Search for a command to run...
Series
AI coding assistants like GitHub Copilot and ChatGPT promise faster development, but they often hide subtle pitfalls that can snowball into serious technical debt. In this series, I’ll break down the 9 most common traps developers fall into when relying on AI-generated code—from misleading abstractions to silent performance issues—and show you how to avoid them. Whether you’re a beginner experimenting with AI
The way you phrase your request determines the quality of AI’s output. Most developers treat AI like a search engine—asking vague questions and accepting the first answer. In this series post, we’ll e
AI can generate code faster than any human, but it doesn’t understand your business logic, your data, or your quality standards. In this post, we cover five critical validation mistakes that lead to u
AI models are trained on vast amounts of public code, which often includes insecure practices. Without careful prompting and review, AI can introduce critical security vulnerabilities. This post cover
When production is on fire, AI can seem like a lifeline. But using AI carelessly during an incident often makes things worse. This post covers five mistakes developers make when using AI to debug or f
AI is great at generating functional code, but it often misses performance considerations. The result can be slow endpoints, database overload, and wasted cloud costs. This post covers five common per
AI can generate tests quickly, but quantity doesn’t equal quality. Many AI‑generated tests either assert the wrong thing, miss edge cases, or are not idempotent. This post shows you five testing pitfa
