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Image courtesy of Google Gemini

This is the third article in my AI Code-Off experiments series. You can read the first article and second article for context.

Line 184. That’s where the Quick Start section lived in our project README.

Think about that for a moment. A new developer lands on your project, excited to try it out, and has to scroll through 183 lines of architectural philosophy, memory bank explanations, and universal design principles before learning the single most important thing: how to actually use it.

Image courtesy of Google Gemini

This blog is a continuation of my AI Code-Off experiments series. You can read the first article here: I Pitted Gemini, Claude, and GPT in a 4-Stage AI ‘Code-Off.’

1,717 lines. That’s how long my Streamlit UI rule, intended as a guide for LLMs and Agents, had grown.

This wasn’t documentation for humans—it was a rule file for AI coding assistants in Cursor. When you ask an AI to build a Streamlit dashboard, it consults files like this to understand best practices and requirements. But somewhere in those 1,717 lines, the guidance had become contradictory. Plotly or PyDeck for visualizations? The rule said both, with no clear preference. Duplicate sections appeared throughout. Critical production guidance (performance, accessibility, deployment) was completely missing.

Image courtesy of Google Gemini

With over two decades of self-taught programming experience, I’ve seen AI revolutionize my coding over the last ten months. Daily use of LLMs has dramatically sped up projects, from quick prototyping and boilerplate generation to faster debugging. This frees me to focus on architectural decisions and complex problem-solving. However, effective AI use demands a strong grasp of programming fundamentals. LLMs aren’t perfect, and my experience has been crucial in quickly spotting and fixing bugs and logical flaws in AI-generated code.