•1 min read•from Machine Learning
[D] It’s 2026. Can we finally admit TensorFlow is the "COBOL of Machine Learning"?
We keep telling students to learn both, but let’s look at the actual landscape:
- Research: 95%+ of HuggingFace and arXiv is PyTorch.
- Innovation: Even Google's own researchers are using JAX more than TF.
- DX: Debugging a custom layer in TF still feels like a fever dream compared to PyTorch’s native Pythonic flow.
TF has the "legacy enterprise" crown, but for anything moving at the speed of SOTA, it’s not even a contest anymore. Is there any technical reason to start a greenfield project in TF today, or are we just clinging to it for the TFX pipeline?
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