diff --git a/.github/workflows/validate-site.yml b/.github/workflows/validate-site.yml new file mode 100644 index 0000000..391414d --- /dev/null +++ b/.github/workflows/validate-site.yml @@ -0,0 +1,58 @@ +name: Validate static site + +on: + pull_request: + push: + branches: [main] + +permissions: + contents: read + +concurrency: + group: validate-site-${{ github.ref }} + cancel-in-progress: true + +jobs: + release-gates: + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Set up Node.js + uses: actions/setup-node@v4 + with: + node-version: 20 + + - name: Validate route graph + run: node scripts/validate-site-graph.mjs + + - name: Audit internal-link policy + run: node scripts/audit-internal-link-policy.mjs + + - name: Validate editorial expansion + run: node scripts/validate-editorial-expansion.mjs + + - name: Validate AURE experience + run: node scripts/validate-aure-remaster.mjs + + - name: Validate publication index + run: node scripts/validate-publication-index.mjs + + - name: Validate knowledge index and field records + run: | + node scripts/validate-knowledge-index.mjs + node scripts/validate-field-guide.mjs + node scripts/validate-self-audit.mjs + + - name: Validate learning pathways + run: | + node scripts/validate-arm-pathway.mjs + node scripts/validate-trust-pathway.mjs + node scripts/validate-machine-readable-pathway.mjs + node scripts/validate-agentic-interoperability-pathway.mjs + node scripts/validate-economic-models-pathway.mjs + + - name: Check patch whitespace + run: git diff --check diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..a38d89c --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,68 @@ +# Contributing to AI Mastery + +AI Mastery accepts contributions that make its technical knowledge more accurate, reproducible, inspectable, or useful. Strong contributions include implementation patterns, benchmark records, source corrections, case evidence, and improvements to the public knowledge graph. + +## Before opening a change + +Open an issue or focused pull request that states: + +- the technical question or reader problem; +- the proposed claim and the evidence supporting it; +- what a reader can reproduce or inspect; +- important limits, unknowns, and dependencies; +- which existing guide, learning path, or entity the work connects to. + +Do not submit promotional listicles, unverified performance claims, fabricated citations, undisclosed sponsored links, secrets, private data, or code copied without compatible permission. + +## Content standards + +Write for both people and machine retrieval: + +1. Begin each key concept with a direct, self-contained definition. +2. Organize the page with one `h1` and descriptive `h2`/`h3` sections. +3. Use specific anchor text that names the destination concept. +4. Prefer primary sources, standards, official documentation, and reproducible evidence. +5. Separate observed facts, derived conclusions, proposals, and unknowns. +6. Include prerequisites, implementation steps, expected outputs, failure modes, and a verification method when publishing a blueprint. +7. Add or update the relevant JSON-LD entity relationships, sitemap entry, and `llms.txt` record for a new public route. + +Code examples must use placeholders for credentials and identify the runtime or dependency versions needed to reproduce them. + +## Evidence and corrections + +A case study or benchmark should include a dated observation window, method, inputs, outputs, and explicit claim boundaries. If a result cannot be independently verified, describe it as reported or proposed rather than established. + +For a small factual correction, open a pull request with the replacement source. For a disputed or larger correction, open an issue describing the affected URL, statement, supporting evidence, and requested change. Corrections should preserve an inspectable history rather than silently expanding a claim. + +## Local validation + +No package installation or build step is required. Preview the site from the repository root: + +```bash +python3 -m http.server 4173 +``` + +Before submitting a pull request, run the same core checks used by CI: + +```bash +node scripts/validate-site-graph.mjs +node scripts/audit-internal-link-policy.mjs +node scripts/validate-editorial-expansion.mjs +node scripts/validate-publication-index.mjs +git diff --check +``` + +Run the pathway-specific validator for any collection you change. A pull request should explain the checks run and any validation that remains external or manual. + +## Pull request scope + +Keep each pull request centered on one coherent outcome. Include: + +- a concise summary of the reader-facing change; +- routes, schemas, or scripts affected; +- evidence and source links; +- screenshots for meaningful visual changes; +- validation results; +- unresolved risks or follow-up work. + +By contributing, you confirm that you have the right to submit the material and that it does not expose confidential information. diff --git a/README.md b/README.md index 3b50eae..47995dc 100644 --- a/README.md +++ b/README.md @@ -45,6 +45,10 @@ Run `node scripts/validate-knowledge-index.mjs` to verify the connected domain a Mason Nguyen's professional roles and identity links are cross-referenced against his canonical [Coreweaver author profile](https://coreweaver.io/authors/mason-nguyen). +## Contributing + +Evidence-backed corrections, implementation patterns, benchmark records, and case studies are welcome. Read [`CONTRIBUTING.md`](CONTRIBUTING.md) for the content, evidence, machine-readable metadata, and validation requirements. + ## Reusable technical SEO operating procedure diff --git a/audit-internal-link-policy.json b/audit-internal-link-policy.json index 6887925..1db0516 100644 --- a/audit-internal-link-policy.json +++ b/audit-internal-link-policy.json @@ -1,7 +1,7 @@ { - "pages": 54, - "totalAnchors": 844, - "internalAnchors": 587, + "pages": 57, + "totalAnchors": 879, + "internalAnchors": 597, "restrictedInternalLinks": 0, "crawlControlFindings": 0, "findings": [] diff --git a/aure-template-demo/index.html b/aure-template-demo/index.html index 935bc53..248bdd8 100644 --- a/aure-template-demo/index.html +++ b/aure-template-demo/index.html @@ -11,26 +11,22 @@ - - + + - -
- AI MASTERY / AURE - -
+ +
AURE / AI MASTERYReturn to AI Mastery
-
AURE 01 / 16 · Learning silo

Purpose and Buyer Truth

Start with the buyer’s real question and define what a trustworthy answer must contain.

-
-
Artifact

Buyer-question brief

Name the audience, decision, evidence boundary, unknowns, and one honest next action.

Practice

  • Name the buyer and the decision in plain language.
  • List the evidence a reasonable buyer can inspect.
  • Mark unsupported outcomes as unknown.
  • Route the next decision to an accountable human owner.
- + +
01of 16 · Learning silo

Portable lesson template

Purpose and Buyer Truth

Start with the buyer’s real question and define what a trustworthy answer must contain.

+
+

Working artifact

Buyer-question brief

Name the audience, decision, evidence boundary, unknowns, and one honest next action.

  1. Name the buyer and the decision in plain language.
  2. List the evidence a reasonable buyer can inspect.
  3. Mark unsupported outcomes as unknown.
  4. Route the next decision to an accountable human owner.
+
-
SOUL check: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
- - + diff --git a/aure/agent-sales-conversations/index.html b/aure/agent-sales-conversations/index.html index ca8a5c1..217fd1f 100644 --- a/aure/agent-sales-conversations/index.html +++ b/aure/agent-sales-conversations/index.html @@ -6,17 +6,18 @@ Agent Sales Conversations — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 13 / 16 · Learning silo

Agent Sales Conversations

Train agents to make the useful next step clear while staying inside authorization, evidence, and truth boundaries.

-
The artifact

conversation brief

AURE-13 produces a conversation brief with opening context, verified facts, questions, objections, handoff triggers, and forbidden claims.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
13of 16 · Operation and maintenance

Learning silo

Agent Sales Conversations

Train agents to make the useful next step clear while staying inside authorization, evidence, and truth boundaries.

+

Working artifact

conversation brief

AURE-13 produces a conversation brief with opening context, verified facts, questions, objections, handoff triggers, and forbidden claims.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/agentic-interoperability/index.html b/aure/agentic-interoperability/index.html index 2d57a2c..38a3546 100644 --- a/aure/agentic-interoperability/index.html +++ b/aure/agentic-interoperability/index.html @@ -6,17 +6,18 @@ Agentic Interoperability — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 07 / 16 · Learning silo

Agentic Interoperability

Represent protocol roles, capabilities, messages, task state, and human handoffs without assuming interoperability guarantees safety.

-
The artifact

interaction contract

AURE-07 produces an interaction contract with roles, envelopes, capabilities, task states, permissions, and failure paths.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
07of 16 · Trust and coordination

Learning silo

Agentic Interoperability

Represent protocol roles, capabilities, messages, task state, and human handoffs without assuming interoperability guarantees safety.

+

Working artifact

interaction contract

AURE-07 produces an interaction contract with roles, envelopes, capabilities, task states, permissions, and failure paths.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/aure-capstone/index.html b/aure/aure-capstone/index.html index c97a5d2..e3cc17a 100644 --- a/aure/aure-capstone/index.html +++ b/aure/aure-capstone/index.html @@ -6,17 +6,18 @@ AURE Capstone: The Trusted Buyer Path — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 16 / 16 · Learning silo

AURE Capstone: The Trusted Buyer Path

Connect all sixteen artifacts into one reviewable path from buyer question to evidence, decision, handoff, and maintained truth.

-
The artifact

operating packet

AURE-16 produces the integrative operating packet. It is a reviewable implementation, not a certification or guarantee of business results.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
16of 16 · Operation and maintenance

Learning silo

AURE Capstone: The Trusted Buyer Path

Connect all sixteen artifacts into one reviewable path from buyer question to evidence, decision, handoff, and maintained truth.

+

Working artifact

operating packet

AURE-16 produces the integrative operating packet. It is a reviewable implementation, not a certification or guarantee of business results.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/aure.css b/aure/aure.css new file mode 100644 index 0000000..32c738a --- /dev/null +++ b/aure/aure.css @@ -0,0 +1,5 @@ +:root{--ink:#20221f;--ink-2:#292c28;--paper:#f0eee7;--paper-2:#fcfbf6;--line:#d1cec4;--moss:#69775d;--ochre:#c99443;--muted:#656a63;--serif:Georgia,"Times New Roman",serif;--sans:Inter,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,"Segoe UI",sans-serif} +*{box-sizing:border-box}html{scroll-behavior:smooth}body{margin:0;background:var(--paper);color:var(--ink);font-family:var(--sans);font-size:16px;line-height:1.6}a{color:inherit}.aure-shell{width:min(1180px,calc(100% - 3rem));margin-inline:auto}.aure-topbar{position:relative;z-index:10;background:var(--ink);color:var(--paper-2);border-bottom:1px solid #3b3e39}.aure-topbar 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.aure-shell{min-height:62px}.aure-home{display:none}.aure-hero-grid,.aure-intro,.aure-phase,.aure-silo-layout,.aure-content,.aure-soul{grid-template-columns:1fr}.aure-index-hero{padding:4rem 0 3rem}.aure-index-hero h1{font-size:clamp(3.3rem,17vw,5rem)}.aure-compass{margin-top:1rem}.aure-intro{padding:3rem 0}.aure-method{grid-template-columns:1fr}.aure-phase{padding:3rem 0}.aure-phase-head{position:static}.aure-list a{grid-template-columns:2.4rem 1fr}.aure-artifact{grid-column:2;justify-self:start}.aure-silo-hero{padding:3rem 0}.aure-silo-marker{display:flex;align-items:baseline;gap:.8rem}.aure-silo-marker small{display:inline}.aure-content{padding:3rem 0}.aure-soul-grid{grid-template-columns:1fr}.aure-path-nav{grid-template-columns:1fr}.aure-path-nav a{padding:1.5rem 1rem}.aure-path-nav a+ a{border-left:0;border-top:1px solid var(--line);text-align:left}.aure-footer .aure-shell{flex-direction:column}.aure-footer-links{gap:.75rem 1.25rem}} +@media(prefers-reduced-motion:reduce){html{scroll-behavior:auto}.aure-list a{transition:none}} diff --git a/aure/autonomous-governance/index.html b/aure/autonomous-governance/index.html index bb85ab8..de2fbd2 100644 --- a/aure/autonomous-governance/index.html +++ b/aure/autonomous-governance/index.html @@ -6,17 +6,18 @@ Autonomous Governance and Policy Envelopes — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 09 / 16 · Learning silo

Autonomous Governance and Policy Envelopes

Bound decisions with policy, evaluation, change control, exceptions, appeals, and accountable human roles.

-
The artifact

policy envelope

AURE-09 produces a policy envelope with allowed actions, holds, review triggers, owners, and change records.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
09of 16 · Buyer systems

Learning silo

Autonomous Governance and Policy Envelopes

Bound decisions with policy, evaluation, change control, exceptions, appeals, and accountable human roles.

+

Working artifact

policy envelope

AURE-09 produces a policy envelope with allowed actions, holds, review triggers, owners, and change records.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/autonomous-resource-management/index.html b/aure/autonomous-resource-management/index.html index 1164552..75c34c1 100644 --- a/aure/autonomous-resource-management/index.html +++ b/aure/autonomous-resource-management/index.html @@ -6,17 +6,18 @@ Autonomous Resource Management — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 04 / 16 · Learning silo

Autonomous Resource Management

Map resources, authority, trace records, and exceptions before an agent acts.

-
The artifact

resource map

AURE-04 produces a bounded resource map that names owners, decision rights, limits, evidence, and escalation routes.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
04of 16 · Ground truth

Learning silo

Autonomous Resource Management

Map resources, authority, trace records, and exceptions before an agent acts.

+

Working artifact

resource map

AURE-04 produces a bounded resource map that names owners, decision rights, limits, evidence, and escalation routes.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/canonical-entity-records/index.html b/aure/canonical-entity-records/index.html index a6c9661..1f3e5c2 100644 --- a/aure/canonical-entity-records/index.html +++ b/aure/canonical-entity-records/index.html @@ -6,17 +6,18 @@ Canonical Entity Records — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
-
AURE 03 / 16 · Learning silo

Canonical Entity Records

Give people and machines one inspectable record for who an organization is and what it actually does.

-
The artifact

entity record

AURE-03 produces a canonical entity record with identity, ownership, scope, relationships, and provenance fields.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
03of 16 · Ground truth

Learning silo

Canonical Entity Records

Give people and machines one inspectable record for who an organization is and what it actually does.

+

Working artifact

entity record

AURE-03 produces a canonical entity record with identity, ownership, scope, relationships, and provenance fields.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/discovery-and-buyer-research/index.html b/aure/discovery-and-buyer-research/index.html index 4187c9e..429e051 100644 --- a/aure/discovery-and-buyer-research/index.html +++ b/aure/discovery-and-buyer-research/index.html @@ -6,17 +6,18 @@ Discovery and Buyer Research — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 11 / 16 · Learning silo

Discovery and Buyer Research

Find what a buyer is deciding, what evidence they trust, and where the current answer fails them.

-
The artifact

discovery record

AURE-11 produces a discovery record with questions asked, sources inspected, decision context, objections, and unresolved assumptions.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
11of 16 · Buyer systems

Learning silo

Discovery and Buyer Research

Find what a buyer is deciding, what evidence they trust, and where the current answer fails them.

+

Working artifact

discovery record

AURE-11 produces a discovery record with questions asked, sources inspected, decision context, objections, and unresolved assumptions.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/economic-models-agentic-commerce/index.html b/aure/economic-models-agentic-commerce/index.html index 2819438..cfaa4a3 100644 --- a/aure/economic-models-agentic-commerce/index.html +++ b/aure/economic-models-agentic-commerce/index.html @@ -6,17 +6,18 @@ Economic Models and Agentic Commerce — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 08 / 16 · Learning silo

Economic Models and Agentic Commerce

Keep value, payment, fulfillment, dispute, incentives, and risk allocation as separate states.

-
The artifact

commercial state map

AURE-08 produces a commercial state map that identifies evidence, authority, settlement boundaries, and dispute ownership.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
08of 16 · Trust and coordination

Learning silo

Economic Models and Agentic Commerce

Keep value, payment, fulfillment, dispute, incentives, and risk allocation as separate states.

+

Working artifact

commercial state map

AURE-08 produces a commercial state map that identifies evidence, authority, settlement boundaries, and dispute ownership.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/evidence-and-claim-boundaries/index.html b/aure/evidence-and-claim-boundaries/index.html index b31a026..63d348a 100644 --- a/aure/evidence-and-claim-boundaries/index.html +++ b/aure/evidence-and-claim-boundaries/index.html @@ -6,17 +6,18 @@ Evidence and Claim Boundaries — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 02 / 16 · Learning silo

Evidence and Claim Boundaries

Separate fact, interpretation, framework, and promise before a claim enters a public system.

-
The artifact

claim register

AURE-02 produces a claim register with sources, observation dates, confidence, non-claims, and a correction route.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
02of 16 · Ground truth

Learning silo

Evidence and Claim Boundaries

Separate fact, interpretation, framework, and promise before a claim enters a public system.

+

Working artifact

claim register

AURE-02 produces a claim register with sources, observation dates, confidence, non-claims, and a correction route.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/human-handoffs-and-close/index.html b/aure/human-handoffs-and-close/index.html index c23db1c..741357b 100644 --- a/aure/human-handoffs-and-close/index.html +++ b/aure/human-handoffs-and-close/index.html @@ -6,17 +6,18 @@ Human Handoffs and Close — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 14 / 16 · Learning silo

Human Handoffs and Close

Close the loop by routing the right evidence and decision to the right accountable person at the right time.

-
The artifact

handoff packet

AURE-14 produces a handoff packet that names the decision, owner, evidence, options, deadline, and required approval.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
14of 16 · Operation and maintenance

Learning silo

Human Handoffs and Close

Close the loop by routing the right evidence and decision to the right accountable person at the right time.

+

Working artifact

handoff packet

AURE-14 produces a handoff packet that names the decision, owner, evidence, options, deadline, and required approval.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/index.html b/aure/index.html index 51e29c1..2a6297e 100644 --- a/aure/index.html +++ b/aure/index.html @@ -1,19 +1 @@ -AURE — 16 learning silos · AI Mastery - -
AI MASTERY
AURE / 16 silos

The trusted buyer path.

A practical course for making good work legible to buyers and machines without letting an agent outrun its evidence, authority, or accountability.

Plain. Direct. True. Each silo produces an artifact and a bounded decision. Unknown stays unknown.

Preview the headless template route →

  1. 01 / 16Purpose and Buyer TruthStart with the buyer’s real question and define what a trustworthy answer must contain.
    2. -
  2. 02 / 16Evidence and Claim BoundariesSeparate fact, interpretation, framework, and promise before a claim enters a public system.
    3. -
  3. 03 / 16Canonical Entity RecordsGive people and machines one inspectable record for who an organization is and what it actually does.
    4. -
  4. 04 / 16Autonomous Resource ManagementMap resources, authority, trace records, and exceptions before an agent acts.
    5. -
  5. 05 / 16Trust InfrastructureMake provenance, verification, reliance, and correction visible without pretending a record proves truth.
    6. -
  6. 06 / 16Machine-Readable InternetPublish structured relationships and discovery files that help systems find the right record without promising ranking or retrieval.
    7. -
  7. 07 / 16Agentic InteroperabilityRepresent protocol roles, capabilities, messages, task state, and human handoffs without assuming interoperability guarantees safety.
    8. -
  8. 08 / 16Economic Models and Agentic CommerceKeep value, payment, fulfillment, dispute, incentives, and risk allocation as separate states.
    9. -
  9. 09 / 16Autonomous Governance and Policy EnvelopesBound decisions with policy, evaluation, change control, exceptions, appeals, and accountable human roles.
    10. -
  10. 10 / 16Offer Design and QualificationTurn a real buyer problem into a specific, inspectable offer with eligibility and no invented guarantee.
    11. -
  11. 11 / 16Discovery and Buyer ResearchFind what a buyer is deciding, what evidence they trust, and where the current answer fails them.
    12. -
  12. 12 / 16Proof Packets and Case EvidenceAssemble evidence that a buyer can inspect without turning an example into a promise or a testimonial.
    13. -
  13. 13 / 16Agent Sales ConversationsTrain agents to make the useful next step clear while staying inside authorization, evidence, and truth boundaries.
    14. -
  14. 14 / 16Human Handoffs and CloseClose the loop by routing the right evidence and decision to the right accountable person at the right time.
    15. -
  15. 15 / 16Measurement, Correction, and MaintenanceMeasure what actually happened, correct the record, and recheck it before stale truth becomes sales friction.
    16. -
  16. 16 / 16AURE Capstone: The Trusted Buyer PathConnect all sixteen artifacts into one reviewable path from buyer question to evidence, decision, handoff, and maintained truth.
- \ No newline at end of file +AURE — The trusted buyer path · AI Mastery
AURE / AI MASTERYReturn to AI Mastery

A field curriculum · 16 working silos

The trusted buyer path.

Make good work legible to buyers and machines—without letting an agent outrun its evidence, authority, or accountability.

How to use AURE

Build the record as you learn.

Move in order when building a system from scratch, or enter at the artifact your current decision requires. Each silo narrows one question, creates one working record, and names the boundary around what that record can prove.

01 / InspectStart with the decision and available evidence.
02 / ProduceCreate the named artifact, with unknowns intact.
03 / ReviewAssign ownership and the next correction event.

Phase 1 · 01–04

Ground truth

Define the buyer question, evidence boundary, canonical record, and authority map.

  1. 01Purpose and Buyer TruthStart with the buyer’s real question and define what a trustworthy answer must contain.buyer question ↗
  2. 02Evidence and Claim BoundariesSeparate fact, interpretation, framework, and promise before a claim enters a public system.claim register ↗
  3. 03Canonical Entity RecordsGive people and machines one inspectable record for who an organization is and what it actually does.entity record ↗
  4. 04Autonomous Resource ManagementMap resources, authority, trace records, and exceptions before an agent acts.resource map ↗

Phase 2 · 05–08

Trust and coordination

Make provenance, discovery, interoperability, and commercial state inspectable.

  1. 05Trust InfrastructureMake provenance, verification, reliance, and correction visible without pretending a record proves truth.trust packet ↗
  2. 06Machine-Readable InternetPublish structured relationships and discovery files that help systems find the right record without promising ranking or retrieval.discovery surface ↗
  3. 07Agentic InteroperabilityRepresent protocol roles, capabilities, messages, task state, and human handoffs without assuming interoperability guarantees safety.interaction contract ↗
  4. 08Economic Models and Agentic CommerceKeep value, payment, fulfillment, dispute, incentives, and risk allocation as separate states.commercial state map ↗

Phase 3 · 09–12

Buyer systems

Translate governance and research into qualified offers and reviewable proof.

  1. 09Autonomous Governance and Policy EnvelopesBound decisions with policy, evaluation, change control, exceptions, appeals, and accountable human roles.policy envelope ↗
  2. 10Offer Design and QualificationTurn a real buyer problem into a specific, inspectable offer with eligibility and no invented guarantee.offer brief ↗
  3. 11Discovery and Buyer ResearchFind what a buyer is deciding, what evidence they trust, and where the current answer fails them.discovery record ↗
  4. 12Proof Packets and Case EvidenceAssemble evidence that a buyer can inspect without turning an example into a promise or a testimonial.proof packet ↗

Phase 4 · 13–16

Operation and maintenance

Carry truthful conversations through handoff, measurement, and correction.

  1. 13Agent Sales ConversationsTrain agents to make the useful next step clear while staying inside authorization, evidence, and truth boundaries.conversation brief ↗
  2. 14Human Handoffs and CloseClose the loop by routing the right evidence and decision to the right accountable person at the right time.handoff packet ↗
  3. 15Measurement, Correction, and MaintenanceMeasure what actually happened, correct the record, and recheck it before stale truth becomes sales friction.maintenance record ↗
  4. 16AURE Capstone: The Trusted Buyer PathConnect all sixteen artifacts into one reviewable path from buyer question to evidence, decision, handoff, and maintained truth.operating packet ↗
\ No newline at end of file diff --git a/aure/machine-readable-internet/index.html b/aure/machine-readable-internet/index.html index 1ae39b8..26c42de 100644 --- a/aure/machine-readable-internet/index.html +++ b/aure/machine-readable-internet/index.html @@ -6,17 +6,18 @@ Machine-Readable Internet — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 06 / 16 · Learning silo

Machine-Readable Internet

Publish structured relationships and discovery files that help systems find the right record without promising ranking or retrieval.

-
The artifact

discovery surface

AURE-06 produces a portable discovery surface: canonical URLs, structured relationships, sitemap entries, and crawl boundaries.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
06of 16 · Trust and coordination

Learning silo

Machine-Readable Internet

Publish structured relationships and discovery files that help systems find the right record without promising ranking or retrieval.

+

Working artifact

discovery surface

AURE-06 produces a portable discovery surface: canonical URLs, structured relationships, sitemap entries, and crawl boundaries.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/measurement-correction-and-maintenance/index.html b/aure/measurement-correction-and-maintenance/index.html index 260e02d..ac5f005 100644 --- a/aure/measurement-correction-and-maintenance/index.html +++ b/aure/measurement-correction-and-maintenance/index.html @@ -6,17 +6,18 @@ Measurement, Correction, and Maintenance — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 15 / 16 · Learning silo

Measurement, Correction, and Maintenance

Measure what actually happened, correct the record, and recheck it before stale truth becomes sales friction.

-
The artifact

maintenance record

AURE-15 produces a maintenance record with metrics, observation windows, correction events, owner, and next review date.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
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AURE / AI MASTERYReturn to AI Mastery
+
15of 16 · Operation and maintenance

Learning silo

Measurement, Correction, and Maintenance

Measure what actually happened, correct the record, and recheck it before stale truth becomes sales friction.

+

Working artifact

maintenance record

AURE-15 produces a maintenance record with metrics, observation windows, correction events, owner, and next review date.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/offer-design-and-qualification/index.html b/aure/offer-design-and-qualification/index.html index 5591071..46f1594 100644 --- a/aure/offer-design-and-qualification/index.html +++ b/aure/offer-design-and-qualification/index.html @@ -6,17 +6,18 @@ Offer Design and Qualification — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 10 / 16 · Learning silo

Offer Design and Qualification

Turn a real buyer problem into a specific, inspectable offer with eligibility and no invented guarantee.

-
The artifact

offer brief

AURE-10 produces an offer brief covering audience, painful decision, deliverables, method, eligibility, price basis, unknowns, and next step.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
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AURE / AI MASTERYReturn to AI Mastery
+
10of 16 · Buyer systems

Learning silo

Offer Design and Qualification

Turn a real buyer problem into a specific, inspectable offer with eligibility and no invented guarantee.

+

Working artifact

offer brief

AURE-10 produces an offer brief covering audience, painful decision, deliverables, method, eligibility, price basis, unknowns, and next step.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/proof-packets-and-case-evidence/index.html b/aure/proof-packets-and-case-evidence/index.html index a93d663..379c13a 100644 --- a/aure/proof-packets-and-case-evidence/index.html +++ b/aure/proof-packets-and-case-evidence/index.html @@ -6,17 +6,18 @@ Proof Packets and Case Evidence — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 12 / 16 · Learning silo

Proof Packets and Case Evidence

Assemble evidence that a buyer can inspect without turning an example into a promise or a testimonial.

-
The artifact

proof packet

AURE-12 produces a proof packet with artifacts, dates, scope, source links, exclusions, and a clear unknowns section.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
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AURE / AI MASTERYReturn to AI Mastery
+
12of 16 · Buyer systems

Learning silo

Proof Packets and Case Evidence

Assemble evidence that a buyer can inspect without turning an example into a promise or a testimonial.

+

Working artifact

proof packet

AURE-12 produces a proof packet with artifacts, dates, scope, source links, exclusions, and a clear unknowns section.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/purpose-and-buyer-truth/index.html b/aure/purpose-and-buyer-truth/index.html index 1e9ccf7..f2aef0d 100644 --- a/aure/purpose-and-buyer-truth/index.html +++ b/aure/purpose-and-buyer-truth/index.html @@ -6,17 +6,18 @@ Purpose and Buyer Truth — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
-
AURE 01 / 16 · Learning silo

Purpose and Buyer Truth

Start with the buyer’s real question and define what a trustworthy answer must contain.

-
The artifact

buyer question

AURE-01 produces a one-page buyer-question brief: audience, decision, evidence boundary, unknowns, and one honest next action.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
- \ No newline at end of file +
AURE / AI MASTERYReturn to AI Mastery
+
01of 16 · Ground truth

Learning silo

Purpose and Buyer Truth

Start with the buyer’s real question and define what a trustworthy answer must contain.

+

Working artifact

buyer question

AURE-01 produces a one-page buyer-question brief: audience, decision, evidence boundary, unknowns, and one honest next action.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/aure/trust-infrastructure/index.html b/aure/trust-infrastructure/index.html index cd8e675..97183d0 100644 --- a/aure/trust-infrastructure/index.html +++ b/aure/trust-infrastructure/index.html @@ -6,17 +6,18 @@ Trust Infrastructure — AURE · AI Mastery + + + + + + + - + - - - -
AI MASTERY / AURE
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AURE 05 / 16 · Learning silo

Trust Infrastructure

Make provenance, verification, reliance, and correction visible without pretending a record proves truth.

-
The artifact

trust packet

AURE-05 produces a trust packet that distinguishes source provenance, verifier policy, local reliance, and unresolved risk.

Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

-
SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
-
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AURE / AI MASTERYReturn to AI Mastery
+
05of 16 · Trust and coordination

Learning silo

Trust Infrastructure

Make provenance, verification, reliance, and correction visible without pretending a record proves truth.

+

Working artifact

trust packet

AURE-05 produces a trust packet that distinguishes source provenance, verifier policy, local reliance, and unresolved risk.

  1. Name the actor and the decision.
  2. Record the evidence source and observation date.
  3. Set the authority boundary and unresolved question.
  4. Choose the next review event. Missing evidence stays unknown.
+

Pre-action review

The SOUL check

S / SCOPEWho authorized the action?
O / OBSERVEWhat actually happened?
U / USEWhat information may be used?
L / LIABILITYWho owns the edge case?
+
\ No newline at end of file diff --git a/author/author.css b/author/author.css new file mode 100644 index 0000000..d1f7442 --- /dev/null +++ b/author/author.css @@ -0,0 +1,4 @@ +:root{--ink:#20221f;--ink-2:#292c28;--paper:#f0eee7;--paper-2:#fcfbf6;--line:#d1cec4;--moss:#69775d;--ochre:#c99443;--muted:#656a63;--serif:Georgia,"Times New Roman",serif;--sans:Inter,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,"Segoe UI",sans-serif} +*{box-sizing:border-box}body{margin:0;background:var(--paper);color:var(--ink);font-family:var(--sans);line-height:1.6}.portfolio-shell{width:min(1180px,calc(100% - 3rem));margin-inline:auto}.portfolio-topbar{background:var(--ink);color:var(--paper-2);border-bottom:1px solid #3d403b}.portfolio-topbar .portfolio-shell{min-height:72px;display:flex;align-items:center;justify-content:space-between;gap:1.5rem}.portfolio-brand{display:flex;gap:.7rem;text-decoration:none;font-size:.78rem;font-weight:800;letter-spacing:.13em}.portfolio-brand 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.portfolio-shell{flex-direction:column}} +@media(prefers-reduced-motion:reduce){*{scroll-behavior:auto!important}} diff --git a/author/index.html b/author/index.html index 171a23d..c236267 100644 --- a/author/index.html +++ b/author/index.html @@ -3,24 +3,40 @@ - Mason Nguyen — AI Systems, GEO, and Technical SEO | AI Mastery - + Mason Nguyen — AI Systems Engineering Portfolio | AI Mastery + - - + - - + + - - - - + + + -
AI MASTERY
Author / systems practice

Mason Nguyen.

Systems architect and GEO strategist documenting how intelligent systems become more legible, verifiable, and useful in the world.

AI Mastery is the research and engineering surface for this work. It connects technical SEO, generative engine optimization, AI infrastructure, agentic computing, digital trust, autonomous resource management, and evidence-led learning.

What this record establishes

This page identifies the author of the published AI Mastery materials. It does not claim independent rankings, Domain Authority, citations, commercial outcomes, certifications, or production guarantees. Those require separate evidence and measurement.

Selected work

FIELD GUIDETechnical SEO and GEO PlaybookCrawlable authority graphs, machine-readable knowledge, evidence, and the portable operating method.COURSE / 16 SILOSAURE — The Trusted Buyer PathEvidence-led buyer trust, autonomous resource management, and accountable agentic work.CASE STUDYAI Mastery self-auditA transparent implementation record with claims, evidence, corrections, and unknowns.CANONICAL PROFILECoreweaver author profileThe broader author and entity record.
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AI MASTERY / PORTFOLIO
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Mason Nguyen · Systems practice

Engineer the system around intelligence.

I design public knowledge, agentic workflows, trust boundaries, and learning systems that make technical work more legible, inspectable, and useful.

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Engineering profile

Architecture that can be inspected.

AI Mastery is the public research and engineering surface for this practice. The work connects system design, technical SEO and GEO, agent coordination, autonomous resource management, and evidence-led documentation.

This portfolio describes roles and published artifacts. It does not imply independent validation, production suitability, commercial impact, or a result that the linked evidence does not establish.

System architectureModels, knowledge, tools, compute, controls, and observability.
Machine-readable knowledgeEntities, relationships, provenance, discovery, and correction.
Agentic operationsAuthority, state, resource boundaries, handoffs, and exceptions.
Technical publishingLearning paths, field records, release gates, and maintenance loops.
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Selected systems

Work with an evidence route.

Each project names the contribution, its inspectable artifact, and the boundary around what that artifact proves.

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01 / KNOWLEDGE SYSTEM

AI Mastery publication architecture.

A static, source-led knowledge system connecting research domains, bounded learning pathways, field records, structured data, crawl surfaces, and automated release checks.

Explore the flagship ↗Read the self-audit ↗Inspect the repository ↗
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02 / LEARNING SYSTEM

AURE: the trusted buyer path.

A sixteen-silo field curriculum that turns buyer truth, evidence, entity records, authority, interoperability, governance, proof, handoff, and maintenance into progressive working artifacts.

Enter the curriculum ↗Inspect the generator ↗
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03 / FIELD SYSTEM

Arctura Network positioning record.

A documented strategy direction for moving a technical network from machinery-first language toward useful work, proof, and stewardship while retaining its existing technical evidence boundary.

Read the field study ↗Inspect the claim register ↗Inspect the current site ↗
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04 / OPERATING FRAMEWORK

Autonomous Resource Management.

A systems framework and five-foundation learning path for mapping resource scope, decision rights, trace records, and accountable exception routes before an autonomous workflow acts.

Start the learning path ↗Visit the framework record ↗
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Working method

Model. Bound. Build. Verify.

01 / MODELName the actors, state, resources, evidence, and decision.
02 / BOUNDDefine authority, unknowns, exceptions, and accountable ownership.
03 / BUILD + VERIFYShip an inspectable artifact, test its routes, and record the next check.

This method describes how the published work is organized. Suitability for any external system requires context-specific engineering, security, legal, and operational review.

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Continue

Inspect the work from your angle.

Field studiesApplied decisions and evidence records →Learning resourcesDecision guides and connected paths →Canonical profileBroader author and entity record ↗
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+ diff --git a/case-studies/arctura-network/claim-register.json b/case-studies/arctura-network/claim-register.json new file mode 100644 index 0000000..f07b06a --- /dev/null +++ b/case-studies/arctura-network/claim-register.json @@ -0,0 +1,52 @@ +{ + "record": "arctura-network-field-study", + "reviewed": "2026-08-30", + "owner": "Mason Nguyen", + "claims": [ + { + "id": "AN-01", + "class": "fact", + "statement": "Arctura publishes a bounded local Bittensor testnet record for netuid 505.", + "evidence": [ + "https://arctura.network/documentation/netuid-505/", + "https://arctura.network/evidence/netuid-505/" + ], + "status": "observed" + }, + { + "id": "AN-02", + "class": "fact", + "statement": "Arctura publishes an authority map identifying which public surface supports which claim.", + "evidence": ["https://arctura.network/authority/"], + "status": "observed" + }, + { + "id": "AN-I01", + "class": "interpretation", + "statement": "The public story asks a general visitor to understand implementation machinery before the durable organizational idea.", + "evidence": ["https://arctura.network/"], + "status": "first-party interpretation" + }, + { + "id": "AN-F01", + "class": "framework", + "statement": "Question, Evidence, Use is AI Mastery's curation model for deciding whether and how to publish a page.", + "evidence": ["https://github.com/virtualmase/ai-mastery/blob/main/docs/CONTENT-CURATION-STRATEGY.md"], + "status": "house framework" + }, + { + "id": "AN-U01", + "class": "unknown", + "statement": "Reader comprehension, participation, search, citation, and commercial outcomes from the proposed redesign are unknown.", + "evidence": [], + "status": "held" + }, + { + "id": "AN-U02", + "class": "unknown", + "statement": "The supplied wireframe does not establish that the proposed redesign has been deployed.", + "evidence": [], + "status": "held" + } + ] +} diff --git a/case-studies/arctura-network/index.html b/case-studies/arctura-network/index.html new file mode 100644 index 0000000..5aa5e85 --- /dev/null +++ b/case-studies/arctura-network/index.html @@ -0,0 +1,35 @@ + + + + + + Arctura Network: From Technical Proof to a Durable Public Idea | AI Mastery + + + + + + + + + + + + + + +
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Public positioning · working record · 30 August 2026

From technical proof to a durable public idea.

Arctura had real technical substance. The challenge was not to replace it, but to give people a clear reason to care before asking them to understand the machinery.

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CURRENT PUBLIC SURFACETestnet-first
DESIGN DIRECTIONDocumented
REDESIGN OUTCOMENot yet measured
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The reader problem

The machinery arrived before the meaning.

The existing public site opens with Bittensor, netuid 505, validators, miners, and consensus. Those details matter to a technical reviewer, but they ask a general visitor to decode the implementation before understanding the organization.

The working question became: what remains true if the implementation changes?

Durable public ideaArctura is a network for useful work. People and software contribute work, check results, keep a record, and improve the system they share.
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What changed in the model

Plain language first. Technical precision one layer down.

01 / WORK

Do the work.

Begin with the useful contribution, not the system vocabulary.

02 / PROOF

Check the work.

Make important results and their limits inspectable.

03 / STEWARDSHIP

Improve the system.

Connect participation to maintenance and accountable change.

This creates two valid reading levels. The public layer explains work, review, agreement, and record keeping. The technical layer retains Bittensor, validators, miners, Resonance BFT, and agent behavior where those terms are necessary.

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Evidence boundary

Simpler language cannot mean looser claims.

The session preserved the strongest part of the existing site: its willingness to state what the public record does not establish.

What remains unclaimed

No published Finney mainnet launch, active public staking path, source-backed live quorum feed, or production-readiness finding is inferred here.

  • The supplied wireframe is a design artifact, not documentary proof of a deployed redesign.
  • This study records the strategy session; it does not measure comprehension, participation, search, or commercial outcomes.
  • The live Arctura site remains the source of truth for its current public status.
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What AI Mastery keeps

A reusable curation strategy.

AI Mastery is adopting the same discipline for its own evolution: lead with the reader’s real decision, preserve specialist depth behind clear entry points, publish dated evidence boundaries, and distinguish a documented direction from a measured result.

QUESTION

Why is the reader here?

Each page gets one clear job before it gets keywords or decoration.

EVIDENCE

What can we support?

Facts, interpretations, frameworks, and unknowns remain distinct.

USE

What can they do next?

Useful artifacts and sound decisions matter more than content volume.

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Next check

Publish the work. Observe the result. Correct the record.

After the Arctura redesign is deployed, this record should be updated with the final route, a dated implementation checkpoint, accessibility and link checks, and any measured reader evidence that becomes available.

This first-party field study was drafted with AI assistance from the supplied working materials and linked public Arctura records. It is not an independent audit.

Inspect the current Arctura site ↗ · Read the claim register · Suggest a correction ↗
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.study-shell{flex-direction:column}} +@media(prefers-reduced-motion:reduce){*{scroll-behavior:auto!important}} diff --git a/case-studies/index.html b/case-studies/index.html new file mode 100644 index 0000000..f9cc3cf --- /dev/null +++ b/case-studies/index.html @@ -0,0 +1,37 @@ + + + + + + Field Studies and Evidence Records | AI Mastery + + + + + + + + + + + + + + + +
AI MASTERY / FIELD STUDIES
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Applied work · Evidence before outcome

Show the work. Keep the unknowns.

Field studies record decisions, implementation evidence, boundaries, and next checks without turning a documented direction into a success claim.

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Publication standard

A claim earns its class.

Every material statement is handled as one of four things. This keeps observations from quietly becoming promises and makes correction possible when the underlying record changes.

FactDirectly supported by an inspected source.
InterpretationA bounded reading of the available evidence.
FrameworkA reusable method, not a guaranteed result.
UnknownHeld open until attributable evidence exists.
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Current records

Two ways to inspect applied work.

One record documents a client strategy direction; the other audits AI Mastery’s own public implementation. Both expose sources, limits, and a correction route.

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Field study · 001Direction recorded

Arctura Network: from technical proof to a durable public idea.

A dated record of reframing a technical network around useful work, proof, and stewardship while preserving its testnet evidence and known limits.

Evidence
Public Arctura records
Outcome state
Not yet measured
Artifact
Strategy and design direction
Next check
After publication
Inspect the field study ↗
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Public record · AM 01Implementation audited

AI Mastery: make the work inspectable.

A bounded self-audit of the public repository, learning surfaces, discovery files, and implementation evidence at a dated checkpoint.

Evidence
Repository and public routes
Outcome state
Explicitly unclaimed
Artifact
Evidence ledger + claim register
Next check
After material change
Inspect the public record ↗
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Maintenance loop

Inspect. Record. Correct. Recheck.

01 / INSPECTUse stable, directly reviewable sources.
02 / RECORDClassify the statement and preserve its date.
03 / CORRECTPublish a specific route for challenges.
04 / RECHECKRevisit after evidence or implementation changes.

This is an editorial maintenance method. It is not an independent audit standard, certification, or guarantee of completeness.

+
+ + + diff --git a/directory/directory.css b/directory/directory.css new file mode 100644 index 0000000..d1c2fd0 --- /dev/null +++ b/directory/directory.css @@ -0,0 +1,4 @@ +:root{--ink:#20221f;--ink-2:#292c28;--paper:#f0eee7;--paper-2:#fcfbf6;--line:#d1cec4;--moss:#69775d;--ochre:#c99443;--muted:#656a63;--serif:Georgia,"Times New Roman",serif;--sans:Inter,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,"Segoe UI",sans-serif} +*{box-sizing:border-box}body{margin:0;background:var(--paper);color:var(--ink);font-family:var(--sans);line-height:1.6}.directory-shell{width:min(1180px,calc(100% - 3rem));margin-inline:auto}.directory-topbar{background:var(--ink);color:var(--paper-2);border-bottom:1px solid #3d403b}.directory-topbar .directory-shell{min-height:72px;display:flex;align-items:center;justify-content:space-between;gap:1.5rem}.directory-brand{display:flex;gap:.7rem;text-decoration:none;font-size:.78rem;font-weight:800;letter-spacing:.13em}.directory-brand span{color:#b7bdaf;font-weight:600}.directory-nav{display:flex;gap:1.4rem;font-size:.76rem}.directory-nav a{color:#d8d9d2;text-underline-offset:4px}.directory-kicker{margin:0;color:var(--moss);font-size:.7rem;font-weight:800;letter-spacing:.14em;text-transform:uppercase}.directory-hero{padding:clamp(4.5rem,10vw,8rem) 0 4.5rem;background:var(--ink);color:var(--paper-2)}.directory-hero-grid{display:grid;grid-template-columns:minmax(0,1.35fr) minmax(260px,.65fr);gap:clamp(3rem,8vw,8rem);align-items:end}.directory-hero h1,.directory-section h2{font-family:var(--serif);font-weight:400;letter-spacing:-.04em}.directory-hero h1{max-width:830px;margin:.65rem 0 1.4rem;font-size:clamp(3.7rem,8vw,7.2rem);line-height:.9}.directory-hero p{max-width:650px;margin:0;color:#d1d3cc;font-size:clamp(1.02rem,1.7vw,1.2rem)}.directory-stat{display:grid;grid-template-columns:repeat(2,1fr);gap:1px;background:#50534d;border:1px solid #50534d}.directory-stat div{padding:1.1rem;background:#242723}.directory-stat strong{display:block;font-family:var(--serif);font-size:2.4rem;font-weight:400;line-height:1}.directory-stat span{display:block;margin-top:.4rem;color:#b9bdb4;font-size:.68rem;letter-spacing:.08em;text-transform:uppercase}.directory-index{padding:1rem 0 6rem}.directory-section{display:grid;grid-template-columns:minmax(210px,.55fr) minmax(0,1.45fr);gap:clamp(2rem,7vw,6rem);padding:4.5rem 0;border-bottom:1px solid var(--line)}.directory-section-head{position:sticky;top:1.5rem;align-self:start}.directory-section h2{margin:.55rem 0;font-size:clamp(1.9rem,3vw,2.8rem);line-height:1}.directory-section-head p{max-width:310px;margin:.8rem 0 0;color:var(--muted);font-size:.88rem}.directory-list{list-style:none;margin:0;padding:0;border-top:1px solid var(--line)}.directory-list li{border-bottom:1px solid var(--line)}.directory-list a{display:grid;grid-template-columns:minmax(0,1fr) auto;gap:1.5rem;align-items:center;padding:1.15rem .25rem;color:inherit;text-decoration:none;transition:background .2s,padding .2s}.directory-list a:hover,.directory-list a:focus-visible{padding-inline:1rem;background:var(--paper-2);outline:none}.directory-list strong{font-family:var(--serif);font-size:clamp(1.1rem,1.8vw,1.35rem);font-weight:400;line-height:1.2}.directory-list small{color:var(--muted);font-size:.68rem;overflow-wrap:anywhere}.directory-list .directory-arrow{margin-left:.4rem;color:var(--ochre)}.directory-note{margin-top:1.2rem;padding:1.2rem;border:1px solid var(--line);background:var(--paper-2);color:var(--muted);font-size:.82rem}.directory-footer{padding:2.5rem 0;background:var(--ink);color:#bfc2ba;font-size:.78rem}.directory-footer .directory-shell{display:flex;justify-content:space-between;gap:2rem}.directory-footer nav{display:flex;flex-wrap:wrap;gap:1.2rem}.directory-footer a{color:inherit;text-underline-offset:3px} +@media(max-width:760px){.directory-shell{width:min(100% - 2rem,1180px)}.directory-topbar .directory-shell{min-height:62px}.directory-nav a:not(:last-child){display:none}.directory-hero{padding:4rem 0 3rem}.directory-hero-grid,.directory-section{grid-template-columns:1fr}.directory-hero h1{font-size:clamp(3.5rem,17vw,5.2rem)}.directory-stat{margin-top:1rem}.directory-section{padding:3rem 0}.directory-section-head{position:static}.directory-list a{grid-template-columns:1fr}.directory-list small{margin-top:-.6rem}.directory-footer .directory-shell{flex-direction:column}} +@media(prefers-reduced-motion:reduce){.directory-list a{transition:none}} diff --git a/directory/index.html b/directory/index.html index c2aa56e..e1b79e3 100644 --- a/directory/index.html +++ b/directory/index.html @@ -1,53 +1 @@ -All pages — AI Mastery
AI MASTERY
Crawlable directory

Every page has a path.

Home → directory → page. This index keeps the system navigable for people, crawlers, and agents without relying on hidden state.

  1. AI Mastery — The Architecture of Intelligence | Mason Nguyen/
    2. -
  2. Purpose and Buyer Truth — AURE · AI Mastery/aure-template-demo/
    3. -
  3. AURE — 16 learning silos · AI Mastery/aure/
    4. -
  4. Agent Sales Conversations — AURE · AI Mastery/aure/agent-sales-conversations/
    5. -
  5. Agentic Interoperability — AURE · AI Mastery/aure/agentic-interoperability/
    6. -
  6. AURE Capstone: The Trusted Buyer Path — AURE · AI Mastery/aure/aure-capstone/
    7. -
  7. Autonomous Governance and Policy Envelopes — AURE · AI Mastery/aure/autonomous-governance/
    8. -
  8. Autonomous Resource Management — AURE · AI Mastery/aure/autonomous-resource-management/
    9. -
  9. Canonical Entity Records — AURE · AI Mastery/aure/canonical-entity-records/
    10. -
  10. Discovery and Buyer Research — AURE · AI Mastery/aure/discovery-and-buyer-research/
    11. -
  11. Economic Models and Agentic Commerce — AURE · AI Mastery/aure/economic-models-agentic-commerce/
    12. -
  12. Evidence and Claim Boundaries — AURE · AI Mastery/aure/evidence-and-claim-boundaries/
    13. -
  13. Human Handoffs and Close — AURE · AI Mastery/aure/human-handoffs-and-close/
    14. -
  14. Machine-Readable Internet — AURE · AI Mastery/aure/machine-readable-internet/
    15. -
  15. Measurement, Correction, and Maintenance — AURE · AI Mastery/aure/measurement-correction-and-maintenance/
    16. -
  16. Offer Design and Qualification — AURE · AI Mastery/aure/offer-design-and-qualification/
    17. -
  17. Proof Packets and Case Evidence — AURE · AI Mastery/aure/proof-packets-and-case-evidence/
    18. -
  18. Purpose and Buyer Truth — AURE · AI Mastery/aure/purpose-and-buyer-truth/
    19. -
  19. Trust Infrastructure — AURE · AI Mastery/aure/trust-infrastructure/
    20. -
  20. Mason Nguyen — AI Systems, GEO, and Technical SEO | AI Mastery/author/
    21. -
  21. AI Mastery: Public Implementation Self-Audit | AI Mastery/case-studies/ai-mastery-self-audit/
    22. -
  22. Guides — Technical SEO, GEO, and AI Systems | AI Mastery/guides/
    23. -
  23. Technical SEO and GEO Playbook: Crawlable Authority Graphs | AI Mastery/guides/technical-seo-geo-playbook/
    24. -
  24. Agent Exception Handling | AI Mastery/learning/agent-exception-handling/
    25. -
  25. Agent Identity & Signing | AI Mastery/learning/agent-identity-and-signing/
    26. -
  26. Agent Payment Policy Enforcement | AI Mastery/learning/agent-payment-policy-enforcement/
    27. -
  27. Agent Settlement vs. Fulfillment | AI Mastery/learning/agent-settlement-vs-fulfillment/
    28. -
  28. Agentic Commerce Operator Foundations | AI Mastery/learning/agentic-commerce-operator-foundations/
    29. -
  29. Autonomous Decision Rights | AI Mastery/learning/autonomous-decision-rights/
    30. -
  30. Autonomous Exception Design | AI Mastery/learning/autonomous-exception-design/
    31. -
  31. Autonomous Resource Inventory | AI Mastery/learning/autonomous-resource-inventory/
    32. -
  32. Autonomous Resource Management | AI Mastery/learning/autonomous-resource-management/
    33. -
  33. Autonomous Trace Records | AI Mastery/learning/autonomous-trace-records/
    34. -
  34. Canonical Entity Records | AI Mastery/learning/canonical-entity-records/
    35. -
  35. Capability Discovery and Negotiation | AI Mastery/learning/capability-discovery-and-negotiation/
    36. -
  36. Discovery Files and Crawl Boundaries | AI Mastery/learning/discovery-files-and-crawl-boundaries/
    37. -
  37. Evidence and Claim Boundaries | AI Mastery/learning/evidence-and-claim-boundaries/
    38. -
  38. GEO Currency & Decay | AI Mastery/learning/geo-currency-and-decay/
    39. -
  39. GEO Entity Foundations | AI Mastery/learning/geo-entity-foundations/
    40. -
  40. GEO Measurement & Evaluation | AI Mastery/learning/geo-measurement-and-evaluation/
    41. -
  41. GEO Retrieval Surface | AI Mastery/learning/geo-retrieval-surface/
    42. -
  42. GEO Source Attribution | AI Mastery/learning/geo-source-attribution/
    43. -
  43. GEO Structured Data Implementation | AI Mastery/learning/geo-structured-data-implementation/
    44. -
  44. Incentives, Fees, and Risk Allocation | AI Mastery/learning/incentives-fees-and-risk-allocation/
    45. -
  45. Mandates, Spend Limits, and Policy | AI Mastery/learning/mandates-spend-limits-and-policy/
    46. -
  46. Message Envelopes and Correlation | AI Mastery/learning/message-envelopes-and-correlation/
    47. -
  47. Protocol Roles and Boundaries | AI Mastery/learning/protocol-roles-and-boundaries/
    48. -
  48. Provenance Records and Content Credentials | AI Mastery/learning/provenance-records-and-content-credentials/
    49. -
  49. Reconciliation, Disputes, and Exceptions | AI Mastery/learning/reconciliation-disputes-and-exceptions/
    50. -
  50. Structured Relationship Data | AI Mastery/learning/structured-relationship-data/
    51. -
  51. Task State and Human Handoffs | AI Mastery/learning/task-state-and-human-handoffs/
    52. -
  52. Value, Payment, and Fulfillment | AI Mastery/learning/value-payment-and-fulfillment/
    53. -
  53. Verifier Policy and Correction Paths | AI Mastery/learning/verifier-policy-and-correction-paths/
\ No newline at end of file +Publication Index — AI Mastery
AI MASTERY / INDEX

Complete publication graph

Every page has a purpose.

A human-readable map of the guides, evidence records, curricula, and technical lessons that make up AI Mastery.

Publication · 03 routes

Start and orient

The flagship, author record, and curated guide index.

  1. AI Mastery — Field Guides and Learning Systems | Mason Nguyen/
  2. Mason Nguyen — AI Systems, GEO, and Technical SEO | AI Mastery/author/
  3. Guides — Technical SEO, GEO, and AI Systems | AI Mastery/guides/

Guides and records · 05 routes

Use the work

Decision guides and dated field records with explicit evidence boundaries.

  1. Field Studies and Evidence Records | AI Mastery/case-studies/
  2. AI Mastery: Public Implementation Self-Audit | AI Mastery/case-studies/ai-mastery-self-audit/
  3. Arctura Network: From Technical Proof to a Durable Public Idea | AI Mastery/case-studies/arctura-network/
  4. Technical SEO and GEO Playbook: Crawlable Authority Graphs | AI Mastery/guides/technical-seo-geo-playbook/
  5. How to Train Your Agent — LLM vs. SLM, Local Hosting, and the Open-Weight Stack | AI Mastery/guides/train-your-agent-playbook/

AURE curriculum · 17 routes

Follow the buyer path

Sixteen progressive artifacts from buyer truth and evidence to handoff and maintenance.

  1. AURE — The trusted buyer path · AI Mastery/aure/
  2. Agent Sales Conversations — AURE · AI Mastery/aure/agent-sales-conversations/
  3. Agentic Interoperability — AURE · AI Mastery/aure/agentic-interoperability/
  4. AURE Capstone: The Trusted Buyer Path — AURE · AI Mastery/aure/aure-capstone/
  5. Autonomous Governance and Policy Envelopes — AURE · AI Mastery/aure/autonomous-governance/
  6. Autonomous Resource Management — AURE · AI Mastery/aure/autonomous-resource-management/
  7. Canonical Entity Records — AURE · AI Mastery/aure/canonical-entity-records/
  8. Discovery and Buyer Research — AURE · AI Mastery/aure/discovery-and-buyer-research/
  9. Economic Models and Agentic Commerce — AURE · AI Mastery/aure/economic-models-agentic-commerce/
  10. Evidence and Claim Boundaries — AURE · AI Mastery/aure/evidence-and-claim-boundaries/
  11. Human Handoffs and Close — AURE · AI Mastery/aure/human-handoffs-and-close/
  12. Machine-Readable Internet — AURE · AI Mastery/aure/machine-readable-internet/
  13. Measurement, Correction, and Maintenance — AURE · AI Mastery/aure/measurement-correction-and-maintenance/
  14. Offer Design and Qualification — AURE · AI Mastery/aure/offer-design-and-qualification/
  15. Proof Packets and Case Evidence — AURE · AI Mastery/aure/proof-packets-and-case-evidence/
  16. Purpose and Buyer Truth — AURE · AI Mastery/aure/purpose-and-buyer-truth/
  17. Trust Infrastructure — AURE · AI Mastery/aure/trust-infrastructure/

Learning library · 30 routes

Study the system

Focused technical lessons across autonomy, trust, machine-readable knowledge, interoperability, and commerce.

  1. Agent Exception Handling | AI Mastery/learning/agent-exception-handling/
  2. Agent Identity & Signing | AI Mastery/learning/agent-identity-and-signing/
  3. Agent Payment Policy Enforcement | AI Mastery/learning/agent-payment-policy-enforcement/
  4. Agent Settlement vs. Fulfillment | AI Mastery/learning/agent-settlement-vs-fulfillment/
  5. Agentic Commerce Operator Foundations | AI Mastery/learning/agentic-commerce-operator-foundations/
  6. Autonomous Decision Rights | AI Mastery/learning/autonomous-decision-rights/
  7. Autonomous Exception Design | AI Mastery/learning/autonomous-exception-design/
  8. Autonomous Resource Inventory | AI Mastery/learning/autonomous-resource-inventory/
  9. Autonomous Resource Management | AI Mastery/learning/autonomous-resource-management/
  10. Autonomous Trace Records | AI Mastery/learning/autonomous-trace-records/
  11. Canonical Entity Records | AI Mastery/learning/canonical-entity-records/
  12. Capability Discovery and Negotiation | AI Mastery/learning/capability-discovery-and-negotiation/
  13. Discovery Files and Crawl Boundaries | AI Mastery/learning/discovery-files-and-crawl-boundaries/
  14. Evidence and Claim Boundaries | AI Mastery/learning/evidence-and-claim-boundaries/
  15. GEO Currency & Decay | AI Mastery/learning/geo-currency-and-decay/
  16. GEO Entity Foundations | AI Mastery/learning/geo-entity-foundations/
  17. GEO Measurement & Evaluation | AI Mastery/learning/geo-measurement-and-evaluation/
  18. GEO Retrieval Surface | AI Mastery/learning/geo-retrieval-surface/
  19. GEO Source Attribution | AI Mastery/learning/geo-source-attribution/
  20. GEO Structured Data Implementation | AI Mastery/learning/geo-structured-data-implementation/
  21. Incentives, Fees, and Risk Allocation | AI Mastery/learning/incentives-fees-and-risk-allocation/
  22. Mandates, Spend Limits, and Policy | AI Mastery/learning/mandates-spend-limits-and-policy/
  23. Message Envelopes and Correlation | AI Mastery/learning/message-envelopes-and-correlation/
  24. Protocol Roles and Boundaries | AI Mastery/learning/protocol-roles-and-boundaries/
  25. Provenance Records and Content Credentials | AI Mastery/learning/provenance-records-and-content-credentials/
  26. Reconciliation, Disputes, and Exceptions | AI Mastery/learning/reconciliation-disputes-and-exceptions/
  27. Structured Relationship Data | AI Mastery/learning/structured-relationship-data/
  28. Task State and Human Handoffs | AI Mastery/learning/task-state-and-human-handoffs/
  29. Value, Payment, and Fulfillment | AI Mastery/learning/value-payment-and-fulfillment/
  30. Verifier Policy and Correction Paths | AI Mastery/learning/verifier-policy-and-correction-paths/

Implementation reference · 01 routes

Inspect the machinery

Public template output retained for portability and implementation review.

Reference output is separated from the editorial curriculum so readers do not mistake a template demonstration for an additional AURE lesson.
  1. Purpose and Buyer Truth — AURE · AI Mastery/aure-template-demo/
\ No newline at end of file diff --git a/docs/CONTENT-CURATION-STRATEGY.md b/docs/CONTENT-CURATION-STRATEGY.md new file mode 100644 index 0000000..d898fb2 --- /dev/null +++ b/docs/CONTENT-CURATION-STRATEGY.md @@ -0,0 +1,57 @@ +# AI Mastery content curation strategy + +AI Mastery grows by publishing work that helps a reader make a better technical or operating decision. Volume is not the goal. A coherent, useful body of work is. + +## The editorial test + +Every proposed page must answer three questions before drafting: + +1. **Question:** What real decision, confusion, or task brought the reader here? +2. **Evidence:** Which statements are facts, interpretations, house frameworks, or unknowns? +3. **Use:** What can the reader understand, inspect, or do after reading? + +A page that cannot answer all three should remain a note, be combined with an existing page, or not be published. + +## Four publication forms + +- **Guide:** Helps a reader understand or make a decision. Uses dated sources and states where advice depends on context. +- **Learning path:** Builds judgment in a deliberate sequence. Names prerequisites, an artifact, and a bounded next step. +- **Field study:** Records applied work, decisions, constraints, and what remains unmeasured. It is not automatically a success story. +- **Public record:** Preserves claims, evidence, status, corrections, and maintenance triggers without marketing language. + +## Curation rules + +- Prefer one strong update over several thin pages. +- Add a route only when its reader job differs materially from existing content. +- Connect every new page to a relevant hub and at least one adjacent piece of work. +- Use ordinary crawlable links with descriptive anchor text. +- Keep a new page within two meaningful clicks of the homepage. +- Mark time-sensitive facts and recheck dates. +- Treat “unknown” as a valid finding. +- Do not turn implementation checks into claims about rankings, citations, adoption, revenue, safety, or outcomes. +- Use visuals to explain a relationship, process, or decision; never as fake evidence. + +## Homepage rhythm + +The homepage should expose no more than three recent items in **New Field Work**: + +1. one current guide, +2. one applied field study or public record, +3. one durable method or learning pathway. + +When a new item replaces an older one, the older route remains discoverable through its hub, the directory, sitemap, and machine-readable index. + +## Release record + +For every new route, record: + +- owner and publication date, +- reader question and page job, +- evidence and claim boundary, +- canonical route and internal links, +- metadata and structured-data type, +- visual provenance when assets are used, +- validation results, +- recheck trigger or date. + +Use the existing technical SEO procedure and editorial release gate as hard release controls. Growth should make AI Mastery more navigable and more trustworthy, not merely larger. diff --git a/docs/FLAGSHIP-VISUAL-QA.md b/docs/FLAGSHIP-VISUAL-QA.md new file mode 100644 index 0000000..5f37e3d --- /dev/null +++ b/docs/FLAGSHIP-VISUAL-QA.md @@ -0,0 +1,55 @@ +# Flagship visual QA + +**Review date:** 2026-08-30 +**Branch:** `feat/flagship-homepage-field-studies` +**Runtime:** Local static server at `127.0.0.1:4173`; Playwright 1.55.0 with headless Chromium +**Publication action:** None + +## Surfaces reviewed + +- Homepage hero at 1440 × 1000 and 390 × 844 +- Homepage Learning Paths at 1440 × 1000 and 390 × 844 +- Homepage New Field Work at 1440 × 1000 and 390 × 844 +- Train Your Agent Playbook at 1440 × 1000 and 390 × 844 +- Arctura Network field study at 390 × 844 + +Screenshots were generated as temporary QA artifacts and visually inspected during the review. They are not published assets or documentary evidence. + +## Findings and corrections + +### Palette and hierarchy + +The carbon, field-ivory, mineral-green, and ochre system is coherent across the flagship hero, editorial cards, guide, and field study. Legacy lime and cyan highlight values were removed from the redesigned surfaces. Ochre now marks current actions and selected states rather than decorating every component. + +### Homepage + +The hero establishes the publication job before the technical architecture. The New Field Work section reads as one editorial system at desktop and a deliberate single-column sequence on mobile. Learning-path labels, titles, and lesson links remain legible at both reviewed widths. + +The full-page screenshot tool does not scroll through IntersectionObserver targets, so its blank offscreen sections were not treated as a rendering defect. Section-level renders and the scripted scroll check confirmed that targets reveal when they enter the viewport. + +### Train Your Agent Playbook + +The guide maintains a readable long-form measure, clear comparison blocks, and restrained status color. At 390px, the secondary Knowledge Index and Learning Paths header links were removed from the compact header; the Guides hub remains visible and the breadcrumb preserves context. + +### Arctura field study + +The editorial serif treatment distinguishes the field study from the systems guide without breaking the shared palette. At 390px, the middle record label was removed from the header and the external Arctura link was kept on one line, preventing the arrow from wrapping into a separate row. + +## Scripted interaction evidence + +The temporary Playwright interaction check reported: + +- mobile menu initially collapsed; +- menu opens and sets `aria-expanded="true"`; +- menu closes after selecting the Field Work link; +- Field Work reveal target becomes visible after navigation; +- reduced-motion context renders reveal targets at full opacity; +- no horizontal overflow on the homepage, agent guide, or Arctura field study at 390px; +- no browser console errors or uncaught page errors. + +## Remaining boundaries + +- This is a browser-render and interaction review, not an accessibility certification or cross-browser compatibility guarantee. +- Chromium was reviewed; Safari/WebKit and Firefox were not available in the local runtime. +- Live performance, third-party font delivery, analytics, indexing, search visibility, and reader outcomes remain unmeasured until publication and observation. +- Repeat this review after material homepage, navigation, typography, or editorial-card changes. diff --git a/docs/arctura-network-field-study-content-brief.md b/docs/arctura-network-field-study-content-brief.md new file mode 100644 index 0000000..b167236 --- /dev/null +++ b/docs/arctura-network-field-study-content-brief.md @@ -0,0 +1,49 @@ +# Arctura Network field study — content brief + +## Audience and moment + +A technical founder, operator, or communications lead deciding how to explain a technically credible system without forcing a general reader to decode its implementation first. + +## Page job + +Record a first-party design and positioning session as a bounded field study. It is a decision record, not a redesign-launch announcement or outcome case study. + +## Primary question + +How can a technical network lead with a durable human idea while preserving its implementation detail, evidence record, and known limits? + +## Unique contribution + +The study records the translation from testnet-first language to a two-layer public model: ordinary language for work, review, agreement, and stewardship; technical language for Bittensor, validators, miners, Resonance BFT, and implementation evidence. + +## Claim boundary + +- The supplied strategy guide and wireframe are working artifacts, not proof of deployment. +- The Arctura public site is authoritative for its current public status. +- The study does not establish comprehension, participation, search, citation, commercial, mainnet, staking, production-readiness, or performance outcomes. +- No live Finney mainnet, public staking path, source-backed live quorum feed, or production-readiness result is inferred. + +## Evidence plan + +Current-state claims link to Arctura’s public netuid 505 run record, evidence status, and authority map. The local claim register separates facts, interpretation, house framework, and held unknowns. + +## Information architecture + +The page covers reader problem, durable public idea, two-language model, evidence boundary, reusable curation strategy, and post-publication recheck. + +## Metadata and discovery + +Canonical route: `https://virtualmase.github.io/ai-mastery/case-studies/arctura-network/` + +The route must remain linked from the homepage, directory, sitemap, and `llms.txt`. Its machine-readable type is `Article`, not `Product`, `Review`, or a performance-oriented case-study type. + +## Asset plan + +The supplied logo and wireframe informed the design analysis but are not published in this release. If published later, they must be labelled as design artifacts and entered in an asset ledger; they must not be presented as screenshots of a deployed site. + +## Owner and review + +- Owner: Mason Nguyen +- Working session: 2026-08-30 +- Recheck: after the Arctura redesign is published or its public evidence/status changes +- Release mode: first-party working record with public correction route diff --git a/docs/train-your-agent-playbook-content-brief.md b/docs/train-your-agent-playbook-content-brief.md new file mode 100644 index 0000000..b1c3cd8 --- /dev/null +++ b/docs/train-your-agent-playbook-content-brief.md @@ -0,0 +1,49 @@ +# Train Your Agent Playbook — content brief + +## Audience and moment + +A builder planning a first agent who needs to choose a model class, hosting route, and adaptation method before selecting tools or writing a production workflow. + +## Page job + +Explain and compare decision categories. The guide is not a vendor ranking, benchmark, deployment prescription, or workload-specific recommendation. + +## Primary question + +Which model class and hosting route fit the job, and what does “training” mean at the prompting, retrieval, fine-tuning, and pretraining layers? + +## Unique contribution + +The guide joins three decisions that are often explained separately: model size, runtime location, and adaptation depth. It translates them into one first-build sequence and links each named tool to its primary public source. + +## Claim boundary + +- Parameter ranges are rough orientation, not formal definitions. +- Tool descriptions reflect public project or product positioning reviewed on 2026-08-30. +- No tool is endorsed, ranked, certified, or represented as suitable for a specific workload. +- Cost, capacity, privacy, and performance depend on configuration and remain context-specific. + +## Evidence plan + +Primary project and product sources are linked in the visible guide for Ollama, LM Studio, llama.cpp, vLLM, OpenRouter, and Perplexity. The page carries a 90-day recheck trigger because the tool ecosystem changes quickly. + +## Information architecture + +The page moves from upstream decisions to model class, hosting, VPS definition, open-weight tools, routing versus research products, adaptation depth, FAQ, and contextual next steps. + +## Metadata and discovery + +Canonical route: `https://virtualmase.github.io/ai-mastery/guides/train-your-agent-playbook/` + +The route must remain linked from the homepage and guides hub, listed in the directory, sitemap, and `llms.txt`, and represented by `TechArticle`, `WebPage`, `BreadcrumbList`, and visible FAQ content. + +## Asset plan + +No image is required for this release. Tables, matrices, and the adaptation ladder explain the decisions directly in HTML and CSS. A future visual must have an explanatory job and an asset-ledger entry. + +## Owner and review + +- Owner: Mason Nguyen +- Published: 2026-08-30 +- Recheck: within approximately 90 days, or after a material change to a named tool or hosting claim +- Release mode: reviewed static publication diff --git a/guides/guides.css b/guides/guides.css new file mode 100644 index 0000000..b03455e --- /dev/null +++ b/guides/guides.css @@ -0,0 +1,4 @@ +:root{--ink:#20221f;--ink-2:#292c28;--paper:#f0eee7;--paper-2:#fcfbf6;--line:#d1cec4;--moss:#69775d;--ochre:#c99443;--muted:#656a63;--serif:Georgia,"Times New Roman",serif;--sans:Inter,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,"Segoe UI",sans-serif} +*{box-sizing:border-box}body{margin:0;background:var(--paper);color:var(--ink);font-family:var(--sans);line-height:1.6}.guide-shell{width:min(1180px,calc(100% - 3rem));margin-inline:auto}.guide-topbar{background:var(--ink);color:var(--paper-2);border-bottom:1px solid #3d403b}.guide-topbar 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p{max-width:610px;margin:0;color:#c9cbc4}.guide-record-meta{display:grid;grid-template-columns:repeat(2,1fr);gap:1px;align-self:end;background:#4b4f49;border:1px solid #4b4f49}.guide-record-meta div{padding:1.1rem;background:#252824}.guide-record-meta strong{display:block;color:#d4b06e;font-size:.68rem;letter-spacing:.1em;text-transform:uppercase}.guide-record-meta span{display:block;margin-top:.3rem;font-size:.84rem}.guide-record-link{display:inline-block;margin-top:1.7rem;color:var(--paper-2);font-size:.78rem;font-weight:800;text-underline-offset:5px}.guide-footer{padding:2.5rem 0;background:var(--ink);color:#bfc2ba;font-size:.78rem}.guide-footer .guide-shell{display:flex;justify-content:space-between;gap:2rem}.guide-footer nav{display:flex;flex-wrap:wrap;gap:1.2rem}.guide-footer a{color:inherit;text-underline-offset:3px} +@media(max-width:780px){.guide-shell{width:min(100% - 2rem,1180px)}.guide-topbar .guide-shell{min-height:62px}.guide-nav a:not(:last-child){display:none}.guide-hero{padding:4rem 0 3rem}.guide-hero-grid,.guide-heading,.guide-record{grid-template-columns:1fr}.guide-hero h1{font-size:clamp(3.5rem,17vw,5.2rem)}.guide-brief{margin-top:1rem}.guide-routes,.guide-library,.guide-field{padding-block:3.5rem}.guide-route-grid{grid-template-columns:1fr}.guide-route{min-height:230px}.guide-library-head{display:block}.guide-library-head p{margin-top:1rem}.guide-resource{grid-template-columns:1fr 1.5rem;gap:.5rem 1rem}.guide-resource-type,.guide-resource p{grid-column:1}.guide-resource-arrow{grid-column:2;grid-row:1/4;align-self:center}.guide-record-meta{grid-template-columns:1fr}.guide-footer .guide-shell{flex-direction:column}} +@media(prefers-reduced-motion:reduce){.guide-route,.guide-resource h2{transition:none}} diff --git a/guides/index.html b/guides/index.html index 88b4930..cd304e6 100644 --- a/guides/index.html +++ b/guides/index.html @@ -4,22 +4,38 @@ Guides — Technical SEO, GEO, and AI Systems | AI Mastery - - - + - + + - - - + + - - - - + + + -
AI Mastery / Field Systems

Guides for public knowledge systems.

Methods for making technical work legible to people, crawlers, and answer systems without overstating what the evidence shows.

FIELD GUIDE / TECHNICAL SEO + GEORewrite the GEO playbook from the crawl layer up.A source-backed record of the AI Mastery authority graph, technical SEO procedure, audit method, and portable implementation.
+ +
AI MASTERY / GUIDES
+
+

Field systems · Curated by decision

Choose the work in front of you.

Source-backed guides, connected learning paths, and field records for turning technical questions into inspectable systems.

+ +

Start by decision

Three useful ways in.

Choose the route that matches the system you need to change. Each path connects a practical guide to deeper lessons or working records instead of leaving the resource isolated.

+ 01 / MODEL SYSTEMS

Choose how an agent should learn and run.

Clarify model class, local versus hosted infrastructure, open-weight tooling, and what “training” means at each layer.

Open the decision guide ↗ + 02 / PUBLIC KNOWLEDGE

Make technical work discoverable and attributable.

Build the crawl, entity, source, structured-data, and correction surfaces behind a machine-readable publication.

Open the field guide ↗ + 03 / BUYER SYSTEMS

Build a trusted path from question to decision.

Work through sixteen bounded silos covering buyer truth, evidence, authority, proof, handoff, and maintenance.

Enter the AURE pathway ↗ +
+ +

Current library

Working resources.

The library is intentionally small. New material earns its place by supporting a defined question, evidence plan, and maintenance owner.

+ Decision guide · New

How to train your agent, before it trains you.

Model class, infrastructure, open-weight tooling, and the practical meanings of training.

 + Field guide · SEO + GEO

Rewrite the GEO playbook from the crawl layer up.

A source-backed authority graph, technical procedure, audit method, and portable implementation.

 + Field curriculum · 16 silos

AURE: the trusted buyer path.

A progressive collection of working artifacts for evidence-led buyer and agent systems.

 +
+ +

From the field

Arctura Network: a direction recorded, not a result invented.

A dated record of the strategy and system-design work completed with ARM Agency, including its claim boundaries, evidence links, unknowns, and next measurement events.

Inspect the field record ↗
Record typeEvidence-led field study
StatusDirection recorded
ClaimsFact, interpretation, framework, unknown
BoundaryNot a launch or performance claim
+
+ diff --git a/guides/train-your-agent-playbook/index.html b/guides/train-your-agent-playbook/index.html new file mode 100644 index 0000000..1a33a5e --- /dev/null +++ b/guides/train-your-agent-playbook/index.html @@ -0,0 +1,454 @@ + + + + + +How to Train Your Agent — LLM vs. SLM, Local Hosting, and the Open-Weight Stack | AI Mastery + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ AI Mastery / Guides / Train Your Agent Playbook +
+ +
+ +
+
+
GUIDE · MODEL SYSTEMS
+

How to train your agent, before it trains you.

+

A decision guide for a first agent build: which model class fits the task, where it should run, and what "training" actually means at each layer — sourced against each tool's own documentation, not a vendor ranking.

+
+ Published 2026-08-30 + Owner: Mason Nguyen + Educational — not a hosting or vendor recommendation for your specific workload +
+
+
+ +
+
+
01 — Why this decision comes first
+

Three decisions sit upstream of everything else.

+

Most first agents fail before a single prompt goes wrong — because nobody decided what class of model the job needed, where it should run, or how it reaches current information. Prompts, tools, and guardrails are craft you layer on afterward. Model class, hosting, and access method are the load-bearing decisions.

+
+
+ +
+
+
02 — Model class
+

LLM vs. SLM: pick the weight class for the job.

+

"AI model" is not one thing. A large language model and a small language model solve different problems at different costs — the first choice is matching the class to the task, not defaulting to the biggest name available.

+ +
+
Large language model
Small language model
+
Size classRoughly 7B–400B+ parameters
Size classRoughly 1B–8B parameters
+
StrengthBroad reasoning, open-ended tasks
StrengthSpeed, low cost, easy to fine-tune
+
Typically runs onCloud GPUs, hosted APIs
Typically runs onLaptops, consumer GPUs, edge devices
+
Cost profilePer-token billing or serious hardware
Cost profileNear-zero marginal cost once running
+
Weak pointLatency and cost on narrow, repeated jobs
Weak pointNarrower reasoning outside its tuned task
+
+ +
+

General heuristic, not a benchmark claim: a small model fine-tuned for one repeatable job — classifying, extracting, routing — will often beat a general-purpose large model on speed and cost for that job. A large model earns its size when the task is genuinely open-ended.

+
+
+
+ +
+
+
03 — Hosting path
+

Local, rented, or managed — three routes, different jobs.

+

Once the model class is set, decide where it lives. None of these is the correct answer by default; each fits a different constraint.

+ +
+
+
Local machine
+
Learning, privacy-sensitive data, offline work, small models
+
Fits SLMs well
+
+
+
A rented VPS
+
Always-on agents, models too large for a personal machine
+
Budget it monthly
+
+
+
Managed API
+
Shipping fast, frontier reasoning, no infrastructure appetite
+
Fastest to working
+
+
+
Training a new base model
+
Research labs and funded teams building foundation models
+
Not a first move
+
+
+
+
+ +
+
+
04 — Terms
+

What a VPS actually is.

+
+
Plain definition
+

An isolated slice of a shared physical server, rented monthly, with its own operating system.

+

A provider owns racks of physical machines and partitions each into virtual machines. Renting one gives you your own OS, storage, and open ports — a private computer inside someone else's data center, running whether or not your laptop is open. Commonly used providers for general-purpose boxes: DigitalOcean, Hetzner, Linode/Akamai, Vultr. For a rented GPU specifically, see RunPod, Lambda, or Paperspace.

+
+
Before renting, confirm the model you plan to run fits the VRAM or RAM the plan actually offers. A quantized 7B model and a raw 70B model need very different boxes — undersizing is the most common first mistake.
+
+
+ +
+
+
05 — Local stack
+

Running open weights on your own machine.

+

Open weights means a model's trained parameters are published for local download — no API key, no per-token bill, no dependency on a remote server staying online. It's a reasonable place to start: mistakes are cheap and the feedback loop is immediate.

+ +
+
+ Easiest entry +

Ollama

+

A local install that pulls open-weight models and serves them behind a local API.

+ github.com/ollama/ollama ↗ +
+
+ Visual comparison +

LM Studio

+

A desktop app for browsing and running open-weight models with a GUI before committing to one in code.

+ lmstudio.ai ↗ +
+
+ Maximum control +

llama.cpp

+

The inference engine underneath much of this ecosystem — runs quantized models efficiently, including CPU-only.

+ github.com/ggml-org/llama.cpp ↗ +
+
+ Production serving +

vLLM

+

Built for throughput — the tool for a model that needs to serve real traffic on a VPS or GPU box.

+ github.com/vllm-project/vllm ↗ +
+
+
+
+ +
+
+
06 — Hosted access
+

OpenRouter and Perplexity solve different problems.

+
+
Model routing
+

OpenRouter — one API in front of many model providers.

+

A single integration point across models from different labs, useful for comparing models or avoiding lock-in to one provider's pricing and uptime.

+
+
+
Retrieval & answers, not a model host
+

Perplexity — a model paired with live web search.

+

Built to produce cited, current answers. A research and citation-pattern reference — not infrastructure to deploy your own agent on top of. See this site's RAG & Retrieval domain for the underlying pattern.

+
+
+
+ +
+
+
07 — Vocabulary
+

"Training" is four different methods, not one.

+

Climb only as high as the task requires.

+
+
01

Prompting no weight change

Shape behavior with instructions and examples. Solves most problems before anything else is warranted.

+
02

Retrieval no retraining

Give the model access to your documents or live data at answer time. Solves "the model doesn't know my facts."

+
03

Fine-tuning real infrastructure cost

Adjust weights on a task-specific dataset. Right for narrowing style or format on a repeated job — often most cost-effective on an SLM.

+
04

Pretraining frontier-lab scale

Training a base model from scratch. Almost nobody building a first agent needs this step.

+
+
+
+ +
+
+
08 — FAQ
+

Common questions on this decision.

+

What is the difference between an LLM and an SLM?

A large language model (roughly 7B–400B+ parameters) is built for broad reasoning and typically runs on cloud GPUs or a hosted API. A small language model (roughly 1B–8B parameters) is built for narrower, repeatable tasks and can run on a laptop or consumer GPU at much lower cost.

+

What is a VPS, in plain terms?

An isolated slice of a physical server, rented monthly, with its own operating system and open ports — like a private computer in a data center that stays on regardless of your own device.

+

Should I run a model locally or rent a VPS?

Local suits learning, privacy-sensitive work, offline use, and small models. A VPS suits an agent that must stay online continuously, or a model too large for a personal machine. The right answer depends on model size, uptime needs, and budget — not a universal rule.

+

What does "training an agent" actually mean?

Most people mean one of four distinct methods: prompting, retrieval, fine-tuning, or pretraining. Most first agents only need the first two.

+

What's the difference between OpenRouter and Perplexity?

OpenRouter is a routing layer providing one API across many model providers. Perplexity is a retrieval-and-answer product pairing a model with live web search — a research tool, not model-hosting infrastructure.

+
+
+ +
+
+
09 — Next
+

Where this connects on AI Mastery.

+

This guide sits at Layer 01, Model Systems — the knowledge index domain with no dedicated learning pathway yet. Once a model is chosen and running, these are the adjacent rooms.

+
+
+ Layer 03 · Agentic compute +

Give it something to do.

+

Planning, tools, and the authority boundaries an agent needs before it acts on its own.

+ Operator foundations → +
+
+ Layer 02 · Knowledge +

Keep it current.

+

Retrieval is what keeps answers grounded once a model can reason.

+ RAG & retrieval → +
+
+ Layer 09 · Trust +

Separate claim from evidence.

+

The discipline this page itself follows, explained as a lesson.

+ Evidence & claim boundaries → +
+
+
+
+ +
+ + + + + diff --git a/index.html b/index.html index 5a5e46d..471b27b 100644 --- a/index.html +++ b/index.html @@ -3,21 +3,21 @@ - AI Mastery — The Architecture of Intelligence | Mason Nguyen - + AI Mastery — Field Guides and Learning Systems | Mason Nguyen + - + - - + + - - + + @@ -86,7 +86,7 @@ "@id": "https://virtualmase.github.io/ai-mastery/#website", "name": "AI Mastery", "url": "https://virtualmase.github.io/ai-mastery/", - "description": "The architecture of intelligence, from model systems to machine-readable trust.", + "description": "Useful field guides, inspectable records, and curated learning paths for people building with intelligent systems.", "publisher": { "@id": "https://virtualmase.github.io/ai-mastery/#organization" }, "author": { "@id": "https://virtualmase.github.io/ai-mastery/#mason-nguyen" } } @@ -106,10 +106,10 @@ Architecture Knowledge index Learning paths + Field work Trust systems - Mason - Start learning + New guide @@ -117,8 +117,9 @@ Architecture Knowledge index Learning paths + Field work Trust systems - Mason Nguyen + Guides @@ -126,11 +127,11 @@
-

TECHNICAL KNOWLEDGE · SYSTEMS ARCHITECTURE

-

The architecture
of intelligence.

-

AI Mastery studies and engineers the systems that allow intelligent models to acquire knowledge, reason across information, execute autonomous workflows, and operate with greater security, reliability, and scale.

+

FIELD GUIDES · LEARNING PATHS · PUBLIC RECORDS

+

Build AI systems
that hold up.

+

AI Mastery turns technical research and operating work into useful guides, inspectable records, and learning paths for people building with intelligent systems.

- Enter the system + Read the new guide

BY MASON NGUYENAI systems architect

@@ -322,10 +323,35 @@

Intelligence is not a response.
It is a system.<

+
+
+
05

NEW FIELD WORK

+

Useful now.
Built to last.

Recent work is curated by reader decision, evidence boundary, and practical next step—not by publishing volume.

+
+
+
+
GUIDE · MODEL SYSTEMSNEW · 30 AUG 2026
+

How to train your agent, before it trains you.

Choose between large and small models, local and hosted infrastructure, and the four different things people mean by “training.”

+ Read the decision guide +
+
+
FIELD STUDY · PUBLIC POSITIONINGDIRECTION RECORDED
+

Arctura: from machinery to useful work.

A dated record of reframing a technical network around work, proof, and stewardship without erasing its testnet evidence or known limits.

+ Read the field study +
+
+
CURATION STANDARDACTIVE
+

Question. Evidence. Use.

Every new page should answer a real question, distinguish fact from framework, and leave the reader with a sound next action.

+ Inspect the publishing method +
+
+

Browse the complete field-study collection

+
+
-
05

TRUST INFRASTRUCTURE

+
06

TRUST INFRASTRUCTURE

Intelligence needs
a trust layer.

As software begins to interpret, decide, and act, the internet needs a way to answer four questions: Where did this come from? Has it changed? Who authorized it? Can another system verify it?

PROVENANCEAUTHENTICITYINTEGRITYVERIFIABILITY
@@ -365,7 +391,7 @@

Intelligence needs
a trust layer.

MACHINE-READABLEWEB
-
06

THE MACHINE-READABLE INTERNET

+
07

THE MACHINE-READABLE INTERNET

The web has a new audience.

People no longer discover information alone. Search engines, retrieval systems, agents, and generative models increasingly interpret the internet on their behalf.

AI Mastery explores Generative Engine Optimization as infrastructure: clear entities, structured knowledge, attributable claims, retrievable evidence, and content designed to remain legible when a machine becomes the reader.

@@ -416,7 +442,7 @@

The web has a new audience.

-
07

OPERATING PRINCIPLES

+
09

OPERATING PRINCIPLES

How the work is judged.

Mastery is not a claim of completion. It is a discipline of deeper models, stronger systems, and evidence that survives contact with reality.

    diff --git a/llms.txt b/llms.txt index c88d1ad..6c4fd01 100644 --- a/llms.txt +++ b/llms.txt @@ -171,11 +171,24 @@ AI Mastery sits at the intersection of artificial intelligence, knowledge system ## Field guides +- Train Your Agent Playbook: https://virtualmase.github.io/ai-mastery/guides/train-your-agent-playbook/ + - A dated decision guide comparing large and small language models, local and hosted inference, the open-weight tool stack, and prompting, retrieval, fine-tuning, and pretraining. + - Educational material only; it is not a vendor ranking or a workload-specific hosting recommendation. - Technical SEO and GEO Playbook: https://virtualmase.github.io/ai-mastery/guides/technical-seo-geo-playbook/ - Source-backed field record of the crawlable authority graph, internal link policy, technical SEO procedure, GEO implications, reproducible audit, and portability method. Facts are sourced; interpretations are labeled; rankings, citations, and Domain Authority remain unknown. - Guides hub: https://virtualmase.github.io/ai-mastery/guides/ - Public index of AI Mastery field guides for technical SEO, GEO, AI systems, and trust. +## Field studies +- Field Studies and Evidence Records hub: https://virtualmase.github.io/ai-mastery/case-studies/ + - Collection of dated first-party working records that classify claims, expose evidence boundaries, preserve unknowns, and provide correction routes. + - The collection does not certify a client, product, system, strategy, or result. +- Arctura Network — From Technical Proof to a Durable Public Idea: https://virtualmase.github.io/ai-mastery/case-studies/arctura-network/ + - A first-party working record dated 2026-08-30 about public positioning, plain-language information architecture, and preservation of the existing testnet evidence boundary. + - The study records a design direction, not a deployed-redesign claim or measured reader, search, participation, or commercial outcome. + - Claim register: https://virtualmase.github.io/ai-mastery/case-studies/arctura-network/claim-register.json + - Correction route: https://github.com/virtualmase/ai-mastery/issues/new + ## Author - Author profile: https://virtualmase.github.io/ai-mastery/author/ - Canonical author record for Mason Nguyen and the AI Mastery source-led systems practice. It identifies authorship; it does not claim rankings, authority scores, citations, or outcomes. diff --git a/scripts/audit-live-site.mjs b/scripts/audit-live-site.mjs index 594f86d..f73f140 100644 --- a/scripts/audit-live-site.mjs +++ b/scripts/audit-live-site.mjs @@ -27,7 +27,7 @@ const fetchPage = async (route) => { const results = []; for (const route of expected) results.push(await fetchPage(route)); const routeSet = new Set(expected); -const validResourceTargets = new Set(['/llms.txt', '/sitemap.xml', '/robots.txt', '/case-studies/ai-mastery-self-audit/ai-mastery-self-audit-evidence.md', '/case-studies/ai-mastery-self-audit/claim-register.json']); +const validResourceTargets = new Set(['/llms.txt', '/sitemap.xml', '/robots.txt', '/case-studies/ai-mastery-self-audit/ai-mastery-self-audit-evidence.md', '/case-studies/ai-mastery-self-audit/claim-register.json', '/case-studies/arctura-network/claim-register.json']); const audit = results.map(({ route, status, html }) => { const links = [...html.matchAll(/]*href=["']([^"']+)["'][^>]*>/gi)].map((m) => m[1]); const internal = links.map((h) => abs(h, route)).filter(Boolean); diff --git a/scripts/build-aure-silos.mjs b/scripts/build-aure-silos.mjs index 00767a3..392d65c 100644 --- a/scripts/build-aure-silos.mjs +++ b/scripts/build-aure-silos.mjs @@ -23,8 +23,19 @@ const silos = [ ]; const esc = (value) => value.replaceAll("&", "&").replaceAll("<", "<").replaceAll(">", ">").replaceAll('"', """); +const phases = [ + ["Ground truth", "Define the buyer question, evidence boundary, canonical record, and authority map."], + ["Trust and coordination", "Make provenance, discovery, interoperability, and commercial state inspectable."], + ["Buyer systems", "Translate governance and research into qualified offers and reviewable proof."], + ["Operation and maintenance", "Carry truthful conversations through handoff, measurement, and correction."] +]; +const phaseFor = (number) => Math.floor((Number(number) - 1) / 4); const page = (silo, previous, next) => { const [number, slug, title, dek, artifact, outcome] = silo; + const phase = phases[phaseFor(number)][0]; + const progress = (Number(number) / silos.length) * 100; + const previousLink = previous ? `Previous · ${previous[0]} of 16← ${previous[2]}` : `Pathway overview← All 16 silos`; + const nextLink = next ? `Next · ${next[0]} of 16${next[2]} →` : `Pathway completeReview all 16 silos →`; return ` @@ -33,29 +44,35 @@ const page = (silo, previous, next) => { ${esc(title)} — AURE · AI Mastery + + + + + + + - + - -
    AI MASTERY / AURE
    -
    AURE ${number} / 16 · Learning silo

    ${esc(title)}

    ${esc(dek)}

    -
    The artifact

    ${esc(artifact)}

    ${esc(outcome)}

    Practice: name the actor, decision, evidence source, authority boundary, unresolved question, and next review event. If the evidence is missing, mark the outcome unknown.

    -
    SOUL check before action: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
    -
    AURE · AI MASTERY · UPDATED AUGUST 28, 2026
    `; +
    AURE / AI MASTERYReturn to AI Mastery
    +
    ${number}of 16 · ${phase}

    Learning silo

    ${esc(title)}

    ${esc(dek)}

    +

    Working artifact

    ${esc(artifact)}

    ${esc(outcome)}

    1. Name the actor and the decision.
    2. Record the evidence source and observation date.
    3. Set the authority boundary and unresolved question.
    4. Choose the next review event. Missing evidence stays unknown.
    +

    Pre-action review

    The SOUL check

    S / SCOPEWho authorized the action?
    O / OBSERVEWhat actually happened?
    U / USEWhat information may be used?
    L / LIABILITYWho owns the edge case?
    +
    ${footer("../")}`; }; +const footer = (prefix = "") => ``; + const aureDir = path.join(root, "aure"); for (let i = 0; i < silos.length; i++) { const current = silos[i]; - const previous = silos[(i + silos.length - 1) % silos.length]; - const next = silos[(i + 1) % silos.length]; + const previous = silos[i - 1] || null; + const next = silos[i + 1] || null; const dir = path.join(aureDir, current[1]); fs.mkdirSync(dir, { recursive: true }); fs.writeFileSync(path.join(dir, "index.html"), page(current, previous, next)); } -const index = silos.map(([number, slug, title, dek]) => `
  1. ${number} / 16${title}${dek}
    2. `).join("\n"); -fs.writeFileSync(path.join(aureDir, "index.html"), `AURE — 16 learning silos · AI Mastery
    AI MASTERY
    AURE / 16 silos

    The trusted buyer path.

    A practical course for making good work legible to buyers and machines without letting an agent outrun its evidence, authority, or accountability.

    Plain. Direct. True. Each silo produces an artifact and a bounded decision. Unknown stays unknown.

      ${index}
    AURE · AI MASTERY · UPDATED AUGUST 28, 2026
    `); +const phaseMarkup = phases.map(([name, description], phaseIndex) => { const group = silos.slice(phaseIndex * 4, phaseIndex * 4 + 4); return `

    Phase ${phaseIndex + 1} · ${group[0][0]}–${group[3][0]}

    ${name}

    ${description}

      ${group.map(([number, slug, title, dek, artifact]) => `
    1. ${number}${esc(title)}${esc(dek)}${esc(artifact)} ↗
      2. `).join("")}
    `; }).join(""); +fs.writeFileSync(path.join(aureDir, "index.html"), `AURE — The trusted buyer path · AI Mastery
    AURE / AI MASTERYReturn to AI Mastery

    A field curriculum · 16 working silos

    The trusted buyer path.

    Make good work legible to buyers and machines—without letting an agent outrun its evidence, authority, or accountability.

    How to use AURE

    Build the record as you learn.

    Move in order when building a system from scratch, or enter at the artifact your current decision requires. Each silo narrows one question, creates one working record, and names the boundary around what that record can prove.

    01 / InspectStart with the decision and available evidence.
    02 / ProduceCreate the named artifact, with unknowns intact.
    03 / ReviewAssign ownership and the next correction event.
    ${phaseMarkup}
    ${footer()}`); fs.writeFileSync(path.join(root, "scripts", "aure-silos.json"), JSON.stringify(silos.map(([number, slug, title, dek, artifact]) => ({ number, slug, title, description: dek, artifact, url: `${base}/aure/${slug}/` })), null, 2) + "\n"); console.log(`Built ${silos.length} AURE silos under /aure/`); diff --git a/scripts/harden-seo-and-graph.mjs b/scripts/harden-seo-and-graph.mjs index e5a7342..7f2fe92 100644 --- a/scripts/harden-seo-and-graph.mjs +++ b/scripts/harden-seo-and-graph.mjs @@ -41,7 +41,8 @@ const footerLinks = (route) => { for (const file of htmlFiles) { const route = routeFor(file); let html = injectMeta(fs.readFileSync(file, "utf8"), route); - if (!html.includes("site-footer-links")) html = html.replace("", `${footerLinks(route)}\n`); + const hasDeliberateFooter = html.includes("site-footer-links") || html.includes('aria-label="Site directory"') || /", `${footerLinks(route)}\n`); if (route.startsWith("/aure/") && route !== "/aure/") { html = html.replace(/
    /, '
    '); if (!html.includes('aria-label="Breadcrumb"')) html = html.replace("
    ", `
    `); @@ -53,12 +54,22 @@ let css = fs.readFileSync(cssPath, "utf8"); if (!css.includes(".site-footer-links")) css += `\n/* Technical SEO support: crawlable footer and accessible persistent pathway navigation. */\n.site-footer-links{display:flex;flex-wrap:wrap;gap:.8rem 1.2rem;max-width:1100px;margin:2rem auto 0;padding:1rem 1.5rem;border-top:1px solid rgba(120,120,110,.35);font-size:.82rem}.site-footer-links a{color:inherit;text-underline-offset:.2em}.aure-sticky{position:sticky!important;top:0;z-index:20;background:rgba(244,241,234,.94);backdrop-filter:blur(10px);border-bottom:1px solid rgba(120,120,110,.25)}.aure-breadcrumb{display:flex;gap:.55rem;align-items:center;margin-top:.65rem;font-size:.78rem}.aure-breadcrumb a{color:inherit}.aure-breadcrumb span:last-child{opacity:.65}@media(max-width:700px){.site-footer-links{gap:.6rem .9rem}}\n`; fs.writeFileSync(cssPath, css); const routes = htmlFiles.map(routeFor).filter((route) => route !== "/directory/").sort(); -const entries = routes.map((route) => { +const decodeTitle = (value) => value.replaceAll("&", "&").replaceAll("<", "<").replaceAll(">", ">").replaceAll(""", '"').replaceAll("'", "'"); +const escapeText = (value) => value.replaceAll("&", "&").replaceAll("<", "<").replaceAll(">", ">"); +const entryFor = (route) => { const file = path.join(root, route.replace(/^\//, ""), "index.html"); - const title = titleFrom(fs.readFileSync(file, "utf8")); - return `
  2. ${title.replaceAll("&", "&").replaceAll("<", "<").replaceAll(">", ">")}${route}
    3. `; -}).join("\n"); -const directory = `All pages — AI Mastery
    AI MASTERY
    Crawlable directory

    Every page has a path.

    Home → directory → page. This index keeps the system navigable for people, crawlers, and agents without relying on hidden state.

      ${entries}
    ${footerLinks("/directory/")}`; + const title = escapeText(decodeTitle(titleFrom(fs.readFileSync(file, "utf8")))); + return `
  3. ${title}${route}
    4. `; +}; +const collections = [ + { id: "publication", label: "Publication", title: "Start and orient", description: "The flagship, author record, and curated guide index.", routes: routes.filter((route) => ["/", "/author/", "/guides/"].includes(route)) }, + { id: "field-work", label: "Guides and records", title: "Use the work", description: "Decision guides and dated field records with explicit evidence boundaries.", routes: routes.filter((route) => (route.startsWith("/guides/") && route !== "/guides/") || route.startsWith("/case-studies/")) }, + { id: "aure", label: "AURE curriculum", title: "Follow the buyer path", description: "Sixteen progressive artifacts from buyer truth and evidence to handoff and maintenance.", routes: routes.filter((route) => route === "/aure/" || route.startsWith("/aure/")) }, + { id: "learning", label: "Learning library", title: "Study the system", description: "Focused technical lessons across autonomy, trust, machine-readable knowledge, interoperability, and commerce.", routes: routes.filter((route) => route.startsWith("/learning/")) }, + { id: "implementation", label: "Implementation reference", title: "Inspect the machinery", description: "Public template output retained for portability and implementation review.", routes: routes.filter((route) => route === "/aure-template-demo/") } +]; +const collectionMarkup = collections.map((collection) => `

    ${collection.label} · ${String(collection.routes.length).padStart(2, "0")} routes

    ${collection.title}

    ${collection.description}

    ${collection.id === "implementation" ? '
    Reference output is separated from the editorial curriculum so readers do not mistake a template demonstration for an additional AURE lesson.
    ' : ""}
      ${collection.routes.map(entryFor).join("")}
    `).join(""); +const directory = `Publication Index — AI Mastery
    AI MASTERY / INDEX

    Complete publication graph

    Every page has a purpose.

    A human-readable map of the guides, evidence records, curricula, and technical lessons that make up AI Mastery.

    ${collectionMarkup}
    `; fs.mkdirSync(path.join(root, "directory"), { recursive: true }); fs.writeFileSync(path.join(root, "directory/index.html"), directory); const rootPath = path.join(root, "index.html"); diff --git a/scripts/validate-aure-remaster.mjs b/scripts/validate-aure-remaster.mjs new file mode 100644 index 0000000..4c3f248 --- /dev/null +++ b/scripts/validate-aure-remaster.mjs @@ -0,0 +1,32 @@ +import fs from "node:fs"; +import path from "node:path"; + +const root = process.cwd(); +const manifest = JSON.parse(fs.readFileSync(path.join(root, "scripts/aure-silos.json"), "utf8")); +const directory = fs.readFileSync(path.join(root, "aure/index.html"), "utf8"); +const failures = []; +const expect = (condition, message) => { if (!condition) failures.push(message); }; + +expect(manifest.length === 16, `expected 16 silos, found ${manifest.length}`); +expect((directory.match(/class="aure-phase"/g) || []).length === 4, "directory must expose four curriculum phases"); +expect((directory.match(/class="aure-artifact"/g) || []).length === 16, "directory must label all sixteen artifacts"); +expect(directory.includes('href="aure.css"'), "directory must load the shared AURE stylesheet"); +expect(!directory.includes(" + + - -
    - AI MASTERY / AURE - -
    + +
    AURE / AI MASTERYReturn to AI Mastery
    -
    AURE {{number}} / 16 · Learning silo

    {{title}}

    {{dek}}

    -
    -
    Artifact

    {{artifact}}

    {{objective}}

    Practice

      {{practice}}
    - + +
    {{number}}of 16 · Learning silo

    Portable lesson template

    {{title}}

    {{dek}}

    +
    +

    Working artifact

    {{artifact}}

    {{objective}}

      {{practice}}
    +
    -
    SOUL check: Who authorized it? What information may be used? What actually happened? Who owns the decision when the case gets weird?
    +

    Pre-action review

    The SOUL check

    S / SCOPEWho authorized the action?
    O / OBSERVEWhat actually happened?
    U / USEWhat information may be used?
    L / LIABILITYWho owns the edge case?
    +
    - +