The Framework Debate That Won't Die
Every few months, someone on Twitter declares PyTorch or TensorFlow "dead." The thread gets 500+ quote tweets, tempers flare, and nothing changes. Teams keep shipping models in both frameworks. The debate wastes energy on the wrong question.
The reality? Both frameworks converged years ago. TensorFlow adopted eager execution (basically PyTorch's dynamic graph model), PyTorch added torch.compile() for graph optimization (TensorFlow's strength). The performance gap narrowed to the point where your choice matters less than your team's familiarity with the API.
I've shipped production models in both. The framework didn't determine success — data quality, experiment tracking, and deployment infrastructure did. Yet I still see teams agonizing over this choice for weeks, delaying actual work.
The Convergence Nobody Talks About
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