How I set up Claude Code context: local docs and meeting transcripts in a /knowledge folder, a .claudignore that keeps the agent away from secrets, and a bash script that bundles only the files a given feature needs.
Andrew Adetitun had more than 160,000 followers and no platform of his own. He is a former WordPress developer, and he still used the AI website builder to get The King’s Monologue online fast, then pointed it at his Kickstarter book.
A three-step way to point an agent at a broken WordPress site: let it read debug.log, make it write a hook-level plan before it edits anything, then iterate on what it ships.
Ollama 0.14 speaks the Anthropic Messages API, so Claude Code can run against a local model instead of a $200 plan. Here is the setup, the three environment variables it needs, and the git init step that trips people up.
Notes from the February 4 contributor meeting on the WordPress AI Client merge into 7.0: why OpenAI, Google and Anthropic are moving out to separate plugins, why the off switch is a constant rather than a filter, and what MCP adapters mean for the Abilities API.
Notes on the WordPress 7.0 AI integration roadmap: the WP AI Client merge proposal, the GatherPress demo built on the Abilities API, how the native client compares to the MCP adapter, and what has to land before the February 19 Beta 1 deadline.
WordPress.com now has an official Claude Connector built on MCP and OAuth 2.1. It gives Claude read-only access to site data like traffic, comments and content, with no custom REST bridge to maintain. Here is what it can do, how to switch it on, and where it stops.
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.
Pre-built agent toolboxes break the moment a client asks something you did not plan for. Here is the plan-code-execute setup I use instead, where the agent writes its own Python, plus the hallucinated CSV that taught me why the analyst step is mandatory.
Chat history arrays are a log, not memory. This walks through the LLM memory layer I build instead: DSPy signatures for extracting atomic facts, QDrant for per-user vector storage, and a maintenance agent that decides when an old fact gets updated or deleted.