Timestamps
* [00:00:00] Introductions
* [00:01:28] Statsbot and limitations of natural language processing in 2017
* [00:04:27] Building Cube as the infrastructure for Statsbot
* [00:08:01] Open sourcing Cube in 2019
* [00:09:09] Explaining the concept of a semantic layer/Cube
* [00:11:01] Using semantic layers to provide context for AI models working with tabular data
* [00:14:47] Workflow of generating queries from natural language via semantic layer
* [00:21:07] Using Cube to power customer-facing analytics and natural language interfaces
* [00:22:38] Building data-driven AI applications and agents
* [00:25:59] The future of the modern data stack
* [00:29:43] Example use cases of Slack bots powered by Cube
* [00:30:59] Using GPT models and limitations around math
* [00:32:44] Tips for building data-driven AI apps
* [00:35:20] Challenges around monetizing embedded analytics
* [00:36:27] Lightning Round
Transcript
Swyx: Hey everyone, welcome to the Latent Space podcast. This is Swyx, writer, editor of Latent Space and founder of Smol.ai and Alessio, partner and CTO in residence at Decibel Partners. [00:00:15]
Alessio: Hey everyone, and today we have Artem Keydunov on the podcast, co-founder of Cube. Hey Artem. [00:00:21]
Artem: Hey Alessio, hi Swyx. Good to be here today, thank you for inviting me. [00:00:25]
Alessio: Yeah, thanks for joining. For people that don't know, I've known Artem for a long time, ever since he started Cube. And Cube is actually a spin-out of his previous company, which is Statsbot. And this kind of feels like going both backward and forward in time. So the premise of Statsbot was having a Slack bot that you can ask, basically like text to SQL in Slack, and this was six, seven years ago, something like that. A lot ahead of its time, and you see startups trying to do that today. And then Cube came out of that as a part of the infrastructure that was powering Statsbot. And Cube then evolved from an embedded analytics product to the semantic layer and just an awesome open source evolution. I think you have over 16,000 stars on GitHub today, you have a very active open source community. But maybe for people at home, just give a quick like lay of the land of the original Statsbot product. You know, what got you interested in like text to SQL and what were some of the limitations that you saw then, the limitations that you're also seeing today in the new landscape? [00:01:28]
Artem: I started Statsbot in 2016. The original idea was to just make sort of a side project based off my initial project that I did at a company that I was working for back then. And I was working for a company that was building software for schools, and we were using Slack a lot. And Slack was growing really fast, a lot of people were talking about Slack, you know, like Slack apps, chatbots in general. So I think it was, you know, like another wave of, you know, bots and all that. We have one more wave right now, but it always comes in waves. So we were like living through one of those waves. And I wanted to build a bot that would give me information from different places where like a data lives to Slack. So it was like developer data, like New Relic, maybe some marketing data, Google Analytics, and then some just regular data, like a production database, so it sells for sometimes. And I wanted to bring it all into Slack, because we were always chatting, you know, like in Slack, and I wanted to see some stats in Slack. So that was the idea of Statsbot, right, like bring stats to Slack. I built that as a, you know, like a first sort of a side project, and I published it on Reddit. And people started to use it even before Slack came up with that Slack application directory. So it was a little, you know, like a hackish way to install it, but people are still installing it. So it was a lot of fun. And then Slack kind of came up with that application directory, and they reached out to me and they wanted to feature Statsbot, because it was one of the already being kind of widely used bots on Slack. So they featured me on this application directory front page, and I just got a lot of, you know, like new users signing up for that. It was a lot of fun, I think, you know, like, but it was sort of a big limitation in terms of how you can process natural language, because the original idea was to let people ask questions directly in Slack, right, hey, show me my, you know, like opportunities closed last week or something like that. My co founder, who kind of started helping me with this Slack application, him and I were trying to build a system to recognize that natural language. But it was, you know, we didn't have LLMs right back then and all of that technology. So it was really hard to build the system, especially the systems that can kind of, you know, like keep talking to you, like maintain some sort of a dialogue. It was a lot of like one off requests, and like, it was a lot of hit and miss, right? If you know how to construct a query in natural language, you will get a result back. But you know, like, it was not a system that was capable of, you know, like asking follow up questions to try to understand what you actually want. And then kind of finally, you know, like, bring this all context and go to generate a SQL query, get the result back and all of that. So that was a really missing part. And I think right now, that's, you know, like, what is the difference? So right now, I kind of bullish that if I would start Statsbot again, probably would have a much better shot at it. But back then, that was a big limitation. We kind of build a queue, right, as we were working on Statsbot, because we needed it. [00:04:27]
Alessio: What was the ML stack at the time? Were you building, trying to build your own natural language understanding models, like were there open source models that were good that you were trying to leverage? [00:04:38]
Artem: I think it was mostly combination of a bunch of things. And we tried a lot of different approaches. The first version, which I built, like was Regex. They were working well. [00:04:47]
Swyx: It's the same as I did, I did option pricing when I was in finance, and I had a natural language pricing tool thing. And it was Regex. It was just a lot of Regex. [00:04:59]
Artem: Yeah. [00:05:00]
Artem: And my co-founder, Pavel, he's much smarter than I am. He's like PhD in math, all of that. And he started to do some stuff. I was like, no, you just do that stuff. I don't know. I can do Regex. And he started to do some models and trying to either look at what we had on the market back then, or try to build a different sort of models. Again, we didn't have any foundation back in place, right? We wanted to try to use existing math, obviously, right? But it was not something that we can take the model and try and run it. I think in 2019, we started to see more of stuff, like ecosystem being built, and then it eventually kind of resulted in all this LLM, like what we have right now. But back then in 2016, it was not much available for just the people to build on top. It was some academic research, right, kind of been happ
