Zhangyang

Regarding the version inconsistency of PyTDML on PyPI, we previously discussed this issue but prioritized it lower due to ongoing iterations and the absence of a stable release. To address...

I fully agree with the concerns you raised. The current core focus of PyTDML should strictly adhere to the OGC standard's model definitions and encoding/decoding functionalities. The integrated data access...

Thank you for the thoughtful suggestions! In fact, we’ve already implemented preliminary submodule isolation in the repository (e.g., separating core functionalities from PyTorch/TensorFlow integrations). However, we overlooked architectural concerns in...

> I see more fine lines in the case at hand. The most important being: is the current step a step forward? When thinking too many steps ahead, we might...

We do have a lot of work to do regarding potential performance waste and maintenance issues.  1. Global Code Review: Check all modules for side-effect code (e.g., functions executed...

The current pytdml.type.extended_types_old module lacks clear alignment with specific standard versions (e.g., 1.0 vs. 1.1), which could lead to maintenance and usability issues.  ​1. Current State: The extended_types_old module...

We fully endorse adopting PDM for dependency management. Here is the implementation plan:  ​1. Configuration Integration: In the existing pyproject.toml, declare dependencies using PDM's standardized format. Since PDM is...