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.
A dashboard shows you what moved. These five methods tell you what caused it: doubly robust estimation, instrumental variables, regression discontinuity, difference-in-differences, and CATE, with the Python I run on live WooCommerce data.
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.
AI raised the floor on production, and that changed what designers are actually paid for. Here is where human strategy still decides the outcome, and why judgment now carries the accountability.
Machine learning at scale is an infrastructure problem before it is a modeling one: why availability beats accuracy, how label leakage fakes good metrics, and why heavy models and fallbacks belong on different hardware.
Vibe coding a feature is easy; keeping it out of the technical debt pile is not. A CLAUDE.md handbook, plan mode before any edit, and a review pass that assumes something is broken.
WordPress 7.0 Beta 2 adds one Connectors screen for AI providers and forces Block API v3 blocks into the iframed editor. Both change what your plugin code has to do before the April 9 release.
Claude Skills keep instructions in files the agent reads only when needed, which cuts the token overhead of always-on MCP tools. Subagents push it further by throwing away their own context once the job is done.
AI-generated code ships fast and then stalls, usually around month three, when nobody can safely change the file it all lives in. A WooCommerce example shows the monolith version next to the decoupled one.
Classic RAG runs one retrieval pass and stays predictable. Agentic RAG keeps searching until it has enough evidence, and pays for it in latency and tokens. How I pick between them, and what breaks when a loop has no budget.