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
33 published records
01 / recommendation
“Like we'd like to keep things simple so that's easier to debug, like less layers of infrastructure, less layers of abstraction, make it a lot easier to work with. Like we don't use Kubernetes,”
02 / belief
“I think the most exciting part is just we're writing a lot of blog posts right now.”
03 / uncertainty
“You got the NIC health checks, GPU health check, this space health check, Docker D message. I don't know what that is.”
04 / uncertainty
“The flip side is I don't know of anyone who like is hiding a secret passphrase in a book and needs the model to find it.”
05 / belief
“I mean, one of them, which I think is probably pretty common, like common knowledge by this point.”
06 / belief
“I think Josh said it and you said it well, like code is kind of the God tool. It unlocks literally everything.”
07 / belief
“I think you can spend a lot of time getting really good at these kinds of benchmarks without making, like, kind of more general purpose progress.”
08 / evaluation
“I would much rather have a coding agent that will give me back a thing. And, you know, it's it's actually the code doesn't work like 10% less of the time than some other model, but it will tell me 100% of the time like when it's not sure.”
09 / uncertainty
“I don't know if, John, you had something that you were sort of burning to ask or.”
10 / belief
“I think people will find creative use cases. And like Jon said, I think the multimodality examples will naturally lend themselves to long context.”
11 / belief
“I think we looked at a bunch of services out there like Minio and some other ones, but a lot of these like come with a lot of extra overhead and maintenance.”
12 / belief
“Like I think that's literally the name of a book on programming, if I remember right.”
13 / uncertainty
“Got it. And I don't know about like what you would highlight so far as a post-acquisition, but the most recent news is that you guys released DBRX.”
14 / belief
“You know, I think the joke is Databricks has been 18 months away from IPO for five years.”
15 / belief
“I think that's basically our sort of recap of our discussion based on Imbue's releases today.”
16 / belief
“I think the real question is, you know, releasing models open source is not our day-to-day bread and butter.”
17 / evaluation
“I think a lot of people are exploring that and I think every now and then people get a bout of knowledge graph religion and then it kind of doesn't work out.”
18 / belief
“Take a different tactic, which is, like, at the end of the day at Databricks, I have 12,000 customers, or I think that's the latest number, all of whom are trying to do something with, you know, LLMs or AI or machine learning.”
19 / uncertainty
“I don't know if that's useful or not, but I mean, that's useful to me as a, as someone who might be working with them.”
20 / belief
“I think the Arc ones are like a little bit too, probably, visual for us to really be able to integrate with.”
21 / belief
“I think we've actually done a lot of work in the past and people can see kind of our blog posts about sort of self-supervised learning and some other kind of vision-related stuff in the past as well.”
22 / belief
“I think we purposely stayed away from the very, like there's BigBench, for example, that has a lot of, I think, to me, feels sort of similar types of tasks that are like very unrealistic.”
23 / belief
“I think what's unusual is like, I think Shutterstock's doing multiple deals in multiple labs.”
24 / evaluation
“I spend a lot of time thinking about RAG these days. And RAG, you know, in one sense, you know, the way that I think about RAG is it's the world's simplest agent.”
25 / evaluation
“we thought about this, which is are emergent properties of language models a mirage? And I think if you look at that paper, it actually makes a relatively compelling case that in fact, you know, this emergent behavior that you're seeing is not really emergent behavior, but is really a function of the evaluation metrics that we're using.”
26 / preference
“Like, I'm very happy with my little SQL bot and building 12,000 of those because they're moving the needle for a lot of folks every day.”
27 / evaluation
“I think ours is actually quite a bit lower than that, probably because we've taken the time to like dig into a large, maybe larger number than we should have of these failures and get to the root cause of it and be like, oh, okay, like that's exactly what's going wrong.”
28 / recommendation
“One tool actually that does come into mind that I will call out is Kraken from Uber.”
29 / recommendation
“Yeah, I mean, like Kubernetes, like that point about Kubernetes, I will say as a former AWS employee, like it seems like it would be ideal for imbue to at some point make it more abstracted or agnostic because you're going to want to, you know, replicate your setup.”
30 / evaluation
“I think I really appreciate all the effort that has gone into actually tuning these things because you can tune them, but it's a lot of work to like tune this stuff and do all this stuff from scratch.”
31 / commitment
“To make sure that we're actually, you know, not testing on train. And then I think a final thing that we're releasing there is around 450,000 human judgments about ambiguity and question quality, which we used in the process of cleaning these evaluations and we also hope will be helpful for other people training kind of similar models.”
32 / evaluation
“I think my favorite failure was GPU is just giving wrong math. Like if they give errors, great, because you can see the errors, but if they just give you the wrong math back, not so fun.”
33 / commitment
“Other ones, because, you know, that's, I think evaluations and data are like a core, important part of, you know, the business. And I think we take evaluations very seriously and are spending a lot of effort in terms of like, what exactly do we make as part of the evaluation set?”