[franciscop]

This is very interesting and telling:

> Great API. Most researchers prefer PyTorch’s API to TensorFlow’s API. This is partially because PyTorch is better designed and partially because TensorFlow has handicapped itself by switching APIs so many times (e.g. ‘layers’ -> ‘slim’ -> ‘estimators’ -> ‘tf.keras’).

Arguably, one of the biggest issues Google had with Angular was the switch from 1.x to 2.x. You'd have thought they learned about how not to make major changes on OSS projects.

Facebook on React for instance do an amazing job here, they use prefixes to anything they don't want to support like "UNSTABLE_" and show warnings forever when they actually plan to make something small obsolete.

I tried to learn from both, so in some of my bigger personal OSS projects (amount of work involved) like npm's "server". I purposefully made some APIs a bit more limited than I could to have more flexibility later on if I didn't like the direction. Of course at a different level, I am a single dev doing OSS on my free time after all.

But I understand in a project of the size of e.g. Tensorflow it's not an individual dev learning, it's more about the company learning how to do things better.