Dev Learning Habits: AI Tools vs Documentation

Two readers asked the most important question in software development right now: "If AI writes the code, how do I build real skill?" The answer is more optimistic than you might expect. Hint: the bar didn't get lower. It got higher. And that is very good news for developers who take their craft seriously. Episode 2 of The Secret Life of Claude Code is live: 👉 https://lnkd.in/gyia4XYa #ClaudeCode #AIEngineering #SkillDevelopment #CodingWithAI

I've been diving into the recent surge in AI-driven code generation tools and have mixed feelings. On one hand, the efficiency they offer can be a game changer, especially for repetitive tasks. However, I worry that relying too heavily on them might hinder our growth as developers. It's essential to strike a balance. While these tools can help us push through the mundane aspects of coding, we also need to ensure we're continually honing our skills and understanding the craft deeply. I’d love to hear how others are integrating these tools into their workflow while still prioritizing skill development. #techdebate

while coding with AI, I spend 90% of the time for planning. then ask AI to build it. my vibe code just works, and solves real business problems. dear vibe coders, learn system design and core software architecture. AI coding is the future, but with experience that you have to earn through years of working.

Quick question for school leaders: If 90% of software developers will be using AI coding assistants by 2026 (according to GitHub), shouldn't we be teaching students how to work with these tools? Not someday. Now. AI Code Skills #AIEducation #EdTech #FutureOfWork

My blog series The AI-Ready Software Developer is 20,000 words of things most people evidently don't want to hear about AI-assisted software engineering. Grounded in hard evidence and gleaned from 3 years of experimentation, reading and research. Yeah, I know, right? I'm like James Randi at a spoon-bending convention. But if you're genuinely interested in what's real and what works, you could do worse. https://lnkd.in/eFsTzUMY

Rare evidence-based advice on using AI to help create software. Recommended reading from Jason Gorman. I hear engineers and managers say “everything has changed!” But the evidence doesn’t show that. Instead it turns out that if you don’t want AI to really mess up your ability to deliver with speed and quality, your team needs to do exactly the same things required to deliver with speed and quality *before* AI: iterate in small batches, modularize rigorously, test continuously, refactor continuously, integrate continuously, focus on bottlenecks. Jason spells it out beautifully in this series. Of course, as the DORA research has shown, few engineering teams have taken this advice over the years to achieve the high performance from continuous delivery. For those that don’t, we’re seeing that AI is making matters *worse*, not better. To me it means these time-tested practices are more urgent than ever.

Given how much AI is writing my code these days, I’ve realized something interesting. I might be a part-time software engineer. But I’ve always been a full-time pattern recognizer. Coding was never just about syntax. It was about noticing patterns in systems, behaviour, and problems. AI may generate the code, but recognizing the right patterns is still the real work. #ArtificialIntelligence #SoftwareEngineering #PatternRecognition #ProblemSolving #EngineeringMindset #FutureOfWork

Something interesting is happening in software development right now. For years, the main focus in programming was writing code as efficiently as possible. But with the rise of AI coding tools like Claude Code, it feels like the role of the developer is slowly shifting. Instead of only writing code, developers are increasingly: • guiding systems • reviewing generated solutions • thinking more about architecture and design • validating outputs instead of producing everything manually In a way, programming is becoming less about typing code and more about understanding systems and making good decisions. As a computer engineering student, I find this shift fascinating. It makes me think that learning how systems work and how to reason about problems might become even more important than memorizing specific technologies. Curious to see how development workflows evolve over the next few years. #AI #SoftwareEngineering #DevTools #ComputerEngineering

No wonder AI is reshaping the software development process, but does this mean traditional problems solving skills are no longer needed ? I strongly believe that the ones who deeply understand coding will always be valuable. Fundamentals still matter more than ever for the long-term success.

I periodically see posts about how software engineers lose the ability to write code after they start using AI. This sounds really weird to me, because I went from software engineering to management in 2005 and during the last 20 years I almost never wrote production code, but still could write it if needed (or for pet projects). How using AI makes you forget coding and giving tasks to engineers doesn't, I can't understand.