A Compass for Navigating Cyber Careers When someone says they work in cybersecurity, it could mean anything from cloud engineering to incident response, from red teams to risk governance. The field is vast and only getting more complex. Despite all the frameworks we use to secure systems, most organisations still lack a shared language for describing the people who do the work. That’s what the NICE Framework offers: a way to map roles, skills, and development pathways across the full cyber landscape. ...

Why I Needed Chinese Font Support The reMarkable 2 is an A4-ish-sized eInk tablet made by a Norwegian company, also called reMarkable. It’s designed for reading, writing, and thinking — without the usual notifications, distractions or temptations of a typical smartphone or tablet. I use mine to read and annotate longer documents like standards, regulations, essays, and articles. Over time, it’s become a quiet but essential part of how I do deep work. And the kind of work I do involves more than just English: documents and regulations are often written in or include Chinese, Japanese, or Korean characters — especially in global firms or cross-border risk contexts. So getting proper font support isn’t just a nice-to-have. It’s part of making the tools I rely on actually work in the world I’m working in. ...

What: Run an LLM Locally with Ollama If you’re curious about running a Large Language Model (LLM) on your own laptop — no cloud, no internet connection required — Ollama makes it surprisingly easy. It’s a simple way to use powerful open-source AI models directly on your machine. Ollama is a developer-friendly tool that makes it simple to run Large Language Models (LLMs) like Llama, Gemma, or Mistral locally on your laptop with just a single terminal command. Ollama supports a wide range of open-source models and is used by developers, researchers, and privacy-conscious professionals who want fast, offline access to AI without sending data to the cloud. ...

How Retrieval-Augmented Generation helps organisations protect sensitive information while harnessing AI’s full potential. When you ask ChatGPT or another AI tool a question, it answers based on what it knows from its training data — typically a massive blend of public information from the internet and available literature up to a certain point in time. While this is powerful, it misses something vital: your own institutional knowledge. Your company’s proprietary policies, control frameworks, audit reports and lessons learned — they aren’t part of the public training set (and you don’t want them to be). But imagine if you could blend the vast “hive mind” of general AI with the unique knowledge sitting inside your own documents - all the while keeping it private, local and secure. That’s exactly what RAG — Retrieval-Augmented Generation — allows you to do. ...

Before an AI system can answer a query, write a paragraph of text, recommend a movie, or drive a car, it must first learn - and that process is called AI training. AI training involves teaching an artificial intelligence model to make accurate predictions or decisions. It learns by analysing large volumes of training data, identifying patterns, and adjusting its internal settings like numerical weights to improve its predictions. Throughout training the AI compares its predictions to known correct answers, refining itself over millions of cycles to reduce errors. However it’s important to remember: AI doesn’t understand its tasks the way a human would. It is simply refining its pattern recognition. ...