The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 9 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Biewald: PyTorch beat TensorFlow through developer empathy, not eager execution
“I don't think they really, I think people tell this, the story of sort of the silver bullet. Of like you know, the eager execution model. But I think the reality is they just built a product with so much more empathy.”
Pesenti: Facebook is going end-to-end all-in on PyTorch
“We're definitely going all in as PyTorch, you know, end to end. So I think initially when we launched the Onyx strategy, it was more like a multi-framework world. And we had actually two framework internally between PyTorch and Cafe Two, but we're still suppor…”
Lin Qiao: PyTorch's primary success lesson is that simplicity scales
“I think one of the biggest success we saw from the PyTorch experience is simplicity scales.”
Karpinski: Alternating language layers in AI frameworks prevents compiler optimizations
“They have what I've, I would describe as a sandwich problem, which is that you end up sandwiching a lot of system code with user code, and then, like, adding more and more layers of that, and as you've sandwiched, like, you know, seven or eight layers of that,…”
CMU undergrad AI course has students build an LLM from scratch
“You build a LLM completely from scratch. You use PyTorch, but you build one from scratch that, you know, can be a chatbot. You train it on data. You RL it to solve math problems with tool calls. You do all of this. And this is a undergrad level course.”
Lin Qiao: OpenAI switched completely from TensorFlow to PyTorch
“OpenAI switched to use PyTorch fully.”
Lin Qiao: Meta had hundreds of engineers building PyTorch and its infrastructure
“We have hundreds of engineers building PyTorch and infrastructure around PyTorch, but at the same time, I believe PyTorch within Meta probably has thousands of users.”
Meta spent five years rebuilding PyTorch's backend for internal scale
“It took us five years. Took us five years to get the stage supporting almost all internal needs using deep learning and mass and massive scale.”
DoorDash standardized its core machine learning platform on LightGBM and PyTorch
“We landed on using a framework that enables tree-based models. And we picked light GBM for that after trying a few different packages and also deep learning. And for that, we then used PyTorch. And so we started with those two core libraries.”