Introduction In today’s competitive market, small business owners need to make quick, informed decisions to stay ahead. Artificial Intelligence (AI) offers powerful tools to analyse data and uncover insights, helping owners understand their businesses’ needs. One such technique, Retrieval-Augmented Generation (RAG), combines data retrieval with natural language generation to provide accurate, context-specific answers.

🎮 Introduction: Why Game Asset Creation Is the Hidden Bottleneck in Development Game development is an intricate and deeply creative endeavour—a fusion of interactive systems , emotional storytelling , mechanical precision , and visual immersion . Whether you’re building a cosy farming simulator, a cyberpunk platformer, or an open-world RPG, visual

Introduction In the ever-evolving landscape of software development, AI-powered code assistants have emerged as game-changing tools that are revolutionising how developers write, debug, and maintain code.

Introduction The global mental health landscape faces staggering treatment gaps.

Introduction AI is changing everything, from chatbots that talk like people to cars that drive themselves. But while big tech hubs in North America, Europe and China race ahead, much of the world is stuck on the sidelines. In many low-income regions, people lack the computers, the internet or even the right data to join in. This gap in technology access is creating a kind of “digital colonialism,” where billions miss out on AI’s benefits.

Introduction Code generation powered by large language models is reshaping how developers write software. OpenAI’s GPT series and Meta’s open-source Llama 4 are two leading options in this space.

1 Overview OLMo 2 1B is a new generation of open-source language models launched by the Allen Institute for AI, with a parameter scale of about 1 billion. It provides excellent natural language understanding and generation capabilities while maintaining a small size, and is suitable for a variety of downstream tasks.

Introduction The rise of artificial intelligence in code generation and problem-solving has transformed the way we approach both technical and creative tasks. From automating repetitive coding chores to providing insights into complex problem domains, AI tools have become essential companions for developers, researchers, and learners alike.

Introduction In today’s rapidly evolving AI landscape, conversational agents have become increasingly sophisticated, transitioning from simple text-based interactions to more natural, voice-enabled experiences. This blog post explores how to create an intelligent chatbot that not only understands and responds to text input but also speaks its responses aloud – all without writing a single line of code.

Understanding its Capabilities and Design

Welcome back, readers! In my previous post, I guided you through setting up an LLM pipeline using n8n and Ollama, all hosted locally (catch up here: How to install n8n and create an LLM pipeline using Ollama & Docker! (For Windows)). Today, we’re elevating that foundation by creating an automated email system that integrates n8n’s AI Agent with Outlook.