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 7 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.”
Biewald: Simple operational errors cause more model failures than data drift
“People talk a lot about data drift in the industry. And that's this idea that like, you know, like language changes over time and you want to know that it's changing and sort of like have your model you know, notice that and update it. But I guess like what I …”
Biewald: Data labeling software requires a top-down sales model
“Data labeling, I think really wants to be a top down sale”
Biewald: LLM API developers are less mathematically specialized than traditional ML engineers
“Even the people Working with a lot of these APIs you know, are, like, less huge math nerds than, you know, some of the people that have been training, you know, models for a long time.”
Biewald: Developers building with LLMs focus heavily on qualitative anecdotes over metrics
“In the LL world, like the anecdote is something people really pay attention to.”
Biewald: Technical buyers don't want sales dinners, they just want facts
“Nobody wants to golf or anything. I mean, that's for sure, right? Like, I mean, there's people like a super aggressive salesperson. Some of my salespeople are really competitive. I am actually really competitive myself, but they kind of like suppress it in a w…”
Biewald: Only five out of 100 feature engineering attempts actually improve models
“Every task that you work on has kind of different different features work better or worse, and I spent, when I first was working as a data scientist, I spent all my time on this feature selection, and as you guys know, you try a hundred things and maybe five o…”