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There's a war for attention.
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If you build something that nobody uses, it might as well have not been built.
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This is Jordan Tigani.
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With over a decade of experience at Google, and specifically BigQuery, he shares why he started a new company and how he's building Mother Duck.
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I'm not sure I'll ever have another idea that's this good, so I might as well go for it.
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What is your advice for people that want to get into specifically this niche of engineering?
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You just had the partner meeting.
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How much do you want?
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And I'm like, I don't know, $5 million?
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Could you share the mother duck origin story from your perspective?
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Sure.
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So, I mean, first of all, I was a single store database company, formerly known as MemSQL, and I had just gotten fired.
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I was a chief product officer, and I had gotten fired on a Friday morning, and I'm like, you know,
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it was like, hit me sort of like a ton of bricks, like, what the hell am I going to do?
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And I tried to figure out what I wanted to do.
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And I had seen this great open source database called DuckDB.
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And I've been thinking, you know, somebody should take that and build a SaaS service out of it.
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And I'm like, well, I've built two database SaaS services, you know, first BigQuery and then the SaaS service for a single store.
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And I'm like, huh, maybe that should be me.
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So I'm like, and I really want to learn Rust.
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And so I thought I would use this as an excuse to learn Rust.
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I was going to hack on it for a little bit.
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And then I asked a friend to introduce me to the creators of DuckDB, Honest and Mark, who were in the Netherlands.
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And I had a call with him and he said, hey, it's a cool idea.
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I'll introduce you to Honest and Mark, but you should also talk to my friend Tom.
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Tom invested in my company and he'll give you some feedback on the idea." I'm like, sure.
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Then the next morning I talked to Hannes and Mark and they're like, I was actually asking for a job.
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I was like, hey, would you hire me to help you build a SaaS service?
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And I said, no, we don't really want to do that.
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We want to just focus on the core database.
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and that afternoon I talked to Tom who was Tom Tungas.
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He was the managing director at Redpoint
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and now has his own has his own firm
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and I get about 15 minutes in he's like yeah it's a good idea I want to fund it.
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Something like that.
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Come to the partner meeting next week
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but you know it's sort of a foregone conclusion
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because he was you know the managing director
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and then the next day I was talking to some other VCs about something totally different.
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I thought I was wasting their time.
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I'm like, hey, I had this idea kicking around.
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Do you guys want to hear it?
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And they said yes.
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And it was Martine Cassato from A16Z and Elad Gil.
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And Martine said, you just had the partner meeting.
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How much do you want?
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And I'm like, I don't know.
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Five million dollars?
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Like, I had no idea.
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I didn't even know who these guys were.
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I didn't know if this was a good firm or not.
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I didn't know anything.
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And, um, you know, they said, sure.
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It sounds good.
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Anyway, the long, long story short, like I kind of like stumbled into starting a company and,
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um, and then, uh, you know, it was about a week later and I'm sort of like, like I sat down, I was like,
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is this really what I want to do.
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And, and then I just thought like, well, you know, I've like, of the options that I have in front of me,
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like, you know, there's, this is the one where, you know, it's a bet on myself.
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It's a bet on, I think it's a really good idea.
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I'm not sure I'll ever have another idea that's this good.
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So I might as well, you know, go for it.
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And also I realized that, you know, I've worked at jobs that had great culture and great environment and ones that were horrible and toxic.
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And it's like, well, how do you, how do you know that you're going to have a good place to work?
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And I'm like, well, you know, if you create it yourself,
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then, you know, at least you only have yourself to blame if it doesn't work out.
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You know, it's not, you know, you can't, it's not always, you know, sunshine and roses and everybody is wonderful to each other.
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But, you know, that's sort of, I figured like, it's the best way to actually build a good work environment
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and to win by building a group of people that like each other,
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that work hard because they're doing it for their co -workers who they trust
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and respect and they want to see them succeed versus everybody out just for a payday.
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So that's what we tried to do and that was about four years ago and you know I think we've been having
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We're having a lot of fun ever since then.
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Yeah, that's amazing.
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The relationship of DuckDB and now the company
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Mother Duck, can you go a little bit deeper into that?
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Because I looked up kind of similar companies
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and my mind went to kind of what I know for Vercel and kind of Next .js.
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But that's a very different example from the setup you have currently.
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Sure.
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I think it's a really unique and interesting one.
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And, you know, we basically have this commercial company, Mother Duck, and we're building this hosted cloud version of DuckDB.
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And then we have the open source company, DuckDB Labs.
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They just changed the name to Duck Labs.
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And so the Duck Labs, they focus on building a great database, building a great open source database.
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They have no pressure to do anything like change the license or do anything, remove things from the open source.
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And we're the commercial company and we're trying to build a SaaS service out of it.
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We're not selling DuckTB.
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We're selling, you know, we run DuckTB for you and with serverless and AI, all this, you know, all this stuff,
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all the things you build around it.
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And, you know, when we started, we actually gave them a co -founder share of the company.
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So like, because I didn't want to just, you know, say, hey, thanks for building this great open source tool.
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We're going to take it and, you know, we're going to try to make a bunch of money out of it.
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It was like, and we also realized
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that the only way to really make it work was if they were bought into it.
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And the way to have them bought into it is so that they are incentivized for us to succeed.
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And I think that's been great
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because it's very hard to build a SaaS service without the ability to make changes to the underlying project.
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And so we had a lot of hooks that were put in just for us.
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We have a lot of features, bug fixes.
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We also contributed a lot ourselves.
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I think we have a thousand commits or something into the DuckDB code base.
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But so far, it's been a really nice partnership.
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It's been a sort of high trust, strong relationship between us and the DuckDB founders.
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And kind of the hope is that if this works out, then this can be a model for other open source to use.
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If it doesn't work out, then it's a cautionary tale.
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But I think even if like, I mean, the world changes, people change.
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But, you know, so even if as things change, like we've gotten a really good base and a really good start with this,
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you know, really key partnership for us.
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Yeah, I love the word partnership.
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In essence, that's what it is.
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All open core companies that I've seen is either people build a company
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and then they open source part of
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that kind of to get a community around that
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or they have the inverse where there are the people that maintain the open source component.
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They also need to make money and then they say, OK, we're going to build services on top of that.
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And some functionality is kind of gated.
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But the core part, that's the open core is still open.
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Tell me what you have.
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I don't know of any other company that has kind of this partnership.
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Because you also get these cases where, you know, people who are building, they build open source.
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And, you know, you're trying to build a community and you're really kind of incentive to just sort of grow users.
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And then they're like, well, how are we going to monetize?
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OK, we're monetized with the SaaS service.
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And so what they do is they build the like simplest possible SaaS service because it's not what they're focused on.
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Their focus is on building this great open source project.
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So the SaaS service is like, okay, we just run whatever in Kubernetes and they call it monetized.
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Managed.
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Yeah, they call it managed.
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And I think there's a real opportunity to innovate in how the service is delivered
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and innovate around that project that like
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if your primary focus is building the open source project
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you just don't you know your all your innovation tokens are being spent on on
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that versus on on the delivery and
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so for us the innovation tokens are spent on how do we provide
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this amazing you know end -to -end service just to
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so to make analytics delightful for people yeah i love
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that a lot i'm wondering because your origin story was a lot of, okay, I reached out to these people,
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I asked for help, and all of a sudden you said, okay, they asked me for a number and I gave them a number.
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This vision that you have of this partnership
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and me focusing my innovation tokens on what I think really should be the core of this company,
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this vision, is that why people invested in the end
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or helped you in the first place now that you've reflected on it?
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Or why were people so willing to help?
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I think there were a couple things.
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One was Snowflake had just gone public and I think it was the largest IPO at the time.
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And I think now it seems kind of quaint with all the, you know, you know, Anthropic and, you know, and open, open AI like and.
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But at the time, like, you know, everybody's like, hey, if they miss out on Snowflake, then then they wanted to find the next thing.
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I think, you know, DuckDB was an open source project that had a lot of traction and so it was growing really quickly.
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And so there was a bet on that.
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You know, I also helped create Google BigQuery and I led engineering and product for a while.
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And so I had a decent amount of credibility in that.
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And so it was like, okay, if somebody is going to build something like this, DuckDB is the right thing to build it on.
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You know, I have a pretty good track record, somebody else to bet on.
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And I think those things just sort of like sort of came together and in a nice, nice package.
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Is that also what you looked for
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when establishing kind of the first core team of people that were going to found this company?
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So the history plus the enthusiasm of Duck TV?
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Yes.
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The founding team was like was people that I'd worked with before, customers that I'd had before.
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and two of them were like people that I knew as customers.
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A couple were people that I worked with at Google.
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And then people who were just DuckDB enthusiasts that were like,
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hey, I want to be part of this DuckDB thing and this is sort of the way to do it.
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And we had kind of a big founding team, but that really let us hit the ground running and get a lot of stuff done.
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And also just like we had some quite senior people
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And the nice thing about
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that was it allowed us to like take sort of the obvious
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but wrong designs and just skip past those and get to like, you know, an actual better design
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and the right way to do things um you know at least
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sometimes the senior people they're less like you know the they can be less like sort of like scrappy and like
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but also um you know you just get more done per kind of per like line of code
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or per per hour of uh of uh of of building
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and so that allowed us to have you know i think a quite a good solid foundation
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that we're you know continuing to build how big was initial team?
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It was 11 people.
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Yeah.
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And when was the first milestone that you've delivered something with that team specifically,
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or maybe even with a broader team that you were quite proud of that?
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So we had like our first alpha, you know, kind of showed, you know, this is a hosted DuckDB that, you know, serverless, so you could,
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we could, you know, assign instances and, you know, in milliseconds.
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And I think that took us about three, three to four months to have something that,
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you know, our own storage system and it was, you know, strung together with some bailing wire,
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but, you know, it was enough to sort of show to some people and start to get some early design partners.
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It took us about a year to get to beta, to something that we thought this is like, this is good enough that,
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you know, people will actually, you know, want to use this for real.
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And then it took about another year to get to the point where we're like, well, okay, this is something that people will pay for.
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And I think one of the tricky things was just, you know, the state of the art of data warehouses is,
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you know, is quite, you know, quite far these days.
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And so just the amount of table stakes that you have to do before people are like,
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yes, I will actually use this for my analytics were pretty significant.
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But since then, we sort of have been off to the races on the monetization
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and actually building a business out of it.
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Nice.
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Was there a capability that stands out more so than others?
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So either developer experience or some of the technical components that needed to be in place for the product?
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Or what's your view on that?
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I think some of it is just the reliability and the usability.
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Like, if you're going to use this in production, it's got to actually work.
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It can't lose data.
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It's got to return the right results.
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You know, I think we had, you know, and I think we were also growing up with DuckDB.
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So like, you know, I think DuckDB, when we started, it could do a lot of things really well,
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but it was not fully fleshed out there was a lot of ways you could not get over
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and queries that went off into the weeds and you know
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um you know this was i think duck db 1 .3 or 0 .6 so it was uh it was you know, still quite early.
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And so just hardening DuckDB was also an important part of that.
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Yeah.
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What's the latest version, just to get an understanding of how farts come?
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1 .55, but like there's,
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you know, they were doing releases kind of every couple of months in the early days.
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I think they've slowed down a little bit, just as the, you know, the bar get tired for testing and you want to make sure that,
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you know, everything is solid.
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It's not a, hey, we just throw it over the wall and like, and, you know, and people will tell us what's broken.
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Yeah, gotcha.
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What's the next big problem that you're trying to solve that will really bring value to the users in the end?
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So, you know, in the last six months or so, it's kind of been clear that AI is changing how people are interacting with data.
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And we're sort of assembling the adjacent pieces to the data warehouse and adding this functionality to our product.
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The first one was, after we realized that, hey, Claude is really good at building data visualizations.
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And so what if we just hosted those visualizations, swapped some things out to make them live and querying live
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Mother Duck databases, had them shareable in Mother Duck, and then, you know, voila, you have basically BI.
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It's BI, but it's BI on steroids because there's some things you can do that you can't, the level of interactivity,
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the sort of the level of creativity
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you can do kind of is way beyond what you can do in a BI tool.
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And then if you look at the other sides, the other pieces of the data stack, you know, there's, okay, you have to get data in,
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you have to data ingestion.
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which is also pretty vibe -codable.
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Like if you point Claude at Salesforce and say, hey, pull my data in, it's going to be able to figure that out.
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And so just like we did with the visualization tool, which we call dives, we built flights,
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which is a way of allowing people to vibe -code a pipeline that they can get data in.
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And we did it in a very generic way so that these pipelines called flights are just
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Python code running on a schedule.
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But really, you combine that with Claude and you can do some really, really cool things.
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And so you kind of put that together.
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You have the ingest, analyze with our core data warehouse offering, and then visualize with our dives.
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And then you can like build this sort of feedback loop where you can have a visualization
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that like will cause data to be to be rehydrated or views to be materialized or sort of kick off,
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you know, right, right backs and kicks off a flight.
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Or maybe the maybe the flight is actually sending, you know, a, you know, Slack messages to somebody or maybe it's building a PDF.
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and you know meanwhile you can have flights
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that are sort of building building dives
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that are updating you know with with the latest you know days news or you know running inference
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you kind of put these you know each one of these tools by itself is like is is very interesting
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and then I think the the feedback loops are also where it gets very interesting underneath
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we have we call guides which is just a context layer
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that kind of threads through everything and so in the
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And the last piece that we're working on is sort of just the agent harness, which is, right now you interact with it through Claude,
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ChatGPT, Gemini, or whatever, and we're sort of building our own agent interface on it,
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which will just make it easy to, like when somebody shows up, and you just, instead of like saying,
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okay, well, I'm going to run these jobs or these queries or to build these things, and just, you have a discussion with
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our agent interface that you describe what you want to do
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and we figure out how to do that by combining these pieces together.
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And I think that's sort of the, I mean, to me, that's sort of the holy grail of sort of AI for data interfaces where,
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you know, you might not be super, you know, you might not be a data engineer or a data analyst.
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You might be.
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It might just be like, you know, it's, you know, just like a software engineer,
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you know, it's really nice to have Claude write the code for you.
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And like, yeah, you could write that, you know, you know, command line, you know, interface code or...
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or UI code, but it's just, you know, it's, you can do it a lot faster if you have, if you have cloud to do it.
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And, and so, you know, I think it's going to enable both technical people
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and non -technical people just to get a lot more stuff done with data.
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And also to let people, you know, business users who are actually close to like what's actually happening to be able to,
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you know, get some of this stuff done on your own,
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like the marketing person that wants to understand what's going on with their campaigns that they're running or the salesperson.
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A lot of our salespeople are probably some of the biggest users of MotherDuck internally.
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They use it to find out, hey, what's going on with my customers?
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What's going on with my book?
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Which customers should I talk to?
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How has their usage been changing?
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And on one hand, everybody's nervous about what happens
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when this AI goes off and does something weird or does the wrong thing and hallucinates.
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But I think one of the cool things is, you know, by having the business users do it,
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they're the ones that can actually spot when it's subtly wrong.
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Whereas if it's an analyst and kind of this, you know, separate team and you say, you know, somebody files a ticket and says,
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hey, can you build this dashboard for me?
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if the numbers like turn out they look kind of wonky um the analyst isn't going to know until like
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and until like the business users like hey like i know
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this customer doesn't actually use this feature why does this show up here
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and like you know and they can they can start poking at it
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and there's just a long feedback loop whereas i think
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if you have um you know the business users close to what's actually happening
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they can sort of spot these these problems and and and fix them
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And at the end of the day, you know, humans are fallible as well.
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And so I think, you know, just like self -driving cars, at some point the self -driving cars get, you know, better than, yes, they are.
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They do make mistakes, but, you know, they're not going to be perfect, but it's, you know, they can certainly be better than a human.
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Yeah.
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Having this agent operator kind of in between the tools
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and capabilities that you offer and the user that actually wants to get these insights,
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I feel like allows the flexibility to kind of focus on different types of users.
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But as you say, an analyst has a different kind of reference frame
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and different judgment or even taste compared to a business owner or a salesperson that knows their process in and out.
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Are those then more the primary users as you view it or how do you see that?
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You know, I think that like, it's going to be, it's going to be both.
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It's going to be, it's going to be sort of like,
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there's going to be, somebody's going to be responsible for data at a company and it's, you know, it's not going to be the business users
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and there's going to be a data team
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and they're going to be responsible for making sure the pipelines are working and debugging things and stuff stays, you know, the weekly business reviews can,
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can, can proceed with, you know, with, with real data.
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And when you report data to the board, it's actually uh it's actually you know reliable
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but i also think it's really important that you know questions
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that other people have in the in the business don't have
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to go through like gatekeepers uh uh who can who can figure things out
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because i think once these folks can start to ask questions about their the things
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that they're interested in you notice that like the things that they ask are actually different.
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You know, for instance, the, you know, one of our salespeople, the first time we kind of set him loose on our MCP server,
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you know, his first question that he asked was like, what customers should I talk to today?
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That's not the kind of question that like, that an analyst would ask.
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And, but it's something that Claude actually did a pretty good job.
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You know, it can look at support tickets.
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It can look at, you know, usage.
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It can look at, you know, news about like, hey, this company, you know, just got dinged by the FCC or something like that.
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There's all sorts of, you know, interesting things that Claude can pull together.
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Also, our head of sales, first question he asked was, what are the three biggest risks to my business?
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Oh, yeah.
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And like, I mean, those are things that Claude actually can give a good answer to,
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but also not the kind of thing that sort of like a data team is likely going to,
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you know, they're not going to come up with this report and then tell the, you know, tell the CROs like,
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hey, here's the risks of your business.
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Like, what the hell is this?
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But, you know, I think there's different kinds of questions
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that you can start to ask when different types of people are doing asking.
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Yeah, I love that.
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I was talking to a person from booking .com and he's responsible for developer experience.
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And they also mentioned that now with agents
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and them already being quite advanced and starting early
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and capturing all the data of developer experience on a quantitative level instead of building dashboards
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and metrics and KPIs because this person said if we do
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that then those become the targets so then it kind of defeats the purpose
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They indeed give their users, which could be engineering managers or leadership teams, the capability to kind of debug the data, debug the software development lifecycle,
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find bottlenecks and then solve those and then ask more questions and find more bottlenecks in that way.
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And I feel like this interaction of debugging is one of the phrases, but kind of finding gold in your data together with agents,
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because in that way, you can also have capabilities that you couldn't even imagine, where Claude goes off on the web and pulls that data in
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or looks at some artifacts that it already has next to your data and then makes a decision based on that.
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I think it's fascinating.
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And I hope it's kind of a trend that will continue
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and people are given the tools to kind of solve the problems that they are top of mind of.
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We've had this internal debate a bit about the future of the dashboard.
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And I think there's, you know, I wrote an article a couple years ago called Big Data is Dead.
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And actually, I borrowed the title from that is like, is because people had been writing about dashboards are dead, you know, for a decade.
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And so, you know, he was like, oh, well, you know, with agents, you know, we don't actually need these dashboards.
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And, you know, people are, you know, if you have dashboards, then, you know, people are,
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you know, they're trying to beat that as a KPI versus is actually solving real problems.
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On the other hand, I'm a big believer in dashboards because if I just think about how I, you know, I use AI with our products,
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you know, all over and I use, you know, I do, you know, but I build a lot of dashboards, a lot of data visualizations,
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and I also have a bunch of data visual, you know, dashboards that I look at, I don't know, eight times a day.
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You know, I'm like, you know, where's the number?
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What's the usage?
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Who's using it?
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How is it changing?
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I think what the difference is, is that kind of there was this interactive BI movement where you build a dashboard
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and you can like drill down and click on things.
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And I think one of the problems is like the type of questions you can answer in
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that kind of situation are very, very narrow.
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It's like, okay, well, I can split out this aggregation.
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I can see where something is actually coming from.
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But I can't go a step further.
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I can't ask why, I can't see what actually is happening.
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I can answer the question maybe that had been prepared.
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I think the interesting thing with AI is that like you can have that dashboard and you can be like, that number there is weird.
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Like something is happening today.
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What's going on?
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And then you can, you know, you can have, you know, you can ask Claude and Claude will,
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you know, poke at it and figure it out.
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And at some point, you know, there'll be an agent running and like, and the agent will tell you there's something weird in the first place
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but it's also just sort of very nice to have a kind of this just sort of stable thing
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that you know this is what we report on this is
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what we care about this is like these are the these
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are the you know input metrics to our revenue metric and kind of we
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have a sense of how like we have a mental model
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or of how the business works through through these metrics uh
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and that you know when something changes they should like they should change for a reason
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and we should be able to dig in and understand.
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I want to pivot slightly and from the perspective of the listener who's typically an individual contributor or an engineer,
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if they get really excited about this concept of databases on the one hand, infrastructure on the other hand,
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even the flavor of AI and kind of the user experience that builds on top of that,
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they might want to join a company that is yours or a company that is similar.
377
What is your advice for people that want to get into specifically this niche of engineering?
378
Well, we're hiring.
379
Yeah, that's good.
380
Okay, check out the website.
381
You know, I think it's one of the just interesting things about a database company like ours
382
and a database SaaS company or database infrastructure company is that there's a whole gamut of engineering jobs from storage,
383
database, infrastructure, SaaS, or like the API level,
384
and then UI, and then AI and agent stuff as well.
385
And it takes all of those things.
386
And so kind of people tend to think about it as like, oh, well, I want to work on the optimizer.
387
I think everybody wants to work on the optimizer.
388
And it's like, first of all, there's not many people in the world that work on database optimizers.
389
And second of all, it's not that interesting of a job.
390
Like it's, you know, kind of you're working in this box
391
and you kind of know where the inputs and you know the outputs.
392
And I mean, for me, it's much more interesting to figure out how do we take kind of these things that are...
393
much more unknown and much more ambiguous
394
and figure out how to like make those all make those all work together
395
and make those work in a in a harmonious way so
396
you know I think the you know the important
397
you know a lot of the important jobs in a you
398
know in a database company are not just the like the core the core database stuff yes
399
that is that's obviously very important and like
400
and at the end of the day you know
401
if you're a database company or an infrastructure company you kind of live
402
or die based on the strength of the you know of the of the the core technology um
403
but if you don't have the great stuff around it then
404
then you know it's you're not going to be successful
405
and um you know i i i think
406
there's a lot of people who um only look at the
407
sort of the the the inner the inner loop uh and not the outer.
408
And I think that's just a, it's also a recipe as a, as an engineer to be sort of, um, you know, have, have blinders about,
409
um, what do users actually want?
410
And like, how do you, how do you build something that's actually, that's actually compelling?
411
Because, you know, in my career, I worked on technology that I thought was like, that I was, it was beautiful.
412
Like I was like, I was so proud of this and it was like, and like, and my heart and my soul went into it
413
and it's like but then it doesn't get used and then
414
or the project gets cancelled or the company
415
that it was built you know behind it goes out of business
416
and like and it's it may as well not have existed and and
417
so that's why I think
418
it's important to um uh to sort of pay attention to the other pieces around it is the the users
419
and you know and the the things
420
that we tend to call product I'm curious what your advice is for people to distinguish themselves
421
because we need people that are both kind of enthusiastic about the technical capabilities
422
that and I also see this trend go a little bit more in width
423
and also think about users usability how do we make this stake
424
and what is important from a user persona perspective there's a lot
425
that goes into it but nowadays with AI potential resumes look the same actually all
426
that they all fulfill your hiring profile basically yet you still need to have people on board
427
that are dedicated to the mission that can execute as you would want them to, how do they distinguish themselves in the first place?
428
I think that is a good question.
429
I think understanding product, which means understanding users, how the thing is going to be used,
430
and understanding the business context in which something lives,
431
I think is a superpower for an engineer.
432
Engineers tend to look at down on it, but it's, you know, again, if you wanted to actually turn into something,
433
something that people care about, I think you have to,
434
you have to understand the users and have a lot of respect for them and a lot of like, you know, empathy, empathy with them.
435
And you also have to, you know, it's got, at the end of the day, it's got to be a business that works because,
436
you know, if, if you can't make money off of it, then everybody goes home and,
437
and has to do, do something else unless you're going to, you know, just, um, you know, just work on open source.
438
Uh, but at some point somebody has to pay, somebody has to pay the, somebody has to pay the bills.
439
Um, and I don't think it's, it's not that hard to learn product.
440
You just have to sort of open, you know, open your mind and think about things, um, you know, think about some of these things.
441
I think for me, one of the best ways of doing that was I wrote the first book.
442
I co -wrote, co -authored a book about BigQuery when I was working on BigQuery.
443
And the funny thing was, as I'm describing how to use it, I realized like, hey, that's weird.
444
That's wonky or that's goofy.
445
Like we should change this.
446
So like there were a lot of changes in BigQuery that were the result of, you know, as I'm just as I'm writing this book,
447
like, how do you make it actually so that it makes sense, like the writing makes sense.
448
And there weren't necessarily huge changes, but they were just like, they were, you know,
449
sometimes the command line that you do to do something or the API you do something to do something was like, it just felt goofy.
450
It's like, that's, that's goofy and like and so you you fix it and
451
so like um you know writing things down helps like i think
452
you know whether you know i think
453
that's probably to me in my mind the biggest uh one
454
of the most important skills for for an engineer is to be able to write well um
455
and i was always a terrible writer and i hated writing and i was super slow at it and i
456
but I forced myself to do it.
457
And, you know, and if you force yourself to do it, you get, you get, you get better at it over time.
458
Um, I still hate it.
459
I still like every time I have to write something, I like, I like, I'll, you know, procrastinate.
460
And then I finally start, I write a paragraph and then I get up,
461
you know, and walk around the house and go do something else and eat some gummy bears and then come back.
462
And I like, I'm like, I'm going to write, I'm going to finish this page.
463
It's just like this, that's this, like coding is never like
464
that coding is like this is great
465
i'm gonna just stay in you know work on this for you know for for hours
466
and hours and um
467
but writing like you know it just i i never got into the to
468
that flow state but i do think um
469
if there's like one skill to learn
470
and that's i think it would be it'd be writing
471
and it's also hard in the day in this day
472
and age where people you can like you know send the you know four bullet points to claude
473
and then claude will come up with this you know entirely plausible 30 page document
474
but I think that if you do
475
that then you're doing you're doing yourself a disservice
476
you're also doing the people who would be reading it a disservice because because really like the the amount of
477
actual content in there is just the four bullet points
478
that you have
479
and somehow it blew up to like 30 pages yeah you have to add value on top
480
and sometimes that value is reducing all of the load
481
that is like generated throughout there from what I know from my friends in both open source and open core companies
482
what people contribute towards that open source is for them kind of a hiring funnel.
483
But I don't know if that's the same for you.
484
Have people that contributed to DuckDB in the end also joined Mother Duck?
485
Or how do you see that flywheel?
486
We had a couple of people who were, you know, in the early days who were contributors to DuckDB.
487
But that's actually one of the tricky things with the model that we have is because,
488
like, you know, we really don't want to poach people from DuckDB Labs because of the relationship we have with that company.
489
And so a lot of the DuckDB contributors go to DuckDB Labs.
490
We actually have a decent number of students.
491
So students who have built DuckDB extensions
492
and sort of worked on stuff on DuckDB that we will hire them at Mother Duck.
493
And we have a handful of interns from CWI in the Netherlands,
494
which is like this research institution that spun out DuckDB Labs and spun out and also created
495
Python and some other things as well.
496
But that relationship has been a fruitful one
497
because kind of then we can get people who are just excited about the area, but also have some experience.
498
One of the last thoughts I had is mainly a branding question
499
because the style
500
that you're wearing the product names everything from doc tb already is what it started with mother the doc
501
you mentioned even some of the components it's not pipeline it's flights
502
and dives and guides some of the other product names
503
that are there who came up with that
504
because that's genius um it was a it was a group effort uh
505
but i think like you know if you have a
506
if you have a company that's uh
507
you know based on something with an animal in the name um uh you you have two choices you can either,
508
you know, be like coy and like, yeah, I know it's, I know it's duck in the name, but like, we're too cool for that.
509
But, um, uh, and, uh, you know, we're too classy or you can embrace it and, and, and really lean in.
510
And so, you know, we decided to, to, to, to lean into it.
511
Um, because at the end of the day as a startup, you know, um, there's a war for attention and,
512
you know, just like, like I mentioned before, like, cause if, if you build something that nobody uses,
513
you might as well, it might as well have not, not been built.
514
And, and the way to like, get attention is, you know, you know, with, with interesting,
515
interesting, interesting branding, and, you know, it takes just, you know, being willing to look a little silly or feel a little silly.
516
But, you know, it's, it ends up being, it ends up being memorable.
517
And, and it's also can be self -sustaining is sort of once you sort of like take that step, then it's like, oh, well,
518
we wear the, you know, we wear the goofy, the goofy stuff.
519
And, you know, the people that join the company are people that are willing to do that and, you know, kind of can,
520
can laugh at themselves or, you know, have enough confidence that they can, you know, you know, they can rock the duck,
521
duck swoveralls or the duck, you know, the weird duck hats and stuff like that.
522
And, And, you know, it's, it, so it's, I wouldn't call it a gimmick because it's,
523
you know, it's, Part of our culture is just being able to like to laugh at ourselves and have fun.
524
You know, it's also a way of just sort of signaling that, you know, hey, we're not this like stodgy enterprise brand that everybody,
525
you know, like databases seem so serious and like, and, you know, the competitors seem like seem really serious and it's sort of like,
526
you know, hey, we can win by, we can win and we can have fun and laugh and, and, you know, and that's all,
527
it can be even the secret to how we, how we can be successful.
528
Yeah, absolutely.
529
Jordan, thanks so much for coming on and sharing.
530
Yeah, thank you.
531
It's been fun.
532
That's it.
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Vocabolario e note di pronuncia per questa lezione
Questa lezione di conversazione si basa sul video “How an Ex-Googler Accidentally Founded a Database Company”. Questo video contiene 355 frasi e 7497 parole da ripetere con lo shadowing. Il parlato dura 40:11. Chi parla va veloce, circa 187 parole al minuto: aspettati suoni legati e ridotti. Il 87% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è facile da seguire.
Vocaboli chiave di questo video
15 parole del video che vale la pena imparare, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| duck sostantivo | /dʌk/ | anatra |
| database sostantivo | /ˈdeɪtəˌbeɪs/ | database, base di dati |
| solve verbo | /sɒlv/ | risolvere |
| partnership sostantivo | /ˈpɑɹtnɚʃɪp/ | partenariato |
| hire verbo | /ˈhaɪɹ/ | assoldare, noleggiare |
| capability sostantivo | /ˌkeɪ.pəˈbɪl.ɪ.ti/ | capacità, possibilità |
| goofy aggettivo | /ˈɡufi/ | goffo, buffo |
| infrastructure sostantivo | /ˈɪnfɹəˌstɹʌkt͡ʃə/ | infrastruttura |
| feedback sostantivo | /ˈfiːdˌbæk/ | valutazione, riscontro |
| metric aggettivo | /ˈmɛt.ɹɪk/ | metrico |
| contribute verbo | /kənˈtɹɪb.jut/ | contribuire |
| component sostantivo | /kəmˈpoʊ.nənt/ | componente |
| innovation sostantivo | /ˌɪn.əˈveɪ.ʃən/ | innovazione |
| versus | /ˈvɝsəs/ | contro |
| loop sostantivo | /luːp/ | cappio |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| figure out verbo | scoprire, rendersi conto |
| come up with verbo | ideare, escogitare |
| end up verbo | andare a finire, finire |
| slow down verbo | rallentare, decelerare |
| check out verbo | investigare |
| come back verbo | tornare |
| find out verbo | scoprire |
| get up verbo | alzarsi |
Frasi da ripetere
Frasi brevi e complete del video che puoi riutilizzare nella conversazione di tutti i giorni:
- Do you guys want to hear it?
- How much do you want?
- Can you go a little bit deeper into that?
- What's going on with my book?
- Which customers should I talk to?
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Present perfect continuous have/has been + -ing — un’azione iniziata prima e ancora in corso | I've been thinking · we've been having |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | have not been built · I've built · I've worked |
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | have not been built · were bought · are incentivized |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | people who are · people who were · guides which is |
Pronuncia a cui fare attenzione
Chi parla usa 123 contrazioni e forme ridotte, come I'm, we're, you're. Pronunciale nella forma breve, così come le senti.
- I suoni “th”: empathy /ˈɛmpəθi/, thread /ˈθɹɛd/, underneath /ˌʌndɚˈniθ/, enthusiasm /ɪnˈθuziæzəm/, width /ˈwɪtθ/
- I suoni “sh” e “zh”: partnership /ˈpɑɹtnɚʃɪp/, innovation /ˌɪn.əˈveɪ.ʃən/, distinguish /dɪˈstɪŋ.ɡwɪʃ/, establish /ɪˈstæb.lɪʃ/, essentially /ɪˈsɛnʃəli/
- Parole lunghe — attenzione all’accento: capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, innovation /ˌɪn.əˈveɪ.ʃən/, developer /dɪˈvɛləpɚ/, contributor /kənˈtɹɪb.jə.tɚ/
I suoni difficili per chi parla italiano:
- /h/ — va soffiata, non è muta: hire /ˈhaɪɹ/, harness /ˈhɑɹ.nəs/, whereas /(h)wɛɹˈæz/, warehouse /ˈwɛə(ɹ)haʊs/, horrible /ˈhɔɹ.ɪ.bəl/
- Consonante finale — senza aggiungere una vocale dopo: duck /dʌk/, database /ˈdeɪtəˌbeɪs/, lab /læːb/, solve /sɒlv/, partnership /ˈpɑɹtnɚʃɪp/
- /æ/ — più aperta della “e”: lab /læːb/, analyst /ˈænəlɪst/, feedback /ˈfiːdˌbæk/, whereas /(h)wɛɹˈæz/, analytics /ˌæn.əˈlɪt.ɪks/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come duck, database, solve.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.