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The Hunt for the Magnetic Monopole

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Cut a bar magnet in half. Do you get one piece that is purely north and another that is purely south? In the workshop, the answer is no. Nature never hands you an isolated pole. You get two smaller magnets, each with its own north and south. Keep slicing down to a single atom and you still have a dipole, never a lone pole. Physicists have hunted for the exception for nearly a century: the magnetic monopole, a hypothetical particle carrying a single magnetic pole. Finding one would do far more than settle a bet. It would restore a missing symmetry in the laws of electromagnetism. This article explains why physicists expect monopoles to exist, why one famous sighting still haunts the field, and where the search stands today. The Great Asymmetry: Electricity vs Magnetism Magnetic monopoles matter because of magnetism’s twin: electricity. Electricity and magnetism are two sides of the same coin: electromagnetism. Yet their charges behave very differently: Electric charges exist...
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On Creativity

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I recently read several pieces on AI and mathematics, including Stephen Wolfram's essay on the future of pure math research , Scientific American on the role of mathematicians , and the World Science Festival podcast AI Creativity: Genius or Gimmick? . This got me thinking about the nature of creativity itself. The real question isn't whether AI can produce novel work. It's whether we keep moving the goalposts of creativity just to keep humans at the centre of the story. The truth is, we don't have a single, settled definition of creativity. But whenever a machine masters something we thought was uniquely ours, our instincts kick in. We begin insisting that true creativity demands consciousness, intention, emotion, or lived experience. In other words, we stop judging the work on its merits and start judging the mind behind it. The AI Effect and the Moving Goalposts We've seen this pattern before. In computer science, it is known as the AI Effect —or as Tesler...
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Building a DNA Assembler in Haskell: 5 Algorithmic Insights

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A Puzzle Without a Picture Genome sequencing faces a fundamental physical constraint: modern instruments cannot read a long chromosome continuously from end to end. Instead, they synthesise or capture millions of short sequence fragments called reads. These range from 150 base pairs in high-throughput short-read chemistry to tens of thousands of base pairs on long-read instruments—and well over a million base pairs with Oxford Nanopore ultra-long protocols. Reconstructing a complete genome from these fragments is like solving a multi-million-piece jigsaw puzzle without the picture on the box top. To tackle this challenge, I turned to graph-theoretical assembly frameworks. The Overlap-Layout-Consensus (OLC) paradigm is one of the classic, most intuitive ways to stitch fragments into continuous sequences called contigs. While building reconstruct-strings —a pure and total Haskell implementation of a greedy OLC assembler—I set out to explore sequence reconstruction from first princ...
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First Look at Omarchy Linux

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AI-First Operating System I've spent the past month with Omarchy , and the short version is this: it is a fresh, lightweight Linux distro built for an AI-native workflow. Created by David Heinemeier Hansson (DHH) , Omarchy is unapologetically opinionated. Instead of a blank canvas, it offers a curated 'omakase' experience designed for the age of AI. The premise is simple: if you can use AI to generate an app quickly, you should be able to shape your entire operating system around the same workflow. I recently revived a 12-year-old ThinkPad X1 Carbon with Omarchy, and the difference was palpable. The machine felt notably snappier, more coherent, and more useful. All the hardware was fully recognised, including the fingerprint scanner. That is not a scientific benchmark, but it is exactly the sort of result that matters to a real user. Installation as a Speed Run One of the most striking things about Omarchy is how fast it gets you from power-on to a usable desktop. Th...
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The Fractured Balance Sheet of Globalisation

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Globalisation gets blamed for almost every modern headache: hollowed-out factory suburbs, flatlining wages, and angry politics. At the same time, mainstream economists often speak about open trade as an unqualified miracle that lifted millions out of poverty. The truth sits in the middle. Global trade creates immense wealth, but it comes with a catch: the perks are spread thin across millions of shoppers, while the costs slam into specific suburbs, factories, and working families. To understand the political backlash tearing through Western democracies, we need to look past the talking points and audit who actually won, who lost, and what it cost. The Economists' Promise Mainstream economists have championed open trade since David Ricardo introduced comparative advantage in 1817. The basic logic is simple: when countries specialise in what they make best, overall output surges and prices fall. Bigger markets allow businesses to scale, capital flows to where it works hardest,...
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seL4: Trust by Proof, Not Hope

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The Fragility of Modern Foundations Almost everything in our modern world runs on software stacks that are terrifyingly fragile. From medical infusion pumps and power grids to autonomous vehicles, the systems we trust with our lives routinely depend on operating system kernels containing tens of millions of lines of code. In that kind of monolithic architecture, a single misplaced pointer or buffer overflow in a peripheral driver can compromise the entire machine. For decades, we have treated software security as an endless game of whack-a-mole: patch a vulnerability, ship an update, and hope attackers do not find the next hole first. The seL4 microkernel takes an entirely different path. Rather than trying to make a sprawling code base slightly less vulnerable, it strips the kernel down to a minimal core of roughly 10,000 lines of C. More importantly, it replaces wishful thinking with mathematical proof. It is not just another operating system; it is a demonstration of what soft...
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Will AI Energy Demand Taper Off?

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Anyone following the AI boom will have heard two arguments pulling in opposite directions. The first says hardware and algorithms are improving so fast — chips getting quicker per watt, models shrinking through quantisation and distillation, software stacks tightening at every layer — that energy use should eventually level off. The second says the opposite: tech giants are signing multi-gigawatt power deals, bringing retired nuclear plants back online, and forecasting demand curves that only go up. I wanted to work out which story actually holds, so I went through the industry data myself. My conclusion is that the second camp has it right. Efficiency keeps improving, but it is not enough on its own. Total demand is going to keep accelerating for the rest of the decade. Why Efficiency Doesn't Mean Less Energy The reason the efficiency argument falls short has a name: Jevons' Paradox . Back in 1865, the economist William Stanley Jevons noticed something counterintuitive ab...
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