I just want AI to rename my photos | The Vergecast

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Welcome to the Vergecast, the flagship podcast of using AI models to rename all the files on your computer for better and for worse. I'm your friend, David Pierce, and this is the first in a

two-part series we're doing about AI, and more specifically, how people who are building AI tools are thinking about the AI tools that they're building.

Basically, we're at this moment in time where everyone who makes any kind of app, any kind of software, any kind of hardware, for goodness sake, is trying

to figure out the ways to put AI into this. And on some level, I think that's

this. And on some level, I think that's silly, right? like there's there's a lot

silly, right? like there's there's a lot of stuff out there that actually does not benefit from having chat GPT shoved into it in some ridiculous way. But on

the flip side, there are actually a lot of things that become better and more useful and more functional with these kinds of tools. One thing I think about

a lot is is text transcription. It's a

simple thing, but right, OpenAI put out this whisper model that does really good, really fast transcription of audio that ends up being really powerful for

lots of things. There's this feature in To-Doist, the app that I really like.

It's a to-do list app, and they have this feature called Ramble. I think I've talked about this before, but you can just talk your to-do list, all the things you're thinking about, all the things on your mind, all the things on

your shopping list. you just sort of yell it into the app and then it will attempt to go through and structure it all and make sense of it. And there's

there's a couple of different layers of AI in there, right? But the first one is just take your voice and reliably successfully transcribe it. That's very

powerful. Uh there's also an app I use called My Mind that is using AI to do really great search so that instead of having to like make a bunch of notes and then file them into folders or give them

tags or do any kind of organizing, you just put it all in and trust that you'll be able to sort and search and find things as you need to. This stuff can really work. So, for the next two

really work. So, for the next two Sundays, what I'm going to do is I'm going to talk to two people who are making apps that I think are doing a smart job of AI. It's going to sound in

both of these interviews like I like the products and it I do. It's that's why they're here because I think they're thinking about AI not as just something to like shove into the app to charge you

more money or juice their stock price or whatever, but because there's something it actually makes possible. And

sometimes it makes those things possible in ways that are complicated and messy and privacy threatening and maybe even threaten to like ruin the vibe of the thing you're trying to build in the

first place, but that also have upsides that make the things more useful and more fun and more discoverable. So,

we're going to talk about all of that.

And my guest for this first one is Thomas Paul Man, who is the founder and CEO of a company called Raycast. Raycast

is initially was a Mac app. It's now on iOS and on Windows. The way I would describe it is it's it's sort of a launcher and then some, right? So, you

use it to replace Spotlight on your Mac.

And it then will let you launch apps.

You can use it to store like text expansion things. I have one set up so

expansion things. I have one set up so that when I tape type H for home, it just immediately spits out my home address. That's a thing that lives in

address. That's a thing that lives in Raycast. You can also use it to like

Raycast. You can also use it to like manage the windows on your computer and move stuff around. But increasingly, one of the biggest things that it can do is access AI models. And you can use it

just to chat with chat GPT inside of Raycast, but you can also use chat GPT to use your apps. So I I can go in and I can type, you know, at browser

uh download all of the tabs as a CSV and put it into a text file that I can then send to somebody. And that's like a thing it is in theory capable of doing.

I can open it up. I can say at Findinder, show me all the files that I have created in the last 24 hours. And it's

it's actually an AI system that can use your other apps and even use your computer. We've talked a lot about AI

computer. We've talked a lot about AI browsers. We've talked a lot about these

browsers. We've talked a lot about these sort of tools that have lots of additional context. Raycast has more

additional context. Raycast has more context than just about any other app.

I've been using this app for a long time. I really like it a lot. I have not

time. I really like it a lot. I have not made that much use of all of the AI stuff inside of it. So, I wanted to have Thomas on to both talk me through how he thinks about putting AI into this

product and also what it can do for you when it really starts to work. I really

enjoyed talking to him. I think you'll enjoy hearing it. We're going to take a quick break and then we're going to get to my interview with Thomas. We'll be

right back.

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Thomas Paul Man, welcome to the Vergecast. Hey, thanks for having me.

Vergecast. Hey, thanks for having me.

You and I have talked many times, but we've never talked into a recorder like this. And I'm very excited to have you

this. And I'm very excited to have you here. This is this is when we were like,

here. This is this is when we were like, we're doing this series about people who are sort of building and thinking about AI and what AI can do. Um, which is a conversation you and I have had versions

of many times. Uh, so now we're just going to do it again and I'm excited about it.

>> Sounds good. Yeah, sounds like we have done it a few times already. So, let's

see. Indeed. [laughter]

So, first give me a sense of I think you've been thinking about AI inside of Raycast for a while and I would say just sort of rewind to like the early days of

when you started thinking about how AI models fit into what Raycast was doing a couple of years ago. Like what what were those first conversations you were having like? Yeah. Um, so yeah, Rayos is

having like? Yeah. Um, so yeah, Rayos is like sort of this global search bar on your Mac, right? And actually now also on Windows. Um, but like basically what

on Windows. Um, but like basically what we realized when CHBD came around and suddenly everybody talked about a prompt and everybody was looking for like a

text box to feed in a prompt that we were really well positioned for that because Rikers itself is like a massive search box and so you can open it anywhere and so you can just type

something in and it just used to be just very static text like oh you're searching an app or like a command or you want to do something. Um but then it felt quite natural to extend this and

just put in natural language like a prompt and then get going. So pretty

much right after chat GPD came out we were hey wait a second that suits us really well and so I think the first model was chd3 that we integrated um

which was back like end of 2022. Okay.

So the thinking even then was like we can we can solve some of the way people talk to their computers cuz I think to me it's like the very first good thing

that any of these models did was make it slightly easier to like speak in English to your computer. Is that was that kind of your your thinking too that like we can just make this make a little more sense?

>> Yeah, I think like the very first thing that people did was just like asking questions, right? And so it's kind of

questions, right? And so it's kind of funny because all the way back when we started ray cost we were like oh like we're programmers we sometimes have questions how do I do xy that and then

you used to go to something like stack overflow and find those questions right that somebody else asked and then you like go over it and read the answer yourself and you kind of had to be very

good at basically keywords to find the proper question that leads you to the answer. But now this whole thing got

answer. But now this whole thing got like basically flipped upside down. And

so the very first thing we saw like oh people just ask questions and so get the answers and then carry on with whatever they do. And this can be little things

they do. And this can be little things can be fun things. Um but like it helped them to basically stay informed. Um and

so one of the first big challenges to overcome was like oh but sometimes those models hallucinate and how do we get over that? And I think then the next

over that? And I think then the next sort of wave was very easy like oh well let the models do what we would do and search the web and then take that information and distill it down in into

the style and the tone of voice we wanted to have. So that was sort of the very first things that we've seen picking up and since then it became more and more advanced right and then it was

like okay maybe not just search the web but search your calendar, search your files, read your files, do actions like organizing your folders

and files on your Mac um or like other things. So I think like as with is every

things. So I think like as with is every new technology people kind of adapt to like what's possible and then they take the next step and like pushing the boundaries a bit more and more and given

we have like quite advanced users like they often times on the forefront and so they pushing really hard to the extremes and then we can kind of see what they wanted to do and then integrate that quite nicely and make accessible to many

more people. So, do you have to make a

more people. So, do you have to make a decision early on about like how much of the stack of AI do we want to be part of? Like I I assume there was never a

of? Like I I assume there was never a question of like should we train a raycast LLM, but but it does seem like, you know, you could build something that

is essentially just a text box that replaces the chat GBT text box or you could build something on top of it. you

could try to integrate with an with an API and be a sort of developer on there just seemed like a lot of different ways you could try to do that thing that you just described at like vastly different

levels of complexity.

Was it obvious to you where to land early on?

>> No, [laughter] I mean this thing came out overnight, right? like suddenly the thing was there

right? like suddenly the thing was there and it's like oh wow like what used to be sort of sci-fi um and the movie thing was suddenly like

somewhat possible. Uh but like you kind

somewhat possible. Uh but like you kind of need to get started somewhere. So

early on we kind of were just playing with the APIs and integrated them. Um

initially just open AI because this was literally the only API that was there, right? there was nothing else that was

right? there was nothing else that was even available. And so that like took us

even available. And so that like took us several months until somebody else popped up.

>> And then it became clear like that models are all a bit different. Not even

talking about which ones are smarter um or faster, but just they have nuances and people prefer the one over the other for oftenimes person reasons like, "Oh,

I like how this model talks to me." And

so really quickly we said like, oh, it probably makes sense to integrate all the different models because people going to have personal preferences and they're going to be better and and and worst models or like better models for

certain cases. Like sometimes you just

certain cases. Like sometimes you just don't need the full intelligence. You

just like want to do something simple like oh summarize this blog post or rewrite this message. Those don't need to be the cutting edge models. Um you

want to have them rather fast. Um and

then sometimes you want to have like a model that goes on for several minutes and and does a bunch of research and then coming back with like um like a big research paper for you. And for that you probably need to have a better model.

And so early on we said like okay let's integrate with as many models as possible because we have a quite technical audience. So they will help us

technical audience. So they will help us also guide which models they prefer. And

what we now see after like a couple of years, whenever there is a new model dropping, everybody goes to the new model, tries out, and basically want to have the the latest and greatest, and then they're using that for several

months until the next model drops from a different company most likely, and then they're going over to the next one. So,

the switching costs between those models are extremely low at the moment, at least for us, um, and for our users. Um,

and then I think there's like they're building up a bit of like muscle memory or like even learning how to get the most out of those models. And those

things are sometimes a bit um more tied to let's say a model family. Um, but

early on we said like we're not going to go and build our own models. Um, what we did we did um some optimizations on the prompt level and also some fine tuning to like make the models really good in

our case. Um and so that is for example

our case. Um and so that is for example u making like a lot of like agentic workflows. Um but nowadays like a lot of

workflows. Um but nowadays like a lot of the models are pretty good at that on a basic level already. What were you doing in those early days like that was that was before everybody was talking about agentic stuff but it also seems like

kind of perfectly up the alley of raycast to figure out okay how do we we have this unique access to your device and your files and your data. How do we teach this model how to do stuff? Were

you were you poking at that stuff even in these sort of early days before everybody was talking about Agentic AI?

Yeah. So, we had this pretty early where we said like this makes total sense for us. Like we have this extension

us. Like we have this extension platform. So, there are like over 2,000

platform. So, there are like over 2,000 extensions. They're publicly available.

extensions. They're publicly available.

You can integrate Rayos with Notion, Linear, Google Docs, GitHub, you name it. Anything you can really think of.

it. Anything you can really think of.

and also on your local computer like it can see your files and your calendar etc. And so we had all of those extensions lying around and we're thinking like oh my god the obvious

thing is instead of like you doing everything manual you say what you want to do and the computer does it for you.

um turns out it's a bit harder getting there, right? But the [laughter]

there, right? But the [laughter] promised land is like quite nice, right?

So you you flip it upside down essentially and and kind of change how you do use computers in the first place because if I think about how I used to use a computer, it's like, okay, I have

an idea what I want to do. So in my head, I'm kind of transpiling that into clicks and key strokes and and navigate around my computer. Um, but now it's like instead of doing that, I just write

down or even like speak into my microphone for several seconds and then let the computer handle it for me. Um,

so we had this idea early on. Getting

there was a bit harder because one, we wanted to make sure that um those things work really well. So that isn't that easy. to um you also had to figure out a

easy. to um you also had to figure out a bit the UX because it's still with a prompt where way you can say anything is great but also we used to

have great UI that guides us right we have buttons that we can click and help us basically navigating um and now if suddenly the computer pretends that everything is possible it's a bit of a

lie because that's often times not a truth and so figuring out sort of the middle ground between when it makes sense to have UI and when makes sense to just have an open pron field. Um, that

was like a bit of a challenge.

>> Well, that's also kind of an essential raycast problem, right? Like this is a thing you and I have talked about before, the how do you discover what this thing is able to do problem because

you open it up and it is just a text box. Like you have the exact same

box. Like you have the exact same problem that chat GPT was has, which is that you open it up and it it makes clear that you can do things, but it's not super clear what things to do or how you teach it to do things. And again,

this is where like all the agent companies get really excited because they're like, "You just say it and we'll figure it out for you." I'm extremely suspicious of that as a as a concept.

Uh, but it does seem like you're you're sort of stacking discovery problems on discovery problems here. Uh, is there is there a way to start to push through those things?

>> I think so. Yeah, we got to learn how to use this new technology, right? And it

changes in how our behaviors. If I look for example for a moment at coders right so they probably a bit further ahead in this adoption curve um and it's like they're very close to the technology so

I think that's why it also progresses there really quickly but programming used to be like you write text and like if you write something wrong that's bad and then at some point somebody like a compiler tells it that's bad and then

you correct it right and then we happen to like know it's like oh we can do some autocomp completions so we kind of know what's possible so we can show you possibilities and then you pick them and

that's great. And then the first LLM use

that's great. And then the first LLM use case was like piggybacking those completions and say like oh maybe I can tell you a bit longer what you could write and and kind of suggest you that

and like predict that for you. Um and

then that worked really well and then now it's like well I don't even write code anymore. I just like write what I

code anymore. I just like write what I want to do and let the LLM do it for me, right? And so I think you see like sort

right? And so I think you see like sort of that the pattern where I call this often times prompt first. like if you know what you do and you know the system, you get actually really good

results. But you're right, like there is

results. But you're right, like there is a discoverability phase where you where you need to know um what a system can do, right? And I think we had this like

do, right? And I think we had this like not that long ago when we had all the voice assistants. I'm not meaning like

voice assistants. I'm not meaning like the ones we have right now, but like back in the day the Alexexas and all things where suddenly like voice interfaces were the hot thing and then everybody was like, "Oh my god, this is amazing. I can order an Uber and god

amazing. I can order an Uber and god knows what." and then everybody got them

knows what." and then everybody got them and well we all know how that one turned out right like it wasn't [laughter] that useful after all. So I think like it's sort of like the tech is obviously like

much better now but people still like need to learn how to use the tag and and that just doesn't happen overnight. Um

and so prompting is still like a skill thing like oftent times we get user feedbacks oh this didn't work and then you look at it it's like well I'm not surprised that it didn't work because I didn't really tell it. So yeah,

discoverability is something that I think is extremely important. I think as those system become much more um proactive, I think this will be better like when a system pushes to you like

hey how about that or like you start typing and it suggests you oh I know kind of what you want to do and I know what the system can do so I can suggest intelligently what's possible and and

kind of guiding you in the right direction. So, one place I think that

direction. So, one place I think that approach could be really useful and I'm sort of surprised to have not seen more people try to do it is what you were talking about earlier with the fact that

there are lots of different models that all have lots of different skills. It

seems to me what we need is not just a sort of model switcher, right? And and

Raycast offers you access to lots of different models. There are lots of apps

different models. There are lots of apps out there that are just like we have all the models in one place and that's something but what I actually want is is something that is like an intelligent router between the models that's like

okay this is actually the one that is going to do better image generation and oh what you prompted me is actually a huge research product let me funnel it to this like I think the idea that we all have to understand which model is

best for which thing is like ridiculous and and just bad UI and it seems like this is a thing that you're actually in theory in a pretty good position to

orchestrate. Is is this is this like a

orchestrate. Is is this is this like a possible thing to do? Like why why doesn't this exist yet? Yeah. Um in fact like we started doing that, right? Um I

think like it's basically first you kind of need to understand what are the best models for what thing, right? And like

some of them you can measure but others is also like a personal choice. Um but

we started doing this and I noticed like oh there are some models that just better at like for what you said like image generation or some are better at like um attending workflows where they

use certain tools to get a job done. Uh

and some are better at like um yeah recognition of like images and and and all this kind of stuff. So we we started basically now um abstracting that away

and sort of we think about of like disclosing the complexity over time. So

we think the best experience is like you sort of have an automatic mode which just does what you want right automatically >> and you don't need to worry about it like if if you ask a question where it

needs like a deep research it does it for you if you if you want to uh generate an image it picks the best model for that. Um, but then also like sometimes when you get more advanced,

you maybe want to have the flexibility so you can go a level deeper and and we want to give you still the configurability where you say, "Hey, I kind of know what I'm doing, so I want to have that specific model doing that

job." And so you you kind of like go up

job." And so you you kind of like go up and down in the in the configuration and can pick what you want. And I think you see this like in the industry where um

you saw this on chat GPT they like put out like an auto mode and then everybody was freaking out that you can't select models anymore and then they kind of like had to broaden it up. Um and I think like this is like something which

you which you will see more often cuz I mean if you think about it like we have this massively smart systems and we still need to figure out which one we need to use it as you mentioned is a bit

ridiculous right? So over time I think

ridiculous right? So over time I think this will just go away and it just like does what you want and figures it out.

Um we're just not there yet in a way. Do

you think Raycast is in in a uniquely good position to do that? Because it

seems to me like again when I think about Raycast I think about it as like it's it's just it's just a a box with access to all of my stuff, right? And I

think on the one hand it has it has access to all the files on my computer which I think is is a thing that is sort of unique to Raycast. On the other hand you have like you said all of these extensions and all of these apps that

I'm plugging into and I'm like you know typing my API keys for all of these apps into Raycast. So like you you have this

into Raycast. So like you you have this access and then on the on the third hand you have access to all of these models.

And so what it seems to me is at least in theory there's nothing you don't have to just be able to orchestrate all of this stuff on my behalf. Like is is there some big blocker here that I'm

missing or is it just a matter of figuring out how to make all of this stuff work?

>> Yeah, it's mono ladder just basically for us as you you're right like we're basically in the perfect spot, right? Uh

just like happened to be there at the right time at the right place. Um so yes we have access to all of that on top we also kind of see what you're doing right

and throughout the day which actions you perform through ray cost and so we had like the usage patterns and those things as well so connecting those things together is I think the magic cells here

is like what basically makes this really personal and unique and tied to you right because we all somewhat unique and using our computers differently and we

use different apps and we write differently and we're interested in different things and so you kind of need to have this personalization layer. Um,

but we're also spending like like hours a day in front of a computer and perform things, right? And so collecting those

things, right? And so collecting those and analyzing those and making sure that we basically can predict the next things you want to do and becoming smarter over time and having this sort of

reinforcement learning for you personally. I think that's what really

personally. I think that's what really makes a difference and we call this sort of the contextual AI. Um we talk a lot about context generally with AI right like I think when you talk to people

like prompting is providing context to a large language model so it can produce the best results but sometimes really hard having the relevant context. Um but

if you happen to like be always around while somebody works on computer, you can collect a lot of that and basically become smart over time and and can help the person steer in the right directions

and kind of ideally the computer knows the same as you do, right? From like

what you have read and what you have consumed and building that up over time.

So it can basically like take the same resources and and throw them back at you and form um connected dots because it has read all the things and and consume that and then also tie together and use

the same apps and tools you use that are already connected to ray cost. How do

you start on a project like this? Like I

think a thing that I see a lot of AI developers and people building stuff with these tools do wrong is they try to

do the like 100% version of the thing all at once, right? And like

>> yeah, >> I can say with great confidence most agentic workflows don't work. They just

don't. And there there are a lot of perfectly valid reasons for that. But

there's also a lot this stuff can do.

And and I think to me it seems like the struggle right now is is figuring out how do we do >> how do we sort of sequence this thing that eventually gets us to the place that we think and hope this technology

is going but that actually works today.

And >> the what I see is everybody either builds stuff that doesn't work or it ends up just being a chat GPT text box and you just sort of offload the the

does it work or does it not to chat GPT.

>> Yeah. Like have you have you figured out how to sort of sequence your way to this magical thing that may someday be true but clearly isn't yet?

>> Yeah, I think that's the tricky part, right? Like we all seen the shiny demos

right? Like we all seen the shiny demos on in launch videos and then they fall apart the moment you use it, right? And

it's kind of annoying and I mean sometimes those things don't even ship like they're just videos and they're never getting getting material.

>> It's science fiction, right? And it is like the dream. This is the interesting thing about this moment is like I think people mostly agree on what the dream is, >> but it is still a dream, right? Like it

is it is it's it's a plausible future, but it is still the future.

>> And and I think like everybody would do well to just remember that a [laughter] little more often.

>> Yeah. I think I I wonder like if it ends up being this like um you know like self-driving cars like oh it's just like one more year and then it's going to be self-driving, right? And then this goes

self-driving, right? And then this goes on for 10 years, right?

>> Um the progress that LLM's made shows a bit different the trend, right? The

trend is more like, oh wow, we're like having like a lot of progress in a very short time and it doesn't seem to to stop any anywhere close. Um but I think

like it boils down to like sort of the usual things like do the simple things first, right? to try out because it's

first, right? to try out because it's such a new technology, you kind of need to get an understanding what's possible and what is not. And so it's a lot of like just prototyping and see kind of

what sticks and what brings real value and it's not just like science fiction as you say which maybe works one out of 10 times and then that's not going to be useful right?

>> And so I think like finding that middle ground is extremely hard. Um and you see like some of those things they're happening, right? So where people see

happening, right? So where people see value which maybe not the sci-fi things that they that we all dream up but like say meeting recordings like those happen now basically on a regular basis right

so there's some true value in here that was not possible before um or like just like yeah the research cases like just consuming and finding information about topics that you would otherwise not have

looked up so there's some of those very concrete examples um and I think there's a lot more out there like coding is another one right it makes just so much progress pres in such a short period of

time. Um, but it's not this like super

time. Um, but it's not this like super general stuff which I think for us is in a way like a challenge because Rayos is this like everything app, right? You

open it and then you can type something in. And so finding that middle ground is

in. And so finding that middle ground is is is sometimes hard for us. Um, but

it's coming back to like okay let's let's see what people do very very often every day. See how we can improve those

every day. See how we can improve those workflows. Um, and then go sweat the

workflows. Um, and then go sweat the details do the prototyping and see what actually makes a difference. And then

when you find something like that, you kind of can bring it back and then um and then bring it back to users. And

that usually like resonates as well because that's what people then are are used to. But yeah, it's like there's a

used to. But yeah, it's like there's a lot of like um exploration and at the end of the day, everybody cooks with the same water, right?

>> Yeah. Yeah. Is there is there an example of that in the product now that you can think of that feels like that that sort of medium measure that you either got right or kind of in the middle of

getting right? Yeah, it's it's weirdly

getting right? Yeah, it's it's weirdly the simple things sometimes like oh you open it and like I use it for meetings for example all the time there's like a word pops up you don't know you open

your ass you get the answer done like it's those we also optimize rakos as a tool for something that you basically use like hundreds or thousands of times right like it does a lot of like little

things that pile up and so it's for those short interactions um things that we see people use all the time is like just like plain reformatting text and fix spelling because like well we're

still typing all day long right um so making those things easier and faster to do um and then people when they get comfortable with it they're getting a bit more adventurous right and then it's

like oh I just happen to download a bunch of files I need to move them in a separate folder and also rename them so they make more sense and so they then type in those prompts and see like oh this works as well and then they take

the next step right yeah okay renaming files is actually like a perfect example of the kind I think I want to talk about about Raycast specifically. Yes.

>> Because uh we've been talking a lot >> on this show and elsewhere about the idea that Sati Nadella and Microsoft have right now that before long you're

going to barely use your computer and it will sort of use itself on your behalf.

Um just to put my own cards on the table, I think that is not correct. Uh

at least not in any sort of near future.

But I do think that there is a lot of room for like doing computer tasks without having to do the tasks, right?

And I think about like all of the things that you know we've spent 20 years downloading little tiny utilities to do that that these sort of like oneoff apps

that are like batch resize a bunch of photos. Simple example. Or like rename

photos. Simple example. Or like rename these photos with all the same name in sequential order based on when I took them. Like these are the kinds of things

them. Like these are the kinds of things that we do a lot on our computers that are not hard tasks and they're not particularly like mentally complex

tasks, but it's like a constant part of computing life. Uh it seems like you're

computing life. Uh it seems like you're in a position where I should just be able to say to Raycast, rename all of the photos on my desktop based on what they are and when I shot

them and put them in an order that makes sense. Just clean up my desktop for me.

sense. Just clean up my desktop for me.

Yeah. Are we Are we Are we almost there?

Are we there? Are we nowhere near there?

Where are we? I we we're 90% there. I

would say in fact you can in fact you can do this today in Rayos. Like we have that, right? You can't do this. Um and

that, right? You can't do this. Um and

then the 90% I'd say like every now and then it doesn't work. Right,

>> Thomas? I'm going to try this right now while we sit here and it's not going to work and I'm going to be mad at you about it. I'm scared. [laughter] But so

about it. I'm scared. [laughter] But so but like it it's possible, right? And

you mentioned like this sort of like super attending I think what you mentioned, right? So you like where

mentioned, right? So you like where where the computer does everything for you. I mean when we reach that state we

you. I mean when we reach that state we talk about hi right then and there is a question like why should I even open a computer like what is a computer at that moment right um

>> I think how how we think about it is more what's an intelligent OS what's sort of the AI OS right so how how our operating systems will change to like

adopted this new future where everything can be smart and it's not necessarily static so you mentioned like you maybe want to have a little app to do something what if you could have this app just like by asking AI and it builds

this little app for you and then you have it for yourself, right? And then

you use it for the chop and then and then the job is done and then it maybe gets this post and then it's like that's fine, right? And so it's like this

fine, right? And so it's like this oneoff software um this personal kind of software that is like personal to you but maybe also to your team or your company that is like very tailored to

the use case you want. I think that's like something which is quite fascinating as we like things get smarter and um software maybe gets cheaper to build. Um I think there is

something quite fascinating when your operating system becomes similar right so where you can just like prompt things into existence for like a short period

of moment when you need them and then when the job is done you just like don't need to use them anymore and then tomorrow you have a different one or maybe at some point you have apps that

are just appearing there as you as you progress with your day and it's like oh I saw David needs to like do certain things hey here's a little for you that you probably can use. All right, we got to take one more break and then we're

going to come back and we're going to finish my conversation with Thomas Palm.

Be right back. [music]

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All right, we're back. We're talking AI with Thomas Paul. Man, let's get back into it. You bring up another thing that

into it. You bring up another thing that I've I've been wondering about, which is I think a thing that Raycast did really well early on was make it really easy to build Raycast extensions. Like it's it's just a little bit of fairly

straightforward JavaScript and you can have something up and running pretty fast. and and so you've built sort of an

fast. and and so you've built sort of an an app store on top of Raycast in a way that seems to be working really well and there's a lot of stuff and it's it's

pretty easy to do. Does that all eventually go away if we get a gentic AI that is good enough to just go do all this stuff on my behalf and I no longer need this sort of interim step of

somebody built an extension that helps go do it? Or is actually what we need lots and lots like should I be using AI to build JavaScript extensions for Raycast or should I be using Raycast to

just completely obviate the JavaScript extensions?

>> Yeah, I mean fair point. Uh so yeah, extension was really what put us sort of on the map because we realized really quickly, okay, people just want to integrate and and rake us with everything basically and there's no way

we can build all of that. So we gave it out to community and then we made it like super easy to build them and that allowed us to like have over 2,000 extensions now in the store. So and

every day there is like new contributions coming and so on and so forth. But if you take a step back, what

forth. But if you take a step back, what we really wanted to do is like build a productivity platform. That's sort of

productivity platform. That's sort of like what we wanted to do. And

extensions is almost like an implementation detail or JavaScript itself, but even extensions are an implementation detail, right? So imagine

like like those wouldn't exist for a second, but services still exist, right?

You still want to do something with like Google Docs or Spotify or you name it, right? Or your files for for that. Um

right? Or your files for for that. Um

and so the idea was always like how can you integrate with those things really easily so I can't do the job for you like this illusion that we did is like oh people can build the extensions you

can use them but you could even equally think about like oh like an AI can build something like that for you so you can use it and then your extension might be behaved differently so the notion of

extensions becomes almost a bit blurry right it's just like that's evolving software in a way and and even for yourself you you're probably like just downloading some extensions, but you haven't built them in the first place or

somebody else built them. So, it's not too far off like for you prompting an AI to come back with a solution for you, but it's like tailored towards you, right? The key thing I think is like to

right? The key thing I think is like to make it all cohesive. Like if everything is like different and you can't find yourself around, it becomes quite annoying and not useful, right? Um,

that's like why people prefer like apps in the first place and why apps and mobile phones one because they're like optimized for the phone, right? They

follow the same UI and UX patterns and people know how to use them and so then the mobile web is like also kind of like more and more catering towards that. And

I think like that's going to be similar like you to make it like really useful.

We we want to integrate with everything around us and make it extremely easy for you to consume that information and then also because software becomes like free

in some way to create at least like little apps um you can transform that however you want to consume it and I think that's super exciting because we all like slightly different and we have

maybe different preferences I maybe want to see like a craft versus you have a different representation and like if you could just change that with like just a little prompt and then you have it your

way. I think that's super exciting where

way. I think that's super exciting where basically um software becomes malible and you can change it at hawk and it becomes just what you want and becomes really this personal touch. Um and

that's what I'm personally really excited about and that's what I see like operating systems evolve into something that is like a personal operating system to you and they're not looking all the

same and software is not all the same.

They're like tailored really to the person that sits in front of the screen.

>> Yeah. It's funny. One of the things I talk about all the time with AIS stuff that I think is actually really powerful is just like simple CSS stuff for

styling apps and web pages. Just the

idea that all of a sudden what I now have is the power to tell this app that I want it to be blue and it can be

because that is because like that's a that's a thing that like claude code can do, right? Is is change the CSS to make

do, right? Is is change the CSS to make it blue. that that is a thing it is

it blue. that that is a thing it is capable of doing and and then what you need on the other side is basically just the hooks that give me that tool to do and I think what it's been before is

like okay you have to build a bunch of complicated things and you have to come up with a whole like how do we display the color wheel do we do and it's like that's not like an impossible thing to do but it is a thing to do but it but if

you just let people plug in that way >> yeah you give them all kinds of opportunities and options just by like opening it up to we're we're going to let you build this

however you want to build it. Yeah. I

think like we have all the building blocks right?

>> Right. But I think what I'm getting at with the with the extensions thing is like as the as you're thinking about AI and I guess just just to go back to this I like I want to rename a bunch of photos in a folder on my computer. Uh

which is a thing Raycast is very well set up to do. Um, if I prompt Raycast kind of out of nowhere to just do that, you have a bunch of tools and you have a

bunch of of, you know, a aentic systems that will go try and figure out how to do that for me. or should I build the thing once like vibe code my way into a

raycast extension that that renames files on my computer and then just use that over and over because now I've built a thing that is like reliable and robust and stable and it will do the

same thing every time. And the problem with a lot of these AI systems is they don't do the same thing the same way every time. And sometimes that's

every time. And sometimes that's exciting and interesting and leads you down different roads, but other times it I just want it to rename the photos.

Like I don't need new ideas about renaming photos. I need you to rename

renaming photos. I need you to rename the photos >> and in the same way all the time.

>> Exactly. So I think and especially as you're thinking about this stuff, you're like, okay, well, do we do we want to use this all of these AI models in a way to like

build rigid structured things that you can then do on your computer over and over reliably? Or is the kind of

over reliably? Or is the kind of open-endedness of the system a feature, not a bug? And I I I just can't quite figure out where I land on that spectrum.

>> Yeah, it's a tricky one. But I think like for tools, having something unpredictable is like a no-go, right?

Like you wouldn't use, >> let's say, I don't know, something complex like Photoshop and half of the time the pixel turns red and half of the time it turns blue, right? Like you

couldn't work, right?

>> Right.

>> And so I think that's a strong argument for software, right? So let's say if you can generate a software once, you don't need any AI anymore. It just works and then it does the job perfectly all the

time. I think it's like a feature,

time. I think it's like a feature, right? It's not a buck. Like it's great.

right? It's not a buck. Like it's great.

Um so I think like leaning much more towards that because that's kind of what the world runs on, right? It's software.

It's like getting written once and then you use it >> and you can always adapt it, right?

Tomorrow you say like, oh, rename the files like this way now and then you can use this and And I think that's like something which is quite nice when you get out an artifact that you can use and that's like what we have at the moment

as extensions, right? You get this artifact out, you can use those extensions and use them over and over again. Um, where we sometimes struggle

again. Um, where we sometimes struggle with is like yeah, sometimes those nonsmart things how you do them, they're like just because they're so reliable and fast and become the muscle memory

are somewhat better in a way. And so you kind of want to find a middle ground.

And I think for tasks that are very concrete, you want to have what you mentioned like just you have an app, an extension, whatever it is, but it does the job. It does it all the time the

the job. It does it all the time the same way. Great. And there are some

same way. Great. And there are some other tasks and I feel like they're often times more open-ended. They like

don't have a single solution. They have

like nuances to it. You don't even know exactly what you want. Uh, and those I feel like are the ones that are really good with AI where like where it just goes out and like does something for you

and you come back it's like ah I haven't thought about that. That's cool. That's

a nice solution. Um, great.

>> So yeah, I'm I think like there is like something nice about the concreteness of software. You write it once and then it

software. You write it once and then it works the whole the same way the whole time.

>> Yeah, that makes sense. Does your

quality bar have to be higher than some others because you have this kind of access to all of the apps and even like the system? Like if you wanted to break

the system? Like if you wanted to break my computer or allow chat GBT to break my computer, you could like it's it's you have an unusual level of access to

my computer in that sense. Does that do you have to treat this kind of nent technology differently because of it?

There is certainly a lot of like scrutiny there and then when users come to us they often times ask us like oh is is AI running in the background can it

do something and so we had actually to put a lot of like um just like even UI and call outs um into the product to say okay this is secure this is not running

if you're not triggering it on um you're in control so if there is a disruptive action for example like deleting a file you will be prompted and you can say yes or no to that. Um, and I think like

that's like that's like definitely something that we we need to maybe do more than others which others can go a bit yolo in a way. Um, and because we have this like system that you mentioned

like we we can access your system in a very deep fashion. Um, and so kind of need to build up this trust and that's also what people expect from us like they used it for years already and it

becomes well it always works right. It's

this app that basically can never fail because it's like always there and if you don't have it people like feel like they can't use their computer anymore and so we put a lot of effort into like

making like super stable and so that's like in here the same way like if if you use that it needs to work basically all the time which as we discussed is really challenging right and I think this is

with machine learning and AI generally it will never be 100% right this is just a technology doesn't doesn't get you there so it's always like how far you can push it that's So we have all these

benchmarks where where all the model providers try to climb them up and be on top of each other. Um but you will never be 100% correct and for that is like even more important to have the guard

rails right so if something goes wrong you can either recover or in idea world it never goes uh off rails and you basically give the user the control which is often times described as like

having the human in the loop. Uh even so that feels like again a bit of a sci-fi term the human I mean Yeah. Yeah. So, do

you do you have to uh be extra careful about that stuff kind of at every turn?

Like does it make building raycast harder because you have built in this AI stuff that can do so much but is kind of unpredictable in that way? I wouldn't

say necessarily harder, but it's something which we think about um from the get-go. We say like, hey, we want to

the get-go. We say like, hey, we want to build a private company like don't want to collect your data and this kind of stuff. Um so that is something that we

stuff. Um so that is something that we build trust on. Um you just need to be smart you to know what you build and maybe what you shouldn't build and then

when you build it also in a in an elegant way uh and give the user basically the choice of like do they want to use it and then if they use it give them control. You can also say like

hey always delete my files don't ask me for confirmations. Um that's like like

for confirmations. Um that's like like use a configuration right but by default that's not turned on for reasons. Um,

and so basically keeping flexibility.

Exactly.

>> Yeah. Yeah. Yeah. Full styism. Just

whatever. Delete anything you want. Go.

See what happens.

>> But then you also want to be smart, right? Like if it's like a rename that

right? Like if it's like a rename that you could do undo. Um, you don't want to like prompt the user for that. So this

is I think the complexity you may be referring to. You maybe need to think a

referring to. You maybe need to think a bit more differently about certain things to make sure that the users build up confidence over time.

>> Okay. What's something you wouldn't build? Like you mentioned things things

build? Like you mentioned things things you can and can't do because you have this kind of thing. Is there is there something that feels obviously over the line to you on that front?

>> I should I should watch out now what I say obviously. [laughter]

say obviously. [laughter] Um but I think like it goes to like the the privacy aspect. Um we had certain things like for example give you give you like a sense of what we felt like

quite cool. We have this feature called

quite cool. We have this feature called focus and the idea of it is like basically you can block distractions like websites and other things and then it basically plants them out and if you go there you see like a warning and so

on and so forth. And then initially we had the idea like hey wouldn't it be cool to make this smart so that you don't even need to configure what you want to plug. It just kind of like

detects that this is probably a distraction. And then how you would do

distraction. And then how you would do this is probably like you do a screen recording all the time or some screenshots and then you send them out and then you analyze them and then you

come back. Um but at the end we felt

come back. Um but at the end we felt like ah yeah this is maybe stretching it a bit too far on like analyzing your screen all the time. Um which we don't

really want to do and I think you and what we realized like users probably would be very hesitant. Um, and then we thought about using like local LLMs for that. And then we said like actually the

that. And then we said like actually the person that sits in front of the computer kind of knows what's a distraction. So the better solution is

distraction. So the better solution is probably just letting them define it.

Um, as boring as it sounds. So I feel like sometimes that's the right thing, right? Like I mean we have still

right? Like I mean we have still intelligence we can think. So sometimes

maybe we can also put in what we want.

Um, so that was like just one of the things which came to mind which we like sort of first started as oh let's make this super cool AI solution and then you ask yourself like three times why and you end up it's like yeah maybe like a

more traditional solution actually cuts it here. That's such a good example

it here. That's such a good example because that is the sort of thing that at first glance you're like yeah it would be useful if raycast or my system could understand the places that I'm

wasting my time right cuz it's going to be slightly different for everybody. I

spend too much time on Reddit. You might

spend too much time on Instagram. And if

if I could just be like, just delete all the places that I waste time and it could do that. There's something that is cool about that. And there is something that is like immediately horrifying and off-putting about that.

>> Exactly. Everyone, what what a what a lot of companies have said forever is just we're going to push through that discomfort and trust that actually if people will eventually get used to it, we've made it so convenient that they're

going to get past the ick factor of this. And I think a this stuff just

this. And I think a this stuff just doesn't work reliably enough yet to do that in a really sort of predictable way. And the minute I go to like my work

way. And the minute I go to like my work email and my focus session is like nu uh uh I'm like I'm out, right? Like we

you've now broken the system.

>> But also I think I think frankly every developer has some responsibility here to say it's actually okay that we're not comfortable with this and maybe I shouldn't be pushing you to get comfortable with this. maybe I should be

asking you to make decisions because you're a person capable of making decisions. Not to get over the fact that

decisions. Not to get over the fact that I'm going to make them for you. And I

think we're we're about to go through a million versions of that with all of this AI stuff is like should we just just bet on the tech getting good enough that everybody will get used to it or

have to or should we like continue to make an effort to let people be in charge of their own existence? And like

this gets big and heady and existential really fast, but it does feel like we're encountering that question kind of a million times every day. And even like I just keep thinking back to this thing Sati Nella said about like we're we're

not that far away from people mostly not using their computers and just directing their computers to use themselves. And I

think philosophically there are ways in which that feels wrong to me. I feel

like it's always sort of this value exchange, right? What do you get out?

exchange, right? What do you get out?

What do you put in and what do you get out, right? And so if it's like super

out, right? And so if it's like super valuable, people are willing to put certain things in, right? I mean, people upload hell stuff to chat bots nowadays and all this kind of stuff, but they're

getting something out of it, right? So I

think it's always the question like what is the value exchange here? Um I think it's at this moment really hard predicting the future. If I would look

back two years ago when we basically just started this whole AI wave, right?

Like would you think like the world is as it is right now where everything is AI? I don't know like it's really hard

AI? I don't know like it's really hard to predict like would you think like coding has changed that much. Would you

think like whatever pick any topic really or it's really really hard to predict and I think it's the the classic um we overestimate the short term and

underestimate the long term. In this

case I think it's really like that. I

think um no idea what's going to be happening in the next 6 to 12 months. I

mean everything changed so rapidly. uh

one thing is clear that those things are here to stay like you hear sometimes like even if no models progress any further we by no means have reached the limit of what you can do with even the

state of the art right and I think that's kind of like nice for everybody in the industry because I mean before AI let's be honest like there was bit of a try phase in tech right where everything

was hyper optimized and nothing really radical changed at least in the terms of software um and so now there's a lot of bus and like every every week there is something new and I think like even if

everything stagnates we haven't reached sort of the limits what what we can do with with all the technologies that we invented in the last two years alone yeah I know it is it is strange that it

it feels like everyone is so busy I mean it's the self-driving cars thing is a perfect example right like every everybody is so busy trying to invent the absolute end state of this where it's like what if it reshaped society

it's like no no no what if my car parked itself that's Awesome. Let's let's do that. Like let's figure out how my car

that. Like let's figure out how my car can park itself and then how my car can like run more efficiently. And there

there are like a million things along the way that are cool and exciting and powerful that don't require like rethinking the way an economy works and like let's let's not skip all the steps

because those are interesting things on the way to something potentially bigger.

Yeah, >> before I let you go, let's just spend a couple minutes talking about how you use AI in Raycast and in general. Like where

where does this stuff fit into sort of your day-to-day life and workflows right now?

>> Yeah, I think the biggest change for me is like for me it's prompt first now.

Basically everything I do, I start with a prompt. Like well we launch something.

a prompt. Like well we launch something.

Okay, got to write a blog post. let me

ramble for five minutes into my microphone and then that's my starting point and then iterate on that like that's one of the the things um oh I

need to like um answer emails which I do a lot okay I'm going to do a lot with AI here writing code uh same way one of the things that changed for me quite

radically is um that you can sort of do things in parallel in the background like I can just kick off a bunch of things um oh there is a feature request on Twitter. Okay, let me kick something

on Twitter. Okay, let me kick something off um and address that right away. Um

oh, there's another one here. Let me do that as well. Oh, I have this idea. Let

me like kick off some deep research and figure out um what's a good solution for that. And it's like, oh, I need to like

that. And it's like, oh, I need to like prepare for the board meeting. Oh, let

me put a few things together. So, I

think my thing my brain is like completely rewired and it's like I'm prompt first by now. And I basically just put things on a on like start with a prompt and then see. Do you then wait I have a I have a I have a procedural

question about that.

>> Oh yeah, please.

>> Do you if you start everything with a prompt is is the goal then to kind of filter everything out into somewhere or do you find yourself like living more and more of your life kind of inside the

chats of these LLMs?

>> Oh yeah, there are sometimes things that are just like inside of our AI chat and rayos, but like this never really sort of produces an output, right? Is maybe

me like chatting for a while through something. Oh, like I have this like

something. Oh, like I have this like pick any topic I have like oh I want to think about how we can land a deal.

These are sort of the points we have like what are elegant ways to like maybe continue the conversation. So how could I like find find a solution to like reach our customers better and like sort

of it's almost I think about it as like a a thinking partner like throwing things back and forth and like talk to somebody for like a bit and sharpen

myself up in a quicker fashion. Um

that's how I use it like a whole lot. Um

and so that's how I see also like in our company just changing where more and more people just start with a problem.

Uh a big change that we've seen in a company is all our designers they code now. Um what used to be basically all

now. Um what used to be basically all static designs they more and more become interactive prototypes directly in our product. So

product. So >> they can get something where you can feel it and see it and it works and then often times an engineer like crushes it up. Um but all our designers are

up. Um but all our designers are basically also halfway developers now which is an incredible change. Um, and I think that like just is really nice for

creative people as well because there was always this barrier of like, oh, you you draw a few pixels and then somebody else needs to rebuild them to make it interactive. And so now we cross that

interactive. And so now we cross that bar essentially. It's just like it's a

bar essentially. It's just like it's a plant like if you're a creative person and you have the will, you can make things happen. uh which which I'm super

things happen. uh which which I'm super super happy to see that basically coding becomes more accessible to a way um and are still failing in a lot of ways at

that regard in programming but I think that's like something that that we've seen in our company happening really heavily that designers become also developers.

>> Okay. Uh yeah, I think to me part of the reason I asked is because one of the things that was most sort of unlocking in my brain was the thing in Raycast where you can like you can basically

atmention one of your apps. Oh yes and then prompt it.

>> And it's like that that to me is like okay now we are now we are getting to like the sequence of things that make sense together right where I I don't now

need I don't know I don't I don't now need a bunch of different very specific apps. I I can just ask AI models to talk

apps. I I can just ask AI models to talk to the apps that they already have access to. Um, it sometimes works, it

access to. Um, it sometimes works, it sometimes doesn't. My whole clean up the

sometimes doesn't. My whole clean up the desktop thing has not worked at all as we've been sitting here. Just just

nothing. It gave me a bunch of semi-helpful information about the files that I have.

>> Got to improve it. See, that's the way.

But I I can do more prompting. I'll

figure some stuff out. But I think like there's just there's something that unlocks when you start to see, okay, here here are kind of the things that are available to me. Uh, and you've

just seen more of those things than most people. So I I was curious to know like

people. So I I was curious to know like are you just constantly doing computer activities through prompts now? Like

you're you're starting by everything starts with a prompt.

>> Yeah, pretty much. Like basically for me it's it's a lot of like I'm in a browser. I have a few tabs open. I pull

browser. I have a few tabs open. I pull

them in um with my at browser essentially. I get all the all the tabs

essentially. I get all the all the tabs in. Then I start from there. Then I say

in. Then I start from there. Then I say like oh by the way put this in a notion page. So then it ends up in a notion

page. So then it ends up in a notion page and I can share it with my team. Um

then I iterate on a notion page. Um I do those things like quite a lot. Um but

also like um I let it write code for me uh to do certain tasks. Like I had reason bit of a silly example but I had to do my text return. Well I didn't do the text return with AI, right? But for

that I needed to download all my payroll and all of them had a password. So I was just asking AI is like hey take those 10 PDFs and here's the password. Can you

remove it so I can send it to my accountant? And it did it for me. It

accountant? And it did it for me. It

just wrote some code. I didn't really look at the code because I like I kind of like know okay that's like what it will do and then it like perfect otherwise I would have spent like I don't know five minutes going over each

PDF first of all figuring out how to remove a password which I have no idea and so I think that's like that's I think the change which I'm quite happy about and it's like for programmers this has kind of existed for a long time we

call this things scripts it's like little things that a programmer every programmer you ask they have a script for various random stuff that I do multiple times a day. What is if this

script is just natural language? Like

what if you just say this and then to your point if it solves the problem once just reuse it so you can use it like many times, right? That's I think like those kind of little things that will make a big difference. And that's what

we do with ray cost. We want to speed up every little thing and you use ray cost hundreds of times a day. How can what what are the next hundred things you should do with ray cost? That's how we think about it. what are the problems we

can solve that you use actually super often and not just like once a year or whatever. Um and and that's like I think

whatever. Um and and that's like I think the journey we on that's pretty cool.

Yeah. And it's like once you have computer access the the number of things that can start to comprise becomes just enormous.

>> Yes. which is and and you have access to the browser and it's like again this is why I think Raycast is so fascinating because you have you you you can see the whole stack in a way that is very hard

to do for almost any other app. Uh it

makes it it means the trust bar for you is very high but it also means like we we talk a lot about you know that these AI agents just can't see and do all the

things that they need. Uh Raycast kind of can. Yeah, I think like

of can. Yeah, I think like that's the that's the the nice position to be in like being at this position to do all of this kind of stuff, but we

still got to connect all the dots and build up the discoverability as you mentioned. Make sure that people get it

mentioned. Make sure that people get it and also make sure people get real value out of it. like I've seen so many demos of cool stuff, but then you're never going to use this dayto-day or only so

little um that it doesn't really play well. Um and so that's like for us

well. Um and so that's like for us really the challenge. Like natural

language is great, but discoverability is hard. You don't know what's feasible

is hard. You don't know what's feasible and so on and so forth. Um but yeah, like I'm excited about this. um helping

basically making your computer smarter um by using the same apps and tools you have by having one AI that kind of follows you around across your journey on your computer and not having like an

AI in every app and it's like everything is like isolated. We've been there with apps, right? It's kind of like annoying.

apps, right? It's kind of like annoying.

Um and we don't want to spread that again that all our knowledge, memory, context lives in each and every app. And

I get it like every company of those apps want to have this, right? They want

to lock you in so you stay in that single app. It's like the financial

single app. It's like the financial things that they want to have, right?

They don't want to give it away. But if

you purely think from a user standpoint, AI should be on the operating system level. It just makes so much more sense

level. It just makes so much more sense to be there instead of like in every app and every app needs to rebuild it. Um it

just happened to be this gold rush that everybody sees. Um but um truly from a

everybody sees. Um but um truly from a user's point, I feel like the best thing is if you have a smart operating system that helps you to get your job done.

>> Yeah, I agree. All right, Thomas, this has been very fun. Thank you so much for doing this with me.

>> Well, thanks for having me, longtime listener, and finally making our way here somewhere together. [laughter]

We did it. All right, that's it for the show. Thank you to Thomas again for

show. Thank you to Thomas again for being here. And thank you to all of you

being here. And thank you to all of you for watching and listening. As always,

if you have questions, if you have Raycast extensions you want to tell me about, if you have thoughts, concerns, feelings about any of this, I want to hear all of them. You can call the hotline 866 Verge11. You can email

vergecastverge.com. I'm David

vergecastverge.com. I'm David

attheverge.com. Hit us up. I think this question of how AI belongs in our software is big and fascinating and messy, and I want to know how you feel about it. So, get at us, ask us all your

about it. So, get at us, ask us all your questions. We have another one of these

questions. We have another one of these coming up next week uh about a very different kind of app that I'm [music] very excited to talk about. We'll get to that, but for now, the Vergecast is a Verge production and part of the Vox Media Podcast Network. The show is

produced by Eric Gomez, Brandon Kefir, and Travis Laruk. We'll be back on Tuesday and Friday with all of your usual good Vergecast stuff. We'll

[music] see you then. Rock and roll.

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