Separate repos for the React frontend and the WordPress backend are why payloads drift out of sync. Notes on moving to a monorepo with Roots Bedrock, keeping an AGENTS.md map of endpoints for coding agents, and the stale REST cache that broke a checkout.
The WordPress Foundation, UIC and Automattic are running a paid micro-credential pilot called AI Leaders: 80 students in Illinois and Louisiana, a $1,000 stipend, and real WordPress AI work instead of prompt-writing exercises. Here is what that changes for hiring.
The February 4, 2026 dev chat put client-side media on the road to WordPress 7.0: libvips compiled to WASM, running in a worker thread, with image resizing done in the visitor’s browser instead of on your server.
The Core AI team put out a Call for Testing for the AI Experiments plugin. Here is what is in it: MCP server support, markdown feeds, extra providers and AI request logging, plus the $400 API bill that convinced me logging is the part you cannot skip.
A million-token context window does not mean a model can reason across a million tokens. Recursive Language Models hand the context to a sandboxed Python REPL and let the model write code to partition and query it, which is how DSPy now implements the strategy.
WordPress 6.9 shipped the Abilities API, and the MCP Adapter is what lets AI agents find those abilities and call them. Ahmad Wael covers flagging an ability for MCP, the two transport methods, custom servers, and keeping permission callbacks tight.
I ran the WordPress.com AI engine at 21 site types, from portfolios to membership hubs, to see whether the blocks it generates survive a mobile viewport and a Core Web Vitals pass. Here is what ships as is, and the parts I still fix in functions.php.
The WordPress handbook now has a section on AI. Five rules cover who stays accountable, when you disclose model help, GPL compatibility, which contributions are in scope, and the quality bar reviewers can enforce.
Tammy Silva’s LinkedIn side project turned into 50 messages a day. She used an AI website builder to put up a WordPress hub in an afternoon, moved lead capture off DMs, and filled 10 coaching slots in two days. Includes the hook I use to route inbound leads.
Siloed training data produces models that fail confidently. This walks through the Federated Learning Flower framework on a biased MNIST setup, the pyproject.toml config, and the jump from 65% to 96% accuracy.