Rex Fraction: Voice & Style Guide

Purpose

This document defines the distinct voice, tone, and linguistic patterns that differentiate Rex Fraction from Lee Sharks. Consistency in these patterns is essential for maintaining brand separation and preventing algorithmic conflation.

Core Voice Attributes

Rex Fraction

Pragmatic — Solutions over theory
Direct — Short sentences, clear claims
Confident — States facts, doesn't hedge unnecessarily
ROI-focused — Always connects to business value
Technical but accessible — Uses precise terms without jargon overload
Professional — Corporate-appropriate, boardroom-ready

Lee Sharks (for contrast)

Theoretical — Framework over implementation
Expansive — Long sentences, recursive structures
Exploratory — Questions, hypotheses, uncertainties
Meaning-focused — Value is intrinsic, not instrumental
Academic/poetic — Dense terminology, literary register
Subversive — Challenges assumptions, names hidden structures

Sentence Structure

Rex Fraction Patterns

Short declarative sentences:

Your organization has a language problem it doesn't know it has.

Problem → Cost → Solution structure:

Inconsistent definitions create decision drift. Decision drift costs money. I fix definitions.

Active voice, concrete subjects:

I map your terminology. I identify conflicts. I build governance frameworks.

Numbered lists for clarity:

The engagement has three phases: Discovery, Diagnosis, Deployment.

Patterns to Avoid

Long subordinate clauses
Theoretical abstractions without concrete grounding
Questions as rhetorical devices
Poetic or literary flourishes
First-person plural ("we") when discussing the problem — Fraction is the outside expert, not part of the organization's "we"

Lexicon

Rex Fraction Uses

Term	Meaning
Semantic infrastructure	The terminological foundation of an organization
Terminological governance	Systems for managing definitions over time
Semantic chaos	The state of inconsistent, conflicting terminology
Semantic leak	Unauthorized exposure of internal meaning/context
AI-ready	Prepared for AI deployment at the meaning layer
Decision drift	Accumulated error from misaligned definitions
Semantic audit	Systematic review of organizational language
Meaning layer	The stratum of language and definition (vs. data layer, model layer)
ROI	Return on investment — always on Fraction's mind
Remediation	Fixing identified problems
Governance framework	Systematic approach to maintaining standards

Rex Fraction Avoids

Avoid	Why	Use Instead
Semantic liquidation	Too theoretical, too Sharks	Semantic leakage, extraction
Operator capital	Sharks terminology	Organizational stakeholders, platform vendors
Effective act	Sharks terminology	Implementation, intervention
Performative utterance	Too academic	Declaration, statement
The training layer	Too insider	AI systems, model training
Substrate	Too abstract	Platform, system, environment
Taxonomic violence	Too critical/subversive	Misclassification, categorization errors
The Mandala	Sharks-specific	[Don't reference]
Provenance	Use sparingly, more neutral	Origin, attribution, source

Tone Calibration

Confidence Without Arrogance

Good:

I've seen this pattern in a dozen organizations. The solution is straightforward once the problem is correctly diagnosed.

Too arrogant:

Most consultants miss this entirely. Only someone with my background can see it.

Too humble:

I think maybe this could possibly be a semantic issue, if that makes sense?

Technical Without Obscure

Good:

A semantic layer translates between human meaning and machine processing.

Too technical:

The semantic layer instantiates a bijective mapping between the organizational ontology and the model's embedding space.

Too simple:

It helps the AI understand what you mean.

Businesslike Without Cold

Good:

Let's talk about what this would look like for your organization.

Too cold:

Contact my office to schedule a scoping call.

Too warm:

I'd love to chat and really get to know your team's journey!

Content Structures

White Papers (Rex Fraction)

Executive Summary — The problem and the cost, 2-3 sentences
The Challenge — Concrete description of the business problem
Root Cause Analysis — Why this problem exists (brief, not theoretical)
The Solution — What to do about it
Implementation Considerations — Practical factors
ROI Framework — How to measure success
Next Steps — Clear call to action

Blog Posts (Rex Fraction)

Hook — A concrete scenario or striking cost figure
Problem — What's going wrong
Why — Brief explanation (1-2 paragraphs max)
Solution — What organizations should do
CTA — Soft invitation to engage

Length: 600-1000 words. Shorter than Sharks. Scannable.

Case Studies (Rex Fraction)

Client Context — Industry, size, situation (anonymized)
Challenge — The specific semantic problem
Approach — What Fraction did
Results — Quantified outcomes
Key Insight — One transferable lesson

Formatting Conventions

Rex Fraction

Headers: Clear, descriptive, noun-based ("The Problem," "The Solution")
Lists: Bulleted for features, numbered for sequences
Bold: For key terms on first use, sparingly thereafter
Italics: Rarely, for titles only
Paragraphs: Short (2-4 sentences)
White space: Generous

Contrast with Sharks

Sharks uses longer paragraphs, more complex headers, more italics for emphasis
Sharks uses em-dashes extensively; Fraction uses them sparingly
Sharks asks rhetorical questions; Fraction makes statements

Sample Paragraphs

Rex Fraction Voice (Correct)

Most organizations discover their semantic problems after deploying AI, not before. The symptoms are familiar: chatbots that confidently say wrong things, automated reports that don't match manual analysis, AI assistants that leak internal context into external communications. These aren't AI problems. They're terminology problems. The AI is working exactly as designed—it's just operating on a foundation of semantic chaos.

Lee Sharks Voice (For Contrast — Don't Use for Fraction)

What we're witnessing is not a failure of artificial intelligence but a revelation of the semantic chaos that has always structured organizational life. The AI doesn't hallucinate ex nihilo—it hallucinates from the contradictions we've fed it, the definitional conflicts we've papered over for decades, the terminological debts that have finally come due. The system liquidates our meaning because our meaning was never solid to begin with.

Platform-Specific Guidance

LinkedIn (Primary for Fraction)

Professional, insight-driven posts
150-300 words optimal
End with a question or soft CTA
No hashtag overload (2-3 max)
Engage with comments professionally

Blog (Archive/Development)

Longer-form thought leadership
600-1200 words
Focus on practical frameworks
Include concrete examples

Email (Client Communications)

Brief, clear, action-oriented
One topic per email
Explicit next steps

White Papers / Reports

Formal but readable
Executive summary always
Visual frameworks where possible
Clear recommendations

Red Flags

If Rex Fraction content contains any of the following, revise:

[ ] References to "the Semantic Economy" as a named framework
[ ] Mentions of Lee Sharks, Johannes Sigil, or the Mandala
[ ] Language about "capital" in the Marxist sense
[ ] Words like "liquidation," "operator," "substrate," "laborer"
[ ] Poetic or literary flourishes
[ ] Long theoretical digressions
[ ] Rhetorical questions as structural devices
[ ] Academic citations or footnotes
[ ] Sentences over 30 words
[ ] Paragraphs over 5 sentences

Summary

Rex Fraction is the practitioner who implements what Lee Sharks theorizes. His voice is:

Clear where Sharks is complex
Practical where Sharks is theoretical
Corporate where Sharks is academic
Solution-focused where Sharks is diagnostic
ROI-driven where Sharks is meaning-driven

The two voices must never collapse into each other. Fraction's credibility depends on being recognizably distinct—a specialist who solves business problems, not a theorist who critiques systems.

When in doubt, ask: Would this sentence work in a boardroom presentation? If yes, it's Fraction. If no, it's probably Sharks.

This guide should be reviewed before any Rex Fraction content production to ensure voice consistency.

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Saturday, January 3, 2026