Retries seem simple. Just try again.
But retrying the wrong thing, at the wrong time, can turn small failures into outages and duplicate data. This article explains retry strategies for vibe coders, when retries help, when they hurt, and how to design them intentionally before writing code.
Auditing is how systems explain themselves after something goes wrong. It answers who did what, when it happened, and why it was allowed. For vibe coders, auditing often feels like a future problem, but any system that handles users, data, or decisions eventually needs a trustworthy history of actions. This article breaks down auditing as a design concern, not a compliance checkbox, and shows how to think about it before you write code.
Logs record what happened. Alerts tell you when something needs attention. This article explains why alerting and error notifications matter for vibe coders and how thinking about them before you start coding helps prevent silent failures and user-facing issues.
Performance monitoring shows how your system behaves in production, under real traffic and real conditions. This article explains why monitoring matters for vibe coders and what to think about before you write your first line of code.
AI makes coding fast. It does not decide what matters most. This article introduces Trade-Offs & Constraints for vibe coders, explaining why every feature, every design decision, and every prompt comes with consequences. Learn how to define priorities and limits before writing code so your prototype works for you… not against you.
AI can generate code instantly, but it cannot decide how your application should be structured. This article introduces System Design for vibe coders and explains why thinking in components, boundaries, and data flow before you start prompting leads to prototypes that are easier to debug, extend, and scale.
AI coding tools can turn ideas into code instantly, but they cannot decide what your system is supposed to do. This article introduces Requirements Thinking and explains why defining intent, boundaries, and expectations before you start vibe coding leads to better prototypes, less rework, and code you actually understand.
Debugging isn’t about fixing bugs. It’s about understanding systems. In fast-moving, AI-assisted development, guessing your way through failures creates fragile prototypes. This article explains how real debugging works, why it matters more with AI-generated code, and how to build debugging habits before your prototype breaks.
Many AI prototypes blur the line between frontend and backend logic. UI code talks directly to databases, secrets end up in the browser, and business rules are easy to bypass. This often works early on, but it creates security, scalability, and reliability problems as prototypes grow.
