I Ditched My AI Coding Setup (135 Stars): Why Reuven's is Better

In this video, I break down why my popular "Roomodes" AI coding setup, despite gaining 135 stars and 36 forks on GitHub, is NOT the approach I recommend anymore. Learn from my failures! We'll cover the specific problems: • Microtasking: Why my initial solution for Gemini 1.5 / GPT-4 costs was flawed (01:57). • Cost Issues: How Claude 3.5/3.7 Sonnet (200k context) beats million-token models for most tasks (02:58, 03:24). • Research-Driven Development: Good idea, but now better integrated into Ruben's setup (05:24). • SAPPO Ontology: Theoretically useful for predicting problems, but practical value is uncertain (05:57). • Test-Driven Development (TDD): Why my initial approach wasn't strategic and how the new method using Gemini for planning is superior (07:16, 08:38). Why Reuven's Setup is Better (01:48): I explain why adopting Reuven's setup, combined with strategic use of Claude 3.7 Instruct (for most tasks) and Gemini (specifically for Deep Research & Debugger modes), is more effective and cost-efficient (04:56, 11:52). NEW Resources to Help You: To make this better approach easier, I've developed: • SPARC Universal Workflow: A detailed guide based on deep research into Spark and prompt engineering best practices (10:33). • Dummy Proof Prompt: A comprehensive template to kickstart your AI coding projects, even if you're a beginner (10:57). Get the new workflow and prompt template here: https://github.com/ChrisRoyse/AI-Vibe-Code-Setup Reuven's Setup: https://www.linkedin.com/pulse/automated-code-development-new-sparc-npx-create-sparc-reuven-cohen-8ujwe/ This video is for anyone interested in AI software development, agentic workflows, AI code generation, cost optimization with LLMs (Claude 3.7 / 3.5 Sonnet vs Gemini 2.5 Pro), prompt engineering, and learning from mistakes in coding. #AICoding #SoftwareDevelopment #AgenticWorkflow #Claude3 #GeminiAI #LLM #Programming #GitHub #TechFailure #LearnToCode