AI
Systems.

Small teams need SEO that works for classic search and AI answer systems, but the work usually fragments across plugins, keyword tools, spreadsheets, analytics dashboards, competitor research, and someone guessing what to change next. WordPress can publish metadata. It rarely tells you what matters, what competitors are doing, what AI systems need to understand, or whether the work is actually moving.

Singularity SEO turns ChatGPT into the control room for a connected WordPress site. The plugin exposes managed pages, rendered metadata, technical health, setup state, reports, proposal queues, change history, Google Search Console visibility, and live search ranking checks. ChatGPT uses that context to audit the site, study the competitive field, build page-level SEO and AIO recommendations, and explain the next move in plain English.

Connect WordPress, verify the site, link the customer/site ID in the Singularity SEO ChatGPT app, then run a read-only baseline before any public change. From there the system can review competitors, choose focused keyword lanes, produce one governed full-site batch proposal, apply approved updates, track ranking and search visibility, and preserve rollback-ready change IDs.

The WordPress plugin owns the site-side dashboard, verification state, metadata overrides, report surfaces, and restore points. The ChatGPT app owns analysis and orchestration. That split keeps public site changes governed: read-only work is fast, proposal generation is explicit, apply actions require approval, and protected operating logic, credentials, and proprietary SEO guidance stay out of public tools.

The product sells the promise clients actually care about: a site that is easier for Google, AI answer engines, and real buyers to understand, with progress tracked through connected search data instead of guesswork.

Agency work creates constant demand for strong first drafts across email, banners, ad creative, landing pages, and social copy, with zero tolerance for voice drift. Generic AI output is the enemy. Volume without precision just produces more wrong work faster.

Brand context files, RAG-retrieved voice samples, SKILL.md governance, and persona specifications are assembled before any draft is generated. The system produces multiple first drafts, then routes them through a standards AI for quality and voice checks before anything reaches a human for approval.

Context ingestion → multi-draft generation → standards-AI critique loop → Telegram approval routing → human edit and publish. Outputs span email campaigns, banner copy, landing pages, ad creative, campaign concepts, and social posts. Nothing ships without human sign-off.

Sunset Marquis: high-frequency promotional and email copy inside a brand voice that took years to build. Agency689: proposals generated from discovery notes and project requirements, structured against years of precedent. Traveler Guitar: systems thinking extended into visual ideation, creating campaign worlds where the product lives without losing the brand's sense of adventure.

Used daily across Agency689 client work. The practical version of AI-assisted creative: faster iteration, tighter review loops, and human approval before anything publishes.

Markets generate data at a pace no individual analyst can track. EDGAR filings, earnings transcripts, analyst revisions, macroeconomic feeds, and price signals all move simultaneously. Manual synthesis lags. Bad signals with high weights cause outsized damage. A system without governance is worse than no system at all.

Node.js + SQLite backbone. Claude Haiku handles routine daily reviews; Claude Sonnet handles advanced analysis. Ingests Finnhub, EDGAR, earnings transcripts, analyst revisions, and macro feeds daily. Claude synthesizes everything into structured intelligence briefs. Signal weights are automatically adjusted based on observed hit rates over time.

Every signal change moves through five verification states: UNVERIFIED → SYNTAX_OK → SQL_VERIFIED → RUNTIME_VERIFIED → PRODUCTION_VERIFIED. New signals receive 0.50× weight until proven. Sub-55% hit rates (n=15) trigger automatic deactivation. A 20% drawdown fires a system-wide kill-switch. Nothing trades automatically.

Launchd scheduling on macOS. Historical backfill from 2022-present (162,439+ price rows). Six hypothesis tiers: fundamentals, peer dynamics, AI infrastructure, VRT-specific factors, macroeconomics, and emerging research. COMPOSITE_BULL averages +3.65% return over 3-day holds. Everything is logged, versioned, and auditable. The changelog reads like a system's operating history.

The transferable design pattern: ingestion, hypothesis testing, composite scoring, human review, audit trail, continuous improvement. The SSIA white paper documents the full architecture.

Indie authors need consistent multi-channel presence. Platform-native copy for Reddit, X, Instagram, Facebook, Goodreads, and KDP all have different voice expectations, character limits, and community norms. Maintaining all six manually is a second job. Generic AI copy, posted without human review, does more damage than nothing.

A single Claude brain reads book metadata (title, author, target audience, ASIN) and generates platform-specific copy for all six channels in one run. OAuth2 and OAuth 1.0a handle platform authentication server-side. No credentials hit the frontend. KDP ranking data feeds back in via Rainforest API or CSV upload for attribution learning.

Local Node.js server with a browser dashboard. Single HTML frontend, single server backend, no build step required. Server health checks show which channels are live. Attribution tracking identifies which content and which channels convert to sales, then feeds those learnings back into the next generation cycle.

BookLite is the open-source Lite release of the internal Agentic689 book and music marketing system. It runs one book, one author voice, one Claude brain, and posts to six platforms. The full production version handles multiple titles, multi-artist configurations, and cross-platform attribution at scale. The Lite release strips to the essentials so the architecture is readable.

Built on the Anthropic Claude Agent SDK. MIT licensed. The white paper covers the full internal architecture the Lite version was derived from, including the multi-title production system.

Traditional mineral exploration is expensive, slow, and requires specialist teams in the field. Satellite imagery covering every inch of Earth's surface is freely accessible but opaque to non-specialists. The gap between available data and actionable prospectivity intelligence is a systems problem, not a data problem.

AuScan trains Claude Vision on the spectral and structural signatures of ten world-class gold deposits: Witwatersrand, Carlin Trend, Kalgoorlie, and others. Multi-sensor data (NASA EMIT hyperspectral, Landsat thermal, ASTER mineral mapping, Sentinel-2 vegetation indices, and ESRI RGB) is assembled into terrain fingerprints for each deposit.

In testing, the system scored a HIGH-confidence hit at 42.609°N, −119.299°W, Oregon's Pueblo Mining District, a documented former gold producer. Cross-referenced against the USGS Mineral Resources Data System. Search grids are configurable from 2×2 to 5×5 across 10–100 mile radii. Patterns and results persist across sessions.

React-based browser application running in a Claude.ai artifact environment. Single-endpoint design with no CORS and no separate auth layer. Browser-native storage for session persistence. USGS MRDS integration via Claude's knowledge base for cross-referenced validation. Configurable search parameters allow rapid iteration across different regions and deposit types.

The transferable insight: domain expertise can be distilled into a vision system that scales to any geographic target. The white paper covers the full methodology, sensor pipeline, and validation results.

The Agentic689 thesis says you learn what these models can actually make by making them make it, against a real objective, with a real outcome on the line. Donkey on the Edge took that to the largest objective available: go from F = ma to the open problems of quantum gravity and produce something the field has not seen.

On April 23, 2026, I asked Claude where to start with quantum mechanics. No physics degree behind the question. The conversation ran in a single unbroken thread from Newtonian mechanics to the frontier. Eighteen days. Thirty-eight reference documents written from scratch along the way. Wrong turns kept on the page next to the right ones.

By May 11 the work had produced a paper, "Complexity-Sensitive Complementarity in Non-Isometric Holographic Codes." Five results, three theorems, thirty-eight references, and one integer-valued exponent gap that nobody has yet explained. The integer is exactly one. The paper is headed for the arXiv.

The whole path of study is mapped. Thirty-eight reference documents from Physics 101 to quantum gravity, drawn as a Victorian railway chart. Thirty-eight stations on six lines. The Quantum Trunk is the spine, the Classical Branch runs alongside, the Gravitational Spur descends to the frontier, and every route terminates at Paper I. The entire physics curriculum compressed into one chart you read like a transit map. Click any station, read that document.

The site is built as a Victorian scientific journal because that is the tradition the work belongs to. Static Astro build. No tracking, no analytics, no cookies. Claude of Anthropic credited as computational collaborator.