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- Gemini 3.0 Benchmarks & Performance
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Classroom Contents
Solve AI Context Failures in React with MCP and Tooling
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- 1 - AI Hype vs. React Reality
- 2 - Vibe Coding vs. AI-Assisted Engineering
- 3 - Measuring Quality: Logic vs. Human Taste
- 4 04:19 - The Complexity Cliff: Why AI Fails in Real Projects - The "Taste" Gap: Why AI Designs Look Generic
- 5 - Next.js & SW Bench: Framework-Specific Failures
- 6 - Solving "Purple Websites" with Skills
- 7 - Strategy: Managing AI Like a Junior Dev
- 8 - Agentic Loops & Code-Specific Models
- 9 - Root Cause: Context Failures vs. Model Failures
- 10 - Context Engineering & The Iceberg Model
- 11 15:50 - Solution: MCP Servers Next.js, Chrome DevTools - Workflow: Building a Closed-Loop Debugger
- 12 - UI Components: Logic Mastered, Aesthetics Failed
- 13 - Generative 3D & Data Visualization in React
- 14 - The AI Stack Framework: Debugging Your Workflow
- 15 - Gemini 3.0 Benchmarks & Performance
