Write a clear, versioned specification before asking an AI to implement a feature so the AI has a single source of truth and won’t make inconsistent architectural or security choices.
Use purpose-built SDD tooling that fits your workflow and codebase; tools that produce spec deltas, a living spec, and an auditable archive make it easy to resume, verify, and evolve work.
SDD reduces rework and improves cross-role review, but it has costs — don’t use it for trivial fixes or pure prototyping, keep specs lean, and watch for spec bloat, drift, and review fatigue.
Embrace AI as a core tool — it makes you a faster, more effective engineer and not using it will leave you behind.
Shift your focus from typing code to higher-level software and product decisions like architecture, design principles, and trade-offs, because human judgment matters more than implementation now.
Invest in better workflows: manage context and memory, use multi-agent tools for reviews and refactoring, keep tests and documentation current, and choose models by cost and complexity.
AI is making developers more productive, but it's also slowing down software delivery. This means while developers can code faster, it doesn't always translate to quicker releases.
Larger changes in software deployment can be riskier and slower. It's often better to make smaller changes that are easier to manage.
The speed of AI adoption might be leading to short-term delivery issues, but organizations might eventually find better ways to balance productivity and delivery as they adapt.
Conditionals in code can be refactored into polymorphic behavior for better maintainability.
Applying the Strategy and Template Method design patterns can simplify complex algorithms and improve code structure.
Refactoring procedural code into object-oriented code not only improves functionality but also forces better naming conventions based on domain knowledge.
Serverless architectures reduce user wait time by storing information on disk and processing it later.
Architectural complexity increases in serverless architectures due to processing split in smaller functions, compared to monoliths.
Using a serverless architecture is influenced by application diversity, with more diverse applications leading to increased complexity and potential failures.