Vibe coding helps junior developers move faster, but it can slow real skill growth when fundamentals stay weak. The term, popularized by Andrej Karpathy in 2025, describes using AI to generate code from simple prompts with minimal review. Many developers…
AI-assisted coding is already changing the job. It speeds up workflows and shifts what developers need to know. The idea of “vibe coding,” popularized by Andrej Karpathy in 2025, captures how people now guide AI with simple instructions instead of…
AI gets stuck because its main job is to predict the next likely word, not to search for the truth. That design choice shapes how it behaves. We have watched models write hundreds of lines of code around one broken…
AI often produces inefficient code because it predicts patterns and aims to pass tests, not to optimize performance. It focuses on what looks correct, not on what runs lean under pressure. In projects we’ve reviewed, this shows up as higher…
AI-generated code needs structure and oversight to stay maintainable. It should be treated as a starting point, not a final deliverable. When teams assume it’s production-ready, refactoring costs rise and technical debt builds quietly. The safer approach is simple: enforce…
We’ve hit a wall. The current generation of AI models, for all their dazzling fluency, are fundamentally limited. They cannot reason, they often lie with confidence, and their growth is slamming into physical and economic barriers. This isn’t a minor…
Yes, over-reliance on AI is a real and present danger. It’s not science fiction, it’s happening right now in hospitals, trading floors, and your own web browser. The core peril is automation bias, the uncritical acceptance of AI outputs because…
AI-generated code often fails in predictable ways—calling wrong APIs, creating security gaps, or breaking in production despite looking fine in review. AI speeds up development, but without structure it adds risk. This guide shares a simple, practical approach to secure…
If-else logic doesn’t scale. Piling conditions on conditions creates brittle systems that crack as soon as new data appears. Every exception demands another rule, and each rule raises maintenance cost, slows execution, and obscures intent. Over time, teams stop understanding…
AI generates duplicative classes because it’s mimicking the repetitive patterns in its training data, and its token-by-token generation can get stuck in loops. The model isn’t thinking about architecture; it’s statistically predicting the next most likely piece of code, which…