Agentic coding: how I debug WordPress sites now
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.
Category: Development
Run Claude Code for free with Ollama and a local model
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.
Writing an Etch A Sketch in Python Turtle
A Python Turtle Etch A Sketch is a small program with real event-driven logic in it: onkey callbacks, a pen you treat as an object, and state you manage yourself. Here is how I structure it, plus the penup/pendown detail that stops the turtle drawing a line home.
The WordPress AI Client heading into core 7.0
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.
WordPress 7.0 AI integration: the road to Beta 1
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.
What the WordPress.com Claude Connector actually does
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.
Building CSS bar charts with sibling-index() and attr()
Chart libraries are heavy for four bars. Here is a CSS Grid bar chart that uses sibling-index() and attr() to read its values straight off the markup, plus the PHP helper I use to render it in WordPress.
SageMaker vs Azure ML: how each handles training compute
Part 2 of the SageMaker vs Azure ML series, this time on compute. Azure keeps clusters as persistent workspace assets while SageMaker spins them up per job and tears them down after, plus spot instances, checkpointing and container control.
Profiling Python with py-spy: 169 seconds down to 0.56
A Pandas iterrows() loop over 3.5 million flight records took about 170 seconds. py-spy is a sampling profiler that shows where the time went without slowing the script down, and the vectorized NumPy rewrite does the same work in 0.56 seconds.
Pandas loc vs iloc and the slicing rule that bites
loc selects by label, iloc by position, and their slicing rules disagree: loc includes the stop value, iloc excludes it. Here is when I use each one, plus the duplicate-index case where loc quietly breaks a model.