The Problem: Benchmarking AI Coding Models Falls Short

• AI coding benchmarks rely heavily on synthetic or outdated tasks
• Developers and tech leaders lack realistic performance insights
• Ineffective evaluation leads to poor AI tool adoption and wasted resources
• Continuous integration pipelines miss AI model performance tracking