Waiting for the F1-score to drop means the fraud already happened. A FIDI Z-score on the symbolic layer flagged drift a full window earlier across five seeds. The code is below, along with the covariate blind spot it will not catch.
Vector search on its own leaves an agent repeating things that stopped being true months ago. Here is the memory lifecycle I build instead: decay scores, contradiction detection, confidence values, compression, and a background worker that does the cleanup.
Production models rarely decay on a smooth curve. They hold steady and then lose a fifth of their recall in an afternoon. Here is the R-squared test that tells you which regime you are in, and the three triggers I use instead of a retraining calendar.
Bigger context windows do not fix agent reasoning. Notes on context rot, compaction and folding, the harness that wraps the model, and why agents should pass distilled artifacts instead of raw traces.
WordPress AI Plugin 0.7.0 adds content classification that can stick to your existing terms, context-aware meta descriptions, bulk alt text in the Media Library, and a filter for system instructions.
VLA models put vision, language and motion in one latent space instead of three separate pipelines. Notes on why that unification works, and how action tokenization, diffusion heads and flow matching differ once the model has to output real movement.
The 7.0 release went back from RC to Beta so Real-Time Collaboration data could move out of post_meta and into its own table. Also covers the WP AI Client, pseudo-states in theme.json and the Playground MCP server.
WordPress 7.0 ships three pieces of AI plumbing: the Abilities API for registering what a site can do, the AI Client for provider-agnostic model calls, and the MCP Adapter that exposes both to assistants like Claude.
Notes from the April 8 contributor chat: the AI Client centralizes the transport layer, the Connectors page holds credentials for any service and not just AI, and the MCP adapter is close to 0.5.0.
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.