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Is it time to move from Windows to Linux?
I’ve been developing Windows apps since Windows 3.1, back in the 90’s when the first program I wrote was an energy tracking screensaver along with a DOS TSR (Terminate Stay Resident) equivalent. In those days you booted into DOS first then started Windows with a command. This was before Visual Basic and Delphi existed, when you wrote Windows apps you had to explicitly code everything including font handling, display contexts and message loops. It was quite fiddly. A friend had got into Linux, but as far as I can see he only ever used it to benchmark his hardware by measuring how long it took to build Linux from source. Then around 2011, after fifteen years of working on Windows, I found myself needing to test some C code. MS VC was fine but Microsoft was a bit behind the curve with C99 support and I learned enough Ubuntu to let me install and run gcc and clang C compilers. When I started doing web development, it was easier to setup a local test web server on Ubuntu. I still program on
The Trick to Bulletproof AI Code? Context Engineering
As software engineers, we’ve all been told that the secret to unlocking LLMs lies in the art of the perfect prompt. We’ve experimented with role-playing cues, chain-of-thought instructions, and increasingly complex prompt templates. But treating AI like a magic syntax box misses the broader reality of production engineering. The truth is, isolated prompts fail in complex, real-world systems because they lack architectural awareness. If we want predictable, production-grade output, we have to stop focusing on clever phrasing and start focusing on context engineering—structuring our codebase, metadata, and testing loops so the model actually understands the system it's contributing to. Moving beyond basic code generation requires treating AI not as an oracle, but as a junior developer who needs strict system constraints, modular task sizes, and comprehensive execution contracts to be effective. Should engineers stop worrying about "prompting" and start focusing on "context engineering"—h
Mythos Changed Cybersecurity, Here's What You Need Now.
As Anthropic’s Mythos and other platforms change cyber operations and reshape enterprise security, cybersecurity pros need new skills to keep up. Industry leaders note that AI fluency, human oversight and operational expertise will become critical. In a few short weeks, Mythos upended the cybersecurity community. On April 7, artificial intelligence firm Anthropic announced its latest large language model (LLM), dubbed Mythos, which the company claims has advanced cybersecurity capabilities to detect vulnerabilities – including zero-day flaws – across a variety of applications and operating systems. In one case, the company detailed how the Mythos model found a 27-year-old flaw in the OpenBSD operating system. Anthropic also noted that this LLM can find vulnerabilities in various applications and convert them into active exploits. With this potential set of powerful capabilities, Anthropic originally planned to limit access to Mythos to a handful of large companies and organizations, in
Latin America Emerges as Key Hub for AI Talent
Enterprises are rethinking how they source AI talent as hiring challenges intensify, with a growing number turning to nearshore teams in Latin America (LATAM) to close experience gaps and accelerate delivery. A report from CI&T finds more than three-quarters (76%) of technology leaders plan to increase nearshore hiring over the next two years, signaling a shift away from traditional offshore models built primarily on cost efficiency. The change reflects a broader recalibration of priorities as AI moves from experimentation to execution. While offshore models once optimized for lower labor costs, AI development is placing a premium on speed, alignment and real-time collaboration. Ninety percent of respondents said they have high confidence in LATAM AI talent, and 60% reported already shifting spending toward the region. The core issue is not just a shortage of talent, but a shortage of applied experience. “The toughest AI roles to fill domestically aren’t about job titles, they’re about
Is ‘vibe coding’ the future, or a trend? We ask the experts
AI is intriguing, and powerful. It has mesmerized the tech world with its ability to spring up apps, services, websites, and more via a simple prompt. But ask it to scale those same apps and services to handle thousands of concurrent transactions, and the limitations of natural language prompting quickly become obvious. Though "vibe coding" has democratized software creation, a growing chorus of industry veterans warns that we are conflating raw output speed with disciplined, educated engineering prowess done by real humans. AI-generated code introduces security vulnerabilities that require deep, senior-level architectural insight and experience to catch. The line separating a high-skilled engineer from a low-skilled prompt-engineer isn't the tool they use; it’s the repeatable, rigorous process that is followed. Where do you draw the line between where "vibe" ends and "engineering" begins? Jeff Jack, former PM lead at Tesla, says “I think the ‘vibe’ part comes up when you're just pokin