Agents @ Work: Lindy.ai (with live demo!)

Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, partner and CTO of Decibel Partners, and I'm joined by my co-host, Swyx, founder of Smol.ai.

Hey, and today we're joined in the studio by Florent Crivello. Welcome.

Hey. Yeah, thanks for having me.

Also known as Ultimaur. Uh, always wanted to ask, what is Ultimaur?

It was my, uh, the name of my character when I was playing Dungeons & Dragons-

Always.

-I was, like, 11 years old.

What was your classes?

I was an elf. I was a magician elf.

Okay. All right. Uh, well, you're still spinning magic. Right now you're solo founder, CEO of Lindy.ai. What is Lindy?

Uh, yeah. We are a no-code platform letting you build your own AI agents easily. So y-you can think of we are to LangChain as Airtable is to MySQL. Like, you can just pin up AI agents super easily by clicking around and, you know, no code required.

You didn't have to be an engineer, and you can automate business workflows that, uh, you simply could not automate before in a few minutes.

You've been in our orbit a few times. Uh, I think you spoke at our Latent Space-

Mm-hmm

... anniversary.

Yeah.

You spoke at my, uh, summit, the first summit, which, uh, which was a really good keynote. And most recently, uh, like we actually already scheduled this podcast before this happened, but Andrew Wilkinson was like, "I'm obsessed by Lindy."

He's, like, just created a whole bunch of agents. So basically, why are you blowing up?

Well, thank you. I think we, we are having a little bit of a moment. I think it's a bit premature to say we're, we're blowing up. But why are things going well? We revamped the product majorly. We called it Lindy 2.0.

I would say we started working on that six months ago. We've actually not really announced it yet.

Okay.

It's just I guess, I guess that's what we're doing now.

You're announcing now?

Uh, it's-- A-and so we've basically been cooking for the last six months, like really rebuilding the product from scratch. I think, Alessio, actually, the last time you tried the product it was still Lindy 1.0.

Oh, yeah. It was.

So if you log in now, like the platform looks very different-

Mm-hmm

... and it does, like, a ton more features. And I think one realization that we made, and I, I think a lot of folks in the agent space made the same realization, is that there is such a thing as, as too much of a good thing.

I think many people, when they started working on agents, they were very LLM-pilled and, and ChatGPT-pilled, right? Is they got ahead of themselves in a way and, us included, and they thought that agents were actually, uh, and LLMs were actually more advanced than they actually were.

And so the first version of Lindy was, like, just a giant prompt and a bunch of tools. And then the realization we had was like, hey, actually, the more you can put your agent on rails, one, the more reliable it's going to be, obviously.

But two, it's also going to be easier to use for the user because you can really, as a user, you get-- instead of just getting this, like, big, giant intimidating text field and you type words in there and you have no idea if you're typing the right one or not- ...

here you can really, like, click and select step by step and, and select, like, tell your agent what to do and really give as narrow or as wide a guardrail as you want for your agent. We started working on that.

We called it Lindy on Rails about six months ago, and we started putting, putting it into the hands of users over the last, I would say, two months or so. And that's, like, I, I think things really started going, like, pretty well at that point.

Th-the agent is way more reliable, way easier to set up, and we're already seeing like a ton of, of new use cases pop up.

Yeah. Just a quick follow-up on that. You launched the first Lindy in November last year.

Yeah.

And, uh, you were already talking about, like, having a DSL, right? Like, I remember having this discussion with you and you were like, "It's just much more reliable." Is this still the DSL under the hood? Like, is this a UI-level change or is it, like, a bigger rewrite?

No, it is, it is a much bigger rewrite. I'll give you a concrete example. Suppose you want to have an agent that observes your Zendesk tickets, okay? And it's like, hey, every time you receive a Zendesk ticket, I want you to check my knowledge base, so it's like there's like a RAG module and whatnot, and then answer the ticket.

The way it used to work with Lindy before was you would type the prompt asking it to do that. Every time you receive a Zendesk ticket, you check my knowledge base and so on and so forth. The problem with doing that is that it can always go wrong.

Like you're, you're praying to the LLM gods that they will actually invoke your knowledge base. But, uh, I don't want to ask it. I want it to always 100% of the time consult the knowledge base after it receives a Zendesk ticket.

And so with Lindy you can actually have the trigger, which is Zendesk ticket received, have the knowledge base consult, which is always there, and then have the agent. So you can really set up your agent any way you want like that.

This is something I think about for AI engineering as well, which is like the big labs want you to hand over everything in the prompts in only code of English, and then the, the smaller brains, the GPU poors always want to write more code to make things more deterministic and reliable and controllable.

One way I put it is like, you know, put SugarGlass in a box and make it a very small-- like the minimum viable box. Everything else should be traditional if this, then that software.

I love that characterization, put the SugarGlass in a box. Yeah. We, we talk about, uh, using as much AI as necessary and as little as possible.

Okay.

And what was the choosing between kinda like this drag and drop, low code, whatever, super code-driven, maybe like the LangChains, O-J- AutoGPT of the world? And maybe the flip side of it, which you don't really do, is like just text to agent, you know?

It's like build the workflow for me. Like what have you learned actually putting this in front of users and figuring out how much do they actually wanna edit versus like how much, you know, kinda like Ruby on Rails instead of Lindy on Rails is kinda like, you know, defaults-

Yeah

... whatever configuration.

Yeah. I actually used to dislike when people said, "Oh, text is not s- a great interface." I was like, "Ah, this is such a myth take. I think text is awesome." And I've actually come around. I actually sort of agree now that text is really not great.

I think for people like you and me, because w-we sort of have a mental model, okay, when I type a prompt into this text box, this is what it's going to do. It's going to map it to this kind of data structure under the hood and so forth.

I guess it's a little bit black pilling towards humans. You jump on these calls with humans and you're like, "Here's a text box. This is going to set up an agent for you. Do it." And then they type words like, "I want you to help me put order in my inbox."

Or actually this is a good one. This is actually a good one. What's a bad one? I, some-- Like I would say 60 or 70% of the prompts that people type don't mean anything. Me as a human, as AGI, I don't understand what they mean.

I don't know what, what they mean. It is actually, I think, whenever you can have a GUI, it is better than to have just a pure text interface.

And then how do you decide how much to expose? So even with the tools you have, uh, Sl- you know, I have a bunch of Lindys. Uh, you have Slack, you have Google Calendar, you have Gmail. Should people by default just turn over access to everything, and then you help them figure out what to use?

I think that's the question. You know, when I tried to set up Slack, it was like, "Hey, give me access to all channels and everything," which-

For the average person, probably makes sense because you don't wanna re-prompt them every time to add a new channels. But at the same time, for maybe like the more sophisticated, like enterprise us-use cases, people are like, "Hey, I wanna like really limit-

Mm.

-what you have access to."

Yeah.

How do you kinda thread that, that balance?

The general philosophy is we ask for the least amount of permissions needed at any given moment. I don't think Slack-- I could be mistaken, but I don't think Slack lets you request permissions for just one channel. But for example, for Google, obviously, there's hundreds of scopes that you could work-

Right.

-require for Google.

It's too many.

There's a lot of scopes, and sometimes it's actually painful to set up your Lindy because you're gonna have to ask to Google and add scopes five or six times. Like we've, we've had sessions like this. But that's what we do because, for example, the Lindy email drafter, she's going to ask you for your auth authorization once for, "I need to be able to read your email, so I can, like, draft a reply," and then another time for, "I need to be able to write a, a draft for them."

We just try to do it very incrementally like that, yeah.

Yeah. Do you think OAuth is just overall gonna change? I think maybe before it was like, "Hey, we need to set up a OAuth that humans only wanna kinda do once."

Yeah.

So we try to jam-pack things all at once, versus what if you could, on demand, get different permissions every time from different parts? Like, do you ever think about designing things knowing that maybe AI will use it instead of humans will use it?

Yeah, for sure. One pattern we've started to see is people provisioning accounts for their AI agents, and so in particular, Google Workspace accounts. So for example, Lindy can be used as a scheduling assistant, and so you can just CC her to your emails when you're trying to find time with someone, and just like an a, a human assistant, she's going to go back and forth and offer availabilities and so forth.

Very often, people don't want the other party to know that it's an AI.

Mm.

So it's actually funny. They introduce delays. They ask the agent to wait before replying, so it's not too obvious that it's an AI, and they provision an account on Google Suite, which costs them like 10 bucks a month or something like that.

So we're, we're seeing that pattern more and more. I think that does the job for now. I'm not optimistic on us actually patching OAuth 'cause I agree with you ultimately, like we would want to patch OAuth 'cause the, the new account thing is kind of a kludge.

It's really a hack. You would want to patch OAuth to have more granular access control and, and really be able to, like, put your sugar in the box. I'm not optimistic on us doing that before AGI, I think.

Yeah.

Uh, that's a very close timeline. I'm mindful of like, you know, talking about a thing without showing it, and we already have the setup to show it. Why don't we, um, jump into a screen share? For listeners, you can jump on the YouTube and like and subscribe.

But also, let's have a look at how you show off Lindy.

Yeah, absolutely. I'll give an example of a very simple Lindy, and then I'll graduate to a much more complicated one. A super simple Lindy that I have is I unfortunately bought some investment properties in the south of France.

Uh, it was a really, really bad idea. And, uh, I, I put them on the Holidu, which is like the French Airbnb, if you will. And so I receive these emails from time to time telling me like, "Oh, hey, you made 200 bucks.

Someone booked your place." Okay? When I receive these emails, I want to log this reservation in a spreadsheet. Doing this without an AI agent or without AI in general is a pain in the butt because you must write an HTML parser for this email.

And so it's, it's just hard. You may not be able to do it, and it's going to break the moment the email changes. By contrast, the way it works with Lindy, it's really simple. It's, it's two steps. It's like, okay, I receive an email.

If it is a reservation confirmation, I have this filter here, then I append a row to this spreadsheet. And so this is where you can see the, the AI part, where the way this action is configured here, you see these purple fields on the right.

Each of these fields is a prompt. And so I can say, okay, you extract from the email the day the reservation begins on. You extract the amount of the reservation. You extract the number of travelers of the res- of the reservation.

And now you can see when I look at the task history of this Lindy, it's really simple. It's like, okay, you do this, and boom, I'm appending this row to this spreadsheet, and this is the information extracted. So effectively, this node here, this update, this append row node, is a mini agent.

It can see everything that just happened. It ha- it has context over the, the task, and it's, it's appending the row. Uh, and, and then it's going to send a reply to, to the thread. That's a very simple example of, of an agent.

A quick follow question on this one while we're still on this page. Uh, is that one call? Is that a structured output call?

Yeah.

Um, okay. Nice.

Yeah. Yeah. And you can see here, for every node, you can, you can, you can configure which model you want to power the node. Here I use Claude. For this, I use GPT-4 Turbo. Much more complex example, uh, my meeting recorder.

Uh, it looks very complex 'cause I've added to it over time, but at a high level, it's really simple. And it's like when a meeting begins, you record the meeting, and after the meeting, you send me a summary, and you send me coaching notes.

So I receive-- Like, my Lindy is constantly coaching me, right? And so you can see here in the prompt of the coaching notes, I've told it, "Hey, you know, was I unnecessarily confrontational at any point?" I'm, I'm French, so I have to, have to watch out for that.

Or not confrontational enough. Should I have double-clicked on any issue, right? So I can really give it exactly the kind of coaching that I'm expecting. And then the interesting thing here is like you can see, the agent here, after it's sent me these coaching notes, moves on, and it does a bunch of other stuff.

So it, it goes on Slack. It disseminates the notes on Slack. It does a bunch of other stuff. But it's actually able to backtrack and resume the automation at the coaching notes email if I responded to that email.

So I'll give a, a super concrete example. Um, this is an actual coaching feedback that I received from Lindy. She was like, "Hey," this was a, a sales call I had with a customer, and she was like, "I found your explanation of Lindy too technical."

And I was able to follow up and just ask a follow-up question in the thread here. And I was like, "What did you find too technical about my explanation?" And Lindy restored the context, and so she basically picked up the automation back up here in the tree, and she has all of the context of everything that happened, including the meeting in which I was.

So she was like, "Oh, you used the words deterministic and context window and agent state." And that concept exists at every level for every channel and every action that Lindy takes. So another example here is I mentioned she also disseminates the notes on Slack.

So, uh, this was a meeting where I was not, right? So this was a, a teammate. His Lindy meeting recorder posts, uh, the meeting notes in this customer discovery channel on Slack. So you can see, okay, this is the onboarding call we had.

This was the use case. Look at the questions. Uh, how do I make Lindy slower? How do I add delays to make Lindy slower? And I was able, in the Slack thread, to ask follow-up questions like, "Oh, what did we answer to these questions?"

And it's really handy because, because I know I can have this sort of interactive Q&A with these meetings, it means that very often now I don't go to meetings anymore. I just- ... I, I just, I just send my Lindy, and, and instead of going to, like, a 60 minutes meeting, I have, like, a five minutes chat with my Lindy afterwards.

And she just replied. She was like, "Well, this is what we replied to this customer," and I can just be like, "Okay, good job, Jack." Like, no notes about your answers. So that, that's the kind of, of, of use cases people, people have with Lindy.

It's a lot of, like... There's a lot of, like, sales automations, customer support automations, and a lot of this, which is basically personal assistance automations, like meeting scheduling and so forth.

Yeah. And I think the question that people might have is memory. Um, so as you get coaching, how does it track whether or not you're improving? You know, if these are, like, mistakes you made in the past. Like, how do you think about that?

Yeah. We have a memory module, so I'll show you my meeting scheduler Lindy, which has a lot of memories because by now I've used her for so long. And so every time I talk to her, she saves a memory.

If I tell her, "You, you, you screwed up. Please don't do this." Um, so you can see here it's, um... Oh, it's got a double memory here. This is the meeting link I have, or this is the address of the office.

Uh, if I tell someone to meet me at home, this is the address of my place. This is the code. I guess we'll have to edit that out.

Yeah.

This is not the code of my place.

No, no doxing.

Um, yeah. So Lindy can just, like, manage her own memory and decide when she's remembering things between executions.

Okay. Uh, uh, I mean, I'm just gonna take the opportunity to ask you since you are the creator of this thing, how come there's so few memories, right? Like, if you've been using this for two years, there should be thousands of thousands of things.

That is a good question. Agents still get confused if they have too many memories, to my point earlier about-

Yeah

... so I just am out of a, a call with, uh, a, a member of the, the Llama team at, at Meta, and we were chatting about Lindy, and we were going into the system prompt that we sent to Lindy and all of that stuff.

And he was amazed, and he was like, "It's a miracle that it's working, guys." He was like- ... "This kind of system prompt, like, this does not exist either pre-training or post-training. Like, these models were never trained to do this kind of stuff.

Like, it's a miracle that they can be agents at all." And so, um, what I do, I actually prune the, the memories. You know, it, it's actually something I've gotten to the habit of doing from back when we had GPT 3.5 being Lindy agents.

I suspect it's probably not as necessary in, like, the Claude 3.5 Sonnet days, but I, I prune the memories. Yeah.

Yeah. Okay. The reason is 'cause I have another assistant that also is recording and trying to come up with facts about me. It comes up with a lot of, like, trivial, useless facts that I, that I... So I spend most of my time pruning.

Yeah.

And actually it's not super useful. I'd much rather have high-quality facts that it, like, accepts.

Yeah.

Or maybe I, I, I was even thinking, like, were you ever tempted to add a wake word to only memorize this when I say memorize this?

Yeah.

And otherwise, don't even bother.

I have a Lindy that does this. So this is my inbox processor Lindy. It's kind of beefy because there's, there's a lot of different emails, but somewhere in here there is a rule where I'm like, "Aha, I can email my inbox processor Lindy."

It's really handy. So she has her own email address, and so when I process my email inbox, I sometimes forward an email to her, and it's, it's a newsletter or it's, like, a cold outreach from a recruiter that I don't care about or anything like that.

And I can give her a rule, and I can be like, "Hey, this email I want you to archive moving forward," or, "I want you to alert me on Slack when I have this kind of email. It's really important."

And so you can see here the prompt is, "If I give you a rule about a kind of email, like archive emails from X, save it as a new memory." And I, I give it to the memory-saving skill.

Mm.

Um, and yeah.

One thing that just occurred to me, so I'm a big fan of virtual mailboxes. I recommend that everybody have a virtual mailbox. You could set up a physical mail receive thing for Lindy, and so then people can just...

Then Lindy can process your physical mail.

Uh, that's actually a good idea. I, I actually already have something like that. I use, like, First Class Mail.

Yep.

Yeah. So yeah, most likely- ... Lindy could process my physical mail, yeah.

Um, and then the other, the other products idea I have looking at this thing is people want to brag about the complexity of their Lindys. So this would, this would be, like, a 65-point Lindy, right?

What, what's a 65-point?

The, uh, complexity counting.

Ah.

Like, how many nodes, how many things, how many conditions, right?

Yeah. This is not the most complex one. I have another one. This designer recruiter here is, is, is kind of beefy as well.

Right, right, right.

Um-

Uh, so I'm just saying, like, let people brag. Let people, like, be super users and-

Oh, right.

Yeah.

Give them a score or something.

Give them a score. Then they would... It'd just be like, "Okay, how high can you make this score?"

Yeah, that's a good point. Um, and I think that's, again, is the beauty of this, uh, on rails phenomenon, is like, think of the equivalent, the prompt equivalent of this Lindy here, for example, that we're looking at. It, it'd be monstrous, and the odds that it gets it right are so low.

Yeah.

But here, because we're really holding the agent's hand step by step by step, it's actually super reliable.

Yeah. And is it all structured output base?

Yeah.

As, as far as possible?

Basically.

Or to... Like, there's no non-structured output.

There is, uh, so for example, here, this, like, AI agent step, right? Or this, like, send message step. Sometimes-

That's just-

... it gets to-

Plain text

... that's right.

Yeah.

Yeah.

Yeah.

Yeah. So I'll give you an example. Maybe it's TMI. I'm having blood pressure issues these days, and so I'm, I'm, uh... This Lindy here, I give it my blood pressure readings, and it, it, uh, updates a log that I have of my blood pressure that it then it sends to my doctor.

Oh, so this is a to... Every Lindy comes with a to-do list?

Uh, yeah. Every Lindy has its own task history.

Huh.

Yeah. And so you can see here, this is my main Lindy, sort of like my personal assistant, and I've told it, uh... Where is this? There is a point where I'm like, "If I am giving you a health-related fact..."

Uh, right here. "I'm giving you a health information, so then you update this log that I have in this Google Doc, and then you send me a message." And you can see I've actually not configured this send message node.

I haven't told it what to send me a message for, right? Um, and you can see it's actually lecturing me. It's like I'm giving it my blood pressure readings. It's like, "Hey, it's a bit high. Like, here are some lifestyle changes you may want to consider."

I think maybe this is the most confusing or new thing for people, so- Even I use Lindy and I didn't even know you could have multiple workflows in one Lindy. I think the mental model is kind of like the Zapier workflows.

It's like it starts and it ends. It's not-- doesn't choose between. How do you think about what's a Lindy versus what's a sub-function of a Lindy? Like, what's the hierarchy?

Yeah. Frankly, I think the line is a little arbitrary. It's kinda like when you code, like, uh, when do you start to create a new class versus when do you overload your, your current class. I think of it in terms of like jobs to be done, and I think of it in terms of who is the Lindy serving.

This Lindy is serving me personally. It's really my day-to-day Lindy. I give it a bunch of stuff, like very easy tasks, and so this is just a Lindy I, I go to. Sometimes when a task is really more specialized, so for example, I have this like summarizer Lindy or this designer recruiter Lindy, these tasks are really beefy.

I wouldn't wanna add this to my main Lindy, so I just created a separate Lindy for it. Or when it's a Lindy that serves another constituency. Like our me-- uh, customer support Lindy, I don't wanna add that to like my personal assistant Lindy.

Yeah.

They're two very different Lindys. Yeah.

Yeah. And you can call a Lindy from within another Lindy.

That's right.

You can kinda chain them together.

Yeah. Lindys can work together, absolutely.

A couple more things for the video portion. I noticed you have a podcast follower. We have to ask about that. What, what is that?

Uh, yeah. So this one wakes me up every-- so wakes herself up every week. Uh, and she sends me-- So she woke up yesterday actually, uh, and she searches so for Lenny's podcast, and she, she looks for like the latest episode on YouTube.

And once she finds it, she transcribes the video, and then she sends me the summary by email. I don't, I don't listen as many-- as to podcasts as much anymore. I just like read these, these summaries.

Yep. Yep.

We should make a Lead & Space Lindy- ... in the marketplace.

Yeah.

Um, okay. So and then, um, w- you know, you have a whole bunch of connectors. I've-- I, I saw the list briefly. Any interesting one, complicated one that you're proud of? Anything that you wanna just share?

Yeah.

Connector stories.

So many of our workflows are about meeting scheduling, so we had to build some very open unity tools around meeting scheduling. So for example, one that is surprisingly hard is this find available times action. You would not believe this is like a thousand lines of code or something.

It's just a very beefy action. And you can pass it a bunch of parameters about how long is the meeting, when does it start, when does it end, what are the meeting, like the, the, the weekdays, uh, in which I meet.

Uh, there's like a how many time slots do you return? What's the buffer between my meeting? It's just a very, very, very complex action. Um, I really like our, our GitHub action. So we have like a Lindy, uh, PL reviewer, and it's, it's really handy because anytime any bug happens, so the Lindy reads our guidelines on Google Docs.

By now, the guidelines are like forty pages long or something. And so every time any new kind of bug happens, we just go to the guideline and we add the lines like, "Hey, this has happened before. Please watch out for this category of bugs."

And it's saving us so much time every day.

There's companies doing PL reviews. Where does a Lindy start? When does a company start? Or maybe how do you think about the complexity of these tasks when it's gonna be worth having kinda like a vertical standalone company versus just like, hey, a Lindy's gonna do a good job ninety-nine percent of the time?

That's a good question. Um, we think about this one all the time. I, I can't say that we've really come up with a very crisp articulation of when do you wanna use a vertical tool versus when do you wanna use a horizontal tool.

I think of it as very similar to the internet. I find it surprising the extent to which a horizontal search engine has won, but I think that-- Google, right? But I think the even more surprising fact is that the horizontal search engine has won in almost every vertical, right?

You go through Google to search Reddit. You go through Google to search Wikipedia. I think maybe the biggest exception is e-commerce. Like you go to Amazon-

Mm-hmm

... to search e-commerce, but otherwise you go through Google. And I think the, the reason for that is because search in each vertical has more in common with search than it does with each vertical. And search is so expensive to get right, like Google is a big company, that it makes a lot of sense to aggregate all of these different use cases and to spread your R&D budget across all of these different use cases.

I have a thesis, which is, is-- it's a really cool thesis for Lindy, is that the same thing is true for agents. I think that-

Mm

... by and large, in a lot of verticals, agents in each vertical have more in common with agents than they do with each vertical. I also think there are benefits in having a single agent platform because that way your agents can work together.

Mm-hmm.

They're all like under one roof. That way you only learn one platform, and so you can create agents for everything that you, that you want and, and you don't have to like pay for like a bunch of different platforms and so forth.

So I, I think ultimately it is actually going to shake out in a way that is similar to search in that search is everywhere on the internet. Every website has a search box, right? So there's going to be a lot of vertical agents for everything.

I think AI is going to completely penetrate every category of software. But then I also think there are going to be a few very, very, very big, uh, horizontal agents that serve a lot of functions for people.

Yeah. That is actually one of the questions that we had about the agent stuff. So I guess we can transition away from the, from the screen now and just, um, ask the follow-up, which is that is a hot topic.

You're, you're basically saying that the current VC obsession of the day, which is vertical AI-enabled SaaS, is mostly not gonna work out. Like, and then there are gonna be some super giant horizontal SaaS.

Oh, no, I'm not saying it's either/or. Like SaaS today, vertical SaaS is huge, and there's also a lot of horizontal platforms. If you look at like Airtable or Notion, basically the entire no-code space is very horizontal. I mean, Loom and Zoom and Slack, like there, there's a lot of very horizontal tools out there.

Okay. I was just trying to get a reaction out of you for, uh- ... for hot takes.

Trying to get, trying to get a hot take. No, I, I also think it is natural for the vertical solutions to emerge first because they're just easier to build. It's just much, much, much harder to build something horizontal.

Cool. Some more, uh, Lindy specific questions. So we covered most of the top use cases and you have a academy. That was nice to, to see. I also see some other people doing it for you for free. So like Ben Speights is doing it and then there's some other guy who's also doing like lessons.

Yeah.

Which is kind of nice, right? Like you-

Yeah. Absolutely

... you don't have to do any of that. Um.

Oh, well, we've been seeing it more and more on like LinkedIn and Twitter, like people posting their Lindys and so forth.

Yeah.

Yeah.

I think that's the flywheel, that you built the platform where creators see value in aligning themselves to you. And so then, you know, your incentive is to make them successful so that they can make other people successful, and then it just drives more and more engagement that you're...

Like, it's earned media. Like you don't have to do anything.

Yeah. Yeah.

Uh.

I mean, community is everything.

Are you doing anything special there? Any, any big wins?

Um, we have a Slack community that's pretty active. I can't say we've invested much more than that-

Yeah

... so far. I would say

From having-- So I have some involvement in the no-code community. I would say that Webflow going very hard after no-code as a category got them a lot more allies than just the people using Webflow.

Mm.

So it helps to-- it helps you to grow the community beyond just Lindy.

Right.

And I don't know what this is called. Maybe it's just no-code again. Maybe you wanna call it something different. But there's definitely a, an appetite for this, and you are one of a broad category, right? Like just before you, we had, uh, Dustin and, you know, they're also kinda going after a similar market.

Zapier obviously is not gonna try to also compete with you.

Yeah.

There's no question there. It's just like a reaction about community. Like, I think a lot about community main space is growing the community of AI engineers, and I think you have a slightly different audience of I don't know what.

Yeah. I think the no-code Tinkereds-

No-code Tinkereds

... is, is the community. Yeah.

Okay.

It is going to be the same sort of community as what, yeah, Webflow, Zapier, Airtable, Notion to some extent.

Yeah. The framing can be different if you were-- So I think Tinkered has this connotation of not serious or, like, small.

Mm-hmm.

And if you framed it to, like, no-code EA, we're exclusively only for, for CEOs with a certain budget-

Yeah

... then you just have-- you, you tap into a different budget.

That's true. The problem with EA is, like, the, the CEO has no willingness to actually tinker and, and play with the platform.

What do you mean? Andrew's doing that. Like, a, a lot of your fo- your biggest advocates are CEOs, right?

Solopreneur, you know, small business owners.

Small businesses. Okay.

I, I think Andrew is an exception. Yeah.

Yeah, yeah. He is.

Yeah.

He's an exception in many ways.

Yep.

Just before we wrap on the use cases, is, uh, rickrolling your customers, like, a officially- ... supported use case or maybe tell, tell that story?

It's one of the main jobs to be done really. Um- Yeah. We, we woke up recently-- So we, we have Lindy obviously doing our customer support, and we do check after the Lindy. And so we, we caught this email exchange where someone was asking Lindy for video tutorials.

And at the time, actually, we did not have video tutorials. We, we do now in the Lindy Academy. And Lindy responded to the email. It's like, "Oh, absolutely. Here's a link." And we were like, "What?" Like, "We don't-- What kind of link did you send?"

And so we clicked on the link, and it was, it was a rickroll. We actually reacted fast enough that the customer had not yet opened the email. And so-

Ah

... we reacted immediately like, "Oh, hey. Actually, sorry, this is the right link." Uh, and so the, the customer never reacted to the first link. And so yeah, we-- I tweeted about that. It went surprisingly viral. And I, I checked afterwards in, in the logs.

We did like a database query and, and we found like, I think like three or four other instances of it having happened before.

That's surprisingly low.

Yeah.

It is, it is low. And, and we fixed it across the board by just adding a line to the, the system prompt that's like, "Hey, don't rickroll people, please don't rickroll."

Yeah. Yeah, yeah. Yeah. I mean, so, you know, uh, you can explain it retroactively, right? Like that YouTube slug has been pasted in so many-

Yeah

... different corpuses that obviously-

Yeah

... it learns to hallucinate that.

Yeah.

And it pretended to be so many things.

Yeah.

Right.

That's the thing is like everybody-

That's true. I wouldn't be surprised if that takes one token. Like there's this- ... in a tokenizer, this is just one token.

Let's check the ID- ... of the YouTube video.

Because it's used so much, right? Like, and, and-

It is true

... you have to basically get it exactly correct. It's probably not. I mean, that, that's a long-

Ah, no

... that's a long slug.

It would've been so good. It is not a single token. It's like-

So this is a-- Just to jump maybe into evals from here, how could you possibly come up for an eval that says, "Make sure my AI does not rickroll my customer"? I feel like when people are writing evals, that's not something that, that they come up with.

So how do you think about evals when it's such, like, an open-ended problem space?

Yeah. It is tough. We built quite a bit of infrastructure for us to create evals in one click from any conversation history. So we can point to a conversation, and we can be like either-- In one click, we can turn it into effectively a unit test.

It's like this is, this is a good conversation. This is how you're supposed to handle things like this. Or if it's a negative example, then we, we, we modify a little bit the conversation after generating the eval. So it's very easy for us to spin up this kind of, of, of eval.

Do you use a off-the-shelf tool? We just had Braintrust on the podcast, or did you just build your own, uh-

We built-- We unfortunately built our own. We're most likely going to switch to Braintrust.

Ooh.

Um, it's-- Well, when we built it, there was nothing. Like, there was no eval tool, frankly. And we-- I mean, we started this project like end of twenty twenty-two. It was like, it was very, very, very early. I wouldn't recommend it to build your own eval tool.

There's, there's better solutions out there, and our eval tool breaks all the time, and it's a nightmare to maintain, and that's not something we wanna be spending our time on.

I was gonna ask that basically because I think my first conversations with you about Lindy was that you had a strong opinion that you shou- everyone should build their own tools, and you were very proud of your evals.

You were, you were kind of showing off to me, like, how many evals you were running, right?

Yeah. I think that was before all of these tools-

Yes

... came around.

Yeah.

I think the, the ecosystem has matured a, a fair bit.

What is one thing that Braintrust has nailed that you always struggled to do?

Well, not using them yet, so I, I couldn't-

Okay

... tell. But from what I've gathered from the conversations I've, I've had, like, they are doing what we do with our eval tool but better.

Yeah. And, like, they do it, but also, like, 60 other companies do it, right? So I don't know how to shop, uh, apart from brand.

Yeah.

Word of mouth.

Same here.

Yeah. Like evals on Lindy is there, there is, there's two kinds of evals, right? Like, in some way, you don't have to eval your system as much because you've constrained the language model so much, and you can rely on OpenAI to guarantee that their structured outputs are going to be good, right?

We had Michelle sit where you sit, and the-- and she explained exactly how they do constraint gra-grammar sampling and all that good stuff. So actually, it's-- I think it's more c- more important for your customers to eval their Lindys than you evaling your Lindy platform because you've just built the platform.

You don't actually need to eval that much.

Yeah. In an ideal world, our customers don't need to care about this. And I think the bar is not like, look, it needs to be at 100%. I think the bar is it needs to be better than a human.

And for most use cases we serve today, it is better than a human, especially if you put it on rails.

Is there a limiting factor of Lindy the business? Like, is it adding new connectors? Is it adding new node types? Like, how do you prioritize what is the most impactful to your company?

Yeah. There are capabilities for sure or a big limit. It is actually shocking the extent to which the model is no longer the limit. It was the limit a year ago. It was too expensive. The context window was too small.

It's kind of insane that we started building this when the context windows were like four thousand tokens. Like today, our system prompt is more than four thousand tokens. So yeah, the, the model is actually very much not a limit anymore.

It almost gives me pause because I'm like, "I want the model to be a limit." And so no, the, the integration is all, all one. The core capabilities are one. So for example, we are investing in a system that's basically I call it like the-- It's, uh, Jay Hack gave me these names, like the, the poor man's RLHF.

So you can turn on a toggle on any step of your Lindy workflow to be like, "Ask me for confirmation before you actually execute this step." So it's like, "Hey, I receive an email. You send a reply. Ask me for confirmation before actually sending it."

And so today, you see the email that's about to get sent, and you can either approve, deny or change it and then approve. And we are making it so that when you make a change, we are then saving this change that you're making.

We're embedding it in the vector database, and then we are retrieving these examples for future tasks and injecting them into the context window. So that's the kind of capability that like makes a huge difference for, for users. That's the bottleneck today.

It's really like good old engineering and, and product work.

I assume you're hiring. We'll, we'll, we'll do a call for hiring at the end.

Any other comments on the model side? When did you start feeling like the model was not a bottleneck anymore? Was it 4o? Was it, uh, 3.5?

3.5 Sonnet, definitely. I think 4o is overhyped, frankly. We don't use 4o. I don't, I don't think it's good for, for agentic behavior. Yeah, 3.5 Sonnet is, is when I started feeling that, and then with prompt caching with 3.5 Sonnet, like that fills the cost, cut the cost again and-

Just go back.

Yeah. Uh-

The-- Your prompts are my-- Some of the, the problems with agentic uses is that your prompts are kind of dynamic, right? Like prompt caching to work, you need the front prefix portion to be stable.

Yes, but we have this append-only ledger paradigm, so every node keeps appending to that ledger, and every filtered node inherits all the context built up by all the previous nodes. And so we can just decide like, "Hey, every X thousand nodes, we, we trigger prompt caching again."

Oh, you say you do it like programmatically, not all the time.

No, sorry, Anthropic manages that for us.

Right, right.

But basically it's like because we keep appending to the prompt-

Yeah

... we just-- like the prompt caching works pretty well.

We have this like small podcaster tool that I built for the podcast and I rewrote all of our prompts because I noticed, you know, I was inputting stuff early on. I wonder how much more money OpenAI and Anthropic are making just because people don't rewrite their prompts-

Mm

... to be like static at the top and like dynamic at the bottom, but-

I think that's the remarkable thing about what we're having right now is it's insane that these companies are, are, are routinely cutting their costs-

Right

... by two, four, five. Like they're basically just apply constraints. They want people to take advantage of these innovations.

Very good. Do you have any other competitive commentary, Dust, Wordware, Gumloop, Zapier? If not, we can move on.

No comment. I think, I think the market is... Look, I mean, AGI is coming.

Right. That's, that's what I'm focused on. I think you're helping. Like y- you're s- paving the road to AGI.

I'm playing my small role. I'm, I'm adding my small brick to this giant, giant, giant castle. Yeah, look, when it's here, we are gonna-- this entire category of software is going to create-- It's going to sound like an exaggeration, but it is a fact that it's going to create trillions of dollars of value in a few years, right?

It's going to-- For the first time, we're actually having software directly replace human labor. I see it every day in sales calls. It's like Lindy is today replacing, like we, we talk to even small teams. It's like, oh, like stop.

This is a 12-people team here. I guess we'll set up this Lindy for one or two days, and then we'll have to decide what to do with this 12-people team. Um, and so, yeah. To me, there's this immense uncapped market opportunity.

There's just such a huge ocean, and there's like three sharks in the ocean. I'm, I'm focused on the ocean more than on the sharks.

Cool. So we're moving on to hot topics, like kind of broadening out from Lindy but obviously informed by Lindy. What are the high-order bits of good agent design?

The model, the model, the model, the model. I think people fail to truly, and, uh, me included, they fail to truly internalize the bitter lesson. So for the listeners out there who don't know about it, it's basically like y-you just scale the model.

Like m- GPUs go brr. It's, it's all that matters. I think it also holds for the, the, the cognitive architecture. I used to be very cognitive architecture-pilled, and I was like, "Ah," and I was like a critique, and I was like a generator, and there was all this.

And then it's just like GPUs go brr. Like, just like let the model do its job. I think we're seeing it a little bit right now with o1. It's-- Uh, I'm seeing some tweets that say that the new 3.5 Sonnet is as good as o1 but with none of all the crazy, uh-

It beats o1 on some measures.

On some reasoning tasks.

But, uh, on AME it's still a lot lower.

Mm-hmm.

Like it's like 14 on AME versus o1 it's like 83.

Got it.

So ...

Right. But even o1 is still the model.

Yeah.

Like there's no cognitive architecture on, uh, on top of it. You can just like wait for o1 to get better.

And so as a founder, how do you think about that, right? Because now knowing this, wouldn't you just wait to start Lindy? You know, you start Lindy, it's like 4K context. The models are not that good. It's like-

Yeah

... but you're still kind of like going along and building and just like waiting for the models to get better. How do you today decide again what to build next-

Yeah

... knowing that, hey, the models are gonna get better, so maybe we just shouldn't focus on improving our prompt design and all that stuff and just build the connectors instead or whatever.

Yeah. I mean, that's exactly what we do. Like all day we always ask ourselves, oh, when we have a feature idea or a feature request, we ask ourselves like is this the kind of thing that just gets better while we sleep because models get better?

I'm reminded again when we started this in 2022, we spent a lot of time because we had to around context pruning, because 4,000 tokens is really nothing. You really can't do anything with 4,000 tokens. All that work was throwaway work.

Like now it's, it's like it was for nothing, right?

Mm-hmm.

Now we just assume that infinite context windows are gonna be here in a year or something, a year and a half. Um, and infinitely cheap as well, and, uh, dynamic compute is gonna be here. Like we just assume all of these things are gonna happen, and so we really focus our job to be done in the industry is to provide the input and output to the model.

I really compare it all the time to the PC and the CPU, right? Apple is busy all day. They're not like a CPU wrapper. They have a lot to build, but they don't-- Well, now actually they do build the CPU as well.

But leaving that aside, they're busy building a laptop. It's just a lot of work to build these things.

It's interesting 'cause like, uh, for example, another person that we're close to, Michele from Replit, he often says that the biggest jump for him was having a multi-agent approach, like the critique, uh, thing that you just said that he doesn't need.

And I wonder when-- in what situations you do need that and what, what situations you don't. Obviously, the simple answer is for coding it helps.

Right.

And you're not coding except for... Are you still generating code?

In Lindy?

Yeah.

Mm. No. We do-

Not really, right?

Oh, right. The... No, no, no. The cognitive architecture changed.

Yeah.

We don't. Yeah.

Yeah. Okay. For you, you, you one-shot and you chain tools together and that's, that's it?

And if the user really wants to have this kind of critic thing, you can also edit the prompt.

Mm.

You're welcome to. I have some of my-- Some of my Lindys, I've told them, like, "Hey, be careful, think step by step about what you're about to do." But that, that gives you a little bump for certain use cases, but yeah.

Yeah. What about unexpected model releases? So Anthropic released Computer Use today.

Yeah.

I don't know if many people were expecting Computer Use to come out today. Do these things make you rethink how to design, like, your roadmap and things like that? Or are you just like, "Hey, look, whatever, that's just, like, a, a small thing in their, like, AGI pursuit that, like, maybe they're not even gonna support, and, like, it's still better for us to build our own integrations into systems and, and things like that."

Because maybe people will say, "Hey, look, why am I building all these API integrations when I can just do Computer Use-

Yeah

... and have a go to the product?"

Yeah. No, I mean, we did take into account Computer Use. I-- We were talking about this a year ago or something. Like, we've been talking about it as part of our roadmap. It's been clear to us that it was coming.

Like, we've read reports of OpenAI working on something like that for a very long time. My philosophy about it is anything that can be done with an API must be done by an API or, or should be done by an API for a very long time.

I think it is dangerous to be overly cavalier about improvements of model capabilities. I'm reminded of iOS versus Android. Android was built on, uh, the JVM. There was a garbage collector, and I can only assume that the conversation that went down in, uh, the engineering, uh, meeting room was, "Oh, who cares about the garbage collector?

Anyway, Moore's law is g- is, is here, and so that's all going to go to zero eventually." Sure, but in the meantime, you are operating on a four hundred megahertz CPU, was like the first CPU on the iPhone 1, and it's-it's really slow, and the garbage collector is introducing a tremendous overhead on top of that, uh, especially like a memory overhead.

And so for the longest time, and it's really only been recently that Android caught up to iOS in terms of how smooth the interactions were. But for the longest time, Android phones were significantly slower and laggier and just n-not feeling as good as, as iOS devices.

And so, look, when you're talking about orders of magnitude of differences in terms of performance to rely on r- and reliability, which is what we are talking about when we're talking about API use versus computer use, then you can't ignore that, right?

And so I, I think we're gonna, w-we're gonna be in a, in an API use world for, for, for a while.

o1 doesn't have API use today. It will have it at some point with, um-- It's on the roadmap. There is a future in which OpenAI goes much harder after your business, your market than it is today. Like ChatGPT, it's, it's its own business.

It's making, like, two billion dollars a year or something.

Yeah.

All they need to do is add tools to the desk-- ChatGPT, and now they're suddenly competing with you. And, and by the way, they have a GPT store where they-- a lot-- a bunch of people have already configured their tools to fit with them.

Is that a concern?

I think even the GPT store, in a way, like the way they architect it, for example, the, the plugin systems, we're actually grateful because it's like we can also use the plugins.

Just use it, yeah.

It's, it's very open. No, again, I, I think it's going to be such a huge market. I think there's going to be a lot of different jobs to be done. Today, at least, ChatGPT, I know they have like a huge enterprise offering and stuff, but today, ChatGPT is a consumer app, right?

And so the sort of flow detail I showed you, this sort of workflow, this sort of use cases that we're going after, which is like we're doing a lot of like lead generation and lead outreach and all of that stuff.

Yeah.

That's not something like meeting recording like Lindy today, right now, joins those Zoom meetings and takes notes, all of that stuff. I don't see that so far on the OpenAI roadmap.

Yeah, but they do have an enterprise team that we talked to for, uh, Decibel Summit. Cool. I have some other questions on company building stuff. You're hiring GMs?

We did.

A fascinating way to build a business, right? Like to-- Like what should you, as CEO, be in charge of, and what should you basically hire a mini CEO to do?

Yeah, that's a good question. I think that's also something we're figuring out. The GM thing was inspired from my days at Uber, where we hired one GM per city or per major geo area. We had like all GMs, regional GMs, and so forth.

And yeah, L-Lindy is so horizontal that we thought it made sense to hire GMs to own each vertical and the go-to-market of the vertical and, and, and the customization of the Lindy templates for these verticals and, and so forth.

What should I own as a CEO? I mean, the, the canonical reply here is always going to be, you know, you, you own the fundraising, you own the culture, you own the... What's the rest of the canonical reply?

The culture, the fundraising.

I don't know. Product.

Um, even that, eventually, you, you, you do have to hand out. Yes, the, the vision, the, the culture, and the fundraising.

Vision, culture, and values, yeah.

And it's like if you, if you just do these things and you've done them well, you've done your job as a CEO. In practice, obviously, yeah, I mean, all day. I do a lot of the product work still, and I, I want to keep doing product work for as long as possible.

Obviously, like you're, you're recruiting and managing the, the team. Yeah.

That one feels like the most automatable part of the job, the recruiting stuff.

Well, yeah. Um- ... you saw my designer recruiter here. Yeah.

Relationship between Factorio and building Lindy.

We actually very often talk about how the business of the future is like a game of Factorio.

Yeah.

It's like you just wake up in the morning, and you've got your Lindy instance. It's, it's like Slack, and you've got like five thousand Lindys in the sidebar, and your job is to somehow manage your five thousand Lindys.

And it's going to be very similar to company building because you're going to look for like the highest leverage way to understand what's going on in your, uh, AI company and understand what levers do you have to make impact in that company.

So I think it's going to be very similar to like a human company, except it's going to go infinitely faster. Today, in a human company, you could have a meeting with your team, and you're like, "Oh, I guess we need one more designer.

Okay, I guess I'll kick off a search," and, you know, two months later-

Oh, of course

... you have a new designer. Now it's like, "Okay, uh, boom."

Yeah.

"I'm gonna spin up fifty designers."

That is gonna go away.

Yeah. Yeah.

Like, uh, uh, actually, the-- it's more important that you can clone an existing designer that you know works. 'Cause the hiring process, you cannot clone someone.

Yeah.

'Cause every new person you bring in is, uh, has gonna the, have their own tweaks, and you don't want that.

Yeah. Yeah. That's true. Yeah.

You want an army of mindless drones that all work the same way. The reason I bring this, bring Factorio up as well is, uh, one, Factorio Space just came out. Apparently, a whole bunch of people stopped working. I tried out Factorio.

I, I never really got that much into it. But, uh, the other thing was you had a tweet recently about how the sort of intentional top-down design didn't-- was not as effective as just build.

Yeah

Just like, just ship

I think people read a little bit too much into that tweet. Like I-

Okay

... it went, it went weirdly viral. I was like, I did not intend it as like a giant statement on life or anything.

I mean, you know, you notice you have a pattern of this, right? Like, you've, you've like-

Yeah

... done this for eight years now. You should know.

I legit was just sharing an interesting story about the Factorio game I had, and everybody was like-

Oh

... "Oh my god, so deep, I guess." "I guess this explains everything about life and companies." And, uh, there is something to be said certainly about focusing on the constraint, and I think it is Peter Thiel who said, "People underestimate the extent to which moonshots are just one pragmatic step taken after the other."

Yeah.

And I think as long as you have some inductive bias about like some loose idea about where you wanna go, I think it makes sense to, uh, follow a, a sort of greedy search along that path. I think planning and organizing is, is important and, and, and having order is, is important.

I'm wrestling with that. There's two ways I encountered it recently, one with Lindy, when I tried out one of your automation templates, and one of them was quite big, and I just didn't understand it, right? So, like, it was not as useful to me as a small one that I can just plug in and see all of.

Yeah.

And then the other one was me using Cursor. I was very excited about o1, and I just upfront stuffed everything I wanted to do into my prompt and expected o1 to do everything, and it got itself into a huge jumbled mess, and it, it was stuck.

It was, it was, it was really... There was no amount... Like, I wasted like, uh, two hours on just, like, trying to get out of, of that hole. So I threw away the code base, started small, switched to Claude Sonnet, and built out something working and just added over time, and it just worked.

Yeah.

And to me, that was the Factorio-

Yeah

... sentiment, right?

Totally.

Maybe I'm one of those fanboys that's just like- ... obsessing over the death of something that you, you, uh, just randomly tweeted out. But I think it's true for company building, for Lindy building, for coding. I don't know.

I think it's fair, and I think, like, you and I talked about there's the Tuft mental principle, and there's this other-

Yes. I love that

... um, there's the, the... I forgot the name of this other blog post, but it's basically about this book, uh, Seeing Like a State, that talks about the need for legibility and people who optimize the system for its legibility.

And anytime you make a system so legible, it's basically more understandable. Anytime you make a system more understandable from the top down, it performs less well from the bottom up. And it's fine if that's what you want-

Yeah

... but you should at least make this trade-off with your eyes wide open. You should know, "I am sacrificing performance for understandability, for legibility."

Mm-hmm.

And in this case, for you, it makes sense. It's like you are actually optimizing for legibility. You do wanna understand your code base. But in, in some other cases, it, it may not make sense. Uh, sometimes it's, it's better to leave the, the system alone and, and let it be this...

its glorious, chaotic, organic self and just trust that it's going to perform well, even though you don't understand it completely.

It does remind me of a common managerial issue or dilemma which you experienced in a small scale with Lindy, where, you know, would you wanna organize your company by functional-

Mm-hmm

... uh, sections or by products or, you know, whatever, whatever the opposite of functional is. And you tried it one way and, and it was more legible to you as CEO, but actually it stopped working at the, at the small level.

Yeah. I mean, one very small example, again, at, at a small scale is we used to have everything on Notion. And for me as founder, that was awesome because everything was there. The roadmap was there. The, the, the tasks were there.

The, the post-mortems were there. And so the post-mortem was linked to its task-

Yeah, it's optimized for you

... It was-- Exactly. And so I had this like one pane of glass and everything was on Notion. And then the team one day came to me with pitchforks, and they want- really wanted to implement Linear.

Mm.

And I had to bite my fist so hard. I was like, "Fine, do it. Implement Linear." 'Cause I was like, at the end of the day, the team needs to be able to self-organize and pick their own tools.

Yeah, but it, it did make the company slightly less legible for me.

Another big change you had was going away from remote work, bringing people back in person. I think there's obviously every other month, uh, the, the discussion comes up again. What was that discussion like? How did your feelings change?

Was there kind of like a threshold of employees and team size where you felt like, "Okay, maybe that worked. Now it doesn't work anymore"? And how are you thinking about the future as you scale the team?

Yeah. So for context, I used to have a business called Teamflow. The business was about building a virtual office for remote teams, and so being remote was not merely something we did. It was, it was... I was banging the remote drum super hard- ...

because, like, we were helping companies to go remote, right? And so, you know, frankly, it's, in a way, it's a bit embarrassing for me to do like a, a 180 like that, but I guess, you know, when the facts change, I, I change my mind.

What happened? Well, you know, I think at first, like everyone else, we went remote by necessity. It was like COVID, and you gotta go remote. And on paper, the gains of remote are, are enormous. In particular, from a founder standpoint, being able to hire from anywhere is, is huge.

Saving on rent is huge. Saving on commute is huge for everyone and so forth. But then, look, I'm not going to say anything original here. It's like it is really making it much harder to work together. And I spent three years of my youth, uh, trying to build a solution for this, and my conclusion is at least we couldn't figure it out, and no one else could.

Uh, Zoom didn't figure it out. We had like a bunch of competitors, like Gather Town was one of the bigger ones. We had dozens and dozens of competitors. No one figured it out. I don't, I don't know that software can actually solve this problem.

The reality of it is everyone just wants to get off the darn Zoom call, and it's not a good feeling to be in your home office if you even are lucky enough to have a home, a home office all day.

It's harder to build culture. It's harder to get in sync. I think software is peculiar because it's like an iceberg. It's like the vast majority of it is, is submerged underwater. And so the quality of the software that you ship is a function of the alignment of your mental models about what is below that waterline.

Can you actually get in sync about what it is exactly fundamentally that we're building? What is the soul of a product? And it is so much harder to get in sync about that when you're remote. And then you, you waste time in a thousand ways because people are offline, and you can't get a hold of them or, like, you can't share your screen.

It's just, it's, it's like you feel like you're walking on molasses all day. And eventually, I just... I was like, "Okay, this is it. Like, we're not, we're not gonna do this anymore."

Yeah. I think that is the current builder San Francisco consensus here.

Yeah.

But I still have a big-- Like, one of my big heroes as a CEO is, is Sid Sijbrandij from GitLab.

Mm-hmm.

Matt Mullenweg used to be a hero. Uh, but, like, these people run thousand-person-

Uh-huh

... remote businesses. The, the main idea is that at some company size, your company is remote anyway.

Yeah.

'Cause if you go from one building to two buildings, you're-- Congrats, you're now remote from the other building. Like, if you want to go from one city office to, to, like, two city offices-

Sure

... they're remote from each other.

But the teams are co-located. Every time anyone talks about remote success stories, they always talk about this real false narrative.

Yeah, right.

It's always, it's always, uh, GitLab and WordPress and Zapier and-

Zapier

... it used to be InVision. Um- And, and, and I will point out that in every one of these examples, you have a co-located counterfactual that is sometimes orders of magnitude bigger. Look, I, I like Matt Mullenweg a lot, but WordPress is a commercial failure.

They run sixty percent of the internet and, and they're, like, a fraction of the size of even Substack, right? Or, or, or-

They're trying to get more money.

Yeah, that's my point, right? Uh, like, look, GitLab is much smaller than GitHub. Uh, InVision, you know, is no more, and, and Figma, like, completely took off, and Figma was, like, very in-person. Figma let go of people because they wanted to move from San Francisco to LA.

So I think if you're optimizing for productivity, if you really know, hey, this is a, a support ticket, right? And I want to have my support tickets for back fifty per support ticket and, and next year I will need for, like, a back twenty, then sure, send your support ticket team to offshore, like the Philippines or whatever, and just optimize for cost.

If you're optimizing for cost, absolutely be remote. If you're optimizing for creativity, which I think that software and product building is a creative endeavor, if you're optimizing for creativity, it's kind of like composing an album. You can't do it on the cheap.

You want the very best album that you can make, and you have to be in person and hear the music to do that.

Yeah. Maybe the, the line is that all jobs that can be remote should be AI or Lindy's, and all jobs that are not remote are in person. Like, there's a very, very clear separation of jobs.

Sure. Well, I think over the long term, every job is going to be AI anyway.

Yeah.

Yeah.

It'll be curious to, to, uh, break down what you think is creativity in coding and in product defining and, and how to express that with LLMs. I think that is underexplored for sure. Um-

Yeah

... you're definitely a, what I call a temperature zero use case of, of LLMs. You want it to be reliable, predictable, small. And then there's other use cases of LLM that are more for, like, creativity and, and engines, right?

I haven't checked, but, like, I'm pretty sure no one uses Lindy for brainstorming. Actually, probably they do.

I use Lindy for brainstorming a lot, actually.

Yeah, yeah, yeah. But, like, you know, you wanna, you wanna have, like, something that's anti-fragile to hallucination. Like, hallucinations are good.

By creativity, I mean is it about direction or magnitude? If it is about direction, like decide what to do, then it's a creative endeavor. If it is about, uh, magnitude and just do it as fast as possible, as cheap as possible, then it's, it's magnitude.

And so sometimes, you know, uh, software companies are not necessarily creative. Sometimes you know what you're doing. And I'll say that it's going to come across the wrong way, but Linear, I look up to a huge amount, like such amazing product builders.

But they know what they're building. They're building a task tracker. So Linear is remote, right? Linear is building a task tracker, right? Um- I don't mean to throw shade at them. Like, good for them. I think they're aware that they're not, like-

They recently got shit for, uh, saying that they have work-life balance on their job description.

They were like, "What do you mean by this?"

Uh, we're building a new kind of product that no one's ever built before, and so we're just scratching our heads all day trying to get in sync about, like, what exactly is it that we're building? What does it consist of?

It's an inherently creative struggle.

Yeah.

Dare we ask about San Francisco? And there's a whole bunch of to-touch stuff in here. I don't know if you have any particular leanings. Uh, probably the, the, the biggest one I would just congratulate you on is becoming American, right?

Like, you, uh, very French, but your heart was sort of in the US. Uh, you eventually found your way here. What are your takes for, for, like, uh, founders, right? Like, you-- A, a few years ago, you wrote this post on, like, go west, young man, and now you've basically completed that journey, right?

Like, you're, you're now here and up to the point where you're kind of mystified by how Europe has been so diesel.

In a way, though, I, I, I feel vindicated because I, I, I was making the prediction that Europe was over fourteen years ago or something like that. I, I think it's been a, a walking corpse for a long time.

I think it is only now becoming obvious that it is paying the consequences of its policies from ten, twenty, thirty years ago. I think at this point, I wish I could rewrite the go west, young man article, but really even more extreme.

Um, I, I think at this point, if you are in tech, especially in AI, but if you are in tech and you are not in San Francisco, you either lack judgment or you lack ambition. It's one of the two.

It's funny, I recently told that to someone, and they were like, "Oh, like, not everyone wants to be, like, a unicorn founder." And I was like, "Like I said-" "... judgment or ambition." Uh, it's fine to not have ambition.

It's fine to want to prioritize other things than your company in life or your career in life.

Yeah.

That's, that's perfectly okay, but know that that's the trade-off you're making. If you prioritize your career, you've got to be here.

As a fellow European escapist, I grew up in Rome and-

Yeah, how do you feel? We never talked about your feelings about Europe.

Yeah, I've been in the US now six years. Well, I started my first company in Europe ten years ago, something like that. And yeah, you can tell nobody really wants to do much, uh, and then you're like, "Okay."

It's funny, I was looking back through some old tweets, and I would send all these tweets to, like, Marc Andreessen, like, fifteen years ago, like, trying to, like, learn more about why are you guys putting money in these things that most people here would say you're, like, crazy to, like, even back.

And eventually, you know, I started doing venture, yeah, six, five years ago. And I think just, like, so many people in Europe reach out and ask, "Hey, can you, like, talk to our team?" Or, like, blah, blah, blah, and they just cannot comprehend, like, the risk appetite that people have here.

It's just, like, so foreign to people, at least in Italy and, like, in some parts of Europe. I'm sure there's some great founders in Europe, but, like, the average European founder is like, "Why would I le-leave my job at the post office to go work on this startup that could change everything and become very successful but might go out of business?"

Instead in the US, you have like, you know, we host a hackathon, and it's like four hundred people show up, and it's like, "Where can I go work that is, like, no job security?" You know?

Yeah.

It's just, like, completely different, and there's no incentives from the government to change that. There's no way you can, like, change such a deep-rooted culture of, like, you know, going and wine and Aperol Spritz and, and all of that early in the afternoon.

So I don't really know how it's gonna change.

It's the quality of life.

Yeah, totally. That's why I left. It's-- The quality is so high that I left. Uh, but again, I, I, I agree with you. It's just like, hey, like, there's no rational explanation as to why it's better to move here.

It just, if you wanna do this job and do this, you should be here. If you don't want to, that's fine, but, like, don't cope him.

Right.

You know? Don't be like, "Oh, no, you can also be successful doing this in these," or, like, whatever. No, probably not, you know? So yeah, I've already done my N-400, so I should get my US citizenship interview-

Hell yeah.

Hell yeah.

Damn

... um, soon. Uh, the-

Yeah. And I think, to be fair, I think what's happening right now to Europe is largely self-inflicted. I think they- they've just completely... Again, they've, they've said no to capitalism. They've decided to say no to capitalism a long time ago.

They've, they've, like, completely over-regulated. There's-- taxation is much too high, and so forth. But I also think some of this is, is a little bit of a, a, a, a self-fulfilling prophecy, or it's, it's a self-perpetuating phenomenon because, look, to your point, like, once there is a network effect that's just so incredibly powerful, they can't be broken, really.

And we tried with San Francisco. I tried with San Francisco. Like, during COVID there was a, a movement of people moving to Miami.

Miami.

And, uh -

You and I both moved there .

Right . How did that pan out? You can't break the network effect, you know?

It's so annoying because first principles-wise, tech should not be here. Like, tech should be in Miami-

Yeah

... 'cause it's just a better city. Th- like -

Yeah. San Francisco does not want tech to be here.

San Francisco hates tech.

100%.

This is a thing I actually wrote down, like, San Francisco hates tech.

It is true.

I think the people that are in San Francisco that were here before-

Yeah

... tech hate it, and then there's kinda, like, this passed down thing. But I would say people in Miami would hate it too if there were too much of it, you know? Like, the Nikki Beach crowd would also not gel with-

They're just rich enough and chill enough to not care.

Yeah, I think so too.

They're like, "Oh, crypto kids. Okay, cool."

Yeah.

Like-

Yeah, Miami celebrates success, which is one thing I loved about it.

Eh, a little bit too much . Um, maybe the last thing I'll mention, like, I just wanted a little bit of EU act talk.

Yeah, yeah.

I think that's good. I'll, I'll maybe carve out that I, I think the UK has done really well. That's an argument for the UK not being part of Europe , uh, is that, you know, the, the AI institutions there at least have, have done very well, right?

Sure. But the economy of Britain is in the gutter.

Yeah, exactly.

They've been stagnating at best.

And then France, like, has a few wins.

Who?

Mistral.

Who uses Mistral?

Hugging Face. A few wins. I, that's, it's like-- I'm just, I'm just saying.

It's-

Uh, they just appointed their first, like, you know, AI minister.

You know the meme with, like, the, the guy who's, like, celebrating with his trophy, and then he's, like, miserable?

Oh, yes.

Right. And, like, to me that, that's France. It's like, "Aha, look, we've got Mistral." And it's like champagne, and it's like, it's like maybe 1% of market share. And by the way, I say that, I love Mistral. I love the guys over there, and it's not a critic of them.

It's a critic of France and of Europe. And, and by the way, I, I think I've heard that the Mistral guys were moving to, to the US. Um-

Yeah, they're opening an office

... they have an office. They're opening an office here.

Uh, but I mean, they're very French, right? Like-

Right

... you can't, you can't really avoid it. There's one interesting counter move, which is Jason Warner and isocant moving to Paris for poolside. I don't know. I s- that still remains to be seen what, how, uh, how that move is going.

Maybe the last thing I'll, I'll, I'll say, uh, like, you know, that's the Eu-Europe talk. I, we, we try not to do politics so much, but you're here. One thing that you do a lot is you test your Overton windows, right?

Like, far more than any founder I know. You know it's not your job. Somewhat for, for sure you're just indulging, but also, I think you consciously test, and I just wanna see what drives you there and why do you keep doing it .

'Cause sometimes you tweet very spicy stuff, especially for, like, the, the San Francisco sort of liberal dynasty.

I don't know because so I, I assume you're referring to recently I, I posted something about pronouns and how nonsense-

Just, just in general, right?

Yeah.

I don't want you to focus on any one particular thing-

Yeah, yeah, yeah

... unless you want to.

You know, well, that tweet in particular, when I was tweeting it, I was like, "Oh, this is kinda spicy. Should I do this?" And then I just did it. And I, you know, I received zero pushback, and the, the tweet was actually pretty successful, and I received a lot of people reaching out, like, "Oh my God, so true."

I think it's coming from a few different places. One, life is more fun this way. Like, I, I don't feel like self-censoring all the time. You know, it's just, it's like who... You know? Um, that's, that's number one.

Number two, if everyone always self-censors, you never know what everyone, what anyone thinks, and so it's, it's becoming, like, a self-perpetuating thing. It's like a public lies, private truth sort of phenomenon or, like, you know, the... It's like there's this phenomenon called, uh, preference cascade.

It's like there's this joke, it's like, oh, there, there's only one communist left in USSR. The problem is no one knows which one it is, so everyone pretends to be communist because everyone else pretends to be a communist.

And so I think there's, there's a role to be played for someone to have backbone and just be like, "Hey, I'm, I'm thinking this," and actually everyone thinks the same. Especially when you are like me in a position where it's like I don't have a boss who's gonna fire me.

Uh- It's, it's, like, look, if I don't speak up and if founders don't speak up, I'm like, "Why? What are you afraid of?" Right? Like, there's really not that much downside. And I, I think there's something to be said about standing up what you think is right and being real and owning your opinions.

I think there's a correlation there between having that level of independence for your political beliefs and free speech or whatever, and the way that you think about business too. Like, I, I j- I see that it helps, I think.

I think the word contrarian has become abused, but I think there's such a powerful insight that it's cool, which is groupthink is real and pervasive and really problematic. Like, your brain constantly shuts down because you're not even thinking and you're not aware you're not thinking.

You just look around you, and you, you decide to adopt the same beliefs as people ar-around you. And everyone thinks they're immune, and everyone else is doing it except themselves.

I'm a special snowflake.

That's right.

Like, I, I have free will.

That's right. And so I actually make it a point to, like, look for, hey, what would be a thing right now that I, I can't really say? And then I think about it, and I'm like, "Do I believe this thing?"

And very often the answer is yes, and then I just say it.

Mm.

And so I think the AI safety is an example of that. Like-

Mm-hmm

... at some point, uh, Mark Andreessen blocked me on Twitter, and I-- It hurt, frankly. I, I, I really look up to Mark Andreessen and, and I knew he would block me .

Yeah, it means you're successful on Twitter.

Um, sure.

That's just a rite of passage.

Uh, you know, M-Mark Andreessen was really my booster initially on Twitter. Like, he really made my account, you know? And I was like, "Look, I'm really concerned about AI safety." It is an unpopular view amongst my peers.

Ah, I remember you were one of the few that actually came out, uh-

Yeah

... in support of the bill or something.

I came out in support of S. 1047. A year and a half ago I was-- I, I- Put like some tweet storms about how I was really concerned. And yeah, I was blocked by a bunch of, of A16Z people, and I don't like it.

But- ... you know, I, I-- it's funny, maybe it's, it's my French education, but look, in France, World War II is very present in people's minds, and the phenomenon of people collaborating with the Nazis during World War II is really present in people's minds.

Sure.

And there is always this sort of debate that people have, like at, at dinner, and it's like, "Ah, like would you really have resisted during World War II," right? And everybody is always say, "Oh yeah, I would totally have resisted."

It's like, yeah, but no. Like look, the reality of it is ninety-five percent of the country did not resist, and, and most of it actually collaborated actively with the Nazis. And so ninety-five percent of y'all are wrong. Like, you would actually have collaborated, right?

I've always told myself, like, I will stand for what I think is right, even if I have... Like I've, I've gotten into physical fights in my life, like in the SAF because like some people got attacked. And like the way I was brought up is like if someone gets attacked before you, you get involved.

Like it doesn't matter, you get involved and you, and you help the, the person, right? And so, um, look, I'm not pretending we're like nowhere near like a, a World War II phenomenon, but I'm like exactly because we are nowhere near this kind of phenomenon, like the stakes are so low.

And if you're not gonna stand up for what you think is right when the stakes are so low, are you going to stand up when it matters?

Italian education is that in Italy, people don't have guns when you fight them, so you can always get in a fight but-

Right

... here in the US, I'm always like-

Yeah

... oh man. I, I feel-- I detect some inconsistency in your statements because you simultaneously believe that AGI is very soon, and you also say stakes are low. You can't believe both are real.

Well, the stakes are-- why, why does AGI make the stakes of speaking up higher?

Sorry. The, the stakes of like safety.

Oh yeah, no, the stakes of AI is... Oh, oh, like physical safety?

No, AI safety. Like-

Oh, no, the stakes of AI safety couldn't be higher.

Okay.

I meant the, the stakes of like speaking up about pronouns or whatever.

Oh, okay, okay.

Yeah, yeah, yeah.

How do you figure out who's real and who isn't? Because there was the whole like, uh, manifesto for responsible AI that like hundreds of like VCs and people signed, and I don't think anybody actually, any of them thinks about it anymore.

Was that the pause letter, like six-month pause or something like that?

Some-- no, there was like something else too-

Okay

... that I think General Catalyst and like some fund signed. But-- and then there's maybe the Anthropic case, which is like, "Hey, we're leaving OpenAI because you guys don't take security seriously." And then it's like, "Hey, what if we give AI access to a whole computer to just like go do things?"

Like- ... how do you reconcile like, okay, it, it-- I mean, you could say the same thing about Lindy. It's like if you're worried about AI safety, why are you building AI, right? That's kinda like the extreme thinking.

How do you internally decide between participation and talking about it and saying, "Hey, I think this is important, but like I'm still gonna build towards that, and building actually makes it safer because I'm involved," versus just being like anti, "I think this is unsafe," but then not do anything about it and just kind of remove yourself from the whole thing?

If that makes sense.

Yeah. The way I think about our own involvement here is I, I'm acutely concerned about the risks at the, the model layer, and I, I'm, I'm simultaneously very excited about the upside. Like for, for the record, my p doom, insofar as I can quantify it, which I cannot, but if I had to, like my vibe is like ten percent or something like that.

And so there's like a ninety percent chance that we live in like a, a pure utopia, right? And that's awesome, right? So like let's go after u- u- utopia, right? Let's talk about the ten percent chance that things go terribly wrong, but I, I do believe there's ninety percent chance that we live in a utopia where there's no disease, and it's like a post-scarcity world.

I think that utopia is going to happen through, like again, I'm, I'm bringing my little contribution to the movement. I think it would be silly to say no to the upside because you're concerned about the downside. At the same time, we wanna be concerned about the downside.

I know that it's very self-serving to say, "Oh, you know, like the downside doesn't exist at my layer. It exists at like the model layer."

Yes.

But truly- Look at Lindy. Look at the Apple building. I, I, I struggle to see exactly how it would like get up and start doing crazy stuff. I'm concerned about the model layer.

Okay. Well, this kind of discussion can go on for hours. Uh, it is still daylight, so, uh- ... not the best time for it. But, uh, I really appreciate you spending the time. Uh, any other last calls to actions or, uh, thoughts that you feel like you wanna get off your chest?

AGI is coming.

Uh, okay.

Um.

Are you hiring for any roles?

We are-

Yeah.

We-- oh yeah, I guess that should be the

Don't bother.

Flo, can you stop saying AGI is coming and just talk about that? Uh, uh, we are also hiring, uh, yeah, we're, we're-- we are hiring, uh, design ELs and engineer ELs right now. Yeah. So hit me up at flo@lindy.ai.

And then go talk to my Lindy.

That's right.

You're not actually gonna reach me.

Actually, I, I have wondered how many times when I talk to you I'm talking to a bot.

I won't know. This is on discussion.

And part of, part of that is I, I don't have to know, right?

That's right.

Well, it's actually doubly confusing because we also have a teammate whose name is Lindy.

Yes. I also wondered. I met her, I was like, "Wait, like this-" "Did you hire her first or?"

It's a marketing stunt. No, she was an inspiration after, you know, we named the company partly after her.

Oh, okay. Interesting, interesting. Yeah, wonderful. Yeah, I'll comment on the design piece just because I think that there are a lot of AI companies that very much focus on the, the functionality and the models and the capabilities and the benchmark.

But I think that increasingly I'm seeing people differentiate with design, and people want to use beautiful products, and people who can figure that out and integrate the AI into their human lives. You know, design at the limit, one, at the lowest level it's make this look pretty, make this look like Stripe or Linear's homepage.

That's design. But at the highest level of design, it is make this integrate seamlessly into my life. Intuitive, beautiful, inspirational maybe even. And I think that companies that I-- you know, this is kind of like a blog post I've been thinking about.

Companies that emphasize design actually are gonna win more than companies that don't.

Yeah. I love this Steve Jobs quote, and I'm going to butcher it, but it's something like, uh, "Design is the expression of the soul of a man-made product through successive layers of, of design."

Jesus.

And so it's, it's-- He, he was good.

He was cooking. He was cooking on that one.

He was cooking. Uh, it, it starts with the soul of the product, which is why I was saying it is so important to reach alignment about that soul of the product, right? It's like an onion. Like you peel the onion and there's layers, right?

And, and, and you, you design an entire journey, just like the user experiencing your product chronologically all the way from the beginning of like the awareness stage. I think it is also the, the job of the designer to design that part of the experience.

It's like, okay, what, you know-- And that, that's brand, basically. Uh, so yeah, I agree with you. I think design is, is immensely important.

Okay, lovely. Yeah. Thanks for coming on, Flo.

Yeah, absolutely. Thanks for having me.