WordPress 7.0 (Armstrong) is past the foundational AI work and into ecosystem plumbing. The connector approval experiment blocks plugins from your API keys until an admin says otherwise, and RC1 lands May 14, so provider plugins need updating now.
Vision models and language models look like separate brains, but as they scale their internal structures line up. A look at the Platonic Representation Hypothesis, the pressures behind convergence, and what it means for the data assets you build.
Asking an LLM whether a value changed significantly turns a cheap comparison into a slow, untestable guess. Here is the split I use in WordPress: PHP thresholds decide when to act, and the model only writes the message.
Dumping PDFs into a vector store is why RAG systems hallucinate and cost too much. This covers curating the source data, chunking by meaning, quantizing vectors, mixing BM25 with vector search through RRF, and testing retrieval with DeepEval.
Timer-XL makes the case that decoder-only Transformers beat encoders at forecasting. This looks at how TimeAttention keeps temporal order intact, why patch tokens cut the compute, and what the zero-shot numbers mean for real pipelines.
MARL for logistics: RL picks the routing strategy, Linear Programming handles packing, and ratio-based observations let one agent run at a rural hub or a metro sorting center. Includes the sequential training loop that keeps agents stable.
A “5x improvement” on its own tells you nothing. Three questions pull it apart: which dimension moved, from what baseline, and against which period. Examples from Core Web Vitals, model accuracy, and a transient cache bug that faked a 10x speed boost.
Claude Code gets more reliable when it can run its own output: unit tests through WP-CLI, parallel prompts compared against the goal, and Chrome screenshots over MCP for front-end work. Also why an agent looping with no check-in is worse than no loop.
Google’s Prompt API is Chrome-only, downloads a 4GB Gemini Nano model without asking, and requires developers to acknowledge Google’s Generative AI Prohibited Uses Policy. My case against calling that a web standard.
AI coding tools copy whatever patterns already exist in a repo and know nothing about battery budgets or packet sizes. In IoT that combination turns into technical debt faster than a team can refactor it away.