LLMs are non-deterministic, so three good prompts prove nothing. What to settle before you build an AI feature for WordPress or WooCommerce: the scenarios worth testing, KPIs that map to the business, and a log you can score later.
The SaaS moat is gone now that an agent can rebuild your product in an afternoon. Here is the case for a herd of small automated niche businesses over one unicorn, and the WordPress REST API and WP-CLI setup that makes running a dozen of them possible.
WordPress Core added an experimental wp-env runtime that runs PHP and SQLite through WebAssembly, so a full environment starts in seconds without Docker. Notes on the npx flag, the SQLite tradeoff, and the limits that still send me back to containers.
Slow reporting on WooCommerce is usually a schema problem, not a hardware problem. This covers custom analytical tables, the Inmon and Kimball warehouse schools, lakehouses, and where event-driven architecture fits a WordPress stack.
Dumping a relational database into a vector store gives you a slow, expensive system that has forgotten its own schema. This covers hybrid retrieval instead: ReAct tool routing, why FAISS post-filtering returns nothing, and a PHP dispatcher for WordPress.
Dashboards tell you a delivery was late. They do not say which team caused it. This post covers the MCP setup I use so Claude can read ERP, WMS and TMS timestamps and work out where the delay started.
A plugin showing a green arrow is not proof that a variant won. This post walks through a 2×2 chi-square test in PHP: expected counts, degrees of freedom, the 3.84 cutoff at p=0.05, and the four assumptions that break the result.
Notes on PyTorch distributed operations: what NCCL is doing under the hood, why synchronous and asynchronous do not mean what you would guess, and how a missing wait call hangs a rank forever.
WooCommerce core devs Greg Bell and Lance Willet are running an open office hours session on testing on March 4, 2026. What to test before you ship, and what to ask them on Slack.
Stephanie Kirmer, a staff ML engineer, on why the job leans on sociology as much as modeling, how code assistants took over the boilerplate, and why $200 billion of AI investment looks out of proportion to what the tech does today.