Speakers in the public record
Claim mix
evaluation 9belief 7preference 2recommendation 2prediction 1commitment 1
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
The useful parts, with receipts.
22 published records
“Yeah, it is, you, you definitely need to have a different prompt if you want to stay within a few percentage points degradation Like I, like I said before, and that certainly comes at a cost, like fallbacks and backups and things like this It's really easy for them to go stale and kind of flake out on you because they're off the beaten track And In our particular case inside of Elicit, we do have fallbacks for a number of kind of crucial functions where it's going to be very obvious if something has gone wrong, but we don't have fallbacks in all cases.”
- Publisher
- Latent Space
“One thing I'll, one thing I'll mention quickly is that a lot of the stuff that you mentioned is typically not part of the normal interview loop. It's actually really hard to interview for because this is the stuff that you polish out in, as you go into production, the coding interviews are typically about the happy path.”
- Publisher
- Latent Space
“else? We don't, we don't use GraphQL. Yeah. So we've got the We've got the types defined in Python, that's the source of truth.”
- Publisher
- Latent Space
“I have some appreciation, I think when you had me on your podcast, I was still working at Temporal and that was like a nice Framework, if you live within Temporal's boundaries, you can pretend that all those faults don't exist, and you can, you can code in a sort of very fault tolerant way.”
- Publisher
- Latent Space
“I, I think that people raise this idea of fallbacks to models, but I don't think it's, I don't, I don't see it practiced very much.”
- Publisher
- Latent Space
“We think we could use your skills and a bunch of them will, will, will bite your hand off actually, because it is such an interesting area.”
- Publisher
- Latent Space
“I do think that, The way that you set up the ML problem to kind of ask the right questions, if you see what I mean, rather than the specific phrasing of exactly how you're doing chain of thought or few shot or something in the prompt I think the way that you set it up is, is probably going to be remain to be trickier for longer.”
- Publisher
- Latent Space
“I forgot to mention that you also run Ink and Switch, which is one of the leading research labs, in my mind, of the tools for thought productivity space, , whatever people mentioned there, or maybe future of programming even, a little bit of that.”
- Publisher
- Latent Space
“I think there actually is a lot more what would you call it , handshake emoji between kind of language models and the local first data model.”
- Publisher
- Latent Space
“I mean , I, I selected as one of my keynotes, Justine Tunney, working at LlamaFall in Mozilla, because I think there's a lot of people interested in that stuff.”
- Publisher
- Latent Space
“The other footnote I would put again on the specifics, that's, I think, general sourcing for any kind of role, but for AI engineering specifically, you're not looking for professional experience at this stage.”
- Publisher
- Latent Space
“You even shared your job description, your reading list, and your interview loop. So, , if anyone's looking to hire AI engineers, I expect this to be the definitive piece and definitive podcast covering it.”
- Publisher
- Latent Space
“I'm actually working on an article on that right now, which I'm very excited about, because it's a very interesting world of things.”
- Publisher
- Latent Space
“I'd be interested in understanding better what the specific problem is that they're looking to solve with that, whether it is to do with data security or centralization of billing, or if they have a kind of Suite of prompts or something like this that people can choose from so they don't need to reinvent the wheel again and again I wouldn't be able to say without understanding the problems and their proposed solutions , which kind of situations that be better or worse fit for but yeah for illicit where really the The secret sauce, if there is a secret sauce, is which models we're using, how we're using them, how we're combining them, how we're thinking about the user problem, how we're thinking about all these pieces coming together.”
- Publisher
- Latent Space
“It's kind of also an opportunity to learn at the feet of some real masters in this space, people who have been working on it since it was, before it was cool, if you want to put it that way. So for me, the last couple of months have been this crash course, and why I sometimes describe myself as an internal journalist is I'm helping to write some, some posts, including Supporting James in this article here we're doing for latent space where I'm just bringing my writing skill and that sort of thing to bear on their very deep domain expertise around language models and applying them to the real world and kind of surface that in a way that's I don't know, accessible, legible, that, that sort of thing.”
- Publisher
- Latent Space
“The, the language models is this kind of chaotic medium, this, this dragon, this wild horse you're, you're, you're riding and trying to guide in the direction that is going to be useful and reliable to users, because I think.”
- Publisher
- Latent Space
“Because of course, it's evolving, you need to figure it out, like the tacit knowledge and the experience of putting it in practice and seeing what those real What those real needs are are, are critical, and so it's, it is really about finding the right time to say yes, we can generalize, we can make standards and abstractions, whether it's for a company, whether it's for, , a library, an open source library, for a whole class of apps and it, it's very much a, much more of a A judgment call slash just a sense of taste or , experience to be able to say, Yeah, we're at the right point.”
- Publisher
- Latent Space
“If some AI engineer would not know, I don't know what, , I don't know where we would stoop to, to call something required knowledge, , or you're not part of the cool kids club.”
- Publisher
- Latent Space
“In terms of the system design interview, that's a little easier to prompt this kind of fault first mindset because it's very easy in that situation just to say, let's imagine that, , this node dies, how does the app still work?”
- Publisher
- Latent Space
“The ML first mindset, I think, is something that I struggle with as well, because the errors, when they do happen, are bad.”
- Publisher
- Latent Space
“I, I do often say that I think AI engineering is about 90 percent software engineering with like the, the 10 percent of like really strong really differentiated AI engineering.”
- Publisher
- Latent Space
“We share the types all the way from the Backend Python code all the way to the to the front end in TypeScript and find that is I mean We'd probably do this anyway But it really helps one reason around the shapes of the data which can going to be going back and forth and that's really important When you can't rely upon You you're going to have to coerce the data that you get back from the ML if you want if you want for it to be structured basically speaking and The second thing which is related is we use checked exceptions inside our Python code base, which means that we can use the type system to make sure we are handling, properly handling, all of the, the various things that could be going wrong, all the different exceptions that could be getting raised.”
- Publisher
- Latent Space