AI时代，道与术

v1.4

AI Era: Philosophy and Technique

v1.4

从机器语言到自然语言 From Machine Language to Natural Language

"Talk is cheap,
show me the code"

—— by Linus Torvalds

"Code is cheap,
show me the talk"

编程语言进化了80年

🎁

从千人到千万：开发者定义的演变 From Thousands to Tens of Millions: The Evolution of Developer Definition

回顾历史，每个计算平台的更迭都伴随着开发者规模的跃升 Throughout history, each computing platform transition has been accompanied by exponential growth in developer scale

1 Unix时代（1970s-1980s） Unix Era (1970s-1980s)

开发者规模：Developer Scale:

数千至数万（主要在大学和研究机构） Thousands to tens of thousands (mainly in universities and research institutions)

系统部署：System Deployment:

从1970年代的10个增长到1981年的2000+个系统 Growth from 10 systems in the 1970s to 2000+ systems by 1981

门槛：Barrier:

计算机科学学位，掌握C语言 Computer Science degree, C language proficiency

特征：Characteristics:

学术精英和研究人员的专属领域 Exclusive domain of academic elites and researchers

2 Windows时代（1990s-2000s） Windows Era (1990s-2000s)

开发者规模：Developer Scale:

数百万（2002年美国约61万软件工程师） Millions (approximately 610,000 software engineers in the US in 2002)

微软生态：Microsoft Ecosystem:

1986年IPO就创造了12,000名百万富翁员工 IPO in 1986 created 12,000 millionaire employees

门槛：Barrier:

Visual Basic降低了入门门槛 Visual Basic lowered the entry barrier

特征：Characteristics:

企业IT部门崛起，ISV生态繁荣 Rise of enterprise IT, ISV ecosystem flourishing

3 移动时代（2008-2024） Mobile Era (2008-2024)

iOS开发者：iOS Developers:

3400万注册开发者（2022年Apple官方数据） 34 million registered developers (2022 Apple official data)

Android生态：Android Ecosystem:

每月约3.6万个新应用发布 Approximately 36,000 new applications published monthly

特征：Characteristics:

独立开发者经济兴起，App Store经济形成 Rise of independent developer economy, formation of App Store economy

4 全球开发者现状（2024） Global Developer Status (2024)

全球规模：Global Scale:

4720万专业软件开发者

地域分布：Geographic Distribution:

美国440万，欧洲610万 US 4.4M, Europe 6.1M

GitHub Copilot：GitHub Copilot:

190万+付费用户

ChatGPT用户：ChatGPT Users:

3亿+（许多已开始用AI编程） 300M+ (many have started AI programming)

5 AI时代预测（2025-2030） AI Era Prediction (2025-2030)

预测规模：Predicted Scale:

5亿-10亿"开发者"

门槛：Barrier:

会说话就能编程 Anyone who can speak can code

特征：Characteristics:

开发者定义被彻底改写 Developer definition completely rewritten

第一部分 Part One

AI导致知识大爆炸
该何去何从？ AI Causes Knowledge Explosion
Where Should We Go?

内容太多，读者不够用了 Too Much Content, Not Enough Readers

过去2年AI技术爆发 AI Technology Explosion in the Past 2 Years

无法收敛，焦虑不安 Unable to Converge, Anxious and Restless

2000年前的警告 Warnings from 2000 Years Ago

庄子《养生主》 Zhuangzi "The Primacy of Nourishing Life"

"吾生也有涯，而知也无涯，以有涯随无涯，殆已。" "Life is finite, but knowledge is infinite. To pursue the infinite with the finite is perilous."

老子《道德经》第48章 Laozi "Tao Te Ching" Chapter 48

"为学日益，为道日损。损之又损，以至于无为。无为而无不为。" "In pursuit of learning, every day something is added. In pursuit of the Dao, every day something is dropped. Less and less is done until non-action is achieved. When nothing is done, nothing is left undone."

每个时代都有当时的知识大爆炸 Every era has its own knowledge explosion

对抗熵增的唯一手段就是通过创新去"减熵" The only way to fight entropy is through innovation to "reduce entropy"

朝代分久必合合久必分，也是一个道理 The cycle of division and unity follows the same principle

张三丰教太极 Zhang Sanfeng Teaching Tai Chi

张三丰道："都记得了没有？"张无忌道："已忘记了一小半。" Zhang Sanfeng asked: "Do you remember it all?" Zhang Wuji replied: "I've already forgotten a small part."

过了一会，张三丰问道："现下怎样了？"张无忌道："已忘记了一大半。" After a while, Zhang Sanfeng asked: "How about now?" Zhang Wuji said: "I've forgotten most of it."

张三丰微笑道："好，我再使一遍。"第二次所使，和第一次使的竟然没一招相同。张三丰画剑成圈，问道："孩儿，怎样啦？"张无忌道："还有三招没忘记。" Zhang Sanfeng smiled: "Good, I'll demonstrate again." The second time, not a single move was the same as the first. Zhang Sanfeng drew circles with his sword and asked: "Child, how is it now?" Zhang Wuji said: "There are still three moves I haven't forgotten."

张无忌满脸喜色，叫道："这我可全忘了，忘得干干净净的了。"张三丰道："不坏，不坏！忘得真快，你这就请八臂神剑指教罢！" Zhang Wuji's face lit up with joy: "I've completely forgotten it all, forgotten it completely clean!" Zhang Sanfeng said: "Not bad, not bad! You forgot so quickly, now you may ask the Eight-Armed Divine Sword for guidance!"

笑傲江湖独孤九剑也是强调的得其意而忘其形 The Dugu Nine Swords also emphasizes grasping the essence while forgetting the form

慕容博和萧远山走火入魔 Murong Bo and Xiao Yuanshan's Deviation

学了几十种武功 Learned dozens of martial arts

越学越杂，反受其害 The more they learned, the more chaotic it became, harming themselves

扫地僧不得不让他们"假死" The sweeping monk had to make them "fake death"

Reset归零，才能重生 Reset to zero to be reborn

AI时代的道与术 Philosophy and Technique in the AI Era

今天AI技术之盛行，大有过之而无不及 Today's AI technology prevalence is even more overwhelming

每天被自媒体刺激要变天了 Daily stimulation from media about impending changes

GPT-5、Claude、Gemini...

东学一榔头，西学一棒槌 Learning bits and pieces here and there

技能树不断刷新，很容易走火入魔 Skill trees constantly refresh, easy to lose your way

学习力 = 提问力² × 产品力 (L = Q² × P) Learning Ability = Questioning² × Product Skills (L = Q² × P)

提问力 (Q²) Questioning (Q²)

是输入 / 战略层面 Input / Strategic Level

搞清楚"为谁做"、"解决什么问题" Clarify "For whom" and "What problem to solve"

产品力 (P) Product Skills (P)

是输出 / 战术执行层面 Output / Tactical Execution Level

"用什么手段去做" "What methods to use"

提问是道，产品是术 Questioning is Philosophy, Product is Technique

第二部分 Part Two

AI提问力是一种什么力 What Kind of Power is AI Questioning Ability

提问力=写好Prompt? Questioning Ability = Writing Good Prompts?

大多数人的误解 Common Misconceptions

"你是一个资深产品经理，有10年B端SaaS经验，现在请你..." "You are a senior product manager with 10 years of B2B SaaS experience, now please..."

📝 写了500字Prompt 📝 Wrote a 500-word prompt

⚙️ 调了2小时参数 ⚙️ Tweaked parameters for 2 hours

结果：AI给的答案还是不能用 Result: AI's answer is still unusable

对谁提问决定了如何提问 Who You Ask Determines How You Ask

问老师 Ask Teacher

vs

问同学 Ask Classmate

问医生 Ask Doctor

vs

问病友 Ask Patient

问AI Ask AI

vs

问人类专家 Ask Human Expert

提问对象不同，提问方式完全不同 Different audiences require completely different questioning approaches

对AI提问的首要问题是什么？ What's the Primary Question When Asking AI?

去鱼池钓鱼，最重要的是什么？ What's most important when fishing in a pond?

→ 判断里面有没有鱼 → Determine if there are fish

去森林打猎，最重要的是什么？ What's most important when hunting in a forest?

→ 判断里面有没有猎物 → Determine if there is prey

问AI问题，最重要的是什么？ What's most important when asking AI?

→ 判断AI能不能回答，值不值得问 → Determine if AI can answer and if it's worth asking

爱因斯坦怎么看提问这件事？ Einstein's View on Questioning

"如果有1小时解决问题， "If I had an hour to solve a problem,

我会花55分钟定义问题是什么。" I would spend 55 minutes defining what the problem is."

问错问题的成本 The Cost of Asking Wrong Questions

❌ 反复调整Prompt ❌ Repeatedly adjusting prompts

⏰ 花2小时还搞不定 ⏰ Spending 2 hours without resolution

最后发现：不是Prompt的问题，
是问题本身就不该问AI Finally realizing: It's not the prompt,
the question shouldn't be asked to AI at all

提问的本质：这是不是一个真问题？ The Essence of Questioning: Is This a Real Question?

❓ 这个问题AI能回答吗？ ❓ Can AI answer this question?

✅ 回答了有用吗？ ✅ Is the answer useful?

🤔 你为什么要问这个问题？ 🤔 Why are you asking this question?

跳出问题看问题：就是元问题 Step outside the question to view it: that's the meta-question

对比案例 Comparative Cases

问题A："帮我写一个抖音爆款文案" Question A: "Help me write a viral TikTok script"

AI能回答？ Can AI answer?

能 Yes

有用？ Useful?

没用 No

为什么？ Why?

爆款的核心是洞察人性，不是文字技巧 Viral content requires human insight, not just writing skills

问题B："分析最近100个爆款视频的共同特征" Question B: "Analyze common features of 100 recent viral videos"

AI能回答？ Can AI answer?

能 Yes

有用？ Useful?

有用 Yes

为什么？ Why?

AI擅长模式识别 AI excels at pattern recognition

元问题认知力 = 定义问题的能力 Meta-Question Cognition = Ability to Define Problems

目的不是为了回答，而是判断是否值得回答 The goal is not to answer, but to judge whether it's worth answering

在提问之前先问自己三个问题 Ask yourself three questions before asking

1️⃣ 这个问题的答案，AI能给吗？ 1️⃣ Can AI provide the answer to this question?

2️⃣ 给了有用吗？ 2️⃣ Will it be useful if provided?

3️⃣ 你为什么要问这个问题？ 3️⃣ Why are you asking this question?

第三部分 提问力的"道"与"术" Part 3: The Philosophy and Technique of Questioning Ability

Johari Window

理解AI与人类的认知边界 Understanding the Cognitive Boundaries Between AI and Humans

提问的道 The Philosophy of Questioning

我们与AI的四种认知关系 Four Cognitive Relationships Between Us and AI

AI知道 AI Knows
AI不知道 AI Doesn't Know
我知道 I Know
我不知道 I Don't Know
(0,0)
I

象限I 公开区 Quadrant I: Open Area

简单说 Just Ask

例："今天天气怎么样？"
"帮我把这篇文章精简一下" e.g. "What's the weather today?"
"Help me condense this article"

(+X, +Y)

II

象限II 隐藏区 Quadrant II: Hidden Area

喂模式 Feed Mode

例："我公司的销售数据是..."
"帮我看看这份体检报告的关键注意事项" e.g. "My company's sales data is..."
"Help analyze key points in this medical report"

(+X, -Y)

III

象限III 盲点区 Quadrant III: Blind Spot

提好问题 Ask Smart Questions

例："Macbook M4和M4 pro的性能差异"
"如何提高团队协作效率？" e.g. "Performance differences between MacBook M4 and M4 Pro"
"How to improve team collaboration?"

(-X, +Y)

IV

象限IV 未知区 Quadrant IV: Unknown Area

开放聊 Open Exploration

例："我们一起探讨一下
未来AI的发展可能性" e.g. "Let's explore together
the future possibilities of AI"

(-X, -Y)

公开象限 (AI知道，你也知道) Open Area (AI Knows, You Know)

核心关键词：自动化 (Automation) & 效率 (Efficiency) Keywords: Automation & Efficiency

AI角色： AI Role: 完美的高级助理 Perfect Senior Assistant

应用场景： Application Scenarios:

  • 合同模板生成、会议纪要整理 Contract template generation, meeting notes organization
  • 房源描述撰写、客户跟进邮件 Property descriptions, client follow-up emails
  • 教案格式转换、作业批改建议 Lesson plan formatting, homework feedback suggestions
  • 简历优化、求职信修改 Resume optimization, cover letter editing

关键行动： Key Actions: 授权 (Delegate) & 执行 (Execute) Delegate & Execute

演讲者备注： Speaker Notes: 这是我们最熟悉的区域。所有你明确知道怎么做，AI也知道怎么做的事情，都应该毫不犹豫地交给它，把你的时间解放出来。 This is the area we're most familiar with. All tasks that you clearly know how to do and AI also knows how to do should be handed over to it without hesitation, freeing up your time.

隐藏象限 (你知道，AI不知道) Hidden Area (You Know, AI Doesn't Know)

核心关键词：个性化 (Personalization) & 知识注入 (Injection) Keywords: Personalization & Knowledge Injection

AI角色： AI Role: 需要你来"喂养"的学生 Student that needs your "feeding"

应用场景： Application Scenarios:

  • 提供客户预算和需求，获取精准房源推荐 Provide client budget and needs for precise property recommendations
  • 上传孩子成绩单，制定个性化学习计划 Upload child's report card to create personalized study plan
  • 输入公司财务数据，分析经营状况 Input company financial data for business performance analysis
  • 分享学生课堂表现，定制家校沟通策略 Share student classroom performance for customized parent-teacher communication

关键行动： Key Actions: 投喂 (Feed) & 训练 (Train) Feed & Train

演讲者备注： Speaker Notes: 这是你创造独特价值的地方。AI的通用知识库里没有你的个人经验、项目数据和团队规范。通过'投喂'，你正在把一个通用AI，训练成你的专属AI。 This is where you create unique value. AI's general knowledge base doesn't contain your personal experience, project data, and team standards. Through 'feeding', you're training a general AI into your exclusive AI.

盲点象限 (AI知道，你不知道) Blind Spot (AI Knows, You Don't Know)

核心关键词：学习 (Learning) & 认知拓展 (Extension) Keywords: Learning & Cognitive Extension

AI角色： AI Role: 你的"外置大脑" Your "External Brain"

应用场景： Application Scenarios:

  • "房贷利率变化对我客户购房决策有什么影响？" "How do mortgage rate changes affect my clients' home buying decisions?"
  • "孩子青春期叛逆，我该如何调整教育方式？" "How should I adjust my parenting approach during my child's rebellious phase?"
  • "差异化教学法对提高班级整体成绩有什么作用？" "How does differentiated instruction help improve overall class performance?"
  • "商业保理和传统银行贷款的核心区别是什么？" "What are the key differences between commercial factoring and traditional bank loans?"

关键行动： Key Actions: 提问 (Ask) & 探索 (Explore) Ask & Explore

演讲者备注： Speaker Notes: 这个象限能帮你快速突破知识盲区。把AI当作一个24小时在线、不知疲倦的专家导师，保持好奇心，大胆地问！ This quadrant helps you quickly break through knowledge blind spots. Treat AI as a 24/7 online, tireless expert mentor, maintain curiosity, and ask boldly!

未知象限 (你不知道，AI也不知道) Unknown Area (You Don't Know, AI Doesn't Know)

核心关键词：创新 (Innovation) & 涌现 (Emergence) Keywords: Innovation & Emergence

AI角色： AI Role: 平等的共创伙伴 (Co-pilot) Equal Co-creation Partner (Co-pilot)

应用场景： Application Scenarios:

  • 探索新的商业模式和盈利点 Explore new business models and profit opportunities
  • 创新房产营销策略和客户体验 Innovate property marketing strategies and customer experiences
  • 设计更有效的家庭教育新方法 Design more effective new family education approaches
  • 共创跨学科融合的创新课程 Co-create innovative interdisciplinary courses

关键行动： Key Actions: 共创 (Co-create) & 迭代 (Iterate) Co-create & Iterate

演讲者备注： Speaker Notes: 这是最激动人心的区域，是真正'1+1>2'的地方。在这里，没有标准答案，你和AI通过高质量的对话，共同探索可能性的边界。 This is the most exciting area, where true '1+1>2' happens. Here, there are no standard answers, you and AI explore the boundaries of possibility through high-quality dialogue.

象限迁移：从舒适区到价值区 Quadrant Migration: From Comfort Zone to Value Zone

问题：你的舒适区域在哪个象限？ Question: Which quadrant is your comfort zone?

I

公开区 Open Area

价值：效率
舒适度：★★★★☆
生产力：★★☆☆☆ Value: Efficiency
Comfort: ★★★★☆
Productivity: ★★☆☆☆

II

生产力区域 Productivity Zone

价值：个性化
舒适度：★★★☆☆
生产力：★★★★☆ Value: Personalization
Comfort: ★★★☆☆
Productivity: ★★★★☆

III

盲点区 Blind Spot

价值：学习
舒适度：★★☆☆☆
生产力：★★★☆☆ Value: Learning
Comfort: ★★☆☆☆
Productivity: ★★★☆☆

IV

未知区 Unknown Area

价值：创新
舒适度：★☆☆☆☆
生产力：★★★★★ Value: Innovation
Comfort: ★☆☆☆☆
Productivity: ★★★★★

AI知道 AI Knows
AI不知道 AI Doesn't Know
我知道 I Know
我不知道 I Don't Know

成长策略：努力从象限 I、III 转化到象限 II、IV Growth Strategy: Migrate from Quadrants I, III to Quadrants II, IV

➡️ I → II： ➡️ I → II: 从标准化走向个性化，投喂独有信息 From standardization to personalization, feed unique information

➡️ III → IV： ➡️ III → IV: 从被动学习到主动共创，探索未知 From passive learning to active co-creation, explore the unknown

思维转变：从"信息索取者"到"对话架构师" Mindset Shift: From "Information Seeker" to "Dialogue Architect"

搜索引擎时代： Search Engine Era: 我们是索取者，期待找到现成答案。 We are seekers, expecting to find ready-made answers.

⬇️

AI大模型时代： AI Large Model Era: 我们是架构师，通过搭建框架，让AI生成答案。 We are architects, building frameworks to let AI generate answers.

💡 核心观点：一个好的问题，本质上是为AI构建了一个优质的思考脚手架。 Core Insight: A good question essentially builds a high-quality thinking scaffold for AI.

演讲者备注： Speaker Notes: 在你问出问题之前，请先转变心态。你不是在搜索，你是在编程——用语言对AI进行编程。你给的指令越清晰，程序运行的结果就越好。 Before asking a question, first shift your mindset. You're not searching, you're programming—programming AI with language. The clearer your instructions, the better the program runs.

黄金提问框架：C.R.A.F.T.（提问的术） Golden Prompting Framework: C.R.A.F.T. (The Technique of Questioning)

C
Context
对话的"地基"
你在哪，我是谁？ The Foundation
Where are you, who am I?
R
Role
为AI戴上"职业镜头"
你是谁？ Professional Lens for AI
Who are you?
A
Action
"做什么"和"什么不能做"？ What to do & constraints?
F
Format
给我看"样子"
别只说"样子" Show me examples
Don't just describe
T
Target
对话的"指南针"
我们要去哪？ The Compass
Where are we going?

📺 进一步学习资源 📺 Further Learning Resources

想了解更多 CRAFT 框架的实际应用案例？ Want to learn more practical CRAFT framework application cases?

🎬 观看详细教程视频 Watch Detailed Tutorial Video

无效提问 vs. C.R.A.F.T.提问 Ineffective vs. C.R.A.F.T. Prompting

❌ 无效提问 ❌ Ineffective Prompting
"我的React代码不工作了，帮我看看。" "My React code isn't working, help me check it."

结果：得到一个通用的、可能无效的模糊答案。 Result: Gets a generic, potentially ineffective vague answer.

✅ C.R.A.F.T.提问 ✅ C.R.A.F.T. Prompting
(C) 我在用Hooks写计数器。
(R) 你是React辅导专家。
(A) 帮我修bug，但只能用useState。
(F) 按"问题定位-修正代码-原理解释"的范例回复。
(T) 我的目标是彻底搞懂它。 (C) I'm writing a counter with Hooks.
(R) You are a React tutoring expert.
(A) Help me fix the bug, but only use useState.
(F) Reply following the "Problem identification - Code correction - Principle explanation" format.
(T) My goal is to thoroughly understand it.

结果：得到一个量身定制、解释清晰、能真正解决问题的完美答案。 Result: Gets a tailored, clearly explained, perfect answer that truly solves the problem.

演讲者备注： Speaker Notes: 大家可以直观感受一下，同样一个问题，因为提问方式的不同，得到的结果天差地别。右边的提问，几乎是在'逼迫'AI给出一个满分答案。 You can intuitively feel that for the same question, different prompting methods lead to vastly different results. The right-side prompting almost 'forces' AI to give a perfect answer.

C.R.A.F.T框架案例 C.R.A.F.T Framework Examples

例子1：市场调研 Example 1: Market Research

❌ 无框架提问：

"帮我分析一下咖啡市场"

✅ C.R.A.F.T提问：

C: 我准备在上海开一家精品咖啡店，目标客群是25-35岁白领

R: 你是消费品行业的市场分析师

A: 分析上海精品咖啡市场的竞争格局和机会点，重点关注差异化定位

F: 用"市场现状-主要玩家-空白机会-进入建议"结构呈现

T: 找到一个可防御的市场定位

例子2：商业计划书 Example 2: Business Plan

❌ 无框架提问：

"怎么写商业计划书？"

✅ C.R.A.F.T提问：

C: 我的在线教育项目需要写BP给天使投资人，项目已有1000付费用户

R: 你是看过上千份BP的投资经理

A: 帮我优化一页纸的执行摘要，突出亮点，不要技术细节

F: 按"问题-解决方案-市场规模-竞争优势-团队-融资需求"6个要点

T: 让投资人愿意约见面聊

例子3：品牌定位 Example 3: Brand Positioning

❌ 无框架提问：

"我的品牌怎么做差异化？"

✅ C.R.A.F.T提问：

C: 我做高端茶饮品牌，客单价80元，目前有3家店，竞品是奈雪、喜茶

R: 你是品牌战略咨询顾问

A: 帮我提炼独特的品牌价值主张，要避开"品质"这种同质化表达

F: 给出"品牌口号+3个支撑点+具体落地措施"

T: 让顾客一句话就能记住我们的不同

例子4：谈判策略 Example 4: Negotiation Strategy

❌ 无框架提问：

"怎么谈合作？"

✅ C.R.A.F.T提问：

C: 下周要和某500强企业谈年度采购合同，去年合作额200万，他们想压价15%

R: 你是商务谈判专家

A: 设计谈判策略，既要守住价格底线，又要维护长期关系

F: 列出"谈判筹码-可能异议-应对话术-备选方案"

T: 维持原价或降幅控制在5%以内

例子5：团队管理 Example 5: Team Management

❌ 无框架提问：

"怎么激励团队？"

✅ C.R.A.F.T提问：

C: 10人销售团队，最近两个月业绩下滑30%，团队士气低落，马上年底了

R: 你是有20年经验的销售管理教练

A: 设计一个月的团队激活方案，不能只靠金钱激励，预算有限

F: 按"周计划-具体行动-预期效果"的时间轴展示

T: 月底前恢复团队战斗力，止住业绩下滑

课后作业（可选） Homework (Optional)

任务： Task: 你的工作、学习中分别有哪些例子，可以分别填入四个象限？ What examples from your work and studies can be categorized into the four quadrants?

  • 公开象限： Open Area: 你和AI都知道怎么做的任务（可直接委托） Tasks both you and AI know how to do (direct delegation)
  • 隐藏象限： Hidden Area: 你知道但AI不知道的信息（需要投喂） Information you know but AI doesn't (needs feeding)
  • 盲点象限： Blind Spot: AI知道但你不知道的知识（可请教学习） Knowledge AI knows but you don't (can learn from AI)
  • 未知象限： Unknown Area: 双方都不确定的探索领域（共同创新） Unexplored areas for both (collaborative innovation)

⏰ 完成时间：1周 Completion Time: 1 week

🔧 工具： Tool: 提问框架助手 Questy.top Questioning Framework Assistant Questy.top

演讲者备注： Speaker Notes: 这是一个可选的课后作业，帮助大家把理论用于实践。可以使用Questy.top工具来辅助完成。 This is an optional homework assignment to help you put theory into practice. You can use the Questy.top tool to assist with completion.

课程回顾 Course Review

学习力 = 提问力² × 产品力 (L = Q² × P)

提问力=认知框架（Johari）+提问方法（CRAFT） Questioning Ability = Cognitive Framework (Johari) + Questioning Method (CRAFT)

用提问对知识进行熵减 Use questioning to reduce entropy in knowledge

产品力=？ Product Skills = ?

为学日益，为道日损 Learning Increases Daily, Dao Decreases Daily

为学日益，为道日损，损之又损，以至于无为。
无为而无不为，取天下常以无事；
及其有事，不足以取天下。

求学的人，其知见智巧一天比一天增加；求道的人，这些机巧则一天比一天减少。减少又减少，到最后以至于"无为"的境地。如果能够做到无为，即不妄为，任何事情都可以有所作为。治理国家的人，要经常以不骚扰人民为治国之本，如果经常以繁苛之政扰害民众，那就不配治理国家了。 Those who pursue learning increase their knowledge and skills day by day; those who pursue the Dao decrease these artifices day by day. Decrease and decrease again, until reaching the state of "wu wei" (non-action). If one can achieve wu wei, that is, not acting recklessly, one can accomplish anything. Those who govern a country should always take not disturbing the people as the foundation of governance. If they constantly harass the people with burdensome policies, they are not worthy of governing the country.

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