Transcript: ‘How to Use AI to Become a Learning Machine’

The transcript of AI & I with Simon Eskildsen is below.

Timestamps

Introduction: 00:01:06
How entrepreneurship and parenthood changed Simon’s learning rituals: 00:02:51
How Simon accelerates his learning by using LLMs to find associations: 00:12:59
Simon’s Anki setup and the flashcard template he swears by: 00:18:24
The custom AI commands that Simon uses most often: 00:26:02
How Simon uses LLMs for DIY home projects: 00:37:45
Leveraging LLMs as intuitive translators: 00:40:48
Simon’s take on how AI is reshaping the future of learning: 00:51:38
How to use Notion AI to write: 00:59:10
The AI tools that Simon uses to write, read, and code: 01:08:53

Transcript

Dan Shipper (00:01:07)

Simon, welcome to the show.

Simon Eskildsen (00:01:08)

Thank you so much, Dan. It's good to be here.

Dan Shipper (00:01:11)

It's good to have you. So, for people who don't know, you are the co-founder of Turbopuffer, which is a really cool AI startup doing better vector databases. Is that how you describe it?

Simon Eskildsen (00:01:23)

Yeah, it's essentially a search engine starting with vector search and we're trying to make it much more affordable and easy to run these things at scale, which is a challenge today that a lot of companies are having.

Dan Shipper (00:01:35)

That's awesome. I think you're one of the smartest founders in the space, especially at the layer of the stack that you're working at. We also go back a long way because you are one of the original— I used to do these interviews called Superorganizers interviews on Every, and you were one of the original interviewees. We did an interview together called “How to Make Yourself Into a Learning Machine,” which just went super viral. This is in 2020. And it was one of our first really, really big articles. And it was really incredible. You have this energy about you in that interview. We go through your reading habits and how you find new books and how you take notes on the books you read and how you turn the books you read into flashcards and all this kind of stuff. And it was, I think, super inspiring for any sort of note-taking nerds, of which I am one. And I'm really excited to get to talk to you again and hear how your brain and your mind is doing or adapting in the AI age. Because I think all the stuff that we were nerdy about four years ago, it's completely changed with the level of tooling available. And so I just want to hear what you're up to. I'm sure it's amazing.

Simon Eskildsen (00:02:51)

Yeah, I think AI has certainly changed how I approach my learning. Absolutely. It's an absolute dream come true. But I think my life has also changed dramatically from 2020. I am running a startup, which is more demanding than anything. And I think if you want to make yourself into a learning machine, it's a pretty good path to take.

There's nothing that challenges you more on your breadth and your skills than running a startup and building it from zero. So, that's been absolutely incredible. But it also means that some of my habits and systems have taken a little bit of a loss. But it might also be interesting to hear what the condensed rituals look like now. And then on top of that, I have a four-week-old baby, which means that my schedule is even more ridiculous.

Dan Shipper (00:03:46)

Congratulations.

Simon Eskildsen (00:03:47)

Thank you so much.

So, I think the reading has definitely condensed. Now, I have perhaps an hour or so to read before I go to bed, oscillating between reading articles on Reader and then reading books. I can't get up to 50 to 70 books a year anymore, so my selection process has gotten much tighter than it used to be. And so that's been a big one. Another thing that I used to spend a lot of time on—and I think we talked about in the article as well—is that I used to spend a lot of time writing about the books that I'm reading and that's also had to go. I just don't have the time.

But I still create a lot of flashcards. I joke with my wife that I'm going to have a party when I reach 10,000 flashcards. And I'm sure it will be absolutely— My friends will probably come because they like to indulge and make fun of all of my ridiculous rituals. But sometimes when I'm doing the flashcards, people want to follow along and they're like, why do you have a flashcard about whether it's better to have the window down or the A/C on at various vehicle speeds. And it's funny when you've been doing this for 10-plus years, because all of those things also carry memories of when you used that ridiculous fact, or the time you created the flashcard in the first place. So the flashcards have definitely stuck. I review somewhere between 50 and 200 of them every single day.

Dan Shipper (00:05:27)

Can we see your Anki? Can you show it to us?

Simon Eskildsen (00:05:28)

I could show it. I can show it. I think in the original article you wrote too, I have on my things to do-list, “cut toenails.” My life is completely in these systems that I regularly have nightmares about losing these systems because my brain is completely outsourced to it. This is what my Anki looks like. I don't think I've reviewed it. So this is a really easy day. We only did 11 cards. So we can take a look. I mean, this is a pretty ridiculous one, right? This is a restaurant that I used to go to. And the only distinguishing fact about this guy was that he had a really good radio-deep voice. And everyone has this dream of whatever restaurants they frequent that you get to know the waiters and it's back and forth and it's the usual, but it never happens. So this is my weird attempt. This restaurant doesn't exist anymore. I haven't seen this guy in a decade, but again, it brings me joy to see this kind of thing.

Dan Shipper (00:06:36)

That's amazing. I actually do that. I put it in my Notes app rather than in my flashcards. So then I just search whenever I'm back at the restaurant.

Simon Eskildsen (00:06:42)

That's perfect. And I think it's also things like your colleagues' kids' names and ages, these kinds of things where some people might find it ridiculous that you put a note about this and it's like, why can't you just remember? But let's be honest—most people don't remember those things. And if you write it down, then you will ask about it. And then eventually you'll remember it. That note is not really valuable anymore. So I also here have the ages and names of so many people who I've worked with kids, significant life events for them, wedding dates, whatever. Because I really do want to remember those things. But my memory is not capable of it. But then at some point I was like, oh yeah, it's July. Isn't this when Scott got married, or whatever? Yeah, this one. How many glasses of wine per bottle? I actually don't remember this one.

Dan Shipper (00:07:33)

I think it's four.

Simon Eskildsen (00:07:35)

There you go.

Dan Shipper (00:07:36)

I didn't need the flashcard.

Simon Eskildsen (00:07:39)

You didn’t need the flashcard. I feel like having a newborn, you forget the joys of a bottle of wine.

Good example too. If something where if you don't drink a ton and you're constantly— Then this might be worth it. I think for a lot of people, you don't need a flashcard. Dan, you live in New York. You're not going to need a flashcard for this one. So this one is going to be— And again, I didn't actually remember. I think the number I had in my head was six, but I think for Dan it's probably two, but it depends on how heavy-handed you are on the pour.

Here's another one. So this is also very common. When I peruse technical documentation, I'm constantly adding things into Anki againstead of note-taking. To be honest, especially on the schedule I'm on now with the type of work I do, I don't take that many notes. Most things just make it straight into flashcards right away. This is a Postgres column type. Postgres is a type of database. There's JSON-B and there's JSON. And I constantly forget when you're supposed to use either. This is kind of a bad flashcard because I think this is just in my standard flashcard template. It shows both sides of the card. So this is not really a valuable flashcard. This is the best one. “When should you use JSON vs. JSON-B?” Sometimes I just don't get the time to pull this up. I'll show you actually just, while I think of it. I used to have 20 different types of cards that I use. But I always use the same one now and I think this might be worth it to some people because at some point I had a different card type for every single thing that I was doing. This is the golden card type. It doesn't matter what app you use. This is the template I like. So you might be like, “How many glasses of wine does Dan think is in a bottle?” So, that's the front of the card. Then you have to think about, okay, can this be reversed? So there's two glasses of—

Dan Shipper (00:09:57)

I mean, I'd say the bottle is the glass for me, obviously.

Simon Eskildsen (00:10:00)

Yeah, there you go. This one doesn’t have a good reversal. But let me just see if I can think of— “Six glasses of wine,” right? So I'll just write something like that. Again, the reversal here doesn't really make sense, but it gets you the gist, right? So, if we're talking about an example from before, like, okay Dan's kids names are X and Y, and then you might have to back this as X and Y are whose kids.

Dan Shipper (00:10:31)

Yeah, you got to go back and forth.

Simon Eskildsen (00:10:35)

You gotta go back and forth. And I just— I don't deal with the—.

Dan Shipper (00:10:38)

This is really making me think of some AI stuff. So, there's this whole debate right now about whether or not language models are actually intelligent, right? And one of the big ones is that they don't understand when things are logically entailed, often. So, if they see all the time in their training data, “How many glasses of wine Dan thinks is in a bottle,” they'll be able to answer six, but they won't be able to answer the reverse. And people are like, oh, that's because they're not actually intelligent. And it's really interesting that, at least in the flashcard example, humans actually have to practice this all the time. What do you think of that?

Simon Eskildsen (00:11:16)

I think I haven't seen a ton of examples or tried a ton of examples of where they can't go in the reverse other than in these benchmarks where people pose them these problems to try to not make them think. I don't really have any big ideas of what a language model is and what it isn't. I just think of a large language model as an average of human knowledge or whatever—public human knowledge or public human knowledge plus what you can easily scrape. And whether they can reason, it's not something that I really use them for, probably because I don't really feel like they can right now. As soon as you get two to three levels of reasoning down, it just doesn't really do the trick for me. So, I'm sure a language model would do very well on something like this and probably even the inverse as well. So, I don't know if I have any direct thoughts on your example other than that's—yeah. No, I don't think they're super intelligent yet, but I think they're an incredible example of the average of the internet.

Dan Shipper (00:12:26)

That makes sense. I guess what this is making me wonder about is how the idea of extending your memory changes for you in a world where language models are available. You have the average of all of human knowledge available at your fingertips, where before you kind of did because you had Google, but Google itself is just a much worse version of a language model where you can get exactly what you need in the context you need when you need it. And I'm curious how that's changed for you, the function of and value of doing flashcards like this.

Simon Eskildsen (00:13:00)

The way that it's changed my learning the most is that what Google is really good at is you roughly know where to find what you're looking for and you can find it immediately. I read this paragraph once, and I think about it every single day because I think the best people that I work with or friends and things like this are where— Yeah. Google is good when you know what you're looking for, but when you're just looking for associations, that's when you have to go out and talk to people, right? If you Google, it’s whatever is SEO plus maybe a level out, right? But if we start to talk about something more interesting and associations for me, it's like, okay, I'm using this data structure here. This is what my data looks like. What might be some other things that I could do here? You just kind of got to talk to someone. But this is what the language models have really changed for me, right? Where I can go to it and be like, hey, I think it could be done like this. I don't know a ton about this domain. Can you just riff on this with me?

And then these models place that somewhere in that latent space. And then they can just find associations around it and pump that back to you. So that is a ping-pong-ing tool of whatever it is when you have a rough idea of the island that you want to land on. It can paint the picture for you really well. I found that it works extremely well for learning. And that wasn't accessible to me at my fingertips before. I couldn't be like, hey— Something I did the other day is, I like this brand but they didn't have a product that I was looking for. What are some other brands like this? It will just tell me that. Again, average of the internet. That I find very valuable. Or a year ago we were having— One of the wonders of living in Canada is that these little cabins in the woods are accessible for not too much money. And we have this retaining wall that we needed to build. And it was near the water and it's this whole complex thing. And there's all these legislations and it was all in French because it's in Quebec. And I was using a language model where someone had told me, oh, you need to build this type of retaining wall. I don't know anything about retaining walls. I don't care about retaining walls. I don't care to read 100 pages of French—and I don't really know how to speak French anyway—about what you're permitted to do near a waterline as it pertains to the retaining walls, right? You talk to a language model about this, and then they start to see, well, this retaining wall is not going to work for this reason. But actually, there is— The name slips me. There's this type of retaining wall, where you put it in a grid and then you put some rocks inside the grid. I'm sure you've seen one of these before. I don't remember the name of it right now, but it's like, oh, this might actually be a really good option that fits with these criteria. But no one had suggested it, right?

These types of associations are just like, yeah, if you talk to someone who's an expert on retaining walls could tell you that, but anything associated like this, I have found the language models and incredibly useful both in my daily work, because it feels a bit like conversing with a Ph.D. and whatever vertical you're going in. But also as you go through your daily life, you often have to talk to a contractor or a vendor who is an expert in some vertical, but certainly not an expert in teaching you enough about it. And maybe also you feel a little ripped off because you don't trust them. That's been very, very good.

Dan Shipper (00:16:33)

That's really interesting. And how does that relate to you to the practice of making these flashcards and using them if at all?

Simon Eskildsen (00:16:40)

I think it's mostly— Flashcards to me is a sink, right? I don't really do anything with the intention of like, oh, okay, I'm going to sit down in my chair. I'm going to create some flashcards today. Once I come across a bit of knowledge that I want to retain for whatever reason, I'll put it in here. There might be a flashcard in there on the name of that retaining wall, whose name slips me. But probably not. But that would be the type of thing that would make it in there, but I don't really— I think I used to open in the morning, set aside 45 minutes with the intention of, okay, we're going to create some flashcards today. I don't have time for that anymore. Right now it's like, okay, I just encountered this piece of knowledge, made it into the sink of flashcards. So I know that this is retrievable-ish, right? And the reason for the flashcards and maintaining this knowledge is that lets me make these associations in real time too that are interesting. And suddenly I have high bandwidth through the tool that's right in front of me to do that. Of course, the best person to do that association with technically is my cofounder, Justin, who normally sits in this chair behind me. It's just free-flowing ideas of high-bandwidth. I feel like I can get some bandwidth with the language model, but the breadth of where I can get it to, and the verticals of knowledge that I can get is unencumbered. I don't have the network to know what the best type of heat pump is for these temperatures and what matters, right?

Dan Shipper (00:18:10)

Yeah. That makes a lot of sense. So I want you to finish telling us about these flashcards and then we'll move on to some of the AI stuff.

Simon Eskildsen (00:18:14)

For sure. And yeah, I think the best way to think about flashcards is that they are a sink of your knowledge and they are just a way that these things resurface. This is the card type that I really like. It's the only one I use for the past thousands of cards I've created. I haven't used anyone else. Then here you do the reverse, whether you want to reverse the card or not. In case of something like how many glasses of wine does Dan think is in a bottle—six glasses—you don't really need the reverse. So we're just going to leave that blank and an extra will just be a picture or something like that. You saw it on the other card. This is all you need. And then always put a source in when you create a flashcard. It's nice to know like, okay, this was in 2017. I talked to this person and they said this thing because again, there's a little bit of nostalgia with these cards. If you're actually serious about making this a habit, you're like, oh yeah, that was Naj at Carben in 2014 or whatever, right? You might not delete the card because that brings you a little bit of joy.

Dan Shipper (00:19:20)

Interesting. I haven't actually heard anyone talk about flashcards from that perspective. It's sort of like when people talk about how they get a whiff of someone's perfume and it reminds them of their mother or something like that. And doing flashcards as a sort of an evocative exercise or associative nostalgic exercise for different times in your life in the same way. Like, oh, I hear a song and I think of being in high school or whatever. I kind of love that. There's something romantic about it.

Simon Eskildsen (00:19:49)

I think it's just because I've been doing it. It's a major part of my life, right? I've been doing this since I was like 17, 18 years old, right? So it's been probably 12 years of flashcards. Yeah, there's a lot of history. I think a lot of people do flashcards for a period of their life. But I found it valuable enough to just stick with it, right? It's one of the three or so things that really have stuck with me. Do you want to do a couple more?

Dan Shipper (00:20:22)

I mean, let's do one where you get a good card that you know. I want to get that.

Simon Eskildsen (00:20:29)

Which prestigious university is in Pittsburgh? Oh, you probably know this.

Dan Shipper (00:20:40)

I do know this.

Simon Eskildsen (00:20:42)

I don't remember this. Okay. Carnegie Mellon.

Dan Shipper (00:20:44)

You gotta give me a chance to answer before you flip the card.

Simon Eskildsen (00:20:52)

Then, English is not my mother tongue. But so I used to do a lot of flashcards with words and definitions. And basically, I had this whole flow where I highlighted on Kindle, it syncs to Readwise, and thenI process that as part of my highlights in a flashcard. Then at some point my wife said— It's funny, my wife has two reactions when I tell her one of these new words that I learned proudly. One reaction is, why don't you know that word? And then the other reaction is, Simon, that’s a dumb word. No one uses that word.

And I’m like, Jen, it's kind of like there's a missing third thing here. What about Simon, that's such an amazing word. I can't believe that. I'm so thankful that you taught that to me. So at some point I started scraping Google with the number of results for the word as a proxy for how useful it is. That's something where you could probably use a language model today to ask how common a word is. So “affable,” I mean, that's someone who's an admired or nice person, right? Good natured. Yeah. That’s another one. “What is the main industry of the Jilin province in China?” There's a lot of good tea. This is a really hard day today. I don't know. I mean, this is not a good flashcard because it’s too hard to skim. So I'm going to mark this and then I'll look at it at some point. Alright, I'll close this down, but yeah, there was a pot time in my life where I found the origin of every vegetable. That mattered to me because then I could map back to what vegetables were endemic to cuisine and it's like before the Colombian transfer of tomatoes, blah, blah, blah, blah.

Dan Shipper (00:22:59)

So what's the answer?

Simon Eskildsen (00:22:59)

The answer is Asia. I don't think it's pinned down much further than that, which makes sense, right? It's not really used outside of that cuisine.

Dan Shipper (00:23:07)

That makes sense. Alright, so we've reviewed the previous interview. We've kind of gone back in time and seen what's stuck and what hasn't. Now I wanna talk to you about AI stuff. So, yeah. How are you using AI? Let's start with your work.