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
22 published records
01 / recommendation
“I think if you're running into issues where you have like 20 or 50 or 60 function calls, I think you're much better having those specifications saved in a vector database and then have them be retrieved, right?”
02 / evaluation
“For example, with one system, there are like 780 endpoints. And if you're actually trying to do vector similarity, it's not that good because the people that wrote the specs didn't have in mind making them like semantically apart.”
03 / evaluation
“Just because you can sort of specify relationships between these entities that you can't do in a parallel function calling, you can have a single chain of thought before you generate a list of results.”
04 / evaluation
“In which case, like we either can assume that, or we can assume that like things need to happen in some kind of sequence as a DAG, right? But if it's a DAG, really that's just like one JSON object that is the entire DAG rather than going like, okay, the order of the function that return don't matter.”
05 / belief
“Like I think when we were in like auto GBT land, there was that one example where it's like, I wanted it to like buy me a bicycle overnight.”
06 / belief
“I think now the models also support like JSON mode and some of these things and, you know, return JSON or my grandma is going to die.”
07 / belief
“Whereas like maintaining the library, I think is mostly just kind of like a utility that I try to keep up, especially because if it's not venture backed, I have no reason to sort of go down the route of like trying to get a thousand integrations.”
08 / belief
“I mean, the biggest one really was the fact that I think for just four years, I was so bearish on language models and just NLP in general.”
09 / belief
“I would say that most people would be familiar with Instructor from your talks and your tweets and all that.”
10 / uncertainty
“I don't know if it's fully GA now, if it's GA, if you can get a commercial access really easily.”
11 / belief
“I feel like we've gone too much in the looping route and I think a lot of more plans and like DAGs and data structures are probably going to come back to help fill in some holes.”
12 / uncertainty
“Jason, you are extremely famous, so I don't know what I'm going to do introducing you, but you're one of the Waterloo clan.”
13 / recommendation
“I think right now, the first steps are just how do we take this DAG idea and break it down to modular components that we can like prompt better, have few shot examples for and ultimately like fine tune against.”
14 / evaluation
“I think one of the agents loopholes or one of the things that is a real barrier for agents is LLMs really like to get stuck into a lane.”
15 / prediction
“I definitely think that's the case because for the most part, I imagine you either have like less than three tools or more than a thousand.”
16 / evaluation
“This is the part that feels crazy because really the difference is LLMs give you strings and Instructor gives you data structures.”
17 / preference
“get in a Django app, right? But no one would say, I like went off of Django because I'm using requests now.”
18 / preference
“I think at some point, you know, tools like Guardrails and Marvin came out. Those are kind of tools that I use XML and Pytantic to get structured data out.”
19 / recommendation
“I think function calling gives you a tool to specify the fact like, okay, this is a list of objects that I want and each object has a name or an age and I want the age to be above zero and I want to make sure it's parsed correctly.”
20 / evaluation
“No, so I think because at Stitch Fix, a lot of the machine learning engineers and data scientists were writing production code, sort of every team's systems were very bespoke.”
21 / preference
“Yeah. So I'm not really into that world of tooling, whereas I think, you know, I spent three good years building observability tools for recommendation systems.”
22 / preference
“I think Stitch Fix is a place where you kind of go because you want the work offloaded.”