Most WordPress sites keep their data as loose post meta rows. Healthcare got further by agreeing on shared ontologies before writing any code, and the same approach works in a WordPress codebase, from CPT design through JSON-LD output.
The WordPress Abilities API landed in 6.9 Beta 2, minus the JavaScript registration, which moved to a standalone NPM package in the Gutenberg repo. Notes on that split, the AI Experiments plugin, the WP AI Client 0.1.0 plan and the MCP Adapter refactor.
Low-tech logistics companies do not need AGI, they need to stop counting stock with a pen. Voice-driven inventory counts in n8n, damage reports generated from a photo, and a WordPress REST endpoint that takes the data in without breaking WooCommerce stock.
Perplexity and BLEU tell you nothing about whether an automated customer journey actually goes anywhere. The CDP framework scores continuity, deepening and progression against a taxonomy of journey stages instead.
LLM-as-judge asks a model that hallucinates to catch hallucinations. Displacement Consistency scores the vector between a question and its answer against grounded neighbors instead, with no source document and no second model call at inference time.
The Core-AI team shipped v0.1.0 of the WordPress PHP AI Client SDK. It is a provider-agnostic Composer package you bundle into your own plugin, so swapping the LLM behind a feature stops being a refactor.
Cursor turns a four-hour refactor into a 20-minute one, but only with a plan. My four steps: spot the trigger, write a plan.md, let Claude execute with test scripts, then compare the output against the original.
Six months in, WordPress Core AI has a stable shape: the Abilities API lands in 6.9, WP Client AI abstracts the LLM providers, and the MCP adapter turns a site into a server that agents can call.
Notes on porting statistical models into WordPress: where research gets diluted instead of distilled, how real-time aggregates in user meta leak data through race conditions, the atomic SQL fix, and why a bigger LLM is the wrong answer to arithmetic.
Rolling-window forecasts treat every SKU as independent. This post models a WooCommerce catalog as a graph instead, with GraphSAGE over shared plants and attributes, the PHP that extracts SKU neighbors, and where heterogeneous transformers go further.