生产体系。
@jxmnop · 50.7K 粉丝 · 114.1K 阅 · 504 赞 · 57 转
06/16 07:43
So you want to do AI research? It's true that no one really teaches you how. Not directly, anyway. But it turns out that the way to get started is pretty simple: some combination of (i) reading and
中文介绍 面向想做 AI research 的人,讨论学校很少直接教的研究入门路径:核心不是神秘天赋,而是从阅读、动手与持续探索中建立研究习惯。更像方法论帖,价值在于把「如何开始」讲得去魅且具体。
@AndrewCurran_ · 53.9K 粉丝 · 62.8K 阅 · 569 赞 · 61 转
06/15 09:31
If you used Fable while it was available, you know it is special in ways that will not show up on benchmarks. I post benchmarks all the time because they matter to many people, but for a long time
中文介绍 围绕 Fable 下线后的感受,指出真正特别的模型体验往往无法体现在 benchmark 上。核心角度是反思行业过度依赖跑分与可量化指标,强调实际使用中的主观质量、交互手感与隐性能力。
@addyosmani · 400.1K 粉丝 · 52.9K 阅 · 522 赞 · 41 转
06/16 02:54
Coding agents are extraordinarily good now and getting better fast. The interesting consequence is that the hard part of engineering moved from writing code to deciding whether to trust it, which
中文介绍 随着 coding agents 能力快速提升,工程难点正从「写代码」转向「能否信任代码」,主题聚焦 agentic code review。价值在于把审查、验证与信任机制放到 AI 编程工作流的中心位置。
@unicity_labs · 125.6K 粉丝 · 47.6K 阅 · 534 赞 · 659 转
06/15 17:06
Programmatic agent usage moves onto a separate metered credit today. Here is what changes, and the controls we are building into AstridOS to manage it. What changed today Today, 15 June 2026,
中文介绍 解读 Claude 在 6 月 15 日将程序化 agent 使用拆分为独立计量额度后的影响:Agent builders 需要重新关注计费、用量控制与预算治理。并提到 AstridOS 正补充相应控制能力,偏产品运营实务。
@zaimiri · 54.4K 粉丝 · 46.9K 阅 · 508 赞 · 48 转
06/14 19:15
Most people try to build their AI setup in one chaotic weekend. They install ten tools. Connect five APIs. Create a few automations. Add a giant system prompt. Then wonder why the whole thing feels
中文介绍 提供一套「7 天搭建 Hermes」的渐进式 AI 配置方案,反对周末一次性装 10 个工具、接 5 个 API、塞巨型 system prompt 的混乱做法。重点是分阶段搭建,提升稳定性与可维护性。
@samueljmcd · 857 粉丝 · 42.2K 阅 · 511 赞 · 52 转
06/15 22:14
Loop engineering is the new label. The hard part is the one it has always been. Verification. Tip: You can copy and paste this article into Claude and ask for the best insights if you don't want to
中文介绍 讨论「Loop Engineering」热词背后的真正难点:问题并不新,核心始终是 verification。也就是代理循环能否被可靠检查、纠错与收敛。角度偏去泡沫,把注意力从命名转回验证机制本身。
@Dimillian · 51.8K 粉丝 · 38.2K 阅 · 558 赞 · 37 转
06/14 16:56
How to turn your phone into a Codex control center?? At first glance, it looks like a way to check on a Codex task from your phone. That is useful, but it misses the bigger idea. The power of Codex
中文介绍 讲如何把手机变成 Codex 的工程控制中心,不只是移动端查看任务状态,而是把手机当成远程调度、监控与指挥入口。核心价值在于扩展 Codex 的使用场景,让代理工作流脱离桌面持续运转。