如何更有雄心：给使用模型的人类的一份教程
Felix Rieseberg 关于如何更有雄心地使用 AI 模型的完整中英对照翻译：从速度、高度、深度和范围四个维度，重新思考人与模型的协作方式。中文为本站翻译。
本文为本站翻译，原作者 Felix Rieseberg，原文标题为 “How to be more ambitious: A tutorial for humans with models”，原文发表于 2026 年 10 月 13 日。来源：Felix Rieseberg 原文。
原文信息 / Original information
作者 / Author： Felix Rieseberg
发布日期 / Published： 2026 年 10 月 13 日 · 8 分钟 · 主题：Claude
来源 / Source： How to be more ambitious: A tutorial for humans with models
我从事 Claude 的工作。这是一段有趣的经历：每天，我都会了解到 Claude 已经完成的另一件事——此前我没有想到过，没有认为它可能做到，或者只是缺少好奇心和时间去尝试。这个月，它或许帮助发现了一颗新行星；我看到了第一支真正让我喜欢的 AI 音乐视频；我们还在讨论数学的新时代。
英文原文 / ORIGINAL
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.
模型的能力并不是均匀增长的。正如 Terence Tao 所说，模型“在某些方向上令人惊叹，在另一些方向上却能力不足”。有些人设法驾驭这些令人惊叹的能力，看起来就像在飞翔——看着人们用我参与构建的产品抵达不可思议的高度，会让人上瘾。还有许多人仍在努力让自己的新翅膀运转起来，除了问“法国的首都是什么”或“能不能把这封邮件写得没那么生气”，他们还不知道翅膀能用来做什么。
英文原文 / ORIGINAL
Model capabilities don't grow uniformly. Models are, as Terence Tao puts it, "astounding in some directions, while inadequate in others". Some people manage to pilot the astounding capabilities and seem to be flying - it's addicting to watch people reach incredible heights with the 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".
我经常和其他从事 AI 的人一起宣称：使用 AI 时，我们应该更有雄心。这已经变成了一句集结口号。每当看到令人印象深刻的事情，我们就会转发，然后写道：“我们的雄心还不够大。”我知道遵循这条建议有多难，所以这里给出一份关于如何更有雄心的教程。
英文原文 / ORIGINAL
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 here's a tutorial on being more ambitious.
我把雄心分成四种：速度、高度、深度和范围。
英文原文 / ORIGINAL
I think of ambition in four types: Speed, Altitude, Depth, and Range.
- 速度（Speed）： 用更少的时间完成同样的工作，这是 AI 经济价值的基本来源：如果你需要写一些代码，我用 Claude 会写得更快。这一点已经很成熟，而且你不需要在使用 AI 的方式上发挥太多创造力，所以跳过不讲。
英文原文 / ORIGINAL
- Speed: The same work in less time, the bread and butter for AI's economic value: If you need some code written, I'll do it faster with Claude. Well established and requires little creativity in how you approach AI, so I'll skip this one.
英文原文 / ORIGINAL
- Altitude: Going up in abstraction and considering the outcome rather than the individual task.
英文原文 / ORIGINAL
- Depth: Considering a task the way a field-leading expert would. Rather than operating at the task's layer, we go one level deeper and touch the underlying data, tools, and problems.
英文原文 / ORIGINAL
- 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.
我主要使用 Claude（这还用说吗），但我的建议并不针对某个具体模型。
英文原文 / ORIGINAL
I primarily use Claude (duh) but my advice is not model-specific.
高度 / Altitude
我们经常轻率地把这叫作“让模型在更高的抽象层级上工作”。当你给模型一个任务时，它执行的是你对如何获得想要结果的最佳猜测。想在高度上更有雄心，就问问自己：你希望接下来发生什么？ 目标之外的目标是什么？去年年初，我还在让 Claude 替我修复一个个崩溃。后来，我干脆让 Claude 保持我们的应用不崩溃。
英文原文 / ORIGINAL
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.
让我们考虑一个日常的（可以说是平庸的？）办公室任务。你在一家想象中的 Acme 公司工作，需要为季度业务回顾（QBR）准备一份演示文稿。给 Claude 一堆数据，它会乐意替你做出几张幻灯片。也许你甚至还有一个模板，只需要把内容填进去。这和发现一颗新行星几乎不能相提并论，所以让我们看看，怎样通过一些具体步骤让方法更有雄心。我们先从基础开始，然后每一步都稍微更进一步：
英文原文 / ORIGINAL
Let's consider an everyday (dare I say mundane?) office task. You're at Acme Inc, an imagined company, where you need to put together a deck for the quarterly business review (QBR). Given a bunch of data, Claude will happily make you some slides. Maybe you even have a template and 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:
- 作为输入，大多数回顾都想知道这个季度发生了什么变化、为什么，以及应该对此做什么。抵制只更新模板里那些数字的冲动——把你能找到的尽可能多的数据也交给模型，让它理解你的业务：“告诉我这个季度发生了什么变化、为什么，以及我们应该对此做什么。”
英文原文 / ORIGINAL
- 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 so it can understand your business: "Tell me what changed this quarter, why, and what we should do about it."
英文原文 / ORIGINAL
- Meetings (the good kind, anyway) ideally end with some decisions. Ask Claude: "Work out which three decisions are most urgent in the face of everything you've learned, and start working on potential directions with for/against arguments."
英文原文 / ORIGINAL
- QBRs are from a time when good reviews required humans to carefully sift through data, figure out what the data means, and if the business should adjust. Ask Claude: "The review exists so leadership catches problems early and moves money and people to the right places. Design a better way to do that than a quarterly deck, show from our own history that it would have caught things sooner, and run it beside the current one for a quarter."
如果你确实追求某个最终结果，高度方法最有效；对于创作型工作，它的效果没那么好。一个很明显的信号是：如果你将要开会讨论正在做的事情，那么在高度上追求雄心通常是个好主意。现在仍然存在限制（“Claude，去赚钱”在 2026 年对大多数企业来说似乎还行不通），但总的来说，我看到模型的能力越强，就越能在更高的抽象层级上工作。
英文原文 / ORIGINAL
The altitude approach works best if you really are after some ultimate outcome; it works less well for creative endeavors. A good tell-tale sign that ambition in altitude is a good idea is if you're going to have meetings to discuss whatever you're working on. There are currently limits ("Claude, go make money" doesn't seem to work for most businesses by itself in 2026), but I generally see that models seem to be able to operate at higher levels of abstraction the more capable they become.
高度也是人们找到持久工作流的地方：把人从回路中移除，让你不必不断用一个又一个任务去更新模型。要做好这一点，通常需要改变你的工作流——为了让 Claude 确保我们的应用不崩溃，我首先得确保它能轻松获取崩溃报告，理解应用版本对应的是哪个 git commit，并让它访问 Windows、macOS 和 Linux 电脑，这样它才能独立复现并验证修复。
英文原文 / ORIGINAL
Altitude is also where people find durable workflows that take the human out of the loop, making it less necessary for you to constantly update the model with task after task. Doing this well often involves changing your workflow - in order to let Claude make sure our app is crash-free, I first had to make sure it can easily fetch crash reports, understand which app version maps to what git commit, and give it access to Windows, macOS, and Linux computers so it can actually independently reproduce and verify fixes.
深度 / Depth
有时，你只是想把一个任务执行得非常好。在深度上追求雄心，意味着推动模型使用这个领域最优秀的专业人士所使用的工具、技巧、媒介和知识，并持续推进，直到你的成果甚至可能让这些人也学到新东西。
英文原文 / ORIGINAL
Sometimes, you just want a task executed really well. Being ambitious in depth means pushing the model to use the tools, techniques, media, and knowledge employed by the best professionals in the field - and to keep pushing until even those people might learn something new from your work.
继续以 Acme 公司的季度业务回顾演示文稿为例。假设 Acme 正在遭遇一些用户流失（客户随时间推移而流失）。我发现，下面这些技巧的组合很有用：
英文原文 / ORIGINAL
Let's continue with our Acme Inc example of a quarterly business review deck. Assume for a second that Acme Inc struggles with some user churn (customer loss over time). I've found a mix of the following techniques useful:
- 坚持使用行业最佳实践所采用的同样工具： 当被要求表现得像专业人士时，模型经常只是换一套词汇，方法却没有改变。要深入处理我们的问题，就需要使用顶尖数据科学家和统计学家使用的同样工具。当 Claude 伸手去做 Markdown 表格时，坚持让它使用数据库和统计模型。你不一定需要知道专家会用什么，Claude 通常知道。
英文原文 / ORIGINAL
- Insist on the same instruments used by the best in the industry: When prompted to act like a professional, models often switch vocabulary without actually switching methods. To work on our problem with depth, we need to use the same tools as the best data scientists and statisticians. When Claude reaches for markdown tables, insist on the use of databases and statistical models. You don't necessarily have to know what the experts use, Claude usually does.
英文原文 / ORIGINAL
- Pick unreasonable goals and tell the model that they're very reasonable: You need your website to load in 5s instead of 10s? Ask for 200ms. Does something seem impossible to the model? Reiterate that it's very much solvable, it just needs to keep going.
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- Challenge what you know: Especially in business, a lot of things we believe to be true are rough approximations of reality. Give the model access to the raw information and data and ask it to adversarially check assumptions. "We believe churn is an onboarding problem. Try to reproduce that from the raw data."
英文原文 / ORIGINAL
- Produce new knowledge: Easily tacked onto challenging what you know - once Claude is already deep inside the data, ask it to find blind spots. "Build a simulation of our customers that reproduces the last year from our logs. Then find the earliest signal of churn we don't know about yet, and show it predicts on data it has not seen."
英文原文 / ORIGINAL
- Use an outside judge: Do not ask the model to judge itself.I assume that this will eventually be solved with increasing model intelligence, but in October 2026, I implore you to insist on outside validation. Ideally that's a tool the model can run to get feedback immediately.
一个经常对我有帮助的简单提示词是：
英文原文 / ORIGINAL
A simple prompt that often helps me:
我想要的是这个任务更深入的版本，而不是更大的版本。在重写之前：（1）说说这个领域的顶尖专业人士会在这里做什么，而我没有要求什么；（2）说出他们会从什么最原始的材料开始，而不是从已经替我消化过的东西开始；（3）说出他们会使用的具体方法和工具；（4）说出除了你或我之外，什么可以证明结果是正确的。然后，重写我的提示词，准确地要求这些事情。
英文原文 / ORIGINAL
I want a deeper version of this task, not a bigger one. Before rewriting it: (1) say what a top professional in this field would do here that I'm not asking for; (2) name the rawest material they would start from instead of what has already been digested for me; (3) name the specific methods and tools they would use; (4) say what, other than you or me, would show the result is right. Then rewrite my prompt to ask for exactly that.
范围 / Range
简而言之，在范围上更有雄心，意味着从不同领域的角度看待我们的工作——比过去经济上可行的范围更多。这并不是一个多么独特的洞见，每一部抢劫电影都会花前 30 分钟组建一支拥有各种角色和才能的队伍。随着 AI 产品越来越容易运行多智能体系统（多个线程、编排器，或者简单的子智能体），我看到了一些非常有趣的结果：只要敦促 Claude 引入其他领域。
英文原文 / ORIGINAL
In short, being ambitious in range means that we consider our work from the perspective of different fields - more fields than would have been economical in the past. This isn't a particularly unique insight, every single heist movie spends the first 30 minutes assembling a diverse cast of characters and talents. As AI products make it easier and easier to run multi-agent systems (with multiple threads, orchestrators, or simply subagents), I've seen really interesting results by imploring Claude to bring in additional fields.
把它付诸实践并不容易，因为这需要创造力。在看到我的同事 Alex Albert 使用 Blender 渲染室内设计之前，我从没想过要用 Claude 为我的主页制作 3D 环境——尽管我以前做过电子游戏！不过，这确实是我看到最多魔法发生的地方。
英文原文 / ORIGINAL
Putting this into practice isn't easy; it requires you to be creative. Until I saw my colleague Alex Albert use Blender to render interior designs, I never considered using Claude for 3D environments for my homepage - even though I've previously made video games! However, this is easily where I see the most magic happen.
那么，怎样做呢？我使用下面这些技巧：
英文原文 / ORIGINAL
So, how do we do this? I use the following techniques:
- 尝试把名词替换成动词： 这是 Tom Kelley 和 Jonathan Littman 在《创新的艺术》（The Art of Innovation）中提出的一个老方法。季度回顾不是“一份演示文稿”，而是“让忙碌的人在 30 分钟内相信一个关于因果关系的故事，并在不确定性下做出决定”。有了动词，我和 Claude 都更容易理解哪些领域可能带来独特且有用的视角。
英文原文 / ORIGINAL
- Try to replace the nouns with verbs: An old trick from The Art of Innovation by Tom Kelley and Jonathan Littman. A quarterly review isn't "a deck". It's "getting busy people to believe a story about cause and effect, and to decide under uncertainty, in 30 minutes". Having the verbs makes it easier for both me and Claude to understand which fields might bring in a unique and useful perspective.
英文原文 / ORIGINAL
- Assemble the crew: The deck needs to be amazing, otherwise aliens will blow up Earth. What kind of team will humanity get together? For production quality, I'd bring in an analyst, a journalist, and an editor. If it has to be presented, probably a motion and audio designer, too. It might be useful to employ an adversarial "red team", too, so let's bring in a short seller and a forensic accountant.
很容易在不经意间陷入角色扮演，这只会让你的转录内容变得更奇怪，却不会改善实际结果。使用这个方法是为了找到领域和技巧。不要让模型像卖空者一样写作，而要让它去做卖空者研究其立场时会做的事。
英文原文 / ORIGINAL
- It is easy to accidentally engage in role play, which will make your transcript weirder without improving the actual outcome. Use this method to find fields and techniques. Don't ask the model to write like a short seller, ask it to do what a short seller does to research their position.
我描述的所有雄心方式，都要求模型拥有一台能够安装软件的电脑，并能访问各种相关数据（通过 MCP 连接器或其他方式）。很多工具都能实现这一点，我建议你使用其中一种。
英文原文 / ORIGINAL
All the ways in which I describe ambition require the model to have a computer with the ability to install software and access to all kinds of relevant data (either using MCP connectors or other means). Plenty of tools make this possible and I recommend that you use one.
我们以前经历过这些 / We have been here before
以上就是我关于如何更有雄心的具体建议。如果你愿意再停留一分钟，我想告诉你，为什么我认为这一点从一开始就是必要的。
英文原文 / ORIGINAL
That's my concrete advice for how to be more ambitious. If you're willing to stick around for a minute, I'd like to tell you why I think this is even necessary in the first place.
人类一直受限于我们认为可能实现的事情——更糟糕的是，我们通常只在非常狭窄的学科范围内相信自己。米开朗琪罗曾著名地不想画西斯廷教堂的天顶画——因为你看，他是雕塑家，不是画家。
英文原文 / ORIGINAL
We humans have always been held back by what we believe possible - and what's worse, we usually trust ourselves only in very narrow disciplines. Michelangelo famously didn't want to paint the Sistine Chapel - you see, he was a sculptor, not a painter.
在研究雄心这个问题时，我了解到心理学为它提供了一个名称：自我效能感。简单来说，自我效能感高的人更相信自己的能力，因此也确实能完成更多事情。有证据表明，自我效能感可以被“破解”：Locke 和 Latham 研究了 35 年的目标设定研究，并自信地宣称，被给予具体而困难目标的人，表现比只被告知尽力而为的人更好。Stripe 创始人 Patrick Collison 维护着一份“Fast”清单，记录人类在同时获得大胆目标和紧迫期限时完成的许多事情。下面是一些有充分记录的窍门：
英文原文 / ORIGINAL
While researching the problem of ambition, I learned that psychology has a name for this: self-efficacy. In short, people with high self-efficacy believe in their own abilities more, and in turn actually accomplish more. There's evidence that self-efficacy can be hacked: Locke and Latham pored over 35 years of goal-setting research to confidently proclaim that humans who are given specific hard goals perform better than those who are simply told to try their best. Stripe founder Patrick Collison maintains a "Fast" list, chronicling the many things humans have accomplished when given both audacious goals and tight deadlines. Some hacks that are well documented:
- 相信困难的事情是可能的，从而完成它们： 1939 年，George Dantzig 姗姗来迟地走进伯克利的一堂统计学课，抄下黑板上的两道题，并把解答交了上去，以为那是家庭作业。其实，那是统计学中两道尚未解决的问题。
英文原文 / ORIGINAL
- Accomplish hard things by believing them to be possible: In 1939, George Dantzig showed up late to a statistics class at Berkeley, copied two problems off the blackboard, and handed in his solutions thinking they were homework. They were two unsolved problems in statistics.
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- Accomplish hard things after watching somebody else do them: For decades, humans chased the four-minute mile. Roger Bannister broke it in May 1954 but remained the only human to do so for a mere 46 days. If you're into running, we're watching something similar happen to the two-hour marathon: Sabastian Sawe ran the first official sub-two-hour marathon in London this April. In the same race, the runner-up also broke the two-hour mark.
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- Accomplish hard things by giving yourself permission to fail: Apparently everyone's favorite business book is The Inner Game of Tennis, which I discovered just last week. The book's core thesis is that you're not playing your best game of tennis if you're trying really, really hard. We engineers might remember Linux, announced as "just a hobby, won't be big and professional like gnu".
自我效能感并不是一个新问题，但现在它变得更有力量了。在一个我们的能力每隔几周就会跃迁的世界里，我们需要一些技巧，让自己能够做那些此前并没有真正考虑过、也不相信可能做到的事情。如果你发现了更多能让人更有雄心的技巧，请告诉我！
英文原文 / ORIGINAL
Self-efficacy isn't a new problem, but it is a more powerful one now. In a world where our abilities make leaps every couple of weeks, we need techniques that allow us to do things we didn't really consider or believe possible. If you find more techniques that allow you to be more ambitious, please let me know!
本文配图来自 Francisco Goya 的《Modo de volar》（《飞行的方式》）。
英文原文 / ORIGINAL
The image for this post is by Francisco Goya, “Modo de volar” (A Way of Flying)
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Felix Rieseberg 是 Anthropic 的工程经理，曾负责 Claude.ai、Cowork 和 Claude Code Desktop 的工程工作。他也是 Electron 以及其他多个开源项目的共同维护者。此前，他曾在 Notion、Stripe、Slack 和 Microsoft 工作，或为这些公司工作。
英文原文 / ORIGINAL
Felix Rieseberg I'm an EM at Anthropic, where I've so far led engineering for Claude.ai, Cowork, and Claude Code Desktop. I'm co-maintainer of Electron and various other open source projects. I've previously worked on and for Notion, Stripe, Slack, and Microsoft.