In real data science work, a large amount of time is spent just trying to understand the data before any modeling can begin. Tasks like checking missing values, understanding feature distributions, spotting outliers, or finding data quality issues are usually done with repeated manual code and different tools. This makes the process slow, inconsistent, and sometimes error-prone, especially for beginners. Even experienced users often rewrite similar EDA code for every new dataset. AK-dskit already aims to simplify data science workflows, but there is still a need for a more structured, automated, and easy-to-use way to profile data and detect common issues early. This project addresses that gap by making data understanding faster, simpler, and more reliable.

Although Kornia works well for training and experimentation, users often face difficulties when trying to compile or export models for deployment using torch.compile and ONNX. These issues create a gap between research and real-world usage, leading to confusion and extra debugging effort. This contribution helps reduce that gap by improving compatibility, adding validation tests, and documenting practical usage patterns. The goal is to make Kornia easier to trust and use in real production workflows.

In real projects, API specifications often contain edge cases, unclear structures, and complex schema definitions that are difficult to validate correctly. When ScanAPI fails to handle these situations well, users may see confusing errors or incomplete results, making API testing harder than it should be. This contribution helps reduce that friction by improving support for real OpenAPI scenarios, making error messages clearer, and strengthening test coverage. The goal is to make ScanAPI more reliable, easier to understand, and more practical for developers who depend on automated API testing in their daily workflows.