Learn how I built a custom Claude Code command to automatically detect unused Drupal modules by comparing Composer dependencies with enabled modules. This practical example demonstrates how AI-powered automation can streamline routine development tasks and keep your Drupal projects lean and maintainable. See the complete implementation and real-world output from my production site.

After upgrading to Docker 29.0, my entire Traefik setup suddenly stopped working, even the latest Traefik version failed to connect. The root cause turned out to be a breaking API change: Docker 29 now requires API 1.44+, while Traefik still uses 1.24. Until Traefik updates its Docker client, the only fix is to downgrade to Docker 28.5.2 and hold updates.

As developers, we often bounce between "just ship it" mode and "I should probably plan this," especially now when vibe coding is everywhere. Backlog.md bridges that gap elegantly. It's a markdown-native task manager that lives right in your git repo, bringing just enough structure without killing the flow.

I've been experimenting with Claude Code and other AI coding assistants for months now, and while they're incredibly helpful, I kept running into the same frustration. They know everything about Drupal and React hooks and Python decorators, but nothing about the specific commands and scripts in my projects. I finally decided to try MCP (Model Context Protocol) to see if I could fix this problem, and it worked better than I expected.

Claude Code subagents are specialized AI assistants designed for task-specific workflows. Each subagent has its own context window, system prompt, and tool permissions, allowing Claude Code to delegate tasks without cluttering the main conversation. This setup enables focused expertise, better context management, and reusable configurations for consistent and efficient development workflows.

Most teams want every merge request to receive a quick, thoughtful review, but human reviewers are often busy or unavailable when code is submitted. By connecting an open-source tool called n8n to an inexpensive large language model, we can trigger an automated code review the moment a GitLab MR opens and post the feedback as a comment.

Creating users on the fly through Drupal's entity reference fields doesn't work out of the box because user entities need more than just a label. The core implementation tries but fails to properly generate usernames, emails, and passwords from the provided text. This tutorial shows how to fix this with a custom Entity Reference Selection plugin.

Ever found yourself in a situation where you needed to increase the length of a text field that already contains data in your Drupal site? Maybe you started with a shorter field length and now your content editors need more space. It's a common scenario, but changing field lengths in production can be tricky.

Building on the foundation laid in the previous article, this post dives into integrating Deep Chat JS component for real-time, context-aware conversations. By leveraging semantic search on your vector database, you can retrieve the most relevant information faster than ever.

This is the first article in a series where I describe how I'm building a chatbot for this blog. My goal is to create a system that can quickly provide relevant answers to site visitors. After refining this approach, I plan to implement it on my wife's ecommerce site as well. Stay tuned for more details in subsequent posts.