Efficiency is one slice of AI value, not the whole thing. Where automation, augmentation and innovation each show up, why the accuracy tax cancels out saved hours, and how to queue an AI fraud check so it never blocks checkout.
Prompt caching gets all the attention while the expensive work happens before the model sees a token. Five places to cache in a RAG pipeline, from query embeddings to full query-response pairs, with a transients example for WordPress.
The API bill is the usual reason teams look at their own hardware, and privacy is the better one. Which benchmarks to trust for agents, how far you can quantize before logic breaks, what a single A100 on GCP costs, and serving Qwen 3.5-27B with vLLM.
WordPress.org now runs an MCP server for the plugin directory, so an AI assistant can validate readme.txt, check where a submission sits in the queue and read the guidelines from the source. The review team still holds you responsible for the code.
RC1 for WordPress 7.0 was due today and now lands on March 24, 2026. The holdups are real-time collaboration performance and client-side media optimization, plus a fresh look at release package size. Both are worth watching before you update.
The Plugin Directory now ships a wp-env setup, so you can boot a local copy instead of guessing how its backend behaves. Here is what works inside it, and where it stops short of the live site.
A run at differentiable rule induction: the model wrote its own IF-THEN fraud rules, rediscovered V14 without help, and went dark in three of five runs. Notes on the consistency loss and when the architecture is worth shipping.
Beta 6 lands 132 fixes, drops Client-side Media Processing to shrink the package, and stretches Real-Time Collaboration polling by 4x. Collaboration is opt-in behind a constant now, and RC1 is due March 24th.
AI coding assistants write decent functions and miss the system around them. A naive REST endpoint next to a reviewed one, two outage stories, and the workflow I use to keep an agent on a leash.
Linear regression is a projection: you look for the point where the error vector meets the feature space at a right angle. Vectors, dot products, a forest-and-highway analogy, and the scikit-learn code that matches it.