The PRQL: Large Language Models Haven’t Always Been Large with Ryan Janssen and Paul Blankley from Zenlytic

Eric Dodds 00:13
welcome to the data stack show prequel. This is a short bonus episode where we preview the upcoming show. You’ll get to meet our guest and hear about the topics we’re going to cover. If they’re interesting to you, you can catch the full length show when it drops on Wednesday. Welcome back to the show. We are here with Ryan Janssen and Paul Blankley from Zenlytic. Gentlemen, welcome to the show.

Paul Blankley 00:35
Thanks. Super excited to chat today.

Eric Dodds 00:39
All right. Well, give us just a brief background. You have different backgrounds, but they actually converged at one point. So Paul, why don’t you start and then tell us where your path crossed with Ryan.

Paul Blankley 00:49
Yeah. So I’m a nerd. Nerd. I was a math and CS undergrad, math and CS grad. And Ryan I met actually doing technical master’s degree at Harvard, studying language models, and this was right around the year that attention was all unique came out, and French warmers were like, sort of first becoming a thing. So we got to see a lot of that, the really early versions, back when they were language models, where they became large language models. And after that, we started consulting. Did consulting for about a few years, and then started the analytic during the pandemic, right? Ryan, you’re half the story? Yeah, well, my background is,

Ryan Janssen 01:23
I was a software engineer at the very start of my career in my native Canada. But then after that, I’ve spent 15 years now, in sort of the last mile of data analytics. And, you know, first I was a VC, you know, slash Excel monkey. I was a stool, and became a data scientist. So I worked in data science for a bit. And, you know, Paul and I, that’s where we met. In fact, we started data science consultancy together, and then we founded Zen learning together. And all those have been different, sort of parts of the same problem, which is like, either I’m a non technical end user, or I’m kind of a semi technical analyst, or I’m a, you know, very technical data scientist, all trying to sort of solve problems with

John Wessel 02:01
data. So guys, before the show, we talked about data versus vibes, and you know about founders or CEOs running companies on, sometimes a combination of both, and sometimes, you know, a little bit more slated toward vibes. So I’m excited to dig in on that. What are you guys excited about? I’m

Paul Blankley 02:18
excited for that one because I think that hits on a really important point that I’m excited to sort of expound on. And other than that, I’m excited to dig into just, you know, what is possible, what is not possible for English models. How, you know, how can we account fit language models in how we as humans sort of think about and operate in the world, and talk a little bit more about how that and how language models work actually affects what we do at zoom link, where we are very, you know, AI native, like aI native, first sort of business intelligence product. Awesome.

John Wessel 02:48
What about you? Ryan,

Ryan Janssen 02:50
yeah, excited for all those really excited to chat about, you know, intersection of AI and bi or AI and data in general, which is like, how do we get AI agents to answer problems and data? And it’s a really hard problem, frankly, because you’ve got this huge surface area of potential data types and configurations. On one side, you’ve got this huge surface area of questions people want to ask. On the other side, there’s a little pinch point in the middle. Such a fascinating field to work in. And, you know, llms, there’s just new stuff every day.
So, you know, lots of stuff to talk about

Eric Dodds 03:18
there, all right. Well, hopefully we can get to all of that. So let’s dig in. Let’s do it. All right, that’s a wrap for the prequel, the full length episode will drop Wednesday morning. Subscribe now so you don’t miss it.