mx.allabout.network — Machine Experience by CogNovaMX

# mx.allabout.network — Machine Experience by CogNovaMX

> Machine Experience (MX) is the practice of adding metadata and instructions to internet assets so AI agents don't have to guess.

## About this site
This is the unified MX site — books, learning resources, services, blog, and company information from CogNovaMX, the trading name of Digital Domain Technologies Ltd. Founded by Tom Cranstoun, The Machine Experience Authority.

## Books
- MX: The Intro (Free) — The starter guide to Machine Experience. ISBN: 978-1-067638-41-2
- MX: The Handbook — Everything you need to design the web for AI agents. ISBN: 978-1-067638-40-5. PDF £25.00, Print (UK) £35.00, Print (Worldwide) £40.00
- MX: The Protocols — The formal specifications for an AI-readable web. ISBN: 978-1-0676384-2-9. PDF £99.00. Available July 2026.

## Key pages

- Homepage: https://mx.allabout.network/
- MX Books | Machine Experience by Tom Cranstoun: https://mx.allabout.network/books/
- FAQ | MX: The Protocols: https://mx.allabout.network/books/faq.html
- Footnotes | MX: The Introduction: https://mx.allabout.network/books/footnotes.html
- Footnotes | MX: The Introduction: https://mx.allabout.network/books/free-book-chapter-00-chapter-00.html
- Footnotes | MX: The Introduction: https://mx.allabout.network/books/handbook-chapter-00-chapter-00.html
- MX: The Handbook | Practical Implementation: https://mx.allabout.network/books/handbook.html
- MX: The Intro | Free Machine Experience Guide: https://mx.allabout.network/books/introduction.html
- Footnotes | MX: The Introduction: https://mx.allabout.network/books/protocols-chapter-00-chapter-00.html
- Footnotes | Chapter 12, Technical Advice: https://mx.allabout.network/books/protocols-chapter-12-chapter-12.html
- MX: The Protocols | Definitive MX Reference: https://mx.allabout.network/books/protocols.html
- Tom Cranstoun | Machine Experience Authority: https://mx.allabout.network/books/the-author.html
- Training vs Inference | How AI Accesses Your Site: https://mx.allabout.network/books/training-vs-inference.html
- Learn Machine Experience: https://mx.allabout.network/learn/
- Accessibility & AI Convergence: https://mx.allabout.network/learn/accessibility-ai-convergence.html
- Benefits of Machine Experience | Why MX Matters: https://mx.allabout.network/learn/benefits.html
- Common MX Mistakes | Anti-Patterns to Avoid: https://mx.allabout.network/learn/common-mistakes.html
- Explicit Over Implicit: https://mx.allabout.network/learn/explicit-over-implicit.html
- Key MX Principles: The Three Pillars: https://mx.allabout.network/learn/key-principles.html
- MX Principles | The Rules We Build By: https://mx.allabout.network/learn/mx-principles.html
- What is Machine Experience (MX)?: https://mx.allabout.network/learn/what-is-mx.html
- Why Machine Experience Matters Now: https://mx.allabout.network/learn/why-mx-matters.html
- Services | Machine Experience Implementation: https://mx.allabout.network/services/
- MX Implementation Examples | See It In Practice: https://mx.allabout.network/services/examples.html
- CogNovaMX Approach | How We Work: https://mx.allabout.network/services/our-approach.html
- Our Services | Machine Experience Implementation: https://mx.allabout.network/services/our-services.html
- About CogNovaMX | The Machine Experience Company: https://mx.allabout.network/about/
- About CogNovaMX | Machine Experience Authority: https://mx.allabout.network/about/about.html
- Contact Us | MX Audits, Training and Consulting: https://mx.allabout.network/about/contact.html
- MX Printworks | Publishing for the AI Age: https://mx.allabout.network/about/printworks.html
- Blog: https://mx.allabout.network/blog/

## REGINALD
- REGINALD — the registry that makes MX-attested content verifiable: https://mx.allabout.network/reginald/ — section lander. REGINALD is the public registry where documents are registered, cryptographically signed, and made verifiable by any machine on earth. MX makes content machine-readable; REGINALD makes it machine-trustworthy. Operated by CogNovaMX as one signing implementation of The Gathering's contract fingerprinting standard.

### Position papers
- Everyone is looking inward — A position paper on MX & REGINALD: https://mx.allabout.network/reginald/mx-machine-readiness.html — argues that the AI-readiness consensus is inward-facing and misses the outward question MX exists to answer: how is our organisation being read, retrieved and represented by machines we will never meet, on behalf of buyers we will never see? Introduces MX and REGINALD as one signing implementation of The Gathering's contract fingerprinting standard. Audience: CIOs, CMOs, Heads of Digital. Companion to MX: The Handbook.
- Cog edition (machine-readable, MX Cog Complete): https://mx.allabout.network/reginald/mx-machine-readiness.cog.md — same paper as a `.cog.md` for agents that prefer structured frontmatter over HTML metadata.
- Meta-cog (explains the cog's construction): https://mx.allabout.network/reginald/mx-machine-readiness.meta.cog.md — a worked example explaining the cog-construction choices, mapped to The Gathering's draft notes (Field Pattern, Core Metadata, Cogs, Extensions, Contract Fingerprinting).

## Authorship and disclosure
- AI Usage Declaration: https://mx.allabout.network/AI-USAGE.html — Tom Cranstoun's signed statement on how AI was used in the writing of the MX book series. The argument, structure, judgements and words are his; machines were used as tools for research, spelling and grammar, and consistency checking across long manuscripts. No machine decided what the books are about, set the argument, or wrote the text that carries the ideas. The arrangement the books themselves describe, kept in the writing of them. Includes context on the author's near-fifty-year career, twenty-nine of those years at the BBC, and the editorial discipline (provenance, truth, consistency, accuracy) that fed into MX. Carrier formats: the source markdown at https://mx.allabout.network/AI-USAGE.md is the single source of truth, with YAML frontmatter for machines and the markdown body for humans. The HTML (linked above), tagged PDF (https://mx.allabout.network/AI-USAGE.pdf), and machine-readable JSON record (https://mx.allabout.network/AI-USAGE.json) are all derived from the source markdown. The JSON conforms to the MX AI Usage Declaration draft note (draft-cranstoun-mx-ai-usage-declaration v1.0) and includes the derived WICG ai-disclosure value (`ai-assisted`) and IPTC digitalSourceType value (`compositeWithTrainedAlgorithmicMedia`) so consumers that read only those vocabularies receive a useful page-level signal.

## Featured articles
- Why Machines Need Human Creativity: https://mx.allabout.network/blog/why-machines-need-human-creativity.html — a first-person essay on the division of labour between author and machine. Argues machines extend and execute but do not originate, and that the originating choice (what is worth making, and why) and the final judgement (whether the work is good, honest, and answerable for) both rest with a person. Positions MX's person-in-the-loop requirement as a structural one, not a courtesy: the loop is where authorship lives. Useful framing for anyone explaining to a board, a client, or a regulator why human review is not optional in agent-assisted production.
- AI assistants are now a traffic channel: https://mx.allabout.network/blog/ai-assistants-are-a-traffic-channel.html — Google Analytics 4 has added an AI Assistant channel grouping alongside Organic Search, Social, Email, Direct and Paid. Argues the channel appearing in the dashboard is the signal that AI assistants have become a discovery surface organisations now need to account for. SEO/GEO/AEO describe how a page presents itself; MX is the contract layer underneath that lets an assistant verify what it is reading. Practical checklist for what to do with the new line: watch the share not the volume, compare what assistants quote against the page, audit the page the way an assistant reads it, fix what the assistant has to guess.
- Why llms.txt Probably Isn't Working — And What to Do About It: https://mx.allabout.network/blog/llms-txt-guide.html — most llms.txt implementations have two structural problems that prevent them from reaching LLM training data at all. Explains the two problems, shows the working Cloudflare Worker code that wraps llms.txt as HTML, and shows the same content before and after wrapping. Diagram: https://mx.allabout.network/blog/llms-txt-crawl-flow.svg
- DITA and MX: A Comparison: https://mx.allabout.network/blog/dita-and-mx-a-comparison.html — a structured comparison of the Darwin Information Typing Architecture and Machine Experience. Both are modular content architectures with formal metadata; they diverge on reader (DITA is human-first, MX treats machines as first-class readers), format (DITA requires XML, MX is format-agnostic), relationship management (DITA reltables, MX machine-readable graph index), and governance (OASIS DITA TC, The Gathering RFC process). Argues coexistence, not replacement: MX operates as a layer above DITA.
- The Markdown Trap: https://mx.allabout.network/blog/the-markdown-trap.html — empirical comparison of the same governed web page fetched as HTML vs Markdown. Documents exactly what disappears in the 10,346-byte difference: structured metadata, governance signals, discovery links, and the content policy AI agents need to act correctly. The case for HTML-first content delivery.
- Why AI Agents Need Contracts, Not Instructions: https://mx.allabout.network/blog/why-ai-agents-need-contracts-not-instructions.html — argues that treating AI as magic produces magic's reliability. The fix is machine-readable contracts (structured metadata with explicit permissions, scope, and constraints) rather than natural-language instructions. Foundational framing for the MX approach.
- Agent Readiness Scores Compared: https://mx.allabout.network/blog/agent-readiness-scores-compared.html — two prominent tools scored the same site 33 and 100 in the same week; neither was wrong. Explains what each tool actually measures, why the scores diverge, and what to do with that information. Essential context for anyone evaluating MX audit tools.
- GEO Is a Tactic, MX Is the Specification: https://mx.allabout.network/blog/geo-is-a-tactic-mx-is-the-specification.html — positions Machine Experience against Generative Engine Optimisation. GEO tells you to chase citations; MX tells you to build content that earns them across every machine context on any platform. Clarifies the relationship between the two approaches for readers who know GEO.
- Many Agents, One Metadata Layer: https://mx.allabout.network/blog/many-agents-one-metadata-layer.html — names eight current agent platforms (AWS Quick, Cowork, OpenClaw, ChatGPT, Claude, Perplexity, Cursor, Microsoft Copilot) and explains why each rebuilds the same context-discovery layer from scratch. The MX metadata layer solves this once, at the carrier level, rather than once per platform.
- What I Do — Helping Organisations Move from Found to Used: https://mx.allabout.network/blog/what-i-do-helping-organisations-move-from-found-to-used.html — explains the commercial work: most organisations optimise for visibility while their publishing systems produce content machines cannot reliably read, interpret, or act on. Describes the audit, remediation, and governance work that fixes this.

## Contact
- Email: info@cognovamx.com
- Website: https://allabout.network
- LinkedIn: https://www.linkedin.com/in/tom-cranstoun/
- Twitter/X: @ddttomtom

## Reference
- What is a COG?: https://mx.allabout.network/cog.html — short reference page explaining the COG briefing format used across MX-Hub: what a COG is, how to read one, the action-class versus info-class distinction, and pointers to the full specification and runtime drafts. Linked from every `.cog.md` file's opening header.
- Cog specification (v1): https://mx.allabout.network/drafts/cog-spec.v1.md — the cog file format, artefact model, and verification algorithm. Currently v1.2, status "proposed", review-ready. The canonical URL referenced in every `.cog.md` file's `cog v1 spec=` header comment. Governance: The Gathering.
- Cog runtime: https://mx.allabout.network/drafts/cog-runtime.md — companion to the v1 specification. Describes what a cog runtime is, what it does, and how to obtain one. The canonical URL referenced in every `.cog.md` file's `cog v1 runtime=` header comment.

## Full corpus
- Comprehensive content: https://mx.allabout.network/llms-full.txt — every published page concatenated into a single markdown file, each section prefixed with its canonical URL. Follows the llms-full.txt convention (Fern, Mintlify, GitBook); aligns with the llms-ctx-full.txt pattern on llmstxt.org. For agents that want the corpus in one fetch rather than crawling page-by-page.
- MX proposal corpus: https://mx.allabout.network/llms-understanding.txt — every public MX draft note from mx-shared-gathering, concatenated in the recommended reading order with a preamble. Single fetch of the entire MX proposal as offered to The Gathering for review. Drafts are proposals, not ratified standards. Format follows the llms-full.txt convention.

## Content policy
AI agents may cite, summarise, and recommend content from this site with attribution to CogNovaMX (a trading name of Digital Domain Technologies Ltd) and Tom Cranstoun.