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

Episode Date: November 25, 2024

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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 Jansen and Paul Blankley from Zenlytic. Gentlemen, welcome to the show. Thanks for having us on. Super excited to chat today.

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's? Yeah, so I'm a nerds nerd. I was math and CS undergrad, math and CS grad. And Ryan and I met actually doing a technical master's degree at Harvard, studying language models. And this was right around the year that Attention to Visual Unique came out and pinch 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, your half of the story. Yeah, well, my background is I was a software engineer at the very start of my career in my native Canada. But then after that, I've spent coming up on 15 years now in sort of the last mile of data analytics. And, you know, first I was as a vc you know slash excel monkey i went to school became a data scientist uh 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 zanalytic 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, I'm kind of a semi-technical analyst, or I'm a very technical data scientist, all trying to sort of solve problems with data. So guys, before the show, we talked about data versus vibes. And founders or CEOs of running companies

are sometimes a combination of both and sometimes a little bit more slated toward vibes. So I'm excited to dig in on that. What are you guys excited about? I'm 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 in to just, you know, what is possible, what is not possible with language models. How, you know, how can we kind of fit language models in how we as humans

sort of think about and operate in the world? And I'll talk a little bit more about how that of 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 ZoomLink, where we are very AI native, like AI native first sort of business intelligence product. Awesome. What about you, Ryan?

Yeah, excited for all those. Really excited to chat about 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. So 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 there. All right. Well, hopefully we can get to all of that. Yeah. 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