उलझे नहीं तो कैसे सुलझोगे, बिगड़े नहीं तो कैसे संभलोगे If you never get tangled, how will you learn to untangle? If things never go wrong, how will you learn to set them right? I was part of three AI trials inside a New Zealand government agency. All three gave us invaluable lessons. Out of those three, one progressed quite a lot, through four stages of development, and has shown genuine promise. ...
Your organisation has a document management system. Maybe it’s called Objective. Maybe it’s TRIM. Maybe it’s a home-grown records platform that nobody wants to touch because the person who built it left in 2014. Whatever it’s called, it holds years of institutional knowledge. Briefings, reports, decisions, correspondence, approvals. The kind of material that makes new work possible — if you can find it. You can’t. Not quickly, anyway. And that’s the problem this post is about. ...
Vibe coding is fun. It’s fast. It feels like magic. But unmanaged magic in a production environment? That’s how you get security gaps, compliance headaches, and a quiet email from the risk team asking for a “quick chat”. There’s a better way. For complimentary app development — dashboards, internal tools, workflow helpers, automation layers — managed vibe coding can dramatically increase output without increasing exposure. The trick is simple: structure the chaos. ...
2025 wasn’t the year AI got smarter It was the year our thinking got exposed. By the end of 2025, most conversations had moved past: “Can AI do this?” To harder questions: “Should we trust this?” “Who owns this decision?” “What happens if this is wrong?” What surprised me wasn’t what AI could do. It was what it revealed about how people think when AI is in the room. ...
When the tech world bursts into hype mode, valuations soar, promises multiply, and eventually someone asks: does this actually work? That’s the moment when hype meets the bubble. Artificial intelligence has reached that point — glowing headlines, soaring investments, and a rising hum of scepticism in the background. The thing is, hype isn’t all bad. It’s the spark that gets things moving. Every big leap — from the steam engine to the smartphone — started with an overexcited crowd shouting, this will change everything! And they weren’t entirely wrong. But the danger comes when excitement turns into blind belief. ...
When we talk about artificial intelligence, we often think about how well it remembers — data, facts, styles, even our writing tone. But what happens when we need it to forget? That’s the tricky challenge of AI unlearning, and a new paper — Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning — offers a smart new angle on how to do it. Why forgetting matters As AI models grow larger, they absorb massive amounts of information — some of it private, copyrighted, or simply outdated. From user data that should have been deleted to training examples that contain bias or sensitive facts, keeping everything isn’t always safe or ethical. ...
Long before ChatGPT was caught writing essays or CEOs were bragging about “AI agents,” there was Mickey Mouse — drenched, panicking, and being schooled by a broom. Fantasia’s “The Sorcerer’s Apprentice” isn’t just a cartoon. It’s an ancient warning about delegation without wisdom — or, to put it bluntly, automation without brains. The Old Story, Fresh Eyes 👀 Picture it. The workshop hums with quiet power. The old sorcerer steps out, leaving his apprentice, Mickey, with one boring task: fetch water. Mickey looks at the heavy buckets, looks at the spellbook, and has a brilliant idea — “Why not get the broom to do it?” ...
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 ...