Blast radius is the measure of how far a single change can impact your system. In vibe-coded applications, hidden coupling and shared dependencies often cause small changes to trigger large, unexpected failures. This article explains how blast radius forms, why it matters in modern systems, and the practical habits that help you contain impact and build safer, more predictable software.
Documentation as you go is the practice of capturing decisions, context, and system understanding while you are still building. Vibe-coded systems move fast, but without lightweight documentation, important context is quickly lost and systems become harder to understand and maintain. This article explains how to document effectively during development without slowing down, and how small habits can preserve clarity over time.
Right-sized prompting is the difference between AI that works consistently and AI that produces incomplete or confusing results. Vibe coding makes it easy to ask for too much or too little in a single prompt, which leads to errors, rework, and unpredictable behavior. This article explains how to size prompts correctly, structure work in layers, and build more reliable AI-driven systems.
AI-generated code can make you move fast, but it can also introduce hidden risks if you accept it without review. Vibe-coded systems often rely on code that works on the surface but includes inconsistencies, missing edge cases, or security gaps. This article explains how to evaluate AI-generated code in a practical way so you can keep speed without sacrificing stability.
Prototypes are fast, messy, and constantly changing. Production thinking is the mindset that ensures your system can survive and thrive in the real world. This article shows vibe coders how to anticipate failure, maintain data integrity, monitor behavior, deploy safely, and secure their systems without slowing down iteration.
Speed is a core strength of rapid prototyping, but every shortcut, quick fix, and temporary workaround creates technical debt. That debt is not inherently bad. It becomes a problem when it is invisible and unmanaged. This article explains how vibe coders can take intentional shortcuts, document tradeoffs, and manage technical debt without slowing down iteration or over-engineering early-stage systems.
Prototypes often work perfectly in the happy path but fail in unexpected ways when real users or systems push them to the edge. Edge case thinking is about anticipating unusual inputs, rare scenarios, and partial failures before writing a line of code. This article shows vibe coders how to identify edge cases, plan fallback behavior, and test unusual conditions to make prototypes more resilient and predictable.
