Not everything in a traditional software engineering curriculum is equally worth your time in 2026. Some things AI handles well. Others are more important than ever. Knowing the difference matters.
A decade after microservices became mainstream, the pattern is well-understood in theory and still routinely misapplied in practice. Here is what the field actually knows and where teams still get it wrong.
Security integrated into the development process sounds obvious when stated. What it means in practice - the tooling, the workflow changes, the responsibilities - is less obvious.
Edge computing has moved from concept to infrastructure. Understanding when to run logic at the edge versus centrally in the cloud is now a practical architectural decision, not a theoretical one.
AI tools change how code is written. They do not change what makes a pull request good. But they do change the failure modes - and what reviewers need to watch for.
When AI can write the code, the decisions that matter most are the ones that happen before the code is written. System design was always important. Now it is the primary leverage point.
What does senior actually mean when AI handles the mechanical parts of coding? The bar has not lowered - it has shifted. Here is what matters now.
AI coding assistants make you faster. They can also make your codebase worse if you use them without a framework. The difference is in how you review and own what gets generated.
Andrej Karpathy coined vibe coding to describe accepting AI output without deep engagement. The mode is legitimate for low-stakes work. The risk is when it becomes the default - and you stop noticing the gradual loss of understanding.
GitHub says AI is writing nearly half of all code on the platform. As that number climbs, the developer role is not disappearing - it is shifting to things AI cannot do.
How the three main approaches to memory management work, what they cost, and why the choice of memory model shapes the entire character of a programming language.
Why engineers struggle to communicate technical problems to non-technical stakeholders, and the frameworks that make explanations clear without sacrificing accuracy.
