PyTorch, JAX, and the Evolution of TensorFlow
Most developers treat their choice of ML framework like a religious conversion. They start with TensorFlow because it's what the tutorials use, move to PyTorch because the tensors actually make sense, and then eventually find themselves squinting at JAX because they want to do something truly weird with gradients. It's a predictable cycle of chasing the cutting edge, but the actual shift in momentum is harder to pin down than the anecdotes suggest. I decided to stop guessing and just look at the data. By overlaying search trends from Hacker News, you can see the exact moment the community's collective brain shifted. TensorFlow owned the early gold rush, but PyTorch didn't just nudge it aside, it completely gutted it in the research labs between 2019 and 2021. Now, JAX is the new darling for anyone doing heavy lifting in 2023. The most interesting part isn't the tools, though. It's the players. For a long time, OpenAI's dominance looked like a forego...