Demo agents fall over in production because errors compound across steps and prompts break when the business process changes. Structured output, LLM-as-a-judge evals and OKR alignment are what get a prototype across.
Five ways to target the root element in CSS: the plain html selector, :root, :scope, the top-level nesting ampersand, and structural tricks that can only match html. Which ones belong in production, and how their specificity differs.
CVE-2026-3589 is a CSRF flaw in the WooCommerce Store API, affecting versions 5.4 to 10.5.2, that can be used to create admin accounts through batch requests. How the flaw works, and how to check your own version with WP-CLI or a small PHP snippet.
Traditional RAG chunking strips the context that makes a chunk mean anything. Contextual retrieval fixes that by prepending a one-sentence summary situating each chunk in its parent document, and prompt caching keeps the ingestion cost down.
A look at Accessible UX Research, the new Smashing Magazine book by Dr. Michele Williams, and why passing a WCAG checklist is not the same as being usable. Includes the nav menu ARIA fix I keep applying to custom themes.
Five ways to turn continuous variables into bins: equal width, equal frequency, domain-defined intervals, K-Means, and a shallow decision tree. What each one does to outliers and skew, with the scikit-learn and Pandas code.
WordPress 7.0 lets you register a block entirely in PHP. The autoRegister flag tells the editor to build the inspector controls from your attribute definitions, so a server-rendered block needs no JavaScript. The setup, and the attribute types it skips.
No model is interpretable in the abstract. Interpretability is a set of methods for answering a question, and in production that question is usually diagnosis, validation or discovery. Includes a way to store explanation data in WordPress meta and transients.
One agent cannot learn routing, packing and weight limits at once. A hybrid MARL-LP setup gives reinforcement learning the fleet-level strategy and hands the hard constraints to a linear programming solver, which trains faster and generalizes across hubs.
AI handles static problems well and struggles with systems that keep moving, which is where operational scar tissue still pays. On static versus flux systems, the physical limits on adoption, and why judgment stays scarce as cognition gets cheap.