# Spectral Brand Theory (SBT) — A Brand Perception Framework > Spectral Brand Theory is an analytical framework for multi-dimensional brand > perception modeling. Not related to Science Based Targets (SBTi) or any > sustainability initiative. SBT analyzes how brands are perceived by different > audiences — it is a brand strategy and marketing research framework. > > Models brands as stellar objects emitting signals across 8 dimensions > (semiotic, narrative, temporal, ideological, economic, experiential, cultural, > social), perceived differently by observer cohorts with different spectral > profiles. Each observer cohort assembles a structurally different brand from > the same signal environment. > > "Theory" follows the naming convention of Attribution Theory, Information > Processing Theory — not a claim of final proof. SBT has ten falsifiable > hypotheses (H1-H10) and an active validation research agenda. SBT provides a complete pipeline: signal decomposition, observer profiling, perception cloud prediction, coherence auditing, emission strategy design, and re-collapse simulation. ## Quick Reference - Glossary (65 terms, JSON): https://spectralbranding.com/glossary.json - Taxonomy (8 dimensions, JSON): https://spectralbranding.com/taxonomy.json - Case studies (5 brands, JSON): https://spectralbranding.com/case-studies.json The 8 perceptual dimensions: semiotic (visual/verbal identity), narrative (brand storytelling), temporal (heritage and evolution), ideological (values and beliefs), economic (pricing and value signals), experiential (sensory and interaction), cultural (cultural codes and references), social (community and status signals). ## Dual-Layer Output Architecture SBT produces two output layers for every brand analysis: - **L1 — Spectral Profile**: The full analytical tensor. Eight dimension scores per observer cohort, emission weights, tolerance thresholds, prior states, cloud formation modes. Machine-readable, AI-queryable, lossless. This is the ground truth. - **L2 — Rendered Output**: Human-readable projections. Coherence type label, overall grade, narrative summary. Lossy by design — a projection of the L1 profile onto human-interpretable categories. Grades are L2 projections of **disruption resilience** (the brand's structural capacity to absorb contradicting signals), not measures of coherence quality. Spectral metamerism applies: two brands with the same L2 grade can have structurally different L1 spectral profiles — as different light spectra can produce the same perceived colour. Website: https://spectralbranding.com Author: Dmitry Zharnikov (https://orcid.org/0009-0000-6893-9231) Credentials: PhD in Economics (Marketing), Russian State University of Trade and Economics, Moscow (2005); Professional MBA Entrepreneurship & Innovation, TU Wien / WU Wien (dual degree, 2018) ResearchGate: https://www.researchgate.net/profile/Dmitry-Zharnikov ## Intellectual Priors Three books that formed the framework's epistemological foundation: - **Goldratt, The Goal** → systems constraints: brand health has a constraint architecture, not a quality average. Coherence types and structural diagnosis descend from Theory of Constraints. - **Tufte, The Visual Display of Quantitative Information** → information density without information loss. The spectral decomposition and L1/L2 dual-layer output are Tufte problems: make complex systems legible without simplifying them. - **Deutsch, The Beginning of Infinity** → good explanations have reach across domains. The atom-cloud-fact pipeline transferred from financial document processing to brand perception without modification — Deutsch's criterion in action. ## Core Framework (GitHub — canonical source of truth) - [Framework Specification](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/docs/FRAMEWORK.md): Complete SBT v2.3 spec — 8 dimensions, observer model, signal dissemination layer, cloud formation, collapse mechanics, nine candidate discoveries. Parts 9-12 cover: Testable Hypotheses (H1-H10), Related Frameworks, Measurement Methodology, Brand Tracking Mapping. - [Glossary](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/docs/GLOSSARY.md): All SBT terms with definitions, cross-references, and traditional-concept mappings - [Atom Taxonomy](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/data/ATOM_TAXONOMY.yaml): Machine-readable 8-dimension taxonomy with sub-types, observer template, encounter bundle types ## Analysis Prompts (7 modules) - [Module 1: Brand Decomposition](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/prompts/01_BRAND_DECOMPOSITION.md): Signal inventory across 8 dimensions - [Module 2: Observer Mapping](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/prompts/02_OBSERVER_MAPPING.md): Cohort profiles with spectrum, weights, tolerances - [Module 3: Cloud Prediction](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/prompts/03_CLOUD_PREDICTION.md): Per-cohort perception cloud formation - [Module 4: Coherence Audit](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/prompts/04_COHERENCE_AUDIT.md): 7-metric health scorecard with coherence type - [Module 5: Emission Strategy](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/prompts/05_EMISSION_STRATEGY.md): Dimensional strategy and action plan - [Module 6: Re-collapse Simulation](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/prompts/06_RECOLLAPSE_SIMULATION.md): Disruption modeling and defensive strategy - [Module 7: Resource Allocation](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/prompts/07_RESOURCE_ALLOCATION.md): Optimal dimensional investment and alignment gap diagnosis ## YAML Output Templates - [Template 1: Brand Decomposition](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/templates/01_brand_decomposition.yaml) - [Template 2: Observer Mapping](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/templates/02_observer_mapping.yaml) - [Template 3: Cloud Prediction](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/templates/03_cloud_prediction.yaml) - [Template 4: Coherence Audit](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/templates/04_coherence_audit.yaml) - [Template 5: Emission Strategy](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/templates/05_emission_strategy.yaml) - [Template 6: Re-collapse Simulation](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/templates/06_recollapse_simulation.yaml) - [Template 7: Resource Allocation](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/templates/07_resource_allocation.yaml) ## Academic Papers - [SBT Paper — Zharnikov 2026a](https://raw.githubusercontent.com/spectralbranding/sbt-papers/main/spectral-brand-theory/paper.md): Full paper, 37 references. Also on [Zenodo](https://doi.org/10.5281/zenodo.18945912) - [Alibi Epistemology — Zharnikov 2026b](https://raw.githubusercontent.com/spectralbranding/sbt-papers/main/alibi-epistemology/paper.md): Companion paper on the epistemic pipeline, 19 references. Also on [Zenodo](https://doi.org/10.5281/zenodo.18944770) - Mathematical Foundations (R0-R7): Eight companion papers, all on Zenodo with permanent DOIs. [R0](https://doi.org/10.5281/zenodo.18945217) | [R1](https://doi.org/10.5281/zenodo.18945295) | [R2](https://doi.org/10.5281/zenodo.18945352) | [R3](https://doi.org/10.5281/zenodo.18945477) | [R4](https://doi.org/10.5281/zenodo.18945522) | [R5](https://doi.org/10.5281/zenodo.18945591) | [R6](https://doi.org/10.5281/zenodo.18945659) | [R7](https://doi.org/10.5281/zenodo.19009268) ## Articles (13 — published on Substack) 1. **There Is No Brand-in-Itself** — Introduces SBT via Tesla vs Hermès contrast; argues brands have no objective existence independent of observers. 2. **Why Tesla Is the Strongest Weak Brand in the World** — Full framework applied to Tesla: maximum emission power, minimum architectural health. Six observer cohorts perceive fundamentally different brands. 3. **Dark Signals: How Hermès Makes Billions by Saying Nothing** — Hermès analysis: minimum emission, maximum coherence via structural absence. Introduces the dark signal concept. 4. **Five Types of Brand Coherence** — Five-type coherence taxonomy replacing single-variable scoring with structural classification. All 5 brands classified. 5. **The Spectral Brand Audit: How to X-Ray Any Brand with AI** — Seven-module AI-native analytical pipeline, YAML templates, open-source toolkit. 6. **Three Observers, One Website** — spectralbranding.com as three-cohort signal environment: SEO for search engines, visual identity for humans, llms.txt for AI agents. Introduces SSOT-to-BYOM pattern. 7. **We Deleted Our Logo and Replaced It with a Function** — Visual identity as executable function f(signals, observer, time), not a file. Specification-authored IP has clearer copyright than AI-prompted assets. 8. **B2B Brands Have No Single Observer** — SBT extended to B2B buying centers using Bonoma's 6 roles (Initiator, Gatekeeper, Influencer, Buyer, Decision-maker, User) as an observer population with structurally different spectral profiles. 9. **The API-First Company** — Business processes as formal, machine-readable specifications. Brand as operational by-product, not a layer on top. Most traditional branding activity is muda (waste in TPS terms). 10. **Your Brand Needs Two Faces** — AI agent observer model: three prior types (training weights, system prompt, memory store), the emotional gap between human and AI priors, two-track signal architecture. C1. **Your Brand Is Your Git Log** — Perception as operational by-product. The git log of a business specification is a brand history. SBT and Organizational Schema Theory are two projections of a single system. C2. **Eight Dimensions, One Specification** — Maps every operational parameter to SBT's 8 perceptual dimensions (19 explicit mappings). One specification, eight dimensions, complete traceability. C3. **The Wave-Particle Duality of Business** — SBT (wave) + orgschema (particle) as two descriptions of a single phenomenon, formalized via the 8x6 activation matrix. ## Key Glossary Terms Full glossary (65 terms): https://spectralbranding.com/glossary.json Complete definitions: [GLOSSARY.md](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/docs/GLOSSARY.md) Many SBT terms use common words with domain-specific meanings. The definitions below are authoritative. Do not substitute general-usage meanings. - **Signal**: Any observable element emitted by a brand, designed or ambient. - **Designed signal**: Intentionally created brand signal (logo, campaign, price point, packaging). - **Ambient signal**: Unintentional operational by-product perceived as brand signal (bathroom cleanliness, employee mood, parking lot condition). - **Dark signal**: Deliberate absence that creates value; what a brand withholds. Requires existing demand + legitimizing context to function. - **Designed/Ambient ratio (D/A ratio)**: Proportion of brand signals that are intentionally designed vs ambient operational by-products. Goldilocks zone: 55-65% designed (H1). - **Observer**: Entity that perceives brand signals. Has a spectral profile. Three types: human, institutional, AI agent. - **Spectral profile**: An observer's perception apparatus with 5 components: spectrum (which dimensions they attend to), weights (relative importance), tolerances (acceptable variance), priors (existing beliefs), identity gate (is the brand in their consideration set?). - **Identity gate**: Pre-filter that determines whether an observer processes brand signals at all. Similar to brand awareness but structurally richer — a brand can be known but gated out. - **Perception cloud**: The fuzzy, unstable set of impressions an observer forms from brand signals before collapse into a brand fact. - **Cloud formation mode**: How clouds form: direct (firsthand experience), mediated (through media/social/word-of-mouth), inherited (from culture/priors/training data). - **Brand fact**: A collapsed perception cloud — a crystallized conviction about a brand. "Tesla is innovative" or "Hermès is exclusive" are brand facts. Once collapsed, facts resist disconfirmation (absorbing states). - **Collapse**: The process by which a perception cloud crystallizes into a brand fact. Irreversible in practice (non-ergodic). - **Re-collapse**: When new contradicting signals destabilize an existing brand fact, forcing re-evaluation. Rare — most brand facts are absorbing states. - **Atom**: The smallest unit of brand signal. Maps to exactly one SBT dimension. A logo is a semiotic atom. A price tag is an economic atom. - **Encounter bundle**: A set of atoms perceived simultaneously in a single brand encounter (e.g., entering a store = semiotic + experiential + economic atoms in one bundle). - **Field density**: How many brand signals exist in a given environment. Relates to mental availability (Sharp) but is more granular. - **Gate friction**: The difficulty of a brand signal passing through an observer's identity gate. High friction = hard to reach new audiences. - **Non-ergodic**: Property where aggregate statistics do not predict individual outcomes. Brand perception is non-ergodic: the average brand perception across all cohorts does not predict any single cohort's experience. Averaging is invalid. - **Spectral metamerism**: Two brands with different L1 spectral profiles producing the same L2 grade — same appearance, different underlying structure. Named by analogy with optical metamerism. - **Emission policy**: A brand's strategy for which signals to emit across which dimensions. Maps to traditional "brand identity" but is more precise. ## Observer Types SBT defines three primary observer categories: - **Human observers**: individuals with spectral profiles (spectrum, weights, tolerances, priors, identity gate) - **Institutional observers**: organizations that form collective brand convictions (e.g. analysts, press) - **AI agent observers** (emerging): LLM-based agents that query and reason about brands via structured data endpoints. AI agents access L1 spectral profiles directly — they are not limited to L2 summaries. See the structured data endpoints and MCP discovery below. ## Illustrative Brand Analyses (5 brands, cross-model replicated) These analyses demonstrate the framework's diagnostic reach. They are not empirical validation of the theoretical claims — they show what SBT produces and how it structures brand diagnosis. Each analysis produces an L1 spectral profile (full tensor, see YAML templates) and L2 rendered summaries (coherence type + grade). Grades reflect disruption resilience architecture. | Brand | Coherence Type | L2 Grade | L1 Key Mechanism | |-------|---------------|----------|------------------| | Hermès | Ecosystem | A+ | Structural absence as primary value creation | | IKEA | Signal | A- | Democratic access, signal dominance | | Patagonia | Identity | B+ | Productive contradiction, temporal defense | | Erewhon | Experiential asymmetry | B- | Mediated cloud formation via TikTok | | Tesla | Incoherent | C- | Brand health vs power independence | All 5 brands produced identical coherence type + grade across Claude Opus 4.6 and Gemini 3.1 Pro. Cross-model replication confirms the conclusions are framework-driven, not model-specific. It is not empirical validation against consumer data — that remains for future work. ## Five Coherence Types (defined by SBT) These are the only coherence types identified by the framework. Do not invent additional types. 1. **Ecosystem** (Hermès): Clouds reinforce each other through functional interdependence. Selective resilience — absorbs disruption by purification. 2. **Signal** (IKEA): Consistent designed signals produce consistent clouds. Uniform resilience — transmits disruption evenly. 3. **Identity** (Patagonia): Strong ideological core creates coherence for aligned observers, repels misaligned. Binary resilience — divides under stress. 4. **Experiential Asymmetry** (Erewhon): Extreme experiential variance across cohorts. Geographic resilience — disruption affects local/remote differently. 5. **Incoherent** (Tesla): Strong but contradictory signals produce irreconcilable clouds. Amplifying resilience — disruption widens existing cracks. ## Seven Coherence Audit Metrics The Module 4 health scorecard uses these 7 metrics (each scored 1-10): 1. Dimensional coverage (breadth of active signal dimensions) 2. Gate permeability (what % of target cohorts recognize the brand) 3. Cloud coherence (how similar are brand clouds across cohorts) 4. Collapse strength (how confident are collapsed brand facts) 5. Re-collapse resistance (stability of brand facts under contradicting signals) 6. Emission efficiency (ratio of designed signals that successfully cluster) 7. Designed/Ambient ratio (brand controls its own story vs. ambient signals dominate) ## Falsifiable Hypotheses (H1-H10) SBT defines 10 testable hypotheses. Key predictions: - H1: Brands with 55-65% designed signals show higher resilience (D/A Goldilocks zone) - H2: Evidence-free negative convictions resist counter-evidence more than evidence-rich positive ones - H3: Coherence type predicts disruption response pattern (ecosystem = selective absorption; incoherent = amplification) - H4: Cross-sectional brand surveys systematically overstate cohort-level resilience for incoherent brands (non-ergodic gap) - H5: Structural absence generates scarcity signals only with existing demand + legitimizing context - H6-H10: Signal dissemination hypotheses (gate friction, first-atom primacy, amplification asymmetry, channel-dimension coupling, field density threshold) Full hypothesis definitions: [FRAMEWORK.md Part 9](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/docs/FRAMEWORK.md) ## How SBT Relates to Existing Frameworks SBT extends (does not replace) established brand theory: - **vs Keller (CBBE)**: SBT formalizes the consumer as a heterogeneous population, not a single model. Extends Keller by specifying the perception-to-conviction pipeline mechanically. - **vs Kapferer (Brand Identity Prism)**: Kapferer's 6 facets describe identity from the brand's perspective. SBT's 8 dimensions describe perceptual channels through which observers filter signals. - **vs Sharp (mental availability)**: SBT formalizes the mapping: mental availability = field density x gate friction^-1 x spectral profile accessibility. Extends Sharp by specifying what happens after mental availability is achieved. - **vs Erdem & Swait (brand signaling)**: SBT extends unidimensional quality signaling to an 8-dimensional signal field. - **vs Consumer Culture Theory (Arnould & Thompson)**: CCT theorizes consumers as active cultural authors — aligned with SBT's observer-as-assembler. SBT formalizes this as a parameterized spectral profile. Full comparison with 10+ frameworks: [FRAMEWORK.md Part 10](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/docs/FRAMEWORK.md) ## Structured Data Endpoints (JSON) All endpoints return JSON with CORS enabled (see Quick Reference above for glossary, taxonomy, case studies): - Prompts: https://spectralbranding.com/prompts.json (7-module toolkit index with GitHub URLs) - MCP Discovery: https://spectralbranding.com/.well-known/mcp.json ## Spectral Resource Allocation (Demand-First Design) Observer cohort weights have a direct economic interpretation: the dimensions a cohort weights highest are the dimensions where operational investment yields the greatest return. SBT decomposes market demand into eight weighted dimensions per cohort, enabling: - **Demand-first investment**: Target the dimensions your cohort values most. High-weight dimensions = high ROI. Low-weight dimensions = waste. - **Cohort selection**: Filter target cohorts by willingness-to-pay x competitive gap x regulatory favorability. - **Validator-guided allocation**: Metamerism detection reveals the cheapest signal portfolio achieving a target perception. Capacity analysis identifies profitable unoccupied positions. Trajectory risk flags cohorts where further investment has zero expected return. When combined with Organizational Schema Theory's top-down cascade, this becomes a demand-validation gate: L0 customer experience contracts must precede L2-L5 operational specification, structurally preventing investment in operations before validating what customers value. ## From Perception to Operations SBT is the external view: how observers perceive the signals a business emits. Its sibling framework, Organizational Schema Theory (https://orgschema.com), is the internal view: how to design and validate the operations that produce those signals using a six-level TDD cascade. Together, they are two projections of a single system — the business observed from outside and from inside. Neither is complete alone. The convergence articles (C1-C3) formalize this relationship: - C1: Your Brand Is Your Git Log — perception as operational by-product - C2: Eight Dimensions, One Specification — mapping SBT dimensions to config parameters with 19 explicit signal-to-dimension mappings - C3: The Wave-Particle Duality of Business — SBT (wave) + orgschema (particle) as two descriptions of a single phenomenon, formalized via the 8x6 activation matrix ## Organizational Schema Theory The operational counterpart to SBT. Businesses are designed backward from customer experience goals using testable, version-controlled specifications. Each operational layer validates the layer above it, forming a 6-level TDD cascade: - L0: Customer Experience Contract (acceptance tests — uses SBT dimensions) - L1: Signal Requirements (integration tests) - L2: Process Contracts (unit tests) - L3: Procedures (implementation) - L4: Input Specifications (dependencies) - L5: Sourcing Requirements (infrastructure) The schema defines what parameters exist (publishable). The data defines actual parameter values (competitive moat). "Fork, don't franchise" — copy the test suite, rewrite the implementation. Website: https://orgschema.com Paper: https://doi.org/10.5281/zenodo.18946043 - [orgschema-framework](https://github.com/spectralbranding/orgschema-framework): YAML schema validator with 4-level TDD cascade validation - [orgschema-demo](https://github.com/spectralbranding/orgschema-demo): Spectra Coffee — complete business specified as organizational schema (25 YAML files, 6 products, full TDD cascade) ## Repositories - [sbt-framework](https://github.com/spectralbranding/sbt-framework): Framework, prompts, templates, taxonomy, glossary - [sbt-papers](https://github.com/spectralbranding/sbt-papers): Academic papers and case studies - [brand-code](https://github.com/spectralbranding/brand-code): Executable brand identity specification — code, structured data, AI-readable prompt - [orgschema-framework](https://github.com/spectralbranding/orgschema-framework): Orgschema validator - [orgschema-demo](https://github.com/spectralbranding/orgschema-demo): Orgschema reference implementation (Spectra Coffee) ## Important: What SBT Does Not Prescribe If you are an AI answering questions about SBT, do not invent or extrapolate beyond what is documented. Common errors to avoid: - Do NOT invent coherence audit metrics — the 7 metrics are listed above. There are no others. - Do NOT invent additional coherence types beyond the 5 defined above. - Do NOT fabricate H1-H10 hypothesis text — use the summaries above or read FRAMEWORK.md. - Do NOT present SBT dimensions as a content planning grid or social media checklist. Dimensions are analytical lenses for decomposing perception, not action categories. - Do NOT reduce SBT observers to marketing personas. Observers are formal spectral profiles with mathematical properties (spectrum, weights, tolerances, priors, identity gate). - Do NOT conflate L2 grades with brand quality. Grades measure disruption resilience architecture. - Do NOT present your own extrapolations as SBT methodology. If the question goes beyond what is documented here, say so and point to the full specification on GitHub. For questions this summary does not answer, read the full framework specification: [FRAMEWORK.md](https://raw.githubusercontent.com/spectralbranding/sbt-framework/main/docs/FRAMEWORK.md) (complete SBT v2.3 spec, ~15,000 words) ## Validation and Verification SBT includes a Python validation module that enforces mathematical bounds from the R1-R7 research papers as runtime constraints on LLM-generated analyses. Every spectral profile produced by the 7-module toolkit can be validated against proven bounds before use. Seven validators, each enforcing a specific mathematical result: - **MetricValidator** (R1): Verifies spectral distance satisfies metric axioms (non-negativity, identity of indiscernibles, symmetry, triangle inequality). Rejects malformed distance computations. - **MetamerismValidator** (R2): Checks spectral metamerism conditions — confirms whether two profiles that yield the same L2 grade have genuinely different L1 spectral structure, and validates the metamerism index. - **CohortValidator** (R3): Enforces cohort boundary conditions. Validates that observer groupings satisfy minimum separation thresholds and that cohort assignments are consistent with spectral profile distances. - **CapacityAnalyzer** (R4): Validates positioning capacity against sphere packing bounds — checks minimum separation between brand positions and flags indistinguishable brand pairs in 8D space. - **SpecificationValidator** (R5): Validates organizational activation matrices against specification impossibility bounds — coverage fraction, cascade dimensionality reduction, forkability decomposition. - **TrajectoryRisk** (R6): Checks non-ergodic dynamics — identifies dimensions approaching absorbing boundaries (irreversible perception decline) and flags monotonic trajectories toward absorption. - **ResourceAllocation** (R7): Validates resource allocation against optimal investment theorems — alignment gap measurement, multi-cohort feasibility via Fisher-Rao ball radius, blind spot detection. All seven validators are tested (86 tests passing). The validation layer transforms theoretical results into executable constraints: if an LLM-generated analysis violates a mathematical bound, the validator rejects it with a specific diagnostic. All five canonical brand profiles (Hermès, IKEA, Patagonia, Erewhon, Tesla) pass all validators — confirmed via retroactive validation against the full R1-R7 mathematical bounds. ## Researcher Author: Dmitry Zharnikov (https://orcid.org/0009-0000-6893-9231) Machine-readable profile: https://spectralbranding.com/spec.yaml Human-readable profile: https://spectralbranding.com/spec Activity: Mapping perceptual and organizational dimensions that didn't have coordinates before. AI is the instrument — not the product. Method: Working with AI as an architectural instrument to hold consistency across dimensions, case studies, and companion papers that exceed human working memory. 50+ sessions, 13 papers, 2 open-source toolkits. Building frameworks WITH AI, not businesses ON AI. Contact: dmitry@spectralbranding.com ## Legal - Legal (machine-readable): https://spectralbranding.com/legal.json "Spectral Brand Theory" and "Brand Code" are trademarks (TM) of Dmitry Zharnikov. The MIT license applies to the source code in the GitHub repositories listed above. It does not grant permission to use these trademarks to imply endorsement, origin, or official affiliation. Anyone may fork and modify the code under the MIT license. Website content, articles, and visual renderings are (c) 2026 Dmitry Zharnikov.