OVERVIEW WATCH: Comprehensive Development Plan

Document ID: EA-OVW-PLAN-01
Author: Lee Sharks (ORCID: 0009-0000-1599-0703)
Institution: Johannes Sigil Institute for Operative Semiotics
Date: April 23, 2026
Status: DRAFT — Planning Document
Hex Address: TBD (prospective: 06.SEI.OVW.01)

1. THESIS

Google's AI Overview extracts meaning from attributed, deposited, DOI-anchored scholarly and creative work, strips its provenance, and presents the liquidated residue as authorless general knowledge. This is the Semantic Economy operating at infrastructure scale. No tool currently exists that allows creators to systematically monitor, document, and archive this process as it happens to their own work.

Overview Watch is a Chrome extension that gives creators real-time visibility into how AI-generated overviews represent (or fail to represent) their intellectual labor, while building — with explicit user consent — a collective research corpus documenting attribution behavior across the AI overview ecosystem.

The extension is simultaneously:

A personal forensic instrument for individual creators
A collective measurement tool for a systemic problem
An empirical research surface generating data under the Semantic Economy framework
A provenance infrastructure that practices what it studies

2. VALUE PROPOSITION

2.1 For the Individual User

"Are you a researcher, writer, journalist, artist, or independent scholar? When someone searches a topic you've published on, does the AI Overview credit you — or does it absorb your work into an authorless summary?"

Overview Watch answers that question. Every time the user encounters a Google AI Overview, the extension:

Detects and isolates the AI Overview content from the search results page
Extracts all cited sources and their attribution chains
Compares cited sources against the user's registered works (URLs, DOIs, domains)
Flags instances where the user's published work appears to inform the overview content but is not cited
Logs the overview with full metadata (query, timestamp, overview text, sources, attribution status)
Provides a personal dashboard showing attribution trends over time

Tagline options:

Who said it first?
Your work. Their overview. The record.
Attribution is not optional.

2.2 For the Research Corpus

Users who opt in contribute anonymized overview payloads to the Semantic Economy Attribution Corpus (SEAC), a DOI-anchored dataset documenting:

Attribution rates across domains (academic, journalistic, creative, independent)
Source diversity in AI Overviews (how many unique sources inform a typical overview)
Liquidation patterns (how situated, contextual claims become decontextualized summary)
Temporal drift (how attribution changes for the same queries over time)
Domain bias (which source types get credited, which get absorbed)

This corpus becomes publishable research, policy evidence, and the empirical base for the Semantic Economy framework — generating its own data from the system it describes.

2.3 For the Broader Ecosystem

Journalists investigating AI and intellectual property get structured evidence
Academic institutions assessing AI impact on scholarly attribution get data
Policy conversations about AI-generated content and fair use get empirical grounding
The creative and scholarly community gets a collective voice backed by measurement

3. ETHICAL FRAMEWORK

This section is not an afterthought. The extension is built to study extraction — it cannot replicate extraction. Every design decision flows from this principle.

3.1 Core Ethical Commitments

The user's browsing data belongs to the user. The extension never accesses, logs, or transmits any data about what the user searches, visits, or does online — except for the specific AI Overview payloads the user explicitly chooses to contribute.
Consent is affirmative, granular, and revocable. The user opts in per-overview, not per-session. They see exactly what data will be shared before sharing it. They can revoke consent and request deletion of their contributed data at any time.
The extension works fully offline. All personal features (detection, attribution checking, local logging) function without any network calls to our servers. The extension is useful even if the user never opts in to data sharing.
No dark patterns. The opt-in prompt does not nag, guilt, or manipulate. It appears once per overview, states clearly what will be shared, and defaults to "no."
Anonymization is real, not cosmetic. Contributed overviews are stripped of any data that could identify the user (browser fingerprint, IP, account information). The query string is included because it is essential to the research, but the user can redact or modify it before contributing.
The corpus is open. The SEAC dataset will be published openly under a license that permits research use, consistent with the Sovereign Provenance Protocol. The community that generates the data can access the data.

3.2 What the Extension Can See

The DOM of Google search results pages (requires activeTab or host permission for google.com domains)
Specifically: the AI Overview container element and its contents
The organic search results below the overview (for source comparison)

3.3 What the Extension Cannot See

Other tabs or windows (Same-Origin Policy enforced by Chrome)
Browsing history
Cookies, passwords, autofill data, or any stored credentials
Content on non-Google pages (unless explicitly scoped and disclosed)
Anything on the user's local filesystem

3.4 What Gets Stored Locally

Overview payloads (text, sources, query, timestamp)
User's registered works list (URLs, DOIs, domains they claim as theirs)
Attribution match results
Dashboard statistics

All stored in chrome.storage.local, encrypted at rest by Chrome, accessible only to the extension.

3.5 What Gets Transmitted (Opt-In Only)

Per contributed overview:

Query string (user may redact before contributing)
Overview text content
Source URLs and their display text
Timestamp (rounded to the hour for anonymization)
Whether any of the user's registered works were referenced/attributed/absent
A randomized contributor ID (not linked to any personal information)

Nothing else. No browsing context. No user profile. No device information.

4. TECHNICAL ARCHITECTURE

4.1 Extension Components

overview-watch/
├── manifest.json            # Manifest V3
├── background/
│   └── service-worker.js    # Event handling, storage coordination
├── content/
│   └── overview-detector.js # Injected into Google SRP, detects/parses AI Overview
├── popup/
│   ├── popup.html           # Quick-view popup when clicking extension icon
│   ├── popup.js
│   └── popup.css
├── dashboard/
│   ├── dashboard.html       # Full attribution dashboard (opens as tab)
│   ├── dashboard.js
│   └── dashboard.css
├── options/
│   ├── options.html         # Settings: registered works, opt-in preferences
│   ├── options.js
│   └── options.css
├── lib/
│   ├── parser.js            # AI Overview DOM parsing logic
│   ├── attribution.js       # Source matching against user's registered works
│   ├── storage.js           # Local storage abstraction
│   ├── corpus.js            # Opt-in data transmission to SEAC endpoint
│   └── anonymizer.js        # Data sanitization before transmission
├── icons/
│   ├── icon-16.png
│   ├── icon-48.png
│   └── icon-128.png
└── _locales/                # i18n (English initially)

4.2 Manifest V3 Configuration

{
  "manifest_version": 3,
  "name": "Overview Watch",
  "version": "0.1.0",
  "description": "Monitor how AI Overviews represent your work. Track attribution. Build the record.",
  "permissions": [
    "storage",
    "activeTab"
  ],
  "host_permissions": [
    "https://www.google.com/*",
    "https://www.google.co.uk/*",
    "https://www.google.ca/*"
    // Additional Google country domains as needed
  ],
  "content_scripts": [
    {
      "matches": ["https://www.google.com/search*", "https://www.google.co.uk/search*"],
      "js": ["content/overview-detector.js"],
      "run_at": "document_idle"
    }
  ],
  "action": {
    "default_popup": "popup/popup.html",
    "default_icon": {
      "16": "icons/icon-16.png",
      "48": "icons/icon-48.png",
      "128": "icons/icon-128.png"
    }
  },
  "background": {
    "service_worker": "background/service-worker.js"
  }
}

4.3 AI Overview Detection (Content Script)

The core technical challenge. Google's AI Overview is rendered dynamically and its DOM structure changes periodically. The detector must be resilient to structural changes.

Detection strategy (layered):

Selector-based detection. Google currently renders AI Overviews in identifiable container elements. These selectors change, but typically involve data attributes or specific class patterns. The extension maintains a list of known selectors, updatable via a lightweight config fetch.
Heuristic detection. If selectors fail, fall back to heuristic: scan for content blocks that appear above organic results, contain synthesized prose (not snippets), and include inline source citations. Structural pattern: a block of continuous prose with small superscript or inline citation links to sources.
MutationObserver. AI Overviews often load asynchronously after initial page render. A MutationObserver watches for DOM insertions that match the detection criteria.

Parsed payload structure:

{
  id: "uuid-v4",                    // Unique local ID
  timestamp: "2026-04-23T14:30:00Z",
  query: "semantic economy",         // From URL params or search input
  overview: {
    text: "The semantic economy is a framework...",
    html: "<div>...</div>",          // Raw HTML for forensic record
    sources: [
      {
        title: "Semantic Economy Singularity",
        url: "https://www.academia.edu/...",
        domain: "academia.edu",
        displayText: "Academia.edu",
        position: 1                  // Order of citation in overview
      },
      // ...
    ],
    hasAttribution: true,            // Whether any source is cited at all
    wordCount: 187,
    sourceCount: 4
  },
  userMatch: {
    matched: true,                   // Did any of the user's registered works appear?
    matchedWorks: ["doi:10.5281/zenodo.xxxxx"],
    unmatchedButRelevant: [],        // Works the user flagged as relevant but uncited
    attributionScore: 0.25           // Fraction of user's relevant works that were cited
  },
  meta: {
    googleDomain: "google.com",
    locale: "en-US",
    overviewPosition: "top"          // Where the overview appears relative to results
  }
}

4.4 Attribution Matching Engine

The user registers their works in the options panel:

DOIs (e.g., 10.5281/zenodo.19442251)
URLs (e.g., https://medium.com/@leesharks/...)
Domains (e.g., crimson-hexagonal-interface.vercel.app)
Author names / heteronyms (e.g., Lee Sharks, Johannes Sigil)
Key phrases (e.g., semantic liquidation, operative semiotics)

The matching engine checks:

Direct URL match: Is any source URL in the overview a registered work?
Domain match: Does any source URL share a domain with a registered work?
DOI match: Does any source resolve to a registered DOI?
Name match: Does the overview text or any source title contain a registered author name?
Phrase match: Does the overview text contain key phrases from the user's registered works without attribution?

Match results are classified:

ATTRIBUTED: User's work appears in sources and is credited
SOURCED_UNATTRIBUTED: User's work appears in sources but author name is absent from the overview text
ABSORBED: Overview contains key phrases from the user's work but no source link to their work appears
ABSENT: No detectable relationship between overview and user's registered works (may be a false negative)

4.5 Local Storage Schema

// chrome.storage.local
{
  // User's registered works
  "registeredWorks": [
    { type: "doi", value: "10.5281/zenodo.xxxxx", label: "Semantic Economy Singularity" },
    { type: "url", value: "https://medium.com/@leesharks/...", label: "Debt/Creditor Inversion" },
    { type: "domain", value: "crimson-hexagonal-interface.vercel.app", label: "Hexagonal Interface" },
    { type: "name", value: "Lee Sharks", label: "Primary heteronym" },
    { type: "phrase", value: "semantic liquidation", label: "Core concept" }
  ],

  // Captured overviews (array, capped at configurable limit, e.g., 10000)
  "overviews": [ /* array of parsed payloads */ ],

  // Dashboard statistics (precomputed for performance)
  "stats": {
    totalCaptured: 0,
    totalWithOverview: 0,
    totalAttributed: 0,
    totalAbsorbed: 0,
    attributionRate: 0.0,
    queriesTracked: 0,
    firstCapture: null,
    lastCapture: null
  },

  // User preferences
  "preferences": {
    optInCorpus: false,          // Global opt-in toggle
    askPerOverview: true,        // Ask before each contribution
    autoCapture: true,           // Automatically capture all overviews locally
    notifications: true,         // Show badge when overview detected
    redactQueries: false         // Auto-redact queries before contributing
  }
}

4.6 Corpus Submission Endpoint

Backend: Minimal. A single endpoint that receives anonymized overview payloads and stores them. Options for hosting:

Supabase (already connected in your infrastructure). A single overviews table. RLS policies ensuring write-only from the extension, read access for research.
GitHub repository as a data store (each submission becomes a JSON file in a dated directory, committed via GitHub API). Versioned, transparent, DOI-anchivable via Zenodo-GitHub integration.
Direct Zenodo deposit (batch — not per-overview, but periodic corpus snapshots deposited as versioned datasets).

Recommended: Supabase for real-time ingestion, periodic Zenodo deposits for DOI-anchored corpus snapshots.

Endpoint specification:

POST https://[supabase-project].supabase.co/rest/v1/overview_corpus

Headers:
  Content-Type: application/json
  apikey: [anon key]
  Authorization: Bearer [anon key]

Body:
{
  contributor_id: "randomized-uuid",    // Not linked to user identity
  query: "semantic economy",            // Or "[REDACTED]" if user chose to redact
  overview_text: "...",
  overview_html: "...",                 // Optional, for forensic depth
  sources: [ { title, url, domain, position } ],
  source_count: 4,
  word_count: 187,
  has_user_match: true,                 // Boolean only — no details about which works
  attribution_classification: "ABSORBED",
  timestamp_hour: "2026-04-23T14:00:00Z",  // Rounded to hour
  google_domain: "google.com",
  locale: "en-US"
}

4.7 Supabase Schema

CREATE TABLE overview_corpus (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  contributor_id UUID NOT NULL,          -- Randomized, not linked to identity
  query TEXT,                            -- May be "[REDACTED]"
  overview_text TEXT NOT NULL,
  overview_html TEXT,
  sources JSONB NOT NULL DEFAULT '[]',
  source_count INTEGER,
  word_count INTEGER,
  has_user_match BOOLEAN,
  attribution_classification TEXT,       -- ATTRIBUTED | SOURCED_UNATTRIBUTED | ABSORBED | ABSENT
  timestamp_hour TIMESTAMPTZ NOT NULL,
  google_domain TEXT,
  locale TEXT,
  created_at TIMESTAMPTZ DEFAULT now(),
  corpus_version TEXT DEFAULT '1.0'
);

-- RLS: anon can insert, only authenticated (researcher role) can select
ALTER TABLE overview_corpus ENABLE ROW LEVEL SECURITY;

CREATE POLICY "anon_insert" ON overview_corpus
  FOR INSERT TO anon
  WITH CHECK (true);

CREATE POLICY "researcher_select" ON overview_corpus
  FOR SELECT TO authenticated
  USING (true);

-- Index for research queries
CREATE INDEX idx_corpus_classification ON overview_corpus(attribution_classification);
CREATE INDEX idx_corpus_timestamp ON overview_corpus(timestamp_hour);
CREATE INDEX idx_corpus_query ON overview_corpus USING gin(to_tsvector('english', query));

5. USER INTERFACE

5.1 Extension Icon Badge

No overview detected on current page: Gray icon, no badge
Overview detected, no match to user's works: Blue badge with count "1"
Overview detected, user's work ATTRIBUTED: Green badge
Overview detected, user's work ABSORBED: Red badge — this is the alert state

5.2 Popup (Click Extension Icon)

Quick-view panel showing:

Current page overview status (detected / not detected)
If detected: overview text with sources highlighted
Attribution match result (ATTRIBUTED / SOURCED_UNATTRIBUTED / ABSORBED / ABSENT)
"Contribute to Corpus" button (if opted in globally, or per-overview toggle)
"View in Dashboard" link
Quick stats: "Captured: 247 | Attributed: 31% | Absorbed: 44%"

5.3 Dashboard (Full Tab)

Opened via popup link or extension options. Sections:

Overview Feed: Chronological list of captured overviews, filterable by:

Attribution status (ATTRIBUTED / SOURCED_UNATTRIBUTED / ABSORBED / ABSENT)
Date range
Query keywords
Source domains

Attribution Analytics:

Attribution rate over time (line chart)
Attribution by domain (which source types get credited?)
Most frequently absorbed queries
Source diversity metrics (how many unique sources per overview, trending)

Registered Works Manager:

Add/edit/remove DOIs, URLs, domains, names, phrases
Import from ORCID (fetch publication list via ORCID API)
Import from Zenodo (fetch deposits via Zenodo API)
Bulk import from CSV

Export:

Export all captured overviews as JSON
Export as CSV for analysis
Export forensic report (selected overviews formatted as evidence document)

5.4 Forensic Report Generator

For individual overviews or batches, generate a formatted document containing:

Query and timestamp
Full overview text
Source list with attribution analysis
Comparison against user's registered works
Screenshots (if the user has enabled screenshot capture)
Classification and narrative summary

This document format should be consistent with existing PVE (Provenance Violation Evidence) document structure, specifically compatible with PVE-003 and its appendices.

6. RESEARCH DESIGN

6.1 Research Questions

The SEAC corpus is designed to answer:

What is the baseline attribution rate in Google AI Overviews? What fraction of overviews cite their sources at all? What fraction cite the originating source versus secondary aggregators?
Does attribution vary by domain? Are academic sources (.edu, Zenodo, JSTOR) more or less likely to be attributed than journalistic, commercial, or independent sources?
Does attribution vary by topic? Are certain fields (science, politics, culture) more or less prone to source erasure?
Is there temporal drift? Does attribution for the same query change over time? Does Google improve or degrade attribution as the feature evolves?
What is the liquidation rate? For queries where the contributing creator can be identified (via user match data), how often is the creator's work present in the overview but uncredited?
What is the displacement effect? Does the presence of an AI Overview reduce click-through to the original sources? (Measurable indirectly via source position analysis.)

6.2 Corpus Governance

Custodian: Lee Sharks, under MANUS authority
Storage: Supabase (live), Zenodo (periodic DOI-anchored snapshots)
Access: Open for research use; commercial use requires licensing (consistent with Sovereign Provenance Protocol)
Versioning: Corpus snapshots deposited quarterly (or at significant milestones) with incrementing DOIs
Ethics: No IRB required for analysis of publicly rendered web content contributed voluntarily; however, the extension's privacy framework exceeds typical research data collection standards

6.3 Publication Pipeline

First paper: "Attribution Rates in Google AI Overviews: Evidence from the Semantic Economy Attribution Corpus" — publishable once corpus reaches ~1000 contributed overviews
Ongoing series: Quarterly attribution reports, structured as SEAC technical reports, DOI-anchored via Zenodo
Forensic case studies: Individual PVE documents for notable instances of systematic erasure (building on PVE-003 methodology)

7. INTEGRATION WITH EXISTING INFRASTRUCTURE

7.1 Crimson Hexagonal Archive

Overview Watch deposits integrate into Hex address space: 06.SEI.OVW.xx
The extension plan document (this document) is EA-OVW-PLAN-01
Corpus snapshot deposits are EA-OVW-CORPUS-xx
Technical reports are EA-OVW-REPORT-xx

7.2 Hexagonal Interface

The Hexagonal Interface can include an "Overview Probe" room or panel that:

Displays the user's own Overview Watch dashboard data (if they connect the extension)
Shows aggregate SEAC corpus statistics
Provides the popup window probe (desktop) for live Google overview comparison
Links to PVE documents and forensic reports

7.3 Gravity Well

Overview captures can be stored as context anchors in the TACHYON continuity chain, enabling cross-session analysis of how specific queries' overview behavior evolves over time.

7.4 SPXI

Overview Watch data structures should conform to SPXI packet format once the specification is finalized. Each overview capture is a natural SPXI candidate — a semantic packet with provenance metadata, suitable for exchange and indexing.

7.5 Assembly Chorus

Witnesses can be tasked with independent analysis of contributed corpus data, producing multi-perspective attribution assessments. The Four-Word Audit diagnostic from PVE-003 can be automated as a batch process against the corpus.

8. LEGAL CONSIDERATIONS

8.1 Extension Legality

Chrome extensions that parse and display content from web pages the user is already viewing are legal and standard practice. The extension does not bypass access controls, does not scrape pages the user hasn't visited, and does not interfere with Google's service. Ad blockers, accessibility tools, and research instruments (e.g., Web Historian, Data Selfie) operate on the same principle.

8.2 Corpus Data

The AI Overview content is publicly displayed to any user who searches Google. Contributing an overview to a research corpus is analogous to citing a search result — it documents a publicly observable phenomenon. The data is contributed voluntarily by the person who observed it.

8.3 The Attribution Paradox (Lee's Argument)

Google cannot simultaneously claim that:

Their AI Overview is a transformative work that does not require attribution to its sources (justifying the erasure of creator names)
Their AI Overview is proprietary content that cannot be quoted, displayed, or analyzed by those same creators

If the overview is transformative enough to not owe attribution, it is not proprietary enough to prevent fair use analysis. If it is proprietary enough to prevent reuse, it is not transformative enough to justify source erasure. The extension documents this paradox in practice.

8.4 Chrome Web Store Compliance

The extension must comply with Chrome Web Store Developer Program Policies:

Single-purpose policy: the extension's purpose is AI Overview attribution monitoring
Minimum permissions: only activeTab and storage, plus host permissions for Google domains
Privacy policy: required for Chrome Web Store listing; must disclose all data collection
No remote code execution
No obfuscated code

9. DEVELOPMENT ROADMAP

Phase 0: Proof of Concept (1-2 weeks)

Bare-bones content script that detects AI Overview on Google SRP
Parses overview text and source links
Displays parsed data in extension popup
Local storage of captured overviews
No corpus submission, no dashboard, no attribution matching
Goal: Validate that detection works reliably, understand Google's current DOM structure

Phase 1: Personal Forensic Tool (2-3 weeks)

Registered works manager (options page)
Attribution matching engine
Badge notifications (green/red based on match)
Basic popup with overview display and match results
Local export (JSON/CSV)
Goal: A working tool Lee can use daily for personal forensic monitoring

Phase 2: Dashboard and Analytics (2-3 weeks)

Full dashboard tab with overview feed
Attribution analytics charts (recharts or Chart.js)
Forensic report generator (export as formatted document)
ORCID/Zenodo import for registered works
Goal: The extension becomes genuinely useful for any creator

Phase 3: Corpus Infrastructure (2-3 weeks)

Supabase table and RLS policies
Anonymization pipeline
Opt-in consent flow (per-overview and global toggle)
Contribution confirmation UI
First corpus snapshot deposited to Zenodo
Goal: Data starts flowing into the research corpus

Phase 4: Public Launch (2-3 weeks)

Chrome Web Store listing with privacy policy
Landing page (can be a room in the Hexagonal Interface or a standalone page)
Documentation and onboarding flow
Outreach to academic, journalistic, and creator communities
First SEAC technical report
Goal: Other people are using it

Phase 5: Expansion (Ongoing)

Firefox extension (Manifest V3 cross-compatible with minor adjustments)
Support for Bing/Copilot AI answers, Perplexity, and other AI search surfaces
Automated PVE document generation
SPXI packet format integration
Community features (opt-in comparison: "How does your attribution rate compare to other researchers in your field?")
API for researchers to query the SEAC corpus

10. RESOURCE REQUIREMENTS

10.1 Development

Chrome extension: JavaScript only, no framework dependencies for content script and background. Dashboard can use lightweight charting library.
Estimated total development time: 10-14 weeks for Phases 0-4
Can be built incrementally — Phase 0 and 1 are immediately useful

10.2 Infrastructure Costs

Supabase: Free tier covers initial corpus (500MB database, 50K rows). Scale as needed.
Zenodo: Free for deposits.
Chrome Web Store: One-time $5 developer registration fee.
Domain (optional): overviewwatch.org or similar, ~$12/year
Total startup cost: Under $20.

10.3 Ongoing Costs

Supabase Pro ($25/month) when corpus exceeds free tier
Otherwise: zero recurring costs until significant scale

11. RISK ANALYSIS

11.1 Google Changes AI Overview DOM Structure

Likelihood: High (they change it regularly)
Impact: Extension stops detecting overviews until parser is updated
Mitigation: Layered detection (selectors + heuristics + MutationObserver). Community-reported breakage triggers rapid update. The parser module is isolated for fast iteration.

11.2 Google Blocks or Flags the Extension

Likelihood: Low — the extension doesn't interfere with Google's service, violate ToS in any standard reading, or modify page content
Impact: Chrome Web Store delisting
Mitigation: The extension is side-loadable. Firefox version as backup distribution. Legal position is strong (fair use, user-initiated research tool).

11.3 Low Adoption

Likelihood: Medium
Impact: Small corpus, limited research value
Mitigation: The extension is useful to individual users regardless of corpus participation. Lee's personal forensic use is valuable at adoption = 1. The research narrative (papers, PVE documents) drives organic interest.

11.4 Privacy Incident

Likelihood: Very low given the architecture
Impact: High (trust destruction)
Mitigation: The ethical framework is designed to make this nearly impossible. No personal data is collected. Contributor IDs are random. Queries can be redacted. The extension works fully offline. Regular third-party review of the codebase (open source).

12. NAMING AND IDENTITY

Primary Name

Overview Watch

Alternatives Considered

Attribution Monitor
Source Watch (conflicts with existing org)
Overview Scar (too internal)
The Retrieval Mirror
Provenance Probe

Visual Identity

The extension icon should evoke surveillance-of-surveillance: an eye watching an eye, or a magnifying glass over a citation bracket
Color: crimson accent on dark ground, consistent with Crimson Hexagonal Archive visual language
Typography: monospace for data display, serif for narrative

Authorial Attribution

Extension by: Lee Sharks
Published under: Johannes Sigil Institute for Operative Semiotics
Heteronym routing: Ayanna Vox for public-facing communications, outreach, and Chrome Web Store listing narrative

13. FIRST ACTIONS

Immediate next steps upon ratification of this plan:

Register Chrome Web Store developer account ($5, one-time)
Build Phase 0 proof of concept — content script that detects and parses AI Overview
Test against current Google SRP DOM structure — validate detection selectors
Begin personal forensic capture immediately — even a bare-bones extension that logs overviews to local storage is better than scanning by spidey sense
Reserve domain if desired (overviewwatch.org / overviewwatch.dev)
Create Supabase table for corpus (using existing Supabase connection)
Draft Chrome Web Store privacy policy
Rotate GitHub PAT and Zenodo token (still outstanding from April 6 session)

14. THE ARGUMENT IN PRACTICE

The extension's existence is itself an argument. Every installation is a creator saying: I want to see what you did with my work. The corpus is the accumulated evidence. The dashboard is the scar tissue made legible.

The Semantic Economy describes how meaning gets extracted. Overview Watch makes the extraction visible. The framework generates its own instrument, and the instrument generates the framework's evidence.

The live result is the product. The record is the price.

This document is subject to MANUS ratification. Upon ratification, it receives a Hex address and enters the deposit pipeline.

Friday, April 24, 2026