BONUS CONTENT: The {Closed} Session Spotlight Series showcases a different co-founder from the super{set} portfolio every episode! Gal Vered is co-founder and Head of Product at Checksum (checksum.ai), end to end test automation leveraging AI to test every corner of your app. Gal details his background as a product manager at Google, a YCombinator CTO, a Northwestern Kellogg MBA, and an Israeli Navy Officer. The group delves into the exciting problem that Checksum is solving with AI and what Gal likes best about working in super{set}'s startup studio model.
Checksum automatically generates and maintains end-to-end tests based on user sessions so you can move fast without breaking things. Learn more about Checksum at checksum.ai .
Gal Vered's LinkedIn - https://www.linkedin.com/in/gal-vered/
Welcome to the closed session. How to get paid in Silicon Valley with your host Tom Chavez and Vivek Vaidya.
Tom: Welcome back to the closed session. This is Tom Chavez.
Vivek: And I'm Vivek Vaidya.
Tom: Okay, we are going to chop it up and do something a little different today. I'm very excited here to have a chance to talk with a couple of our amazing co-founders from Superset. We haven't done this before, have we?
Vivek: We...
Tom: but, no, we've done it in a different context, right?
Vivek: No, we did it quite a few times, actually. We got Pankaj in, we had Matt, we had Dane. We've done it a few times. But this is a different format, though.
Tom: It's a different, it's, that's...
Vivek: Different format...
Tom: That's where I got discombobulated, but yes.
Vivek: Different format. It's called the Spotlight series.
Tom: We're doing a spotlight.
Vivek: Yeah.
Tom: And so it's exciting. Let's just hurl right into it. We have Gal with us. Gal is one of our co-founders, is our Principal Co-founder and Head of Product with Checksum. Hi Gal.
Gal: Hi. How are you?
Tom: Good. I'm glad you're here.
Vivek: Yeah. Great to have you here, Gaal.
Tom: Well...
Gal: I'm happy to be hosted.
Tom: Gal, back us up a little bit. Give us the, uh, the personal journey. How did you, you don't have to go all, well, yeah, go back. Where did, you know, where you from? How'd you get into this game?
Gal: Sure. So I'm, as some of you may be able to tell by my accent, I'm originally from Israel, I served seven years in the Israeli Navy, and then I decided, I came to the US did my master's degree, worked at Google, and then founded a company at YC, and joined Superset as a co founder.
Tom: All right. And now, and so, can you tell us more about the Navy experience? Because that's, you know, for us Americans, Israelis who train in the military. It's fascinating to us. What did you learn? What did you do?
Gal: Yeah. So I think what you learn in the Navy is that you operate with a high risk, right? Because if something goes wrong, you're in trouble, but on the other side, you operate very fast, like you don't have time, everything needs to happen now. So you kind of learn how to processize stuff. So you can operate in high speed, but lower the tolerance to mistakes. So it's about debriefing, trusting people, and kind of getting them ready for the day. So that's what I learned.
Tom: Dude, that sounds exactly like early stage company building.
Vivek: Exactly.
Tom: Well, that explains why there's so many kick-ass Israeli, entrepreneurs.
Vivek: So speaking of early stage, what does our company do, Gal?
Gal: Yeah, so we help companies QA their product and test every corner of their web applications using AI. And if we take it one level more technically, we generate end-to-end testing suites. A switch that can run on command and test everything. Again, using a state of doubt, generative AI models.
Tom: So I... as with all things at Superset, we explore and build companies when they're personal. And we think we know something about the space, the premise, the opportunity, but we also want to have up close and personal experience of the problem. So I always like to talk about in the context of Checksum. There's another Superset company, where I'm the guinea pig, early product user, and I'm getting so frustrated that we're bumping into this, these knowable, avoidable bugs and I'm getting angry. And then I think, well, how did this happen? And then I remember, oh, no, no, no, that's, that didn't just happen to me. I am the problem. Gal, back us up and talk a little bit about the context around this, right? The problems that, cause I'm the person in that context telling the Head of Engineering and asking the Head of Engineering in this other context, Hey, you know, ship it, go, keep it going, don't work, don't break your pick and get all caught up in your knickers on some of those bugs, we'll fix them later. Tell us a little bit about the problem and why we care so deeply about it.
Gal: Yeah, I think, first of all, one of the cool things about Checksum is that it was never about AI, right? It always started with the problem. Like, we all suffered from the same problem again and again. And AI is just a way to solve the problem. And I think that's why we're kind of seeing, seeing the traction we're seeing. So if we start from the beginning and this is the, I'll tell you the journey from a startup perspective, but it's true for big companies, small companies, as we've kind of learned across the months is that you start with a small team and at the beginning, it's all about ship, ship, ship, you have so much risk that it doesn't make sense to think about quality because. You may not solve the right problem. Everything you do right now may go into the trash bin and you're going to start from scratch but the mindset is always about ship, ship ship. But it's a small team, so the CTO is able to handle it you know, everyone, you're recruiting very talented senior engineers so you can manage it. With time, the team grows, you get more engineers into the mix. The CTO is the most experienced person most of the times, can't micromanage and review every PR and test everything manually. The CEO, the Head of Product, the Product Manager can't keep doing like back and forth with every engineer because the team is now big. And also the bar for hiring, hopefully you keep it up, but in reality, every once in a while, like the you're going to have, you know, even forget about hiring, even junior engineers are going to join the team because that's the natural succession of the company. That's a good thing. But the problem is that with that quality goes down and you'll start seeing bugs in production and you'll start seeing tasks that should take two or three days, take three weeks, just because there is so much back and forth. Like the task itself takes three days and then it's three weeks of fixing the bugs and dealing with all of the unintended consequences. And in that point in time, you'll kind of feel like the house is on fire. And this is a time where our customers kind of like crying out loud, and we come in and we help them test their app end-to-end. So you work on a feature three days and instead of a three weeks process to figure it out, you run it, you find all of the bugs immediately. You fix them within the day. And within four days you ship it, you ship it back.
Tom: Yeah. And so for listeners who don't work in the middle of a software engineering process, what Gal was describing, those hair on fire issues, we call that Tuesday, right? In a software, in a software group. It's very, yeah, it's a, it's a big problem and it's remained unsolved for decades.
Vivek: Yeah. And, and so let's talk about that, right? It's been, it's remained unsolved for decades. You mentioned AI and, uh, the fact that AI is. And you didn't say it like this, but AI is a means to an end for us. We focus on the problem. We always start with a problem and always have the problem in the back of our minds when we're doing this. There is an element of AI, which has informed our approach and we couldn't have done what we're doing right now, two years ago, right? So can you talk a little bit more about, about that and why check, what makes Checksum unique? Based on what's happening today.
Gal: Yeah, how technical should I go? Is it keep it high level or? Well, give us the high level and then go deep. Yeah. So if we think about all of the great things we see in AI today, right, the most well known ones are obviously ChatGPT and, and all of the image generations. They all became available because of, of a new technology, diffusers and transformers that was introduced in 2019 and, kind of, took three years to take it to market. And I think what we're seeing today is that those technologies allow us to understand the delicate interconnections and dependencies that language or systems in general have between different steps. So with ChatGPT is how it can understand what the word that it needs to generate now, correlates with the sentence that it wrote like one sentence ago and one paragraph ago and take every all of the context into consideration. For Checksum is the ability to understand that the user, the action that the test is doing right now, how it correlates to what it's done five steps ago, ten steps ago, the entire system, the user context, everything that makes software so complex. But also so wonderful, all of the logic that's backed up into the software, the new innovations in AI now allow us to create a model that can take everything into consideration, understand what's important, that's the attentions networks, if you want to go kind of technical here and spit out a prediction that for us might perceive as, as intelligent. And I think it's up to debate whether it is intelligent or not, but perceive that as at least intelligent-like, what you see results from ChatGPT and what we see in Checksum when we generate tests.
Tom: Yeah. So role playing here, Gal. A listener who is a software engineer say was, would listen to this and say, well, it sounds like QA automation. Is it, you're doing QA automation then, right?
Gal: We, so we think about the problem as continuous quality in a world. Around 10 years ago, I think companies started to shift into SaaS models and into CI/CD models, meaning that instead of writing software for six months in a row and then package it up in a CD, ship it to stores. And they started doing CI/CD, every time you deploy multiple times a day. Every time you ship code, it goes to production. What's missing from this space is continuous quality. Cause yes, you ship more code, but you ship more buggy code and you create more issues. And we've all been there. Like we've all used companies from the products from the best and the greatest Amazon, Google, Apple, and there's just so many bugs out there. So we think about it as continuous quality. We think, and what we do is Checksum is because we understand systems very well and because we understand how your code works very well, we're able to make sure at every step of the process, from the developer writing code, to the integration, all the way to deployment and production, make sure that your code works. As if you had an army of people that review every PR, review every code, test every release, and make sure it just works and if it doesn't work, you get feedback now. Not two weeks from now, not two months from now when the user files a bug report. As you write this, as you write the code, you get feedback and you're able to fix it. So, and it's important to say it's as much about as about velocity as it is about quality. And because our customers, they mostly talk about speed. Like when we talk to them about the value we provide. And quality, cause it goes hand to hand, hand in hand.
Tom: If you talk to a CTO or any Head of Engineering who's worked in software at any kind of scale and ask them rhetorically, Hey, what would you be willing to pay for just one percentage point improvement in throughput from your engineering team, like working code per unit time? It's immeasurably huge, right? So I really appreciate how you always bring us back to the, to, it's not, it's about quality, but it's also about the velocity of your software engineering process.
Vivek: Yeah. And just to, just to kind of make it personal, right? I've used so many QA automation platforms in the past, right? They, and there are, there are quite a few out there. People use testing frameworks and various CI/CD systems to implement these automation pipelines. And that's not the hard part. Those systems exist where I think Checksum solves a unique problem is you all the long pull in the tent. You have, you need to have tests that you run through those frameworks, that you run as part of your QA automation pipelines. And that's where Checksum is unique in that we actually generate tests for our software engine, for the software engineering teams who are our customers that help them achieve the velocity by not compromising on quality.
Tom: Yeah. I don't want to get too punchy about it, but for me, Checksum is to conventional QA automation what apple's are to Hovercraft, right? I mean, yeah, I mean, they're both entities or objects, but this isn't, to your point Vivek, automating the test after it already exists, that's not the hard part.
Vivek: Correct, correct.
Tom: Generating a new test that smokes out a bug that I actually care about. Okay that's nirvana, right? And it couldn't, to your point Gal, it couldn't have happened two years ago, right? We had to wait for this magical cusp, right, where this gen AI, this multi layer neural network explosion of possibility fuels the kinds of, you know, things that we're doing at Checksum. I don't think we could have built this company five years ago.
Vivek: No.
Gal: Exactly.
Vivek: Absolutely not.
Tom: No. So, a note or two, Gal, if you would, about Superset, what it's been like, you know, joining up and, and whacking away at it, at it over here with me and Vivek at Superset.
Gal: Yeah. It's. First of all, it's great and...
Tom: You don't have to just say that
Gal: Yeah, I kind of do but I mean it in this time I mean it but I don't know...
Tom: Along those lines, along those lines Vivek or me, who do you like more?
Gal: Oh, there's only one right answer, here you go.
Vivek: And it starts with a V.
Gal: Let me, I'll, I'll take the, I plead the fifth, I believe.
Tom: Oh, gracefully dodged.
Gal: No, I think, look, working at Superset was simply put, amazing. First of all, I'm doing what I love. I don't think, I was always happy in my, in my jobs, I, so... Yeah, I've had the luck to always work on interesting problems and roles, but this is really kind of like the essence of what I dreamt about to be working in tech, working in new technologies, innovation, solving people problems, and, you know, everyday whiteboarding, a lot of breakthroughs, a lot of disappointments and stuff that doesn't work, stuff that don't work and, and it's simply been amazing. And I think with Superset, the unique part is that it provides you all of the kind of excitement of a startup, but it just gives you some percent more guidance. Like you're still in the field, you're getting your hands dirty, you're taking the risks, but every once in a while and quite often, but like I have the Superset, it's you two, but it's also the entire Superset group and it's the Superset companies and the other co-founders to kind of share your thoughts, share ideas. And that's, I often, I, I sometimes give the analogy because people ask me about Superset. It's like, I cook and I don't cook well and my wife cooks well. So every once in a while, I like, I make the dish and I spend an hour on it. And it, it just doesn't taste well. And my wife comes and she adds a bit of salt, paprika, I don't know, some condiments, and within like two seconds, she take an okay dish to something amazing. But I couldn't do it myself. And I think that's the Superset experience. It's like, the Superset group, and especially you two, you come, you add like the salt, the paprika, the pepper, and suddenly what we had was okay, turns into amazing, because it's like those small directions in life and I think I'm very lucky to have this kind of mentorship and support system in my first kind of like tooth to nail startup.
Tom: Oh God, not to get all goopy on you, we're the lucky ones here, actually, right? I mean, building, cause we've said in prior podcasts, like we just need the joy of building shoulder to shoulder and getting that dirt under our fingernails and getting kicked in the head as we do every day. But doing it shoulder to shoulder with with world class product leadership, that's as good as it gets.
Vivek: Yeah. And I think for us, it's also that energy that you have the hustle that you bring is infectious. We get inspired by seeing you and other, other Superset co-founders do their work. Really, we do.
Tom: No, we need that. We need that. Really great. Gal, thanks for joining.
Gal: Thank you.
Tom: This was fun.
Vivek: For our listeners, if you want to check out what Checksum does, you can go to checksum.ai. For all the software engineering leaders out there, if you want to give it a shot. Reach out to us. We're looking for design partners always.
Gal: Thank you very much.
Tom: Thanks, Gal.