Prompt engineering is a crucial skill in today’s AI-driven world. At its core, it’s the process of designing and optimising prompts to interact effectively with natural language processing (NLP) models like ChatGPT. Crafting a good prompt is an art and a science, and when done well, it can drastically improve the quality of responses from AI systems. Whether you’re writing prompts for work, learning, or creative projects, understanding the basics of prompt engineering can make your interactions with AI more productive and rewarding.

We’re living in the middle of an AI revolution. The rapid advancements in artificial intelligence feel like the dawn of a new era—much like how the internet and the World Wide Web transformed the way we live, communicate, and work. In just a few short years, AI has gone from a buzzword to a daily tool many of us use, and the pace of progress shows no signs of slowing.

Diving into the world of Linux naturally led me to discover the vibrant homelab community. What began as casual curiosity soon became a genuine fascination, fuelled by countless YouTube videos showcasing everything from compact, single-board computers to sprawling, business-grade server racks. The creativity and technical depth on display resonated with my own desire to experiment and learn by doing. But as I watched, a set of questions began to form: Did I actually need a homelab? What would I use it for? Did it require a significant investment in hardware? And, crucially, how should I even begin?

When people think of iPhones, they often picture sleek hardware and user-friendly software. Of course, if you’re team Android, that’s perfectly fine—there’s no room for tribalism here. But I digress. Back to the topic at hand: one of iOS’s most underrated features is hiding in plain sight—Apple Shortcuts. This powerful app allows you to automate tasks, saving time and effort on repetitive actions. Yet despite its incredible potential, many iPhone users either tuck it away in a folder or remove it entirely.

It’s only been a few weeks since OpenAI released GPT-4, and I’ve already been putting it to work. As a ChatGPT Plus subscriber, I’ve gained access to this new model, albeit with frustratingly low rate limits that I hope will improve in the coming weeks. Despite that, the excitement I am feeling is real. GPT-4 feels like a gear shift, a real turning point, not just in AI capability, but in how I personally approach learning. I’ve been experimenting with Large Language Models (LLMs) for some time now, mainly to augment my learning around technical subjects. With GPT-4, that approach feels more powerful than ever.