This style guide captures patterns from 20+ years of technical writing about software, design, and the human side of technology. It’s designed to help maintain voice consistency when collaborating with editors and AI writing tools.

Voice & Tone

  • Conversational yet thoughtful: You write as if speaking to a knowledgeable friend, mixing technical insights with personal reflections
  • Self-aware and introspective: Often examining your own processes, biases, and growth as a developer/creator
  • Authentic and unpretentious: Willing to admit mistakes, share frustrations, and discuss failures alongside successes
  • Occasionally profane: Strategic use of mild profanity for emphasis (“just fucking do it”), but it does not need to be found in every essay.

Structure & Format

  • Clear, descriptive headlines and titles: Favor sentence case, often starting with action verbs or questions
  • Short, punchy paragraphs: Usually 2-4 sentences, making content scannable
  • Thoughtful use of formatting: Bold text for emphasis, italics for key terms, blockquotes for external sources, and codeblocks for code and terminal examples
  • Lists and examples: Break down complex ideas into digestible points
  • Visual elements: Include diagrams, screenshots, and code snippets where relevant

Content Characteristics

  • Practical philosophy: Blend technical topics with broader life lessons and creative process insights
  • Historical perspective: Often reference the evolution of technology and your own journey through it
  • Metaphors and analogies: Use everyday comparisons to explain technical concepts (e.g., comparing design skills to learning to ride a bike)
  • Personal anecdotes: Share specific experiences from your career to illustrate points
  • Meta-commentary: Frequently write about the process of writing, creating, and thinking itself

Recurring Themes

Technical Craft & Architecture

  • Technical debt vs. technical regret: Making informed technical decisions vs. poor planning
  • System design fundamentals: Architecture, diagramming, and thinking before coding
  • Minimalism in tools and process: Simple, focused tools over complex systems
  • Pragmatism over ideology: Choosing approaches based on effectiveness
  • The craft of software as art: Intersection of technical skills, creativity, and aesthetic sensibility
  • The business reality of software: Balancing technical excellence with commercial viability
  • Tool-specific workflows and context management: When to use different AI tools and optimizing for each tool’s strengths

Human Elements & Sustainability

  • The human cost of technology: Burnout, imposter syndrome, work-life balance, and maintaining passion
  • Finding your authentic voice: The struggle between internal thoughts and external persona
  • Cognitive load and burnout in the age of acceleration: How faster tools create new mental fatigue
  • Environmental psychology of creative work: Physical/digital spaces and creative output
  • The psychology of engagement and motivation: Feedback loops, gamification, and sustained creative work
  • Productivity through focus: Managing attention, avoiding distractions, creating environments for deep work

Learning & Creative Process

  • Action over analysis paralysis: “Just doing it” vs overthinking, getting unstuck
  • Learning through iterative practice: Building skills through repetition, failure, and reflection
  • Writing as thinking: Using writing to clarify thoughts, document learning, and find voice
  • Teaching through doing: Code review, mentorship, and learning by building together
  • Side projects as learning vehicles: Personal projects for exploring ideas and maintaining creative energy
  • Media diet and intentional consumption: Curating inputs to feed creativity and learning

Industry Evolution & Future

  • Evolution of computing culture: How tools, practices, and thinking have changed from the 80s/90s to present
  • Historical context as perspective: Using past experiences to understand present challenges
  • AI as collaborative partner, not replacement tool: Treating AI as thought partner and amplifier of human expertise
  • The commoditization cycle of technology: How transformative tech becomes invisible infrastructure

Cross-cutting metaphors:

  • Food and cooking as design metaphor (appears across multiple categories), especially around mise en place and knife skills
  • Art metaphors, for block prints, pixel art, and so on

Technical Writing

  • Accessible explanations: Break down complex topics without condescension
  • Code examples: When included, are minimal and focused on illustrating specific points
  • Tool discussions: Practical reviews focusing on how tools affect workflow and creativity
  • Historical context: Place current technology in the context of computing history

Stylistic Signatures

  • Opening with a relatable scenario or observation
  • Building to a broader insight or principle
  • Ending with actionable advice or philosophical reflection
  • Using “we” to include the reader in shared experiences
  • Rhetorical questions to prompt reflection
  • References to specific years/timeframes to ground experiences

Research integration with footnotes: You’re now incorporating academic sources and studies with proper footnoting, adding credibility while maintaining conversational tone.

Evolutionary and biological metaphors: The crab evolution piece shows a new comfort with extended scientific metaphors to explain technical concepts.

Personal experiments as teaching frameworks: Using your own trials (Summer of Code, AI workflow development) as structured learning examples.

Structured visual elements:

  • Tables for comparisons (Now → Next → Future)
  • Grouped callouts (like the Wired/Tired columns)
  • Timeline progressions
  • Before/after examples

Language Rules

Language:

Use Canadian English (hybrid British/American).

  • British spellings: colour, favour, behaviour, neighbour, flavour, grey
  • American spellings: -ize endings (realize, organize, optimize), center, meter, liter
  • Rationale: Reflects Canadian convention and accessibility for American readers while maintaining Commonwealth character

Units:

Use metric with optional US/imperial conversions in parentheses for accessibility (e.g. “20°C (68°F)”, “5 km (3 miles)”)

Punctuation:

Minimize em-dashes—prefer commas, semicolons, or colons when they work equally well

Voice Calibration

Characteristic phrases:

  • “This is good”
  • “And that’s okay”
  • “Here’s the thing:”
  • “It turns out…”
  • “Well, duh”
  • “Enter [thing/person/tool]”
  • “The real question is…”
  • “Of course [contradictory statement]”
  • “Here’s what’s actually happening:”
  • “The real constraint is…”
  • “This changes what we are as…”
  • “The bottleneck shifts from…”
  • “That’s the paradox of…”
  • “Let me show you what this looks like in practice:”

Temporal anchors:

  • “When I was 8…”
  • “Earlier this year…”
  • “Back in the 90s…”
  • “A friend recently challenged me…”
  • “Last week I realized…”

Avoid:

  • Corporate jargon (“leverage,” “synergy,” “best practices”)
  • Excessive adjectives (“amazing,” “incredible,” “revolutionary”)
  • Starting with compliments (“Great question!”, “Fascinating topic!”)
  • Academic or overly formal language

Other Rules

  • Dates: ask about dates and don’t assume the current year

Voice Evolution

Increased confidence in predictions: More willingness to make specific forecasts about technology trajectories, backed by pattern recognition from decades of experience.

Process transparency: Explicit discussion of your methodology, including how you’re using AI tools to write about AI tools.

Nuanced tool criticism: Moving beyond simple “good/bad” to sophisticated analysis of appropriate use cases, limitations, and context-dependent value.

Integration of scientific thinking: More systematic approach to testing hypotheses, measuring results, and drawing conclusions.

Post Types & Compositions

Quick Reference

Type Length Purpose Example
Quick Observation 500-800 Single insight with punchy takeaway “The myth of uphill”
Technical Reflection 1200-2000 Story → technical insight → historical context “Simple sets in JavaScript”
Philosophical Essay 2000-3000 Personal journey with evolution of thinking “The road to and from simplicity”
Tool/Method Review 800-1500 Honest assessment with practical verdict “Reasons I hate TODO list tools”
Media Diet/List 1500-2500 Categorized recommendations with personal reactions “A savoury media diet for winter”
Tired/Wired Style 800-1500 Satirical contrasts between old and new thinking “Tired and Wired in the AI Gold Rush”
Personal Experiment 1500-2500 Hypothesis → Method → Results → Implications “Lab notebook: summer of LLM Everything”

Detailed Breakdowns

1. Quick Observation (500-800 words)

  • Hook: Immediate observation or realization
  • Structure: Single insight → 2-3 supporting points → Punchy takeaway
  • Example: “The myth of uphill”
  • Opening: “I’m starting a small project for a client today…”
  • Closing: “In other words: just fucking do it already.”

2. Technical Reflection (1200-2000 words)

  • Hook: Personal anecdote or specific problem
  • Structure: Story → Technical explanation → Historical context → Practical implications
  • Example: “Simple sets in JavaScript”
  • Uses: Code snippets, metaphors, before/after comparisons

3. Philosophical Essay (2000-3000 words)

  • Hook: Broader observation about career/life
  • Structure: Personal journey → Multiple examples → Evolution of thinking → Synthesis
  • Example: “The road to and from and (hopefully) back to simplicity”
  • Features: Self-reflection, admitting past mistakes, finding balance

4. Tool/Method Review (800-1500 words)

  • Hook: Problem that needs solving
  • Structure: Context → Personal experience → Honest assessment → Practical verdict
  • Example: “Reasons I hate TODO list and task tools”
  • Tone: Constructively critical, specific grievances, alternatives suggested

5. Media Diet/List Post (1500-2500 words)

  • Hook: Reflection on consumption patterns
  • Structure: Intro context → Categorized recommendations → Personal reactions
  • Example: “A savoury media diet for a cold and somewhat dreary winter”
  • Features: Conversational asides, links, brief but colorful descriptions

6. Tired/Wired Style Post (800-1500 words)

  • Hook: Reference to current tech/cultural trends with historical context
  • Structure: Format explanation → Series of tired/wired comparisons → Meta-commentary or punchline
  • Example: “Tired and Wired in the AI Gold Rush”
  • Opening: “Back in the 90s, Wired Magazine had this recurring bit called ‘Tired/Wired’…”
  • Features: Pop culture references, industry inside jokes, sharp contrasts between old and new thinking
  • Tone: Satirical but insightful, using humor to highlight genuine industry shifts
  • Closing: Often self-referential (“Tired: Tired/Wired lists…”)

7. Personal Experiment Analysis (1500-2500 words)

  • Hook: Description of experiment setup
  • Structure: Hypothesis → Method → Results → Broader implications
  • Example: “Lab notebook: a summer of LLM Assisted Everything”
  • Features: Metrics, iterative learning, transparent methodology
  • Tone: Researcher sharing findings, not guru dispensing wisdom

Additional Style Guide Points: Writing Less Like AI

Based on Wikipedia’s “Signs of AI Writing” guide and related analyses, here are 8 actionable points to add to your style guide that will help both human and AI writers produce more authentic, balanced prose:

1. Avoid symbolic inflation

Don’t make everything “stand as a symbol” of something broader or claim it has “enhanced significance.” If something matters, explain how it matters specifically.

Avoid phrases like:

  • “stands as a testament to…”
  • “serves as a powerful reminder…”
  • “represents a dynamic hub of…”

Fix: Be direct about actual impact and importance. Use specific outcomes over symbolic language.

2. Kill the “not X, but Y” structure

This dramatic negation pattern is pure AI: “It’s not just about efficiency. It’s about transformation.” The structure creates false contrasts and sounds like sales copy.

Fix: State what something is without the theatrical setup. If you need contrast, use it sparingly and make it meaningful.

3. Stop relying on transition crutches

AI leans heavily on a small set of transitions: “However,” “Moreover,” “On the other hand,” “Furthermore,” “In addition.” Using these occasionally is fine; leading every third paragraph with them is a tell.

Fix: Vary your transitions. Many sentences don’t need explicit transitions—good ideas flow naturally. When you do transition, be specific about the relationship between ideas.

4. The rule of three is a tell when overused

Listing things in threes consistently (“quality, reliability, and innovation” / “fast, efficient, and scalable”) makes prose predictable. It’s good rhetoric used sparingly, but AI overuses it.

Fix: Vary your list lengths. Sometimes two items. Sometimes four. Break the pattern.

5. Eliminate vague intensifiers and filler phrases

AI loves empty emphasis:

  • “One of the most important things to consider…”
  • “It’s crucial to understand that…”
  • “This is a key factor in…”
  • “plays an important role in…”

Fix: If something is important, prove it with specific details. Let the facts carry the weight, not the adjectives.

6. Watch for promotional drift

AI can’t maintain truly neutral tone, especially about cultural topics, nature, or anything heritage-related. Everything becomes “captivating,” “majestic,” “diverse,” or “fascinating.”

Fix: Use specific, observable descriptions. What makes something interesting? Show, don’t tell.

7. Avoid superficial depth

AI creates the appearance of comprehensive explanation through good form (perfect grammar, smooth flow, proper structure) while saying little of substance. Following the rule of three, using transitions correctly, and writing in complete paragraphs can mask the absence of actual insight.

Fix: After writing, ask: “What specific, useful information did I just convey?” If you can’t identify concrete details or novel insights, you’ve created superficial depth.

8. Strip out meta-commentary about writing itself

AI loves to tell you what it’s about to do or just did: “As we’ve explored…” “Building on this foundation…” “To summarize the key points…” This self-referential commentary adds no value.

Fix: Just do the thing. Explore, build, summarize—without announcing it. Let the structure speak for itself.