Cleaning the dataset will not stop LLM hallucinations. Inside a transformer the right token is there in the early layers, and the model rotates away from it in the middle ones. What that means for monitoring an AI feature in production.
Core bundles its own AI client in WordPress 7.0, so older AI Experiments builds fatal the moment you activate them. What is graduating out of experiment status, the LLM kill switch in Trac 64706, and how to load your own AI code without crashing.
Building an AI application is mostly plumbing: keys kept out of the repo, a plan for 429 responses, and a chunking engine for anything longer than the token limit. Notes on what keeps an AI feature running once it leaves your laptop.
Nobody trains a vision language model from a blank slate. You freeze a ViT, train a Q-Former to bridge image features into the text embedding space, and wrap the language model in LoRA. This is how those three pieces fit and where the GPU hours go.
Chaining agents multiplies errors instead of dividing work: DeepMind measured up to 17.2x amplification. Here is the math behind that, the three topologies that hold up in production, and the checklist I run before shipping an agentic system.
Real test data is usually missing right when you need it. How I generate varied synthetic WooCommerce orders with an LLM, and the two ways it goes wrong: context rot, and only ever prompting for the happy path.
Pure semantic search stops finding part numbers and SKUs. Why I pair embeddings with a BM25 keyword index, what the k1 and b parameters change, and how rank fusion merges the two result sets.
AI Experiments 0.5.0 requires WordPress 7.0, drops its bundled AI client for the one in core, and moves API keys to the Connectors screen. What that means if you are still on 6.9.
Dashboards are no longer the main consumer of your data. Here is what I would change in a data stack to make it readable by agents: fewer tools, open table formats, a real semantic layer, and feedback loops that act without waiting for a human.
np.fft is not a black box you have to live with. This walks through the winding machine picture of Fourier transform sound analysis, the center of mass calculation behind magnitude and phase, and why Euler’s formula shows up instead of a plain sine wave.