Despite its shortcomings, I share the same vision as this article. Here are my reasons: - Tensorflow has a way too large API surface area: parsing command line

Despite its shortcomings, I share the same vision as this article. Here are my reasons: - Tensorflow has a way too large API surface area: parsing command lines arguments handling, unit test runners, logging, help formatting strings... most of those are not as good as available counterparts in python. - The C++ and Go versions are radically different from the Python version. Limited code reuse, different APIs, not maintained or documented with the same attention. - The technical debt in the source code is huge. For instance, There are 3 redundant implementations in the source code of a safe division (_safe_div), with slightly different interfaces (sometimes with default params, sometimes not). It's technical debt. In every way, it reminds me of Angular.io project. A failed promise to be true multi-language, failing to use the expressiveness of python, with a super large API that tries to do things we didn't ask it to do and a lack of a general sounding architecture.