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.
The promise of AI tools is that they handle cognitive work for us. But there’s a cost that’s easy to miss: when you let AI do your thinking, your thinking skills erode. The good news is this isn’t inevitable. You can use AI in ways that strengthen your critical thinking rather than replacing it. The key is shifting from “human in the loop” to “human as decision owner” and asking yourself one simple question: can I defend it?
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 agents fail in production at twice the rate of traditional IT projects – not because the technology doesn’t work, but because we deploy them wrong. The pattern repeats: impressive demo, promising pilot, production disaster. The problem isn’t capability. It’s autonomy without context. This article examines why agents fail when given broad authority without explicit boundaries, and what a better deployment architecture looks like.
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.
Most LLM advice focuses on what to type. But the real issue isn’t your wording; it’s understanding how these models decide what to say. This article introduces 22 patterns for getting past polite nonsense and toward genuinely useful AI interactions.
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.
