WooCommerce for Claude came out of Automattic’s Radical Speed Month. It uses MCP to give the model analytics lookup tables, a store knowledge layer and an AI-readiness score, all on one endpoint. Plus the provider pattern and how to run it locally.
Ghost is an agent-first Postgres platform. Notes from using its MCP server and database forking to let AI agents test schema changes and index strategies without touching a shared staging database.
Prompt caching only pays off when the prefix stays byte-identical. I go through the mistakes that break it, a semantic cache layer for WordPress, loading tool definitions on demand, and what to keep or drop between turns.
A logistics director with all-green KPIs and 18% late shipments does not have a data problem. Scoped AI agents watching the TMS can tell you which handoff actually broke, and tell you on the morning it happens.
Vibes-checking AI output is the new bottleneck. What actually moved my Claude Code performance: terminal permissions for the agent, a test requested with every feature, and a no-stop rule until they pass.
Cowork gets written off as Claude Code for people who don’t code. I use it for architecture work and multi-file debugging, and this is how I keep the context window lean, why I stay in plan mode, and which repeat jobs I turned into skills.
Claude Code is not only for refactoring PHP. Three things I run through it that never touch a codebase: LaTeX decks kept in Git, cross-app search over Slack and Notion, and prospect research.
Woo ran a Bug Blitz: support engineers with Claude Code and Copilot shipped over 170 fixes across 20 products, every line reviewed by quality engineers. Why it worked and what to copy.
LLMs forget your stack the moment you close the tab. Here is what actually sticks: a project rules file in the repo, global rules for your own preferences, and MCP when the assistant needs data from outside the codebase.
One LLM cannot draft emails, read documents and optimize a sales pipeline well. A look at splitting the work across reasoning, orchestration and execution layers, with TD learning doing the sequential decisions.