Output entropy tells you a translation model is unsure but not why. This walks through bidirectional cross-attention, the misalignment features it exposes, and a small QE head that scores tokens without retraining the NMT model.
Vectorless RAG is accurate, slow, and expensive to index. Proxy-Pointer RAG puts the document structure into the embeddings and metadata instead: regex skeleton trees, breadcrumbs on every chunk, and pointers back to the full section.
Claude Code will scaffold a WordPress plugin faster than you can spec one, which is the problem. The workflow I use: a CLAUDE.md spec first, the terminal agent second, and a manual review of everything before it ships.
Gutenberg 22.9 lands background.gradient support so a gradient and a background image can coexist, adds an EmptyState component to wordpress/ui, and gives the experimental Forms block hidden input fields.
An LLM pointed at a folder of documents will confidently quote a 2019 policy at a customer in 2026. What decides whether a RAG pipeline holds up is the chunking, the retrieval and the evaluation, not the model you picked.
Models trained on the output of earlier models lose the tails of the distribution and drift into nonsense. The clean data sits behind logins, and the PROPS framework is one way to train on it without anyone handing it over.
A RAT sitting on the victim’s own phone gets a clean IP, a real device fingerprint, and a valid MFA code. What it cannot fake is how the owner scrolls, and that is the signal behavioral biometrics reads.
The WordCamp Asia 2026 schedule is out: Mumbai, April 9 to 11, leaning hard on AI, enterprise scale, and the Interactivity API. My notes on which sessions are worth a slot in your day.
Embedding models turn text into coordinates, and that is what vector search is really matching on. This covers tokenization, chunking, cosine similarity and fine-tuning a model so it fits your own catalog rather than the general web.
Tool vendors promise 40% productivity gains and the backlog never moves. Amdahl’s Law explains why speeding up 5% of the work buys you 5%, and why I would rather measure cognitive load than coding speed.