ChatGPT has become more than just a place where I ask questions. It has become part of how I think through ideas and learn new skills. As my usage grew, one problem became obvious. My chats were starting to blur together and became difficult to manage. Personal projects overlapped with professional thinking. It all worked, but it wasn’t structured. And for someone who values organised systems, that friction started to bother me.

As a technical professional and somebody who is constantly learning new skills, I’ve always taken time to document what I learn. I learned early on that relying on memory simply doesn’t scale, and documentation isn’t an afterthought for me, it’s a deliberate part of my workflow. There are two main reasons I document things: To help future me remember how I did something To share knowledge with others If you’ve worked in IT for any length of time, you’ll know how fast things move. A tool that feels essential today can be quietly deprecated tomorrow. In the age of AI, that pace has only accelerated. New frameworks, platforms, services, and workflows appear constantly, and the number of rabbit holes you can go down in any given tech stack is endless.

GPT-5.2 was released on 11 December 2025, and it feels like the release that the original GPT-5 should have been. It builds on the lessons learned from the mixed reception of GPT-5 and the corrective update that followed with GPT-5.1, delivering a powerful model that is more capable, more balanced, and more aligned with how people actually use AI day to day. Before getting into GPT-5.2 itself, it’s worth briefly acknowledging GPT-5.1. Released on 12 November 2025, GPT-5.1 was a clear signal that OpenAI had listened to user feedback. After criticism around tone, personality, and control in the original GPT-5 launch, GPT-5.1 focused on making the model warmer, more conversational, and better at adapting its reasoning depth to the task at hand.

In the fast-evolving world of technology, staying ahead means constantly learning new skills, whether that’s mastering Linux, diving into Docker, or exploring a new cloud service. That hasn’t changed. Reading documentation, exploring blog posts, and watching tutorials are still essential parts of the learning process. What has changed for me is how I bring all of that material together. The tool I use to do that is NotebookLM which has slotted neatly into my learning toolkit, helping me research, retain, and make sense of the information.

I first dived into containerisation around 2020, eventually settling on a Virtual Private Server (VPS) setup where the focus shifted from managing hardware to mastering software and services. For a long time, the backbone of that software setup was Portainer. Portainer served as the graphical interface for managing the 20+ services I run in stacks on my VPS, handling everything from my reverse proxy to monitoring tools. Portainer is a robust GUI for managing containers and stacks, and I want to make it clear that my migration wasn’t driven by any failing on its part; it’s an excellent piece of kit. However, as my homelab evolved, I started looking for something that offered an even simpler and easier management experience. I wanted a dedicated tool that stripped back the complexity, allowing me to focus entirely on tweaking my Docker Compose files and stacks. That search led me straight to Dockge.