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. ...

Authors: Daniel Kokotajlo, Scott Alexander, Thomas Larsen, Eli Lifland, Romeo Dean Published 3 April 2025 AI 2027 report We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution. https://ai-2027.com/ Topics covered AI Agents (Unimpressive and error-prone at first, but starting to replace some employee tasks and companies inserting into workflows regardless. And getting better) Autonomous Coding AI Research (AIs creating better AIs) After being trained to predict internet text, the model is trained to produce text in response to instructions. This bakes in a basic personality and “drives.” For example, an agent that understands a task clearly is more likely to complete it successfully; over the course of training the model “learns” a “drive” to get a clear understanding of its tasks. Other drives in this category might be effectiveness, knowledge, and self-presentation (i.e. the tendency to frame its results in the best possible light). ...

Prompt engineering is a term coined in the 2020s to describe the practice of creating and crafting effective prompts to guide the behaviour and output of large language models (LLMs). Most people ask simple questions and can get simple answers. Somewhat like in the real world, the art of asking questions will bring the best results. Effective prompt engineering involves understanding the model’s capabilities - as well as its limitations - to achieve the most optimal results. ...