If you want to avoid unnecessary complications, it’s crucial to clearly specify that in your prompts when working with AI. Communication is key—don’t just stand on the sidelines expecting simpler outputs; guide the AI by being explicit about your preferences.

With the rise of AI coding tools, verbose and overly complex coding patterns have become more common. From what I’ve observed, most popular AI models tend to favor adding extra classes, functions, and code blocks that don’t necessarily add value. This usually stems from the training data filled with “enterprise-grade” style code, where developers sometimes over-engineer just to meet perceived standards or team conventions.

To all fellow vibe coders and professionals—using AI coding tools is great, but be cautious not to sacrifice simplicity for elegance. Avoid over-engineering your solutions. Always return to first principles, critically evaluate why a practice is being suggested, and adopt it only if it truly addresses a problem or improves your codebase.

Keep it simple and intentional!

Best,
[Your Name]

Your advice to stick to first principles and prioritize simplicity over elegance is crucial. It’s a reminder that we, as developers, need to actively guide AI tools rather than passively accepting their output. I’ve found that being explicit in prompts—like specifying a preference for minimalistic, straightforward code—can help steer the AI away from overcomplicating things. It’s all about communicating intent clearly and not just standing on the sidelines, as you mentioned.

Thanks for highlighting this. It’s a great call to action for all of us to question every practice and ensure it genuinely solves a problem before adopting it. Let’s keep the focus on building clean, effective solutions!

It's important to remember that we need to communicate effectively with these AI tools, not just passively accept their output. If you want less complicated code, you have to specifically ask for it in your prompts. It's about guiding the AI towards simplicity and questioning the practices it suggests. Don't fall into the trap of over-engineering just because the AI suggests it! As you said, always stick to first principles and question why a given practice is being applied.