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Vibe知识大赏丨如何更有野心的使用 AI

陈与小金 · 2026-10-22 · 赞 2,576 · 收藏 2,320 · 评论 72 · 读取于 2026-10-23

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要点（每条带原文引文）

  • Anthropic Claude工程负责人Felix Rieseberg将使用AI的“野心”拆解为四个维度：速度、高度、深度和广度。 — ✓ “野心拆成了四个方向:速度,高度,深度,广度” (0:15)
  • 速度指利用AI减少体力劳动时间，如写代码或打字更快，这是基础应用，视频建议跳过此部分直接关注后三者。 — ✗ “这是大模型挣钱的家常便饭...我们今天也跳过”
  • 高度要求用户从执行具体任务转向追求终极目标，例如不问“怎么修报错”，而是问“如何保证APP永远不崩”。 — ✗ “保证我们的APP永远不崩才是一个结果”
  • 提升高度的关键在于给AI开放权限和工具环境（如连接日志、代码库），使其能独立复现问题并闭环解决。 — ✓ “人必须先从具体循环里退出来,把工具和环境的门给它打开” (3:57)
  • 深度要求AI使用行业顶尖专家的工具、方法和原始数据，产出连专家都能学到新东西的成果，而非仅仅模仿语气。 — ✓ “逼他使用这个行业最顶尖的专家才会用的工具” (5:10)
  • 实现深度的五招包括：强制使用工业级工具、设定离谱但合理的高目标、对抗性验证常识、挖掘盲区产生新知识、引入外部裁判自评。 — ✓ “找外部裁判,绝不让他自评自审” (6:37)
  • 视频提供了一个改写提示词的模板，要求AI在动手前先回答四个问题：顶尖专家会做什么、用什么原始材料、什么专业方法、什么外部标准证明结果正确。 — ✓ “我要这个任务更深的版本,不是更大的版本” (7:50)
  • 广度指跨行业借用思路，通过把名词转化为动词（如将PPT转化为“让高管做决定”）来打开AI的思路联想。 — ✗ “它的动词是,让一群注意力分散的高管...拍板做决定”
  • 构建跨行业团队时，应让AI像特定角色那样去做实事（如做空者去扒财务疑点），而不是进行空洞的角色扮演。 — ✓ “让他去扒财务疑点,去交叉比对数据” (9:43)
  • 心理学中的“自我效能”限制了人类对可能性的认知，明确的高难度目标能显著提升表现。 — ✓ “被定下明确高难度目标的人,表现会比只被告知尽力而为的人更好” (10:27)
  • 打破自我效能限制的三个窍门是：相信不可能之事也能做成、观察他人做到过、以及保持轻松心态别太用力。 — ✓ “允许自己随便试试,别太用力” (11:21)
  • 这套方法适用于追求明确结果的场景，对艺术创作效果有限，但能帮助创作者克服不敢向AI索要结果的障碍。 — ✓ “卡住你的,从来不是你的灵感,而是你敢不敢开口向他要” (12:06)

要点由 LinkDigest 的模型写出，引文逐字核对过：✓ 在原文里找到了，✗ 没找到。

画面文字（前 10 行）

  • 0:00 Felix Rieseberg @felixrieseberg Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible. We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial: How to be more ambitious: A tutorial for humans with models 来自 felixrieseberg.com 下午11:02 · 2026年10月13日 · 515万 查看 Anthropic 做 Claude 的
  • 0:19 Felix I work on Claude. It's an interesting experience: Every day I learn about yet another feat accomplished with or by Claude that I hadn't considered, didn't think possible, or simply lacked the curiosity and time to try. This month, it may have helped discover a new planet, I've seen the first AI music video that I really like, and we are discussing a new era of math. Model capabilities don't grow uniformly. Models are, as Terence Tao puts it, "astounding in some directions, while inadequate
  • 0:39 AI 音乐 MV I work on Claude. It's an interesting experience: Every day I learn about yet another feat accomplished with or by Claude that I hadn't considered, didn't think possible, or simply lacked the curiosity and time to try. This month, it may have helped discover a new planet, I've seen the first AI music video that I really like, and we are discussing a new era of math. Model capabilities don't grow uniformly. Models are, as Terence Tao puts it, "astounding in some directions, while inadequ
  • 0:58 学 1.0 2.0 I work on Claude. It's an interesting experience: Every day I learn about yet another feat accomplished with or by Claude that I hadn't considered, didn't think possible, or simply lacked the curiosity and time to try. This month, it may have helped discover a new planet, I've seen the first AI music video that I really like, and we are discussing a Model capabilities don't grow uniformly. Models are, as Terence Tao puts it, "astounding in some directions, while inadequate in others".
  • 1:18 products I've built. Many others are still struggling to get their new wings to work, wondering what they're good for beyond asking "What is the capital of France" or "Can you make this email sound less angry". I frequently proclaim (together with other people working on AI) that we ought to be more ambitious when using it. It's become a rallying cry. Whenever we see something impressive, we repost and write "we're not ambitious enough". I understand how tricky it is to follow that advice, so he
  • 1:37 rather than operating at the task's layer, we go one level deeper and touch the underlying data, tools, and problems. • Range: Drawing on multiple fields rather than just the one the task might need at the surface, using knowledge, tools, and techniques you normally wouldn't. I primarily use Claude (duh) but my advice is not model-specific. Altitude We often flippantly call this "letting the model operate at a higher abstraction". When you give the model a task, it's executing your best guess at
  • 1:57 在给 AI 发指令前 / We often flippantly call this “letting the model operate at a higher abstraction”. When you give the model a task, it’s executing your best guess at how to get the outcome you want. To be more ambitious in altitude, ask yourself — what do you want to happen next? What’s the goal beyond the goal? At the beginning of last year, I still asked Claude to fix individual crashes for me. Eventually I asked Claude to simply keep our apps crash-free. / Let’s consider an everyday (dare I say m
  • 2:17 “保证我们的 App 永远不崩” / App / We often flippantly call this “letting the model operate at a higher abstraction”. When you give the model a task, it’s executing your best guess at how to get the outcome you want. To be more ambitious in altitude, ask yourself — what do you want to happen next? What’s the goal beyond the goal? At the beginning of last year, I still asked Claude to fix individual crashes for me. Eventually I asked Claude to simply keep our apps crash-free. / Let’s consider an everyday (
  • 2:36 填模板 / 季度业务回顾 / AI 确实能给你排好版 / Altitude / We often flippantly call this “letting the model operate at a higher abstraction”. When you give the model a task, it’s executing your best guess at how to get the outcome you want. To be more ambitious in altitude, ask yourself — what do you want to happen next? What’s the goal beyond the goal? At the beginning of last year, I still asked Claude to fix individual crashes for me. Eventually I asked Claude to simply / Let’s consider an everyday (dare I say
  • 2:56 做决定 / 变了什么 / 为什么变 / 接下来干嘛 / “根据你看到的所有数据 / it just needs to fill it in. That’s hardly comparable to finding a new planet, so let’s look at some concrete steps to make our approach more ambitious. We’ll start basic and get a little bit more ambitious with each step: / As input, most reviews want to know what changed this quarter, why, and what should be done about it. Resist the urge to just update whatever numbers are in the template — give the model too as much data as you can get your hands on

逐字稿

都说对AI要有野心,但没人告诉你,野心到底怎么落到一句提示词里。 Anthropique做cloud的工程负责人Fenix写了一篇文章,叫做《怎么更有野心》。 发出来一周,已经有五百多万浏览。 他在这篇文章里把野心拆成了四个方向:速度,高度,深度,广度。 今天把原文带大家拆完,顺便附带我的使用心得。 同时,原文的提示词,你可以直接截屏带走。 Fenix开篇讲了一段大白话,他说自己在cloud的团队干活,每天最上瘾的事,就是看到全世界的用户,用用cloud做成了一件他自己连想都没想过,甚至没勇气去试的事。 比如就在这个月,有人用它可能发现了一颗新行星。 他看到了第一支自己真心喜欢的AI音乐MV。…

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