Frankly speaking ...

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...

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...

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,...

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...

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...

Linux is celebrating its 35th birthday , and commentators are once again asking if 2026 will finally be the "year of the Linux desktop". For the broader industry, that phrase has been a running joke for decades. But for me, the answer is much simpler: Linux has been the right environment for my work for years, and I have never really looked back. My own journey with Linux began in 1999, when I set out to build an open source development environment. That was a decision driven by my professional work. In those early days, I was focused on development tools and machine performance. I tuned kernels, adjusted desktop settings, and stripped systems down to maximise the performance of my modest hardware. I moved between RPM- and DEB-based distributions, and I even spent a rewarding period on FreeBSD. It was a good learning experience, and it was fun. That sense of fun is often overlooked, but it matters. As a developer, having access to a broad range of command-line tools and...

A Reader Pushes Back Why Lean? argued that formal methods are the industry's answer to a world where testing can no longer keep up with the cost of failure. A reader, commenting on that post , pointed out something the post had left out entirely: sometimes bugs survive not because testing failed to catch them, but because nobody was ever going to fix them. That comment, and the conversation it started, turned into more than a reply. It was the second half of the argument. The Bugs We Choose Not to Fix The reader's point was blunt and specific: a company they had just left refused to fix known bugs because the market and investors wanted to see year-on-year growth, and growth comes from shipping new features, not from fixing what already exists. Bugs get "fixed" only when a client trips over one in production. In effect, the user base becomes an informal, unpaid QA layer — crowdsourced testing whether they like it or not. This is a different failure mode from ...

The Real Issue Is Trust Modern society runs on software that is increasingly essential and increasingly fragile. The problem is not simply that code has bugs. The deeper issue is that we have built a digital civilisation on systems whose correctness is assumed rather than demonstrated. The examples are familiar, and they are getting more costly. In 2025, a routine firewall upgrade caused the Optus emergency calling outage : ordinary calls were automatically rerouted to another network without incident, but Triple Zero calls were silently left undelivered for hours. The fault was narrow, yet it landed precisely on the one function that mattered most, raising serious questions about how deeply society depends on systems we assume are safe and reliable. In 2026, the Telstra outage brought widespread disruption across a continent, showing how a single infrastructure failure can cascade through communications, commerce, and daily life. In a world where a single logic error can disabl...

It is a comfortable belief: AI is just a mirror. Critics often dismiss large AI systems, especially large language models, as statistical parrots that reflect our words, images, and ideas without any deeper agency. They argue that such systems cannot come up with new ideas. I think that is wrong. The question is not whether AI has consciousness or human-style understanding. The question is whether it can see associations and links that humans often miss. Human civilisation also advances through copying and recombination. We build on what came before. AI does something related, though in a different manner. It notices connections across vast bodies of data and turns those relationships into outputs that can feel surprising to us. In this essay I will look at several examples — a Go match, a mathematical proof, a rocket engine, a strange portrait, and a drug candidate — where I think AI has done something novel and interesting. Move 37: A Strategy That Broke Human Intuition In Mar...

Introduction: The Informational Universe We typically perceive our world through the lens of tangible matter and flowing energy. We see a landscape of atoms, forces, and heat. However, modern science is beginning to realise that these physical properties might merely be the surface of a deeper architecture. At its most fundamental level, the universe appears to be composed of “bits” of information. Consider the extraordinary feat of a single microscopic cell: how does it “know” how to construct a complex human being? The answer lies not just in chemistry but in the sophisticated processing of data. By shifting our perspective to information theory, we can begin to decode the underlying software of existence and see ourselves as nodes within a vast, self-optimising system. This article follows that idea from first principles to wider systems. It moves from Shannon’s formal definition of information, through biology and thermodynamics, then into active theoretical debates in physics...