Task Runner

Accessibility (often abbreviated a11y) is the practice of designing and building websites and apps so they can be used by everyone, including people with visual, motor, auditory, or cognitive disabilities. This involves proper semantic markup, keyboard navigation support, sufficient color contrast, and screen reader compatibility. Beyond being an ethical responsibility, accessibility is increasingly a legal requirement in many jurisdictions.

Smoke testing is a quick, automated verification that the most critical functions of an application work after a new deployment or build. Rather than running the full test suite, smoke tests check essential paths — can the app start, does the login work, do key API endpoints respond. They are typically the first automated gate in a deployment pipeline, catching catastrophic failures before more expensive tests run.

Auto-scaling is the ability of a system to automatically adjust the number of running instances or allocated resources based on current demand. When traffic spikes, new instances are provisioned; when demand drops, excess resources are released to save costs. Cloud platforms like AWS, GCP, and Azure offer auto-scaling groups with configurable policies based on CPU usage, request count, queue depth, or custom metrics.

CI/CD (Continuous Integration / Continuous Deployment) is a set of practices that automate building, testing, and deploying code every time a developer pushes changes. Continuous Integration merges code into a shared branch frequently and runs automated tests, while Continuous Deployment automatically releases validated changes to production. Together they form the backbone of modern software delivery, reducing manual errors and accelerating release cycles.

Media queries are a CSS feature that allows you to apply styles conditionally based on device characteristics like viewport width, height, orientation, color scheme preference, or reduced motion settings. They are the backbone of responsive web design, enabling different layouts for mobile, tablet, and desktop screens. Modern media queries also support user preference detection with `prefers-color-scheme` and `prefers-reduced-motion` for more accessible experiences.

Prompt engineering is the practice of crafting and optimizing input instructions to guide AI models toward producing desired outputs. Techniques include few-shot examples, chain-of-thought reasoning, role assignment, and structured output formatting. Effective prompt engineering can dramatically improve the quality, accuracy, and consistency of LLM responses without modifying the underlying model.