Large Language Models (LLMs) like ChatGPT, Claude, and Google Gemini have become invaluable tools for developers, offering assistance with code review, debugging, and general programming queries. However, these tools vary in the files they handle, creating challenges when seeking help with larger codebases.

At work, getting production releases green-lit requires keeping systems clear of software vulnerabilities. This applies to both programming language packages and operating system components, even for internal-only systems.

Testing npm packages locally before publishing can be tricky, as you will want to verify everything works correctly in a real project before pushing to npm. Let me walk through some common approaches and what has worked best for me.

To maximise a window/application minimised in the Dock on OS X, follow these steps.

Here are my personal study notes from my preparation for the Certified Kubernetes Application Developer (CKAD) exam. While the main focus of these notes is the CKAD syllabus, they also touch upon additional aspects of Kubernetes that I think provides a more complete understanding and may prove useful to someone.

Here are some things to consider when working on the design of GraphQL schemas

A pattern to automatically retry API requests when the quota limit is reached.

Switched from hexo to hugo installed via homebrew.

To use rust and webassembly requires installing a fair amount of tooling. I decided to create a docker image with everything required: