How I Got Prompt Engineering Wrong At my work, one of my colleagues (whose age shall not be discussed publicly, though we regularly joke he may have been around during the Jurassic age) has unexpectedly become one of the most effective Claude users I know. While I was building carefully engineered prompts full of instructions, formatting rules, context, edge cases, and constraints, he would casually type things like: “Give me everything about topic X.” ...
Here is a real story. Back in early 2025, one of my very AI forward colleagues and I were talking about where all this was heading. He was convinced that by the end of 2026 AI would already have dramatic reach across society. The paranoia was real. Jobs gone. Massive disruption. Entire industries changing overnight. I had a different view. Not because I thought the technology would fail. Quite the opposite. I thought the technology would become capable of doing almost everything surprisingly quickly. ...
I finished high school in Delhi in 1999. That year still sits somewhere in the background of everything that came after it. Not sharply. More like a low hum. The world felt like it was shifting, but nobody had a clear map of where it was going. Y2K was part of that atmosphere. Not in a precise way. More like background tension. Computers might fail. Systems might misread time. Something about the infrastructure of modern life felt slightly untrusted, even if nobody could quite point to where the risk actually was. ...
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?” ...