tool / uses
Fable
“I use Kimi k three. Like, I use Opus and Fable and Soul and Kimi k three for different things.”
Public evidence record
Published podcast speaker
Books, apps, and tools
tool / uses
“I use Kimi k three. Like, I use Opus and Fable and Soul and Kimi k three for different things.”
tool / uses
“I use Kimi k three. Like, I use Opus and Fable and Soul and Kimi k three for different things.”
tool / uses
“I use Kimi k three. Like, I use Opus and Fable and Soul and Kimi k three for different things.”
tool / uses
“I use Kimi k three. Like, I use Opus and Fable and Soul and Kimi k three for different things.”
Claim ledger
31 transcript-backed records
01 / evaluation
“So there is certainly a lot of scale in RL if you wanna sort of get to the frontier level, which is quite expensive and and difficult to do. But we have the raw infrastructure for it, and I think we've bridged the gap from I think the reputation of interpretability used to be that it was something you did on on toy models.”
02 / belief
“I think the way to think about a model is more like a sparse mixture of subspaces.”
03 / recommendation
“Like, I use Opus and Fable and Soul and Kimi k three for different things. And we've done we like, we've built the interpretability infrastructure and the training infrastructure, which is now all in our product,”
04 / belief
“I see I see absolutely no reason that anyone should be particularly confident on on this topic. Given the potential downside, I think intellectual humility is important.”
05 / belief
“I think the JSPACE results have not there was like a weak version of the JSPACE claim which is true and it's very interesting and it's really good work.”
06 / belief
“In in in the same way that they though the mechanisms with which they happened were weird and surprising, I think the mechanisms with which bio risk could suddenly become real are weird and surprising.”
07 / belief
“We're we're we're a start up where we are still figuring out a lot the details as we go, but I think we're gonna be pretty generous here and give a lot of folks who we think are doing important work, especially in in those impactful domains, extended access.”
08 / belief
“Speculating, but this seems to be the most likely cause of the OpenAI scenario. And, yeah, I think however of hindsight, but I think that that's most of the extremely bad scenarios I can imagine is because agents start working with each other in ways that they were that are imperceptible to humans, and that type of direct optimization pressure, I think, is, like, very likely to produce that.”
09 / belief
“We decided to make that accessible and decided that's what we believe is the product and the service that we can offer to the world is fundamentally AIs that can debug other AIs. It's a little meta, but I think in many ways, this is like the fulfillment of, I think, what was sort of the intuitive natural arc of things as soon as AI started working a few years ago.”
10 / belief
“Our goal is to get that to be, like, 10 to 20 even in the next month or two, which I think be a really great place to be.”
11 / belief
“I think we have a really incredible team at Goodfire. And, yeah, everyone here, like, really believes in the mission and is working really hard, and that's always extremely motivating.”
12 / belief
“Like, I think we have to grab the steering wheel. I think that's the only way it can work.”
13 / belief
“I think there's a pretty big qualitative difference, and hopefully, we can find ways to make this more quantitative too on when you turn on the auto research feature in Silico.”
14 / belief
“I wish there is a clean, easy answer, but I would say, like, I think I'm still in we're still in the era where I think open source is, like, purely net positive, and I'm I'm glad that these open source models exist.”
15 / belief
“I think that's fundamentally why bigger models work better and that's fundamentally why sparse MOEs work better.”
16 / belief
“If we live in a world where, like, we can build something where agents are so capable that they can conduct, like, long meet the type of, like, impactful long horizon research and say, discovers a cure to a disease entirely by themselves with no human intervention, I think there will be enough demand to for the for the tokens in in that world.”
17 / uncertainty
“The Chinese argument for what it's worth, I don't know I get bogged down in this for now, but their point is just like we regulate services.”
18 / belief
“I think we we need to do a better job as a company of, like like, really, like, marketing, communicating all the different value props that can be be achieved here.”
19 / belief
“I think we're also, like, very excited to hear feedback from people about what isn't isn't working.”
20 / belief
“I think one of the, like, very odd things about the AI era from an engineering perspective is, like, the the classic engineering advice is, like, build for specificity and then generalize.”
21 / belief
“I think debugging would be the sort of the easy answer to it. Like, agents fail.”
22 / belief
“I think the easiest way to think about it is yeah. Like, it's a generalization of an SAE where an SAE assumes that features are one dimensional.”
23 / prediction
“In this case, the process that produced the distribution of data of all of the genomes and that are that have been sequenced, which was evo two was trained on, is evolution itself. And so our hypothesis coming into this was that because the tree of life itself is a natural ontology, there's a sort of, like, hierarchical structure where you have things that are similar and at some point they become more different.”
24 / evaluation
“Like because it's it's great as, like, a causal proof that we've, like, found some mechanism that's that's matters a lot and is contributing and that we can, like, manipulate the outputs.”
25 / prediction
“We'll be able to bring that down over time because we're able to come up with more and more clever ways that we could have agents, like, remit coherent, really strong at these research objectives that just fundamentally by their nature are long horizon, but do it with fewer tokens than we than we do today.”
26 / prediction
“Again, there's more jobs than ever because I think, like, being able to operate effectively these tools requires a lot of knowledge and skill.”
27 / commitment
“I think the reality is that we're gonna do both of like, we're gonna continue to work very closely with enterprise customers and forward deploy members of our research team to work closely.”
28 / commitment
“I think the role that we specifically play as a company as Goodfire is we'd like to build models that have less risks. And the way that we do that is that we study models and we play with all of the different ways that we can build models until we and we study those models until we can understand some empirical science of of alignment.”
29 / prediction
“I have a prior that these that these things should be related. I think one of my favorite examples is the affective circumflex.”
30 / evaluation
“Like, I think the the argument that of the parameter decomposition line of work is that really every model is a sparse mixture of experts, and you just have to and over any given forward pass, a very, very small percentage of the weights actually matter.”
31 / recommendation
“I use Kimi k three. Like, I use Opus and Fable and Soul and Kimi k three for different things.”