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.
Healthcare treats data as an ontology rather than a row, which is why its knowledge graphs outlive the systems built on them. Layered ontologies, regulated vocabularies, and what web developers could borrow from both.
Version 0.2.0 of WordPress AI Experiments adds excerpt generation that authors can edit or reject, and an Abilities Explorer screen for testing every registered ability in the browser.
After 14 years I moved most of my work out of the IDE and into the terminal: Claude Code in Warp, a Git worktree per agent so they stop colliding, and stored slash commands so I am not retyping the same prompt.
Junk topics usually mean the pipeline is wrong, not the data. Here is how seeded KeyNMF and an LLM summarization step keep a topic model focused without burning the whole compute budget.
Ninety percent accuracy sounds fine until the wrong JOIN lands on a production database. This covers why Execution Accuracy beats Exact Match, what Spider 2.0 exposed about enterprise schemas, and how to ground an LLM on the schema you actually have.
Notes on building a data-driven goal tracker with Python, Streamlit and Neon: one schema for daily habits and quarterly milestones, ISO-8601 week boundaries so the weekly numbers do not drift, and a REST API bridge when the data has to land in WordPress.
A knowledge graph has three layers: an ontology, controlled vocabularies and observational data. This post walks through what each one does, and shows the WordPress version of the shift from flat meta_query lookups to taxonomy-based entity relationships.
Retrieval-augmented generation is past the demo stage. Notes on chunk size as an experimental variable, why HNSW precision degrades as the index grows, the latency cost of extra retrieval layers, and a transient wrapper for caching retrieval in WordPress.
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.