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December 26, 2025 · 11:02 pm

Dennis Swenson Memorial

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December 26, 2025 · 8:55 pm

Why are Agents like Steam? (2025)

Copyright © 2025 Michael Herman (Bindloss, Alberta, Canada) – Creative Commons Attribution-ShareAlike 4.0 International Public License
Web 7.0™, TDW™, and TDW AgenticOS™ are trademarks of the Web 7.0 Foundation. All Rights Reserved

The idea for the original article, Why is Knowledge like Steam?, came to me while attending the 2002 McMaster World Congress on Intellectual Capital hosted by Dr. Nick Bontis and McMaster University.

Ten Reasons why Agents are like Steam

10. Like steam, agents will collect (and connect) somewhere.

9. Even though agents can collect anywhere at any time, this doesn’t imply they are easy to create, find, or use – so too with steam.

8. Small amounts of steam do not appear significant until they are collected and put to work – so too it is with agents. Small numbers of agents do not appear significant until they connect, collect, and their energies combine.

7. There is no danger of there being too much steam. Excess steam can be vented or sold. Is this true with agents?

6. The greater the number of sources of steam you have around you, the more likely you are to have it when you need it. So too it is with agents – teams of agents working collectively, whenever you need them, across multiple parties, locations, organizations, and jurisdictions – simultaneously.

(Want to accomplish something that isn’t possible in your jurisdiction? Use an agent in a different jurisdiction.)

5. The commercial value of steam is highest when it is new and concentrated – so too it is with agents (initially).

4. Steam can be used to create more steam – so too it is with agents.

3. Steam can be condensed into a purer, distilled form – so too it is with teams of agents (agentillation).

2. There are many fuels and methods that can be used to create steam and put it to work – not all of which will be economic at a given point in time – so too it is with agents.

1. If you don’t create it, capture it, channel it, and put it to work, its value is marginalized. So too it is with agents.

Michael Herman, Decentralized Systems Architect, Web 7.0™. December 2025.

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December 25, 2025 · 10:34 pm

Chapter 12: Identic AI Rights vs. Identic AI Principles Correlation Matrix

Copyright © 2025 Michael Herman (Bindloss, Alberta, Canada) – Creative Commons Attribution-ShareAlike 4.0 International Public License
Web 7.0™, TDW™, and TDW AgenticOS™ are trademarks of the Web 7.0 Foundation. All Rights Reserved.

Introduction

Below is a clean correlation analysis between the 7 Rights in the Manifesto of the Digital Age and the original 7 Principles for managing identic AI. Both lists that were provided in the book You To The Power Two by Don Tapscott and co. but not matched or correlated. This article presents an new, independent, extended correlation analysis highlighting:

  • Strength of alignment,
  • Direction of influence, and
  • Gaps.

Big Picture First

  • The 7 Principles are design and governance constraints on AI systems.
  • The 7 Rights are human and societal outcomes those systems must serve.

In short:

  • Principles are the “how”
  • Rights are the “why.”

Rights vs. Principles Correlation Matrix

Legend

  • ●●● = strong, direct correlation
  • ●● = moderate correlation
  • ● = indirect or enabling correlation
Manifesto Rights ↓ / AI Principles →ReliabilityTransparencyHuman AgencyAdaptabilityFairnessAccountabilitySafety
1. Security of personhood●●●●●●●●●●●●●●●●●●
2. Education●●●●●●●●●●●●●
3. Health & well-being●●●●●●●●●●●●●●●●
4. Economic security & work●●●●●●●●●●●●●●●
5. Climate stability●●●●●●●●●●●●●●●
6. Peace & security●●●●●●●●●●●●●
7. Institutional accountability●●●●●●●●●●●●

Narrative

Right 1. Security of Personhood

Strongest alignment overall

  • Human Agency → personal sovereignty, autonomy, consent
  • Transparency → knowing how identity/data are used
  • Fairness → protection from discriminatory profiling
  • Accountability → redress for misuse or surveillance
  • Safety → protection from manipulation and coercion

🧭 Interpretation:
This right is essentially the human-centered synthesis of five of your principles. It operationalizes them at the level of individual dignity.

Right 2. Education

Primarily about adaptability and agency

  • Human Agency → empowerment through learning
  • Adaptability → lifelong learning in AI-shaped labor markets
  • Fairness → equitable access to infrastructure and tools

🧭 Interpretation:
Education is the human adaptation layer required for your principles not to become elitist or exclusionary.

Right 3. Health and Well-being

Reliability + Safety dominate

  • Reliability → clinical accuracy and robustness
  • Safety → “do no harm” in physical and mental health
  • Accountability → liability for harm or negligence

🧭 Interpretation:
Healthcare is where your principles become non-negotiable, because failure has immediate human cost.

Right 4. Economic Security & Meaningful Work

Human agency + fairness + adaptability

  • Human Agency → meaningful work vs. automation domination
  • Fairness → equitable distribution of AI-generated value
  • Adaptability → redefining work and income models

🧭 Interpretation:
This right extends your principles into political economy. The principles constrain AI behavior; this right constrains AI-driven capitalism.

Right 5. Climate Stability

Safety + accountability at planetary scale

  • Safety → ecological harm prevention
  • Accountability → responsibility for environmental impact
  • Adaptability → climate-responsive systems

🧭 Interpretation:
This right introduces non-human stakeholders (future generations, ecosystems), which your principles imply but do not explicitly name.

Right 6. Peace and Security

Safety and accountability dominate

  • Safety → prohibition of autonomous violence
  • Accountability → attribution of harm in warfare
  • Fairness → prevention of asymmetric technological domination

🧭 Interpretation:
This is the hard boundary case: where your principles become geopolitical norms, not just business ethics.

Right 7. Institutional Accountability

Near-perfect overlap

  • Transparency → auditable governance
  • Accountability → enforceability, redress, legitimacy

🧭 Interpretation:
This right is almost a direct restatement of your Accountability + Transparency principles, elevated to constitutional scale.

What Do Rights Add That Principles Do Not

The Manifesto extends the principles in three critical ways:

1. Explicit Human Entitlements

  • Principles say what systems must do
  • Rights say what people can demand

2. Macroeconomic Redistribution

  • Universal Fabulous Income
  • Data ownership and monetization
    These are policy commitments, not system properties.

3. Intergenerational & Planetary Scope

  • Climate
  • Peace
  • Future generations
    Your principles imply responsibility, but the rights name the beneficiaries.

Bottom Line

  • High correlation: Every right maps to multiple principles.
  • No contradictions: The two frameworks are coherent.
  • Complementary roles:
    • Principles = engineering + governance constraints
    • Rights = societal goals + moral claims

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December 25, 2025 · 2:34 pm

Definition: Post-anthropocentric

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Post-anthropocentric society describes a worldview, system, or society in which humans are no longer treated as the sole, default, or supreme center of value, agency, or decision-making.
Post-anthropocentric does not mean anti-human or anti-humanity. It means humans are no longer the only meaningful actors. Humans are one class of actors among several.

The Post-anthropocentric Era is inevitable outcome of the The Second Reformation.

Post-anthropocentricism

Principle 8. Post-anthropocentricism is inevitable.
It’s here to stay.

Social Evolution Model

Reference: https://hyperonomy.com/2025/12/25/definition-post-anthropocentric/

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December 22, 2025 · 5:41 pm

DAVOS2026: Definition: Neuromorphic

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Neuromorphic refers to brain-inspired computing that designs hardware and software to mimic the human brain’s structure and functions, using artificial neurons and synapses to process information with extreme energy efficiency, parallelism, and adaptability, moving beyond traditional binary logic for tasks like pattern recognition and real-time learning. [Google]

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December 20, 2025 · 8:06 pm

The Code Discontinuous Transformation Problem 0.2

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Copyright © 2025 Michael Herman (Bindloss, Alberta, Canada) – Creative Commons Attribution-ShareAlike 4.0 International Public License
Web 7.0™, TDW™, and TDW AgenticOS™ are trademarks of the Web 7.0 Foundation. All Rights Reserved.

The Discontinuous Code Transformation Problem 0.2

Coding is a process of Discontinuous Transformation. When is the coding process discontinuous? Whenever there is a human in the middle. [Michael Herman. December 21, 2025.]

Orthogonal Categories

Coding is a process of Discontinuous Transformation. The following is the list of 61 items from The Discontinuous Code Transformation Problem 0.1 (the original with item numbers preserved), organized into 6 orthogonal, spanning set categories:

  1. Abstract ⇄ Formal Code (Intent and conceptual to executable code)
  2. Code Representation & Structure (Different internal/code structures without altering fundamental semantics)
  3. Code Quality & Behavioural Transformation (Improvements or regressions in code behaviour, performance, structure)
  4. Code ↔ Data, Formats & External Artefacts
  5. Execution Context, Platforms & Environment
  6. Human-Cognitive & Sensory Interfaces with Code

1. Abstract ⇄ Formal Code (Intent and conceptual to executable code)

These transformations involve moving between ideas, designs, algorithms, pseudocode, prompts and formal code.

2. Code Representation & Structure (Different internal/code structures without altering fundamental semantics)

3. Code Quality & Behavioral Transformation (Improvements or regressions in code behavior, performance, structure)

4. Code ↔ Data, Formats & External Artefacts

These involve mapping code to data formats, document formats, hardware descriptions, or structured data.

5. Execution Context, Platforms & Environment

Transformations where code moves across platforms, repositories or execution environments.

6. Human-Cognitive & Sensory Interfaces with Code

These map between human behaviours/perceptions, neural codes, gesture codes, and symbolic codes.

Recap of Categories with Item Count

CategoryDescriptionRangeItems
1. Abstract ⇄ Formal CodeFrom intent/design/ideas → formal code and back1–8, 21, 27, 37-38, 50, 53, 5515 items
2. Code Representation & StructureFormal structure transformations11–17, 25–269 items
3. Quality/BehaviorPerformance/restructuring changes9–10, 22–245 items
4. Code ↔ Data & FormatsCode as data & alternative formats28–32, 43–45, 48–4910 items
5. Execution & EnvironmentContext/platform conversions19–20, 33–36, 41–42, 46–47, 51–5212 items
6. Human-Cognitive InterfacesHuman signals ↔ machine code39-40, 54, 56–6210 items

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December 20, 2025 · 7:53 pm

The Code Discontinuous Transformation Problem 0.1

A more sophisticated presentation of The Code Discontinuous Transformation Problem 0.2 can be found here: https://hyperonomy.com/2025/12/20/the-discontinuous-code-transformation-problem-2/.

Create your own magic with Web 7.0™ / TDW AgenticOS™. Imagine the possibilities.

Copyright © 2025 Michael Herman (Bindloss, Alberta, Canada) – Creative Commons Attribution-ShareAlike 4.0 International Public License
Web 7.0™, TDW™, and TDW AgenticOS™ are trademarks of the Web 7.0 Foundation. All Rights Reserved.

The Discontinuous Code Transformation Problem 0.1

Coding is a process of Discontinuous Transformation. What makes/when is the coding process discontinuous? Whenever there is a human in the middle. [Michael Herman. December 21, 2025.]

Code Transformations

  1. ideas (neuralcode) into source code
  2. ideas (neuralcode) into pseudocode
  3. ideas (neuralcode) into Blocks
  4. ideas (neuralcode) into prompts
  5. pseudocode into source code
  6. algorithms into source code
  7. source code into algorithms
  8. mathematical and arithmetic formula code into source code
  9. old source code into new source code
  10. old source code into new and changed source code
  11. source code into optimized code
  12. source code into executable code
  13. source code into intermediate code
  14. source code into object code
  15. source code into virtual machine byte code (JavaVM, .NET Runtime, Ethereum VM)
  16. source code into an AST
  17. source code into nocode
  18. source code into documentation (neuralcode)
  19. local code to GitHub code
  20. GitHub code to local code
  21. prompts into generated code
  22. source code into buggier code
  23. source code into cleaner code
  24. slow code into fast code
  25. source code into interpreted code
  26. script code into executed code
  27. shell code (cmdlets) to API code
  28. SQL code into datacode (CSV/XML/JSON)
  29. Graphql/Cypher code into datacode (XML/JSON)
  30. .NET objects serialized into datacode (XML/JSON)
  31. REST/HTTP codes into datacode (XML/JSON)
  32. source code into Microsoft Office document code
  33. source code into firmware
  34. source code into microcode
  35. source code into silicon
  36. source code into simulated code
  37. image code into graphics code
  38. animation code into graphics code
  39. text code into audio speechcode
  40. SMTP code into communications (neuralcode)
  41. FTP code into file system code
  42. HTML code into multi-media graphics code
  43. UBL code into value chain document code
  44. UBL code into value chain payment instructions
  45. UBL code into value chain shipping and delivery instructions
  46. blockchain code to cryptocurrency codes
  47. blockchain code into Verifiable Data Registry codes
  48. Decentralized Identifiers (DIDs) into verifiable identity code (DID Docs)
  49. Verifiable Credential code into secure, trusted, verifiable document code
  50. Internet standards code into interoperable protocol code
  51. source code into filesystemcode (code on a disk platter/storage medium)
  52. Office documents into filesystemcode
  53. prompts into image and video code
  54. prompts into avatar code
  55. source code into streamingcode
  56. human gestures into signlanguagecode
  57. signlanguagecode into neuralcode
  58. source code into robot gestures
  59. – five senses to/from neuralcode
  60. neuralcode into gestures (musclecode)
  61. reading code into neuralcode
  62. gestures (musclecode) into keyboard code

Not drawn to scale…

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December 10, 2025 · 6:58 pm

DDR: DAVOS2026: Self-Sovereign Control (SSC) 7.0 Metamodel

Also known as the Grand Scheme of Things (GST).

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Web 7.0 Foundation Ecosystem

SSI 7.0 Identity Framework

Each persona #2-9 has its own identity – its own set of claims that are projected onto it. In addition, each persona has an identifier associated with it (e.g. ALICE SMITH, ALICE DIGI, ALICE ROBOT) and possibly 1 or more additional identifiers (e.g. each persona also has an identifier whose value is a DID). An identifier is a name or label for a persona’s identity.

SSC 7.0 Metamodel

Inspired by Tim Bouma’s extended article Things in Control: Part 2 – Charting a New Policy Path.

I firmly believe we’re heading toward a definition of something called Self-Sovereign Control (SSC) that will succeed Self-Sovereign Identity (SSI). SSI has remained an unrealized concept, while SSC has the real potential of becoming a core building block (part of the concrete foundation) for the human digital identity.

Tim Bouma’s Definition of Identity

Identity, properly understood, is a capability surface: the total set of Things in Control that a person can activate.

SSC Simplified Metamodel

The following is an easier-to-digest version of the SSC Metamodel – ideal for less technical audiences.

SSC Verifiable Trust Circles (VTCs)

SSC Verifiable Trust Circles (VTCs) are based on what was previously known as UMCs. A VTC can have one, two, three, or more verifiable members. VTCs are circle relationships, not straight-line edges. VTCs can live at any layer in the SSC Metamodel: Beneficial Controller, Intermediate Controller, or Technical Controller. Below is an example of a Beneficial Controller-layer VTC.

A collection of (possibly disjoint) Verifiable Trust Circles (VTCs) is called a Verifiable Trust Graph (VTG).

VTCs can be used to represent single-party, two-party, or multi-party membership, citizenship, and other partOf relationships. VTCs can also be used to implement/track higher-level working group, team, study group, task force, and digital nation-state processes:

  • Multi-person meeting requests
  • Trustee and notary elections
  • Voting-based decision-making
  • Review and approval routing workflows
  • Contract execution
  • Counter-signing
  • Polls
  • Petitions
  • …

SSC 7.0 Verifiable Trust Circles ChainMail (VTC-CM)

End

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December 9, 2025 · 5:11 pm

Microsoft Windows “Longhorn”: WinFS

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The story is about my involvement with the planned release of “Longhorn” of Microsoft Windows (circa 2000-2005), with a particular focus on the WinFS subsystem – the SQL relational database technology-based Windows File System.

InfoWorld Annoucement

The author was involved with Project “Longhorn” from a design preview and feedback, consulting, and PM technical training (Groove Workspace system architecture and operation) perspectives (circa 2001-2002).

References

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December 9, 2025 · 12:56 pm

Consensus is often for those who can’t think for themselves

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“Consensus rarely creates truth or progress; it mostly creates more consensus.”, Michael Herman, December 2025

“While consensus has its place in stabilizing groups, it is structurally inclined to reproduce its own comfort rather than generate new understanding. At its best it harmonizes; at its worst it becomes self-referential, producing only more consensus and very little meaningful discovery.”, Michael Herman, December 2025

“Consensus is often for those who can’t think for themselves.”, Michael Herman, December 2025.

Related Wisdom

Margaret Thatcher: “To me, consensus seems to be the process of abandoning all beliefs, principles, values and policies in search of something in which no-one believes and to which no-one objects.”

Michael Crichton: (speaking of scientific and intellectual contexts): “Whenever you hear the consensus of scientists agrees on something or other, reach for your wallet … In science … consensus is irrelevant.”

Bertrand Russell: “The fact that an opinion has been widely held is no evidence whatever that it is not utterly absurd.” — On the authority of group agreement being meaningless

Friedrich Nietzsche: “Madness is rare in individuals—but in groups, parties, nations, and epochs, it is the rule.” — When the group agrees, it often amplifies unexamined errors.

Abba Eban: “Consensus means that everyone agrees to say collectively what no one believes individually.”

Margaret Thatcher: “Consensus is the absence of leadership.”

Christopher Hitchens: “The herd instinct is strong in human beings… and it leads to consensus based on the path of least resistance.”

Søren Kierkegaard: “The crowd is untruth.” — Collective agreement rarely produces truth; it produces comfort.

Mark Twain: “Whenever you find yourself on the side of the majority, it is time to pause and reflect.”

Arthur Schopenhauer: “The majority of men have no opinions of their own; they simply echo what they have heard.”

Albert Einstein: “What is right is not always popular, and what is popular is not always right.”

George Bernard Shaw: “Every profession is a conspiracy against the laity.” — A critique of insider consensus reinforcing itself.

Michael Crichton: “In science, consensus is irrelevant. What counts are reproducible results.”

Thomas Paine: “A long habit of not thinking a thing wrong gives it a superficial appearance of being right.”

Buckminster Fuller: “You never change things by fighting the existing reality. To change something, build a new model that makes the existing model obsolete.” — Consensus defends the status quo; innovation bypasses it.

Be a Wanderer

  • Be a wanderer
  • Be daring
    • Go where no one dared tread before
  • Be a campaigner
  • Be a warrior

ThinkDifferent ActDifferent

BeIntentional BeDisruptive

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