Exploring the AI revolution: large language models, coding assistants, robotics, developer workflows, and the rapidly evolving landscape of artificial intelligence. This section covers tools, news, critical analysis, and perspectives on how AI is transforming technology, work, and society.
The pace of AI development over the past three years is genuinely unlike anything in recent economic history. The Stanford AI Index has tracked frontier model capability roughly doubling on a yearly cadence, and private AI investment has reached levels that dwarf the dot-com peak in inflation-adjusted terms. What’s less widely understood is what that pace actually means for competition, investment, and the structure of the economy. The Build Time Collapse It’s not just that AI is writing code faster. Build times are collapsing across the entire software stack - design, implementation, testing, deployment - and that changes the rules of competition. ...
Most predictions about the future of AI fall into two flavours. One camp says we are months away from machines that can do everything a human can do, and we should brace for either paradise or extinction. The other camp says the whole thing is a bubble, the models have plateaued, and in five years we will be talking about something else. Both are wrong, and both are wrong for the same reason. They are trying to forecast a single headline event - arrival of AGI, collapse of the hype - when the actual future of AI is not an event. It is a slow, uneven transformation of how ordinary work gets done. ...
AI cloud subscriptions have fragmented into a crowded market. Frontier-lab APIs compete with open-weights challengers, consumer chat plans compete with agent platforms, and every provider is reshuffling model tiers every few months. This guide organizes the 2026 landscape so you can pick a plan without reading six pricing pages. For background on how these costs behave over time, see Token Economics: Why Costs Aren’t Going Down and Local vs Cloud AI in 2026. ...
TL;DR Best value: Mac Studio M4 Max at $1,999 for most local LLM work Best prefill speed: DGX Spark at $4,699 (3.8× faster prompt processing) Best token generation: Mac Studio M3 Ultra at $3,999 (819 GB/s bandwidth) Best for fine-tuning: DGX Spark (CUDA ecosystem wins) Best combined setup: DGX Spark + M3 Ultra = 2.8× faster than either alone Introduction The market for personal AI supercomputers has exploded in 2025-2026. Two standout options have emerged: NVIDIA’s DGX Spark and Apple’s Mac Studio lineup. Both promise desktop-scale AI compute, but they approach the problem very differently. This guide breaks down the specs, costs, and real-world performance to help you decide which is right for you. ...
The AI landscape is evolving rapidly, and knowing where to find reliable guidance on best practices has become essential for developers, researchers, and organizations. This post curates the most valuable resources and practices that will help you work more effectively with modern AI systems. Key Best Practices to Master Prompt Engineering Fundamentals Clear, specific prompts produce better results than vague requests. The foundation of working with any LLM is understanding how to communicate your intent precisely. Break complex tasks into smaller, manageable steps and provide context about what success looks like. ...
TL;DR Best entry point: M2 Max 32-64 GB (~£1.4k-£2k) for 7B-13B models at 25-40 tok/s Best sweet spot: M2 Ultra 64-128 GB (~£3k-£4.5k) handles 30B+ models comfortably Best for 70B models: M3 Ultra 128 GB+ (~£5.5k+) with 800+ GB/s bandwidth Newer alternative: M4 Max (£2k-£4k) - lower bandwidth (410-546 GB/s) than Ultra chips, but still solid for 7B-13B models Key rule: Memory bandwidth matters more than raw compute for token generation Reality check: A RTX 5090 rig is 2-3× faster for similar money - buy Mac for simplicity and unified memory You want to run large language models locally on a Mac Studio. Good idea - unified memory is genuinely useful for LLMs. But the specs matter, and there are some hard truths about what “works” versus what feels responsive. More importantly: the right Mac depends entirely on which model you want to run. ...
If you’re paying attention to your Claude usage, you’ve probably noticed something: your token bills don’t scale linearly with your productivity. Sometimes a conversation that feels quick costs three times more than expected. Other conversations that took hours feel suspiciously cheap. The difference isn’t randomness. It’s a mental model problem. The Problem With “Just Ask” Most people treat Claude like a search engine with a long context window. You dump information, ask a question, wait for an answer. If you don’t like it, you ask again. Iterate until satisfied. ...
Design-to-development handoff has always been a friction point. Designers create something beautiful. Engineers interpret Figma specs, argue about spacing, squint at color values. SVG assets get lost. Responsive behavior gets reimplemented. By the time the code matches the design, half the polish is gone. Claude Design, Anthropic’s new design collaboration tool, attacks this problem directly. Instead of designers creating static files that engineers have to decode, Claude Design lets both sides work in the same tool - with Claude as the bridge. ...
Opus 4.7 is a meaningful step forward. Not a revolutionary rewrite, but a targeted upgrade that addresses friction points developers actually experience: vision quality, autonomous task handling, and creative output. The headline feature is deceptively simple - images up to 2,576 pixels. That’s 3.75 megapixels, roughly three times the previous limit. In practice, this means Claude can now read a dense screenshot without losing details, extract data from complex charts without ambiguity, and handle UI testing images that show real context instead of cropped fragments. ...
We’ve embraced the future. AI agents like Cline are now the primary “builders” of software, executing complex engineering plans from high-level specifications. As I’ve argued in “The Architect vs The Builder”, the human role is shifting from execution to architectural oversight and defining intent. But this shift introduces a profound, often uncomfortable, question: How do we know it actually works? In a world where AI is writing the code, generating the data, and even orchestrating deployments, traditional notions of testing and reliability are breaking down. AI reliability is weird, and it demands a complete re-evaluation of our verification strategies. ...