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I'm starting up an effort to improve the TensorFlow debugging experience. If you have obscure stack traces you want to share, or if you have ideas/suggestions, send them to my work email.
Debugging is central to the way people experience a framework. It's what we spend most of our time doing. And the clarity of the feedback provided by the framework during debugging is what minimizes time to solution and maximizes developer happiness.
Best of luck! Seems we’re on similar fronts 
It would help if reading TensorFlow source code were easier. I once tried to understand how `dataset.shuffle` was exactly implemented, but quickly gave up. Following Linux kernel code is easier (after you read one book :).
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