1. Learning isn't for high scores.
I mistakenly believed learning was about high scores. That wasn't my decision; it was the inevitable output of a dozen-plus years in a rotten education system. They preach knowledge is power, but they only ever test compliance.
1 学习不是为了高分
我错误地将学习当作是为了考试得高分。这不是我的观点，而是我过去十几年学生生涯所做的事。这个烂掉的教育体制，宣扬知识就是力量，实际上关注的是应试。
Now I realize learning isn't for those stupid tests. On the surface, it’s adding more "plugins" to your mind. Fundamentally, it's about nourishing the mind, pushing life's boundaries, and finding depth. We spend a decade learning things just to pass the next level. In real life, those obsolete spells or dragon-slaying techniques become useless.
直到脱离那教育体系多年我才明白，学习不是为了那愚蠢的考试。浅一层的意义，是给头脑拓展更多“技能”插件，更深一层是为了滋养头脑、不断深入生活、拓展生活边界。想想受教育十几年，却是在学习一些为了通过一个个不断升级关卡的东西。一旦进入到实际生活，那些过了时的法术或者屠龙术，再无用武之地。
If I could go back, I wouldn't choose liberal arts, especially politics and history. Both are packed with ideology you must parrot exactly. Politics is just disgusting. History offers a decent frame, but it’s a poor model for thinking.
假如回到学生时代，我不会再选文科，尤其是政治和历史。二者充斥大量意识形态的观点，而且还要背诵，写出他们想要你写出的答案。政治除了恶心人，没有别的作用。历史能够带来整体框架，但不值得以它们为范本去钻研。
Back then I didn't know how to think and couldn't handle science subjects. Even if the current me went back with better knowledge, it wouldn't matter now. What matters is that if I have kids, I can pass on the lesson. Shifting the time spent grinding through exams toward nourishing the mind would have been infinitely more valuable.
可是那时我不知如何思考，学不好理科。尽管现在的我回到过去，可能知道如何绕过考试去学习，但这已没有意义。有意义的是如果我有孩子，我可以跟他分享教训。毕竟把考试通关的学习时间，转移到关注滋养头脑，要有意义多了。
2. Learning isn't short-term.
I wrongly saw learning as a short-term project that ends when the course or exam does.
2 学习不是短期行为
我错误地将学习当作短期的行为。课程会完结，考试会结束，学习就告一段落。
I was staggeringly dumb about this until thirty. Learning is about building lifelong habits: the habit of thinking and expressing in English, of making things with code, of modeling the world with math, of predicting trends with economics, of making money with business knowledge, of reaching abstractions with philosophy.
我蠢得要死，三十岁才明白这点，学习是养成终身习惯的过程。是用英语描述与表达的习惯，用编程实现效果的习惯，是用数学建模事物的习惯，是用经济理论预测走向的习惯，是用商业知识赚到钱的习惯，是用哲学抵达世界抽象的习惯。
And when those tools fail—as they usually do—you dig deeper, using the habit of thinking to get closer to reality and build something that works.
并且当它们不凑效时——通常如此——再进一步深入，靠着思考的习惯去贴近现实，试图在现实里构建成果。
I told my nephew: don't study English just for tests. Treat it as a ten-year commitment. Don't let temporary scores shake you. Just keep pushing the pawn forward one step each day.
有一天我跟外甥说，不要盯着考试去学英语，而是当做一种你要花十年时间才能掌握好的东西，不要被一时成绩的好坏所影响，重要的是日拱一卒。
Then I thought: why don't I do the same? Ever since, whenever I decide to learn something, I push one step a day. Now my life is full of English words. When I don't know one, I ask AI how natives say it.
转念一想，为什么我不这么做呢？从那以后，当决定要学习什么，我就日拱一卒。在现在的生活里，我看到的全是英语单词，不会的则问人工智能某某的地道英语怎么说。
I no longer care if a word is for some advanced exam. I just need it to express myself. Only then did I understand why my high-school English teacher was always wondering how to say everyday things in English.
也从不关心，这个单词是专八还是GRE级别，只知道我需要它来表达。我也才明白，为何高中的英语老师总会想生活中各种东西如何用英语表达。
3. Learning isn't about memorizing facts.
I wrongly thought learning meant knowing lots of details. Real learning is about use. If you don't plan to use something habitually, don't bother learning it. Learning must equate to habitual use.
3 学习不是熟悉知识
我错误地认为，学习是熟悉各种细节知识。学习应该是使用，如果不打算习惯性使用，就不要学，学应当意味着习惯性使用。
To be clear, in any field, use frequency follows a normal distribution: some things you use rarely, some constantly, most in the middle. Habitual use covers the middle-to-high frequency band.
需要澄清的是，我现在发现某领域的知识，使用频率是呈现正态分布，即部分是高频率和低频率使用，部分属于中度的使用频率。因此习惯性使用，所涵盖的范围，是中度至高度频率的区间。
Use—or practice—is the core. Knowledge that hasn't been tested in reality is just fantasy. Only collision with the real world turns it into useful knowledge.
使用，或者说实践，才是学习的核心所在。没有经过实践验证的知识，只是头脑中的臆想，唯有与现实碰撞，这个臆想才能贴近有效认知。
I hated school partly because it was so detached from life. In the real world I hit walls plenty, but I’m glad for it. I'd rather get a bloody nose in reality than sit in an ivory tower clutching useless ideas, thinking I'm smart.
我讨厌学校，一方面就在于它总是那么脱离实际。在现实里我有很多碰壁，但令人高兴。我情愿在现实里碰一鼻子灰，也不愿意在象牙塔里捧着无效认知自以为是。
If I remember right, my entire school career never taught me the word "faucet"—for the thing I touch every day! What kind of education fails to make you notice the faucet?
如果没记错，我整个学生生涯都没接触过faucet水龙头这个单词，然而它就在我天天接触的范围内！！到底是一种怎样愚蠢的教育，没有令我学会关注水龙头faucet。
4. Learning doesn't respect disciplines.
I wrongly thought learning happened in separate subjects. I’ve been practicing holistic thinking for about ten years. Only through practice have I started to grasp that everything is one.
4 学习不用区分学科
我错误地认为，学习是分学科的。从我接触整体性思维至今，应该有十年时间。在这十年里，我才在实践里理解了一些万物为一的理念。
I now believe you don't divide learning by discipline. It's not about learning everything; it's about fusing what you learn into a single whole, because life itself is one big mash-up of information. Learning advances your cognitive map of that single entity we call life.
我如今相信学习不用区分学科。不是什么都学，而是指将所学融为一体，就像生活本身是一个多类别信息聚合的实体。学习增进的是对生活这一实体的认知。
All kinds of lives emphasize different things—education and psychology, medicine and biology, law and academia, technology and engineering, art and music—but everything happens inside the same entity.
各式各样的生活里，可能侧重教育、心理，侧重医疗、生物，侧重法律、学术，侧重技术、工程，侧重艺术、音乐，但是无论哪一种侧重，统统在一个实体内。
Bayes' theorem isn't just in probability and statistics; it’s in everyday causal reasoning, like when a wife finds a long hair, strange lipstick, or odd perfume in her husband's car.
贝叶斯公式并不是在概率与统计里，而是在日常因果推断里，在老婆看到老公车里有长头发、陌生口红、衣服有异香的推断里。
Asynchronous thinking isn't only in programming; it’s in pausing to answer the phone while putting on shoes, or going to the bathroom and coming back to the grind.
异步的编程思想，也不仅是在电脑里，还在接完电话再继续穿鞋里，在中途上个厕所继续回来当牛做马的生活里。
Accounting entries aren't just in ledgers; they're about keeping both sides of a scale balanced, or pouring water from one bucket to another. Knowledge from different fields is just different lenses on the same reality.
会计的分录核算思想，也不仅是在账本上，还在天平保持平衡的两端上，还在很多个水桶，从这个桶倒进另一个桶。各个领域的知识，是用不同的视角看同一个实体。
5. Learning isn't a steady curve.
A key dimension of learning is iterating on metacognition, not just piling up facts. The ideal growth curve shouldn't be flat; it should steepen. You get faster at understanding new things. Iterating metacognition brings acceleration.
5 学习不是平缓曲线
学习重要的维度，是迭代元认知，而不仅仅是停留在具体知识上。理想的成长曲线，不应该一直平缓，而是上升，即理解事物的能力越来越快，迭代元认知能带来加速度。举个例子，
I often use PowerPoint for notes and sometimes edit videos. One day I asked what they have in common. Both follow the basic logic of selecting an object and then doing something to it—like pointing at a person before talking to them. Selection and processing happen in two kinds of windows: visual and structural. All the complex operations live inside those.
我经常使用ppt做笔记，偶尔会用剪辑软件剪视频。有一天思考二者有什么共同点，发现它们遵从选中对象从而处理它的基本逻辑。这很容易理解，就像指定某个人，再跟他对话。而选择和处理对象，又分两类窗口，即视觉类和结构窗口。各种繁复的细节操作，则在这两类窗口内实现。
That insight made learning Photoshop much easier—I'd always been confused by its logic. Basically, you can understand most audio-visual software with the simple select-and-process frame.
这个发现，令我在后来学习ps大有帮助，我一直搞不清楚ps的操作思路。可以说，对于音视图类的软件，基本都可以用选中-处理的通俗框架去理解学习。
Earlier learning, once abstracted one level up, accelerates everything new. That's why iterating metacognition matters.
前面的学习，在上升到抽象一层认知时，它会提升人理解新事物的速度。这就是元认知迭代的重要性。
6. Learning needs principles.
I never paid much attention to principles until recently. In Discourse on Method, Descartes laid out four rules for thinking.
6 学习不能没有原则
以前，我并没有注重原则，直到前一阵子也没有。笛卡尔在《谈谈方法》里，揭露了他思考的四条原则。
First, never accept anything as true unless it's clear. Second, divide problems into as many parts as needed. Third, start with the simplest and easiest, then climb step by step. Fourth, review everything to make sure nothing is missed.
第一，凡是不清晰的，绝不当真。第二，分可能和必要去区分思考的对象。第三，从最简单最容易开始，一点点推理。第四，尽可能全面普遍地考察。
Philosophy in the seventeenth century shifted from ontology ("what do I think exists") to epistemology ("how do I know I'm right") largely because of such principles.
而哲学之所以在十七世纪，从“我认为是什么”的本体论，转向“我怎么知道我是对的”的认知论，还得归功于这样的思考原则。
Feynman was relentless about working through integrals himself to verify results before accepting others' conclusions.
类似的，费曼总是不遗余力去计算那些微积分，就是确保他要亲自验证，通过验证才去接受别人的学术观点。
Only when I relearned grammar did I grasp how important principles are. By principles I mean consciously testing the views, rules, or principles you already hold.
再直到我重新学语法，我才理解这种原则的重要性。这种原则，是指有意识地检验已经秉持的某个观点、某条规则或者某些原理。
You consciously apply them to whatever you're examining. When they don't hold, you ask why—is it a special case, a new rule, what is the new rule, or is the old one flawed and needs fixing? That's how you slowly build better models.
在任何情况下都有意识地秉持着它们去分析某个对象，当其不成立时，则追问为什么不成立，是情况特殊，有新的规则，那新规则是什么，还是原准则有缺陷，哪里有缺陷需要修正，此来一点点建模。
Conclusion / 结语
Some wrong turns you have to take yourself before you can see the right road.
有些弯路可能是必须走一遍，才能知道好的路应该是什么样。
Now at thirty, I know my input bandwidth is average, my CPU isn't fast, I prefer slow thinking over quick. I'm small-built, with limited energy, and no idea how much time is left.
现在我三十岁，已然知道自身输入带宽一般，CPU处理速度一般，偏好慢思考，并不是快思考。而且体格偏小，体能和精力有限，也不知道还有多少时间。
Chances are I'll never amount to much or do anything impressive.
很有可能以后也无法学有所成，做出什么像样的事情。
The good part is that at least now I enjoy learning. But it's not enough. It could still be faster.
好的是，至少现在我能享受学习的乐趣了。但不能算满意，它一定还可以再快一些的。
It definitely can be faster. I just don't know how yet.
一定可以再快，只是我现在不知道。