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Beyond Autocomplete: AI Prompting Strategies for Software Architects
Software engineering is changing quickly. We are moving from manual coding to using AI to help manage complex systems. As large language models (LLMs) become standard tools, senior engineers and CTOs face a new challenge: how to use AI to learn new architectures, understand legacy code, and check that AI code is actually correct. If you treat an AI like a simple autocomplete tool, you’ll get generic, flawed results. To really get ahead, you need to stop asking simple questions and start using clear, structured instructions. This guide shares practical ways to get more out of AI - because let’s face it, you’re using AI every day anyway. By developing the skills and mindset for effective LLM prompting, you can shorten your learning curve, improve code reviews, and build better tools. How to use AI to learn software architectures When migrating systems or exploring unfamiliar architectural paradigms—such as shifting from monolithic backends to event-driven microservices—engineers frequent
Local-First Computing: The Case Against Cloud, Thin Clients and Age Gates
It’s probably hard to believe but when I started my career as a programmer in the early 1980s there were no networks, no hard disks and no laptops. Everything was done on standalone personal computers like Apple II and later IBM PCs. The only way to get code in or out was by typing it in, or loading it from a floppy disk. Networking didn’t come along until a decade later. What about thin clients? The idea of thin clients has been pushed forward at least on two different occasions, mainly as a way for employers to save money. Instead of developers needing powerful PCs with TBs of storage and 32 GB or more of RAM, the idea is to use a dumb terminal and edit/compile applications on a server. You don’t need to worry about backing up and builds can be automatically triggered and then tested when you commit code. What’s not to like? Well, I for one wasn’t too keen on the idea. About 10 years ago I was invited to try one but it quickly turned out there were a couple of snags: I was in the Uni
Is Your Degree Still Worth It in the Age of AI?
Generative AI has shifted the hiring landscape, sparking ongoing debate among employers and job seekers about whether a traditional computer science degree still holds value. Though degrees remain a powerful signal of structured, algorithmic thinking in the traditional hiring process, the skills gap between credentialed graduates and self-taught, AI-assisted developers is narrowing. As technical vetting pivots away from rigid gatekeeping toward practical, skills-based assessment, how should universities and developers evolve to ensure they’re not just building software but mastering the underlying systems that AI tools can't yet solve on their own? How has the rise of generative AI changed your valuation of a formal computer science degree versus hands-on portfolio experience? Bryan Wall, Senior Competency Leader, Software & Cloud Engineering at Experis, says “because of Gen AI, employers have now shifted to how we think about evaluating talent. Having a computer science degree still s
The Human Edge: Soft Skills That Keep Tech Pros Irreplaceable
As artificial intelligence transforms the tech landscape, traditional "code-first" thinking is quickly dying. While AI can churn out functional code and solve complex technical problems, it lacks the human nuance required to build, manage, and scale real-world solutions that drive actual revenue; something AI is still poor at. But what separates the engineers who thrive in this new era from those who get left behind? Industry leaders from ONLC, The SaaS Jobs, and Praxica.io weigh in on the essential soft skills all technologists need to master, from strategic problem framing to AI oversight. These skills are quickly becoming the true currency for tech professionals who want to remain irreplaceable while the machine minds continue to gobble up the hard work of writing code. What 4-5 soft skills make a candidate irreplaceable by AI? Mitchell Ruebush, Chief Technology Officer and Head of Product at ONLC, gives us his soft-skills wishlist when vetting candidates: Networking - getting the r
From Perk to Prerequisite: Cybersecurity Training Is Now an Operational Requirement
A recent report from research firm Gartner predicts that worldwide spending on artificial intelligence (AI) could top a staggering $2.5 trillion this year, creating significant challenges for IT and cybersecurity teams tasked with understanding how these rapidly evolving AI tools operate while ensuring their safe and secure use. The adoption of AI requires a well-trained and well-educated workforce as more of these technologies move from testing into production environments. At the same time, the growing use of AI increases the risks enterprises and other organizations face, especially when it comes to securing corporate and sensitive personal data used to train large language models (LLMs) that underpin these platforms. ISC2, a nonprofit cybersecurity training organization, finds that with the influx of AI technologies, large and small organizations are investing more in training to keep up, particularly in cybersecurity. An analysis published in June – 2026 Security Training Trends –