Interactive development environments like Jupyter Notebooks enable incremental coding through cells with immediate feedback, but their linear structure and global namespace limit scalability for large software projects. We present CodePod, a hierarchical extension of Jupyter that introduces a novel scoped execution model with formal semantics. Our key contribution is a language-agnostic runtime system that performs source-level transformations to implement hierarchical scoping rules, enabling true incremental evaluation across nested modules without requiring language-specific kernel modifications. We formalize the scoping semantics as a mathematical framework with precise visibility relations and prove key properties including uniqueness of symbol resolution and correctness of the resolution algorithm. A qualitative user study with seven senior developers demonstrates that CodePod enables significant improvements in project scalability compared to Jupyter, with notable reductions in navigation effort. We validate the system's effectiveness on large-scale projects with thousands of lines of code, demonstrating its applicability beyond traditional notebook boundaries. Our tool is open-source and available at https://codepod.io