AI coding assistants and the WordPress code they get wrong
AI coding assistants write PHP that reads fine and falls over in production. A WooCommerce audit, the naive fetch they keep producing, and the refactor I run it through.
Tag: AI Assistants
Reviewing Claude Code output is the new bottleneck
Generating a thousand lines of PHP takes seconds, so review is where the time goes. The review skill I run before opening a PR, why I ask for HTML reports, and the WooCommerce checkout bug that nearly shipped.
Building a Claude Code Skill that actually triggers
I moved a 500-line prompt into a Claude Code Skill and stopped re-pasting it every morning. This covers the three loading stages, how to write a description Claude will act on, and the three implementation patterns.
Building an AI application is mostly infrastructure work
Building an AI application is mostly plumbing: keys kept out of the repo, a plan for 429 responses, and a chunking engine for anything longer than the token limit. Notes on what keeps an AI feature running once it leaves your laptop.
Vision language models are not trained from scratch
Nobody trains a vision language model from a blank slate. You freeze a ViT, train a Q-Former to bridge image features into the text embedding space, and wrap the language model in LoRA. This is how those three pieces fit and where the GPU hours go.
Why multi-agent AI systems fail, and three patterns that work
Chaining agents multiplies errors instead of dividing work: DeepMind measured up to 17.2x amplification. Here is the math behind that, the three topologies that hold up in production, and the checklist I run before shipping an agentic system.
Why your AI search evaluation is probably wrong
Vibe-checking search results is how teams end up rebuilding a backend for a system that scores worse than the SQL it replaced. What a real AI search evaluation needs: a golden set, repeated trials, and an ICC check before you ship.
Causal inference in data science, the skill AI can’t fake
AI infrastructure spending hit nearly $400 billion in 2025 against barely $100 billion of revenue. The skills that survive that correction are the classical ones: causal inference, experiment design, Bayesian reasoning, domain modeling and SPC.
Agent skills architecture: why Markdown is not enough
A tool is one capability, a skill is the orchestration around it. Notes on writing agent skills in Python instead of Markdown, and where MCP fits when you build agents outside Claude.
How I get production-ready code out of Claude Code
Vibe coding a feature is easy; keeping it out of the technical debt pile is not. A CLAUDE.md handbook, plan mode before any edit, and a review pass that assumes something is broken.