Notes from the WordPress performance chat: bridging classic scripts with script modules for the CodeMirror upgrade, memoizing wp_normalize_path, and why Copilot kept reintroducing a PCRE bug that had already been fixed.
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 7.0 may merge the WP AI Client into Core, giving plugins one provider-agnostic API for calling models instead of each shipping its own SDK. Notes on the Connectors keychain, the package size argument, and what it changes for developers.
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.
A custom VS Code theme takes hours rather than weeks once you move past CSS variables and into TextMate tokens. Ahmad Wael on scaffolding the first pass, debugging scopes in the Extension Host, and picking contrast that does not fry your eyes by lunchtime.
Basic GNNs treat every SKU connection the same. Heterogeneous Graph Transformers weight each edge type separately, which cut WAPE from 0.86 to 0.58 on an FMCG dataset. The post also shows the typed-edge table I use instead of flat post meta.
The January 2026 Towards Data Science pieces I actually used: Ryan Pegoud cutting LLM memory 84% with Triton fused kernels, ACE context engineering, and Hugo Lu on the ceilings at Databricks and Snowflake.
RoPE rotates query and key vectors instead of adding an index, so the distance between two tokens stays the same wherever the sequence sits. Covers the rotation intuition, why dimension pairs rotate at different speeds, and a PyTorch implementation for a custom transformer.
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.