AI

A hammer helps you build. A violin asks you to learn a phrase. AI is closer to the violin. We often hear AI described as a tool, a neutral extension of human intent. Something you pick up, point, and apply. But that language downplays its nature. Tools don’t talk back. Instruments, on the other hand, respond. They shape the way you play, reveal new sounds you didn’t expect, and demand practice to master. If we keep calling AI a tool, we miss the more interesting truth: it’s an instrument. ...

If you reward fluency over truth, don’t be surprised when your AI speaks nonsense beautifully. That is the sobering lesson from recent work on why large language models (LLMs) hallucinate. The research is clear: hallucinations are not mysterious glitches, but the rational outcome of how these systems are trained and evaluated. When the training signal rewards confident answers, models learn to manufacture them—truthful or not. The problem with beautiful nonsense The paper Why Language Models Hallucinate makes a blunt claim: hallucinations arise because LLMs are optimised for being useful and fluent, not necessarily correct. In other words, they are rewarded for looking right more than for being right. That incentive structure guarantees some degree of dishonesty, even if the model has no intention in the human sense. ...

Extending the Experiment I saw this TechRadar article and thought—what would ChatGPT say about “EA”? Spoiler: ChatGPT first assumed I meant Executive Assistant—and honestly, it nailed it. They’re the understated powerhouse behind the scenes. The TechRadar piece that sparked this reads beautifully simple: a Senior Data Analytics Consultant becomes “someone who counts toys and tells people which toys are played with the most.” A journalist turns into a curious storyteller, a management consultant is a fixer of lemonade stands, a CTO is a ship’s technology captain steering through storms. It’s equal parts hilarious and enlightening—stripping away jargon to reveal the human heart of a job. Read it here ...

Context for new readers: This document builds on my earlier blog about vibe coding an RFI where I explored whether a single “vibe coder” could respond to a government Request for Information (RFI) and even begin building the product. Here, I’ve reframed that exercise as a formal project brief, as if written by a product vendor. It also includes an architectural view to highlight what an internal team might consider. 1. Executive Brief (Senior Executive) Context: TEC seeks to add a CV and Cover Letter building capability to its Tahatū Career Navigator site. Purpose: Support learners to create tailored, professional documents integrated into the wider careers ecosystem. ...

Relatively short post today. I came across this amazing tool called Transformer Explainer. It lets you see how a Transformer model (like GPT-2) actually works, layer by layer, right in your browser. You can watch tokens flow through embeddings, self-attention, and MLPs, and even play around with parameters like temperature and top-k to see how text generation changes. Check it out here: https://poloclub.github.io/transformer-explainer/ Written for KiwiGPT.co.nz — Generated, Published and Tinkered with AI by a Kiwi ...

Once upon a time, customer service meant walking into a building. If you needed to pay a bill, change a booking or renew your licence, you went to the branch down the road. Banks, shops, post offices, council offices — all ran on face-to-face chats and paper forms. It worked, but it came with queues, short hours, and a long drive if you lived out in the wop wops. Then came the phone. In the 80s and 90s, private companies jumped on 0800 numbers and central call centres so you did not have to show up in person. Government got on board too, but it was slower going. Often you still had to ring the right regional office, and sometimes that meant calling three different numbers. For private businesses, the phone was a way to win customers. For government, it was more about easing the pressure on the counter. ...

Last updated: August 15th, 2025 How to AI OpenAI Academy Learn Machine Learning with Andrew Ng Veo course Midjourney course Perplexity Labs Claude from A to Z ChatGPT images Gemini prompting Guide to AI agent Deep dive into LLMs

MCP—or Model Context Protocol—is the latest open standard that’s supercharging AI agents by giving them a reliable, universal connection to tools, services, and data. Imagine AI as a savvy assistant… but one stuck in a room with no windows. Until now, every new tool—whether a database, file system, or API—needed a bespoke connection. MCP breaks those walls down. It gives AI the ability to speak a consistent “plug-and-play” language, no matter what it’s connecting to. ...

Summary “A new way of coding is emerging where AI writes most of the code and humans just guide the process — meet vibe coding.” Vibe coding is a buzzworthy approach to software development where developers (and even non-developers) describe what they want in natural language, and a Large Language Model (LLM) generates the code. Instead of meticulously crafting every line, humans focus on testing, tweaking, and guiding the AI’s output—letting the “vibes” steer the process. ...

Enterprise Architecture (EA) has long been about the big picture—vision, strategy, and ensuring the technology landscape doesn’t collapse under the weight of its own complexity. Agile, on the other hand, is all about delivering value fast, embracing change, and avoiding big upfront designs. When these two worlds meet… sparks fly. Agile vs EA — The Friendly (Mostly) Tension From Agile’s perspective: “Why are we waiting for an architecture review when customers want this feature yesterday?” “We value working software over comprehensive documentation… remember?” From EA’s perspective: ...