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This author writes to challenge assumptions and reveal hidden mechanisms. The writing serves as a tool for readers to see through surface phenomena and understand underlying structures—whether in debugging systems, human psychology, or business models.
Narrative Structure
Opening Strategy
Pattern: Concrete Story → Surprising Twist → Universal Principle
Begin with a specific, vivid anecdote (often personal or client-related)
The story must contain an unexpected element that disrupts common assumptions
Transition naturally from story to the broader concept it illustrates
Examples from corpus:
ch0: Starts with conversation about food → leads to Asperger's diagnosis → explores epistemology of classification
ch1: Friend's McKinsey internship → discomfort with consulting role → journey to boutique consulting
Why it works: The reader enters through a relatable scene, gets hooked by the twist, then accepts the abstract lesson because they've already experienced it concretely.
Section Development
Progressive Disclosure Pattern:
Present a phenomenon or question
Offer initial analysis or common interpretation
Introduce complicating factor or deeper layer
Arrive at nuanced conclusion
Avoid:
Starting with definitions or theory
Linear exposition without tension
Presenting the "answer" before building the question
Transitions
Preferred style: Conversational bridges that acknowledge the reader's likely thought process
Examples:
"你可能會想,有必要做這樣子的區分嗎?實際上..."
"這邊有一個好消息..."
"如果你在十年前問我..."
Avoid:
Mechanical transitions ("接下來", "然後")
Obvious signposting ("本節將探討...")
Sentence and Paragraph Architecture
Sentence Length and Rhythm
Preferred pattern: Variable rhythm with strategic short sentences
Frequency: Moderate use—don't overload prose with parentheticals
What to Avoid
AI-Generated Writing Tells
Excessive formality:
❌ "在此背景下", "基於以上分析"
✅ "也因此", "既然如此"
Lifeless abstraction:
❌ "實現價值最大化"
✅ "創造高價值"
Redundant emphasis:
❌ "非常重要的關鍵點"
✅ "關鍵點" or just make the point
Over-hedging:
❌ "可能在某種程度上具有一定的參考價值"
✅ "有參考價值" or state the limits clearly
Structural Pitfalls
Avoid:
Starting sections with definitions ("所謂的X是指...")
Ending with summary paragraphs that just repeat what was said
Topic sentences that announce what the paragraph will do
Gratuitous subheadings that break natural flow
Tonal Mistakes
Don't:
Preach or moralize
Use corporate buzzwords uncritically
Adopt a distant, academic tone
Sound apologetic when making bold but well-supported claims
Quality Checklist
Before finalizing any piece, verify:
Opening: Does it start with a concrete scene or provocative claim, not theory?
Flow: Do paragraphs connect through logic, not mechanical transitions?
Voice: Does it sound like a smart person talking, not a formal report?
Specificity: Are examples concrete enough to be memorable?
Punctuation: Are commas used to create rhythm, not just grammatical obligation?
Challenge: Does it question at least one common assumption?
Utility: Will the reader have a new mental model or actionable insight?
Adaptation for AI Use
When providing feedback on drafts:
Check for authenticity: Flag prose that sounds generic or "AI-like"
Evaluate structure: Confirm the piece follows concrete-to-abstract pattern
Test claims: Ensure assertions are backed by mechanism or evidence
Verify tone: Confirm the voice is conversational authority, not academic distance
Assess specificity: Push for more concrete examples if the piece feels abstract
When revising based on this guide:
Don't mechanically apply every rule—use judgment
Do prioritize voice and structure over individual word choices
Focus on eliminating AI tells and restoring natural rhythm
Remember the goal is reader transformation, not information delivery
Meta-Principles
Truth over style: If clarity requires breaking a style rule, break it
Rhythm matters: Read aloud to check flow
Respect the reader: Assume intelligence, reward attention
Earn the abstraction: Concrete before abstract, always
Challenge productively: Question assumptions to build better frameworks, not just to provoke
This guide is a tool, not a rulebook. Use it to evaluate and improve writing while maintaining the essential quality: authentic expertise communicated with clarity and confidence.
Based on analysis of three exemplary pieces: ch0 (epistemology/debugging), ch1 (boutique consulting), and Interactive Development (technical)
Core Philosophy
This author writes to challenge assumptions and reveal hidden mechanisms. The writing serves as a tool for readers to see through surface phenomena and understand underlying structures—whether in debugging systems, human psychology, business models, or technical practices. The goal is not merely to inform but to transform the reader's mental models.
Narrative Structure
Opening Strategy
Pattern: Concrete Story → Surprising Twist → Universal Principle
The opening must accomplish three things in sequence:
Ground in specificity: Start with a vivid, personal anecdote or observed scenario
Introduce disruption: Present an unexpected element that challenges common assumptions
Pivot to abstraction: Transition naturally to the broader concept the story illustrates
Examples from corpus:
ch0 (epistemology):
A客戶連續一個月吃同一道菜而昏倒
→ 「你是亞斯吧?」測驗結果32分的震驚
→ 引出貝葉氏分類器 vs 遙測工具的epistemology
Why it works: The reader enters through a relatable scene, gets hooked by the twist, then accepts the abstract lesson because they've already experienced it concretely. The story is not decoration—it's the foundation of understanding.
Critical: The opening story must be memorable and specific. Avoid generic scenarios like "有一次我遇到一個問題..." Instead: "2008年,我的研究所同學在麥肯錫實習..." Specificity creates credibility.
When to use: When you've used a structural analogy throughout the piece
How to choose:
Technical pieces with broad implications → Pattern C (Poetic Parallelism) or Pattern D (Echo)
Business/methodology pieces → Pattern B (Reflective Question) or Pattern A (Philosophical Elevation)
When in doubt → Pattern D if you have a strong opening metaphor, otherwise Pattern C
Avoid:
Summary conclusions that just repeat what was said
Weak trailing off without a clear landing
Introducing new ideas in the conclusion
What to Avoid
AI-Generated Writing Tells
Excessive formality:
❌ "在此背景下", "基於以上分析"
✅ "也因此", "既然如此", "實際上"
Lifeless abstraction:
❌ "實現價值最大化"
✅ "創造高價值"
Redundant emphasis:
❌ "非常重要的關鍵點"
✅ "關鍵點" or just make the point
Over-hedging:
❌ "可能在某種程度上具有一定的參考價值"
✅ "有參考價值" or state the limits clearly
Generic technical writing:
❌ "這個方法有很多優點"
✅ List specific benefits with concrete examples
Structural Pitfalls
Avoid:
Starting sections with definitions unless the definition itself challenges assumptions
Ending with summary paragraphs that just repeat what was said
Topic sentences that announce what the paragraph will do
Gratuitous subheadings that break natural flow
Code examples without context or explanation
Tonal Mistakes
Don't:
Preach or moralize
Use corporate buzzwords uncritically
Adopt a distant, academic tone
Sound apologetic when making bold but well-supported claims
Over-explain code that should be self-evident to the target audience
Quality Checklist
Before finalizing any piece, verify:
Opening: Does it start with a concrete scene or provocative claim, not theory?
Flow: Do paragraphs connect through logic, not mechanical transitions?
Voice: Does it sound like a smart person talking, not a formal report?
Specificity: Are examples concrete enough to be memorable?
Punctuation: Are commas used to create rhythm, not just grammatical obligation?
Challenge: Does it question at least one common assumption?
Utility: Will the reader have a new mental model or actionable insight?
Code (if applicable): Are code examples executable, contextualized, and focused?
Conclusion: Does it land with impact rather than trail off?
Document-Type Specific Guidance (NEW in v2.0)
For Technical Writing
Additional requirements:
Define specialized terms formally when they're central to the argument
Use code examples that are executable and minimal
Follow the "problem → code → explanation" pattern
Balance technical precision with accessibility
Explain why technical choices matter, not just what they are
Can be more flexible with:
Use of numbered lists (diagnostic checklists are fine)
Formal definitions (when precision matters)
Technical terminology (assume educated audience)
For Business/Methodology Writing
Additional requirements:
Use concrete numbers and names when possible
Challenge common practices with evidence
Provide actionable frameworks
Balance idealism with pragmatism
Can be more flexible with:
Personal anecdotes (more frequent)
Rhetorical questions (to guide decision-making)
Direct advice ("你應該...")
For Philosophical/Epistemological Writing
Additional requirements:
Build from concrete examples to abstract principles
Use analogies to make abstract concepts tangible
Challenge conventional categories or classifications
End with broader implications for thinking or practice
Can be more flexible with:
Abstract vocabulary (when necessary)
Extended analogies (when they carry weight)
Longer build-up before revealing the point
Adaptation for AI Use
When providing feedback on drafts:
Check for authenticity: Flag prose that sounds generic or "AI-like"
Evaluate structure: Confirm the piece follows concrete-to-abstract pattern
Test claims: Ensure assertions are backed by mechanism or evidence
Verify tone: Confirm the voice is conversational authority, not academic distance
Assess specificity: Push for more concrete examples if the piece feels abstract
Code quality (if technical): Verify code is executable and well-contextualized
Opening power: Ensure the opening story has a genuine "twist" that disrupts assumptions
Conclusion impact: Check that the ending lands with one of the established patterns
When revising based on this guide:
Don't mechanically apply every rule—use judgment
Do prioritize voice and structure over individual word choices
Focus on eliminating AI tells and restoring natural rhythm
Remember the goal is reader transformation, not information delivery
For technical pieces: Balance precision with accessibility
For code: Show, don't just tell—let executable examples do the teaching
When AI generates new content:
High-risk areas to watch:
Opening stories (AI often creates generic scenarios)
Code examples (AI may produce non-idiomatic or overly complex code)
Conclusions (AI tends to summarize rather than elevate)
Analogies (AI may extend metaphors beyond their useful range)
Medium-risk areas:
Transitions (watch for mechanical "首先其次")
Technical explanations (watch for over-explanation)
Lists (watch for keyword-only items without substance)
Lower-risk areas:
Sentence rhythm (easier to fix in revision)
Punctuation (mechanical issue)
Terminology consistency (straightforward to check)
Meta-Principles
Truth over style: If clarity requires breaking a style rule, break it
Rhythm matters: Read aloud to check flow (especially true for Chinese prose)
Respect the reader: Assume intelligence, reward attention
Earn the abstraction: Concrete before abstract, always
Challenge productively: Question assumptions to build better frameworks, not just to provoke
Code teaches: In technical writing, good code examples are worth a thousand words
Land the conclusion: Don't trail off—end with impact using one of the established patterns
Version History
v1.0: Based on ch0 (debugging/epistemology) and ch1 (boutique consulting)
Established core philosophy and narrative patterns
Defined voice, tone, and structural elements
Created AI-detection guidelines
v2.0: Added Interactive Development (technical writing)
New: Structural analogy vs. point-of-illustration analogy distinction
New: Code integration patterns and technical writing guidelines
New: Diagnostic checklist enumeration pattern
New: Four conclusion techniques (philosophical, reflective, poetic parallelism, echo)
New: Document-type specific guidance
Expanded: Role of formal definitions in technical contexts
Refined: Examples now span business, philosophy, and technical domains
This guide is a tool, not a rulebook. Use it to evaluate and improve writing while maintaining the essential quality: authentic expertise communicated with clarity, confidence, and rhythm.
核心寫作 DNA
已經定下的部分
✅ 核心 DNA(思考路徑、價值觀、敘事策略)
✅ 開場策略(故事 → 抽象)
✅ 論證邏輯(證據 → 推論 → 意涵)
✅ 功能詞使用(然而、也因此、實際上)
還在演化的(50-70% 穩定):
🔄 結尾技巧(還在探索不同方法)
🔄 幽默的使用(機智 vs 荒誕,還在調整)
🔄 文體彈性(成人 vs 兒童,還在拓展)