OpenAI highlights a significant issue in AI evaluation: existing coding benchmarks often focus on trivial problems rather than real-world software development challenges. Tasks like asking a model to generate Fibonacci sequences don't reflect the complexities engineers face daily. To address this gap, the cline-bench initiative aims to create high-fidelity benchmarks and reinforcement learning environments based on actual open source development scenarios. By using real projects, cline-bench focuses on tasks that require manual intervention or expose model failures, ensuring that the benchmarks are relevant and useful for evaluating AI performance. Cline-bench is built around authenticity and collaboration. It only includes open source repositories to maintain transparency and reproducibility. Tasks can be contributed through the Cline Provider or directly by engineers in the open source community. Each task is designed as a reproducible environment that reflects the true nature of software development, encompassing challenges like ambiguity and multi-step reasoning. The initiative does not aim to create superficial rankings but strives to provide a foundational resource that benefits the broader AI ecosystem. The primary objectives of cline-bench are reliable evaluation, open scientific progress, and offering training data for fine-tuning AI models. By standardizing environments, researchers can study model capabilities and failure modes more effectively. This initiative is a step toward enhancing AI agents' performance in real-world coding tasks, making them genuinely reliable. Users maintain control over their participation and data security, with options to use their own models or third-party providers. Cline-bench is actively seeking contributions to further develop its benchmark framework, emphasizing the importance of real engineering problems in advancing AI capabilities.