Latent Space / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
16 published records
01 / evaluation
“I just wrapped up a book with a coauthor Hector Yee called Building Production Recommendation Systems.”
02 / belief
“To that end, I think what pickaxes I think are still very valuable is understanding systems that are inherently less predictable, that are inherently sort of experimental.”
03 / belief
“Uh, yeah, I think one of the things that Swyx said when he was opening the AI engineer summit a couple of weeks ago was like, look, most people here don't know much about the space because it's so new and like being open and welcoming.”
04 / commitment
“Like, I don't think it was like particularly amazing or particularly poor, but what I will say is damn, he was right.”
05 / belief
“I think I'm just going to do it because I just like support the idea. I'm also admittedly someone who, when Google Glass first came out, thought that seems awesome.”
06 / belief
“What I would say is like underrated is Kensington. So Kensington is like a little town just a teeny bit north of Berkeley, but still in the Berkeley hills.”
07 / prediction
“I think however much you're integrating with these tools or interacting with these tools, and this audience is probably going to be pretty high on that distribution.”
08 / evaluation
“I think the cool things about these models is like people that are not traditionally technical can do a lot of very advanced things.”
09 / evaluation
“Because that is such a priority for data teams, it becomes an important focus of my team, which is, okay, magic may be an enabler.”
10 / recommendation
“We've had a lot of success in making specific the parts that need to be precise and tightly schemified, and that has really paid dividends. And so other analogies from data science that I think are very valuable is there's the sort of like human in the loop analogy, which has been around for quite a while.”
11 / evaluation
“This is the exact kind of thing that you expect RAG to be good at augmenting. But I think where people who have done a lot of thinking about RAG for the document case, they think of it as chunking and sort of like the MapReduce and the sort of like these approaches.”
12 / evaluation
“There is some irony because I think what the notebook allows is like chat plus plus.”
13 / prediction
“I would say for a large number of our applications, GPT-4 is pretty much required.”
14 / preference
“I did talk to a company recently called gather, which seems to have some cool ideas in this direction, but I haven't seen yet what I, what I really want, which is I want something that is sort of like every time I listen to a podcast or I watch a movie or I read a book, it sort of like has a great vector index built on top of all that information that's contained within.”
15 / preference
“I'd argue no. And ultimately, because we don't have one specific sphere of data that we need to write great data analysis workbooks for, we actually want to provide a platform for anyone to do data analysis about their business.”
16 / recommendation
“I have this little app that I use called Wanderer, which just lets me like kind of keep track of everywhere I've been.”