Black-box models can tell you a customer will probably buy, but not what pushed them there. What explainable AI adds instead: readable feature importance, SHAP and LIME, and a small WooCommerce snippet that logs the rationale on the order.
The host and device paradigm decides whether your GPU works or waits. Where CPU and GPU interaction turns into a bottleneck, how CUDA streams overlap transfers with compute, and which one-line habits trigger a synchronization stall.
Clever WordPress code tends to fall over in production. What changes when you optimize for reliability instead: a chunked WP-CLI batch pattern, debugging a failing WooCommerce checkout methodically, and using LLMs for edge cases rather than features.
The WP AI Client is up for a core merge with Beta 1 ten days out and no PR to test. Notes on that proposal, the admin reskin in ticket #64308, the Release Squad now in place, and what Gutenberg 22.5 changes for 7.0.
How I use Claude Code with WordPress Studio to scaffold plugins: setting up a local site and terminal, installing the CLI, the context a prompt needs, and why the naive admin notice it hands you is an XSS bug waiting to happen.
Agentic AI features need more than a good prompt. These are the patterns I use to keep users in control: intent previews, an autonomy dial, an action audit log with a working undo, and escalation when the model is not confident.
Most WooCommerce stores buy clicks and hope. Expected value modeling puts a number on it: profit per sale, cost per click and purchase probability decide whether a customer is worth targeting, plus a small PHP helper to run the check.
Mechanistic interpretability is the Xdebug of AI research: hook a forward pass, zero out a neuron, and see what the model was actually doing. A walk through the residual stream, TransformerLens, circuit tracing and steering vectors.
Agentic analytics moves the consultant’s job from writing queries to architecting the systems agents read from. What that looks like in practice, including a clean WordPress REST endpoint built for agent consumption.
Voyager 1 still self-corrects on 69.63KB of memory. That comparison is the frame for quantization, knowledge distillation and TinyML, plus a small PHP example of how weight quantization actually works.