Anthropic's Katelyn Lesse & Angela Jiang: Building an Ecosystem, not a Walled Garden

20:26Katelyn Lesse:I want to do more of that thing. We like industries where it's like the answer to that question, you say, I want to do more of that thing. So coding is obviously the one that we all know. And the great thing about coding is that what it's actually doing is that once you've finished a turn, you look at that and you're like, that was incredible. I'm like unlocked. I'm going to do more. I'm going to build more. I can do more. And there's other services where it's like, actually, when you finish that turn, you completed the job and you just move on. You know what I mean? And so we tend to like go into the ones that are a bit more like there's this kind of like iterative flow.

20:54Katelyn Lesse:You're going to build more, generate more together. And then the last angle that we kind of take a look at is just sort of like, you know, there's going to be certain business functions that we're like, they are the buyer that we like to go to. We want to help them optimize their workflows, help them create better products there. And I think we've been pretty transparent with some of the verticalization. Like we've done like finance, we've done like legal. and we've tried to kind of like narrow on into specific areas where we feel like by having the right context and the right tools and putting it together in a good form factor is probably useful for us to be able to do.

21:25Angela Jiang:And in each of those areas, we do, we're trying to do a bit of like showing the art of the possible across all the different ways that you would accomplish those outcomes. And so for, you know, like finance, for example, is a good one. You know, we, you could be a company that solves problems in finance and you could build directly on the messages API and you can just get some tokens and you can build everything else on top. Or you could be someone who builds on cloud managed agents. You can get a lot more out of the box. Or you could say, I'm going to build a plugin that or like a connector, right?

21:57Angela Jiang:That's going to sit within one of our products and within those form factors. When we did recently, we launched like Cloud for Financial Services is like, okay, cool. We've got packages of skills and things like this that you could choose to use within our product, within other people's products. We even launched like cookbooks on here's how you would use Claude managed agents to go and do these things. And so I think for us, it's all kind of an experimentation around like, you know, we provide people all these different pieces and see kind of where they run with it. And then sometimes we put together products that are just packaging of all of these things like Claude Tag, I think, is a really good example.

22:33Angela Jiang:Like we had been seeing people in the industry go and say like Shopify did this with River, Square, Block recently did this with BuilderBot. There's like a few of these examples where people said, I'm going to build like an agentic platform internal to my company and I'm going to try to give it all the right context and I'm going to make it accessible from Slack or from various other platforms that you'd want it to be accessible at. And I think Claude Tag was very much a packaging of all those same things that anybody could choose to build something similar. but this is how we're kind of like, well, this is how we're doing it internally.

23:07Angela Jiang:And if you would like to just kind of plug in and go, here's what that looks like. What do you think people misunderstood about Cloud Tag? Because there was all this like ruckus about, oh my gosh, it's just a Slack bot. Like tell us what the magic of Tag is.

23:19Katelyn Lesse:No, I think it's a great question. And I do think it actually showcases a little bit of where maybe the future could be going. Yeah, I think like the, I think if you look at products in the past, people are like, oh, you really attach to like the form or the UI almost, right? Like it looks like this. So it's like super cool. And I think when you look at like tag, it like, yeah, like the way you interact with it is that you like literally tag it in Slack. And so, yeah, that is like the interface. But that's not really the important part. The important part is all the kind of like context engineering and like architecture that we put underneath the hood so that tag just works.

23:56Katelyn Lesse:It really should just like just feel like a coworker, like a, you know, if you go to a company and you onboard, or the coworker comes into your channel and then you can chat with it. It's proactive. It figured out like what's like useful. And it just gets stuff like done for you. And so if you think about, you know, especially like non-technical audiences, this is like, it's a huge unlock. You just, you literally create a channel and then you add Claude or sometimes you don't even add Claude and you're like, hey, I want to be able to do this and do that. And I can't figure out this. And how do I actually like submit an expense report again?

24:26Katelyn Lesse:And traditionally, do you think about how to solve that workflow? You are going all over the place and you're talking to your manager and you're talking to your spin-up buddy and it's really, really complicated. And today, now you just like go talk to CloudTag. And we do a lot of the hard work on doing the context engineering, the proactivity, a lot of the harness pieces. I think Andre Kaparthi said it really well. It's like, it's like an org level harness. There's a lot of like complexity baked into that. Like Kayla mentioned, like you can use our APIs to go and construct that. You can do a lot of the experimentation yourself, obviously.

24:53Katelyn Lesse:But this is like an opinionated take from Anthropic on like how you can have this really awesome, always on kind of agent for your entire company. And the bit that's like futuristic, I guess, is like a lot of that complexity is actually like, it's like an iceberg. It's like all the stuff underneath it. That's actually becoming the harder and harder and like useful part that we're trying to like push through. And I think we'll see more and more like that kind of like tip bit that's like outside in the water. It's just like the interface can actually constantly swap. Like today, right? Slack is a place where a lot of people collaborate, a lot of business collaborate, but also a lot of people collaborate in teams.

25:31Katelyn Lesse:And some people collaborate by a WhatsApp group or they text each other or some people still email each other. And those could be the form factors that actually completely you can imagine agents just going there and being. And they're almost taking up the same form factors as humans have taken up. It was almost like a very, almost like boring take, but it's actually like, I feel like the most like forward one because you want the agent and you want AI to basically be like another person and it's helping you. But it's like, you know, very intelligent, can figure out all the context and you can always have it to be a really helpful assistant.

26:03Angela Jiang:Totally. You talked about context and then harnesses quite a bit. And so your team is just, you know, has such an opinionated point of view on like what it takes to build an exceptional agent. And I imagine a lot of that comes down to the context engineering and the harnesses.

26:16Katelyn Lesse:Totally.

26:16Angela Jiang:Maybe like what best practices or advice would you would you share with people about what you need to get right on the harness and what you need to get right on the context? Yeah, I think so. It's interesting because we've kind of talked about, you know, we launched Cloud Managed Agents as this like very generic but high performing harness because we've done all the nitty gritty work that's actually like really boring and not super interesting around how do you deal with prompt caching? How do you deal with context management? You like clear old stuff out of the window. Sometimes you like call tools programmatically so you don't pull everything into the context window and you can keep it clean.

26:51Angela Jiang:There's a lot of those sort of details on the lower level harness layer. And I think, honestly, like best practices are just stuff like prom caching, do it, you're going to save a lot of money and token costs. Obviously, like try to keep your context window clear and then putting those things together in a harness that will be performant is, you know, sometimes specific to the tasks that you're trying to accomplish. Right. And then, of course, evals. I'm surprised we got this far into this thing before one of us said the word evals. but you need evals to make sure that what you're trying to accomplish is performance.

27:28Angela Jiang:But I think where we're starting to go, and Angela mentioned this a little bit earlier, is more of a concept of strategies or meta harnesses. Because I do think that, yes, you can, again, make this lower level harness. It's going to be performant. And maybe that's interesting for you to do yourself or maybe not. And you offload it to us. But this concept that you can take any given token and spend that token on just executing, or you could take that same token and choose to actually reflect on your past agentic sessions and write learnings to memory so that the next agent does a good job. Or you could take that token and advise with a bigger model so that a smaller model can execute and do a better job.

28:07Angela Jiang:Or you can say execute, execute. And then like a grader comes in and is like, did you do a good job? No, you didn't try again. Right. And so I think the interesting innovation is going to come more at that higher level on the meta level. And I think optimizing within those strategies is something that our team is really excited about. And we're starting to do a lot of work there. And I think a lot of other people are starting to feel really excited about this concept of strategies and the jobs you give to tokens. because again, like, yes, there's best practices on stuff like your problem caching and exactly how you clear stuff out of your context window and how you write your evals and like a lot of things like this.

28:44Angela Jiang:But I don't know that there's necessarily so much juice to squeeze in a lot of cases out of that layer as compared to a layer higher than that.

28:53Katelyn Lesse:Yeah. And one of the reasons for that, I think it has to do with the generations of the models. If you look like two years ago, a lot of the harness was like a scaffold to kind of like tell the model to go from point A to point B. And you had to like, you really had to like build in a lot, you'd practically build one wall here and one wall here. So to like the thing would go in a straight line. And now the models are actually very, very steerable. And so a lot of that steering, you can just put in the prompt, right? Like go do, go from point A to point B and the model like will go from point A to point B.

29:23Katelyn Lesse:So a lot of, if you have harnesses that are like designed to kind of do that kind of like steering, you can delete that part. Like that part, we actually frequently encourage, we can delete part of those harnesses. I think various people have said things along those lines. And that's, I think, what people oftentimes mean when they're like, you know, the model will kind of consume some of the scaffolding. And like in that sense, like for sure, if your scaffolding is telling it to go in direction that it can just intelligently figure out, like that I think will increasingly continue to be so. But as a result of this, what the harness needs to start doing is more allow it to run longer.

29:55Katelyn Lesse:And so that's where like that execution bit tends to be. I think like it sounds like a maybe somewhat silly point, but I do think it results in a lot of differences because you can go in the direction that you tell it to go, you obviously don't want it to stop at B. You're going to be like, okay, now go from B to C and then go to F and then go to Z and then come back to me on A. You know, something funky like that. In order to be able to do a lot of those things, the kinds of harnesses that you do are less the steering harness and it's more like these kind of strategy harnesses that Caitlin's mentioning, which allows you to operate at a slightly higher level of thinking, which matches, I think, a lot of the intelligence gains that we're starting to see with the model.

30:27Angela Jiang:Do you think task-specific harnesses make sense? or vertical specific or task specific harnesses?

30:33Katelyn Lesse:I think people have different opinions on this. Like our opinion is yes. I don't think there's like a general harness. I think there are some capabilities that are obviously very general and they tend to like be very useful. Like coding is a capability that like is very useful because you can use it across so many things and software has, you know, just like eaten so much of what is capable. So our ability to like write software is therefore useful. I think when you think about like very, very specific types of domains, that were going to require like a couple of pieces of the harness to be sort of like customized.

31:04Katelyn Lesse:One of that I do think is how you choose to kind of like handle sort of like errors between when you do something and you hand something off to the model. So in like domains where you require like an extreme level of verification, that logic of how you handle that, like again, I think it sounds small, but like I totally understand why some people feel like they really want to own the harness because tweaking that last bit will give you a ton of juice. and especially domains like legal and finance where there's a lot of consequences to you're not getting it perfectly correct, like it's really going to matter.

31:35Katelyn Lesse:And that's going to be the difference between your product and someone else's product being the thing that the user ultimately uses. And then there are other domains for which, like I would say, it's not going to matter as much because you're able to compress it into like a general model capability. So the tweaks that I guess, like where we feel like the domain specificity is really going to matter is the specific like verification logic between the model and your execution. And then I think it's going to be about some of these kind of higher order strategies on how well you're able to actually allocate your token budget.

32:09Katelyn Lesse:I think the context bit is actually a little overdone. Yes, you're going to throw in context, but any harness can actually handle a lot of context. And so that's just more like you have the data. And if you have the data, then obviously you're uniquely qualified to do something useful.

32:24Angela Jiang:Yeah, and I think when people say harnesses, they often mean a lot of different things. And I think this is why, in part, there's so many different opinions on this. Like, you can think of a harness as literally just, like, a loop that's, like, okay, cool, like, user model, user model tool, you know, like, that sort of thing. Then you could think of the harness as also all of the tools that are packaged up with the harness, right? And there's just, like, a lot of different definitions of these things. And I think the stuff that can be pretty generic and, like, less interesting to own and deal was, what I was kind of saying earlier, is like getting your prompt caching right, right?

33:00Angela Jiang:Like maybe that is not the world's most interesting thing. Choosing to clear out old tool calls from the context window and things like that, right? Or like maybe a little bit less interesting. And you like go a layer higher into some of the stuff Angela's talking about. And then you get into like, okay, yeah, these are things that I might want to own and control. And so it's interesting with Cloud Managed Agents, like the thing that we built today, we call it higher order, but it's not really like that high order in the sense that you can choose to define all of the tools that you want to bring in as custom tools with the harness, right?

33:33And like, we give you a

33:34Angela Jiang:lot of knobs to control. You can define skills, you can do your system prompts, you can do a whole bunch of different things, MCP servers and things like this. And I think where, you know, we want to get to you as a point where you can literally just tell an agent, here's the outcome I want, and here's the budget that I want to spend, like, ready, set, go. And you may be like, don't think about any of those things underneath. And so I think there's just a few different layers of this, right? That for certain things, like you might want to sit at a different layer of what you actually go and control.

34:01Angela Jiang:And you can probably get better outcomes within some of those layers by doing a little bit more optimization work.

34:07Katelyn Lesse:Very cool. One of the things I'm curious about, and one that I love about infrastructure and platform teams is that you get to see what the most advanced users in the world are using and learn from them. I'm curious, what are some things that you're seeing and learning from the people building on your platform? There's some people that have been doing some really funky ways of like handling context. We ourselves explore this a lot. That's actually like one of the reasons why Tag is like such a great product is like there's a lot of really awesome like context kind of engineering that's happening.

34:36Katelyn Lesse:We've seen some teams be really clever about like how they do that. And they are able to kind of think through like, okay, if I have all these contacts in a bunch of different places, how can I proactively go reach out to them? How can I try to generate enough like permissions across each of them? And then feed that all into like an agent. And it's interesting that like, I guess like this is kind of the level of innovation that like we're actually like very excited by. It doesn't express itself as like a completely different product form factor. but what it actually does express itself as is like maximally useful to users and we've been seeing this more and more with like in actually like internal use cases instead of like external ones so like companies who are becoming more ai native basically they're the ones we're seeing increasingly more and more innovation out of and so you know we've had like customers try to do this for their like they've built their own like custom sdlc kind of setup in very very innovative ways we've had uh ones who do that for like their entire back office and just like the kind of nuances of how they like stream in context, I think has been like actually really interesting in terms of like how they've been putting together the pieces.

35:37Katelyn Lesse:So that's been like one category that's been like really, really like fascinating. Another category that's been like really interesting has actually been with companies that are dealing with like really old school software. And so there's a lot of like healthcare companies that we kind of engage with. And, you know, like they're like the systems I'm working with, they don't even have APIs. Like that's a dream. And so, you know, how can they use computer use and things like this to be able to start to kind of automate and create more connectivity with our systems? And that area of innovation, I think, has been really exciting.

36:09Katelyn Lesse:It's been really interesting to see people try all sorts of crazy stuff from like taking a laptop and trying to like run a bunch of things on it to auto generate a bunch of things that then their agents can go and use. And this has actually been probably like an area of, I think, a lot of innovation coming from a lot of our customers that we want to find ways to like support better and see like, OK, maybe there are like, how can we make this easier for you? How can we help you with some standardization? How can we get it so that, you know, like you can just have a spec and then Claude can then respect it.

36:35And so it's much easier for you to organically connect a lot of these things.

36:38Katelyn Lesse:But yeah, maybe the general theme I would just give you is like, interestingly, a lot of the innovation that's most exciting out there right now has been this kind of like context and connectivity layer, which has been really fascinating.

36:49Angela Jiang:Yeah, like a good one in that we were working with a customer who they built some agents on Claude managed agents. They also have some agents that they built on other models and other platforms. And they've kind of optimized each of these agents to be good at the things that they want. They want these agents to all be able to work well together. And they kind of were like, wow, Galaxy Brain, like what if I expose an MCP server on top of this agent so that I can then go and like have this other agent call a tool on that agent. Right. And have these things just be more modular and be able to work together.

37:20Angela Jiang:And we were like, yeah, totally. And we sat down with them and worked through it and it worked perfectly and it was pretty cool. And so we're seeing a lot of, again, that connectivity layer that I think is one of the cooler areas where people are innovating. But outside of that, one thing that has been cool is just seeing the shift in, I guess, like industry trends of where we're seeing a lot of our usage come from. Like we talked a lot about coding, like coding as a category, like, of course, absolutely exploded. And there's so much going on there. And we're starting to see some of these emerging trends.

37:48Angela Jiang:More recently, we're starting to see manufacturing really pick up as just a category where people are building with AI and one of our PMs getting on a flight to Detroit to go figure out with these customers what they need and what's going on. And so I think we're going to start to see a lot more just kind of like outside of the box of what people think about today sort of use cases, which we're really excited about. It seems like we went through a token maxing moment of history, and now there's the token rationalization moment of history. What are your thoughts on that? And what should companies be doing?

38:24Angela Jiang:And then how does the platform team think about enabling that?

38:28Katelyn Lesse:Yeah, I mean, it makes sense. It makes sense from the high you start to rationalize. I really like that framing. And I think there's a couple things that are top of mind for us on this front. I think, again, it makes sense. And as these models get more and more capable, you're going to hit like levels of intelligence maxing that are like there that then you want to do the next kind of dimension. And the next dimension after intelligence will either be cost or it will be speed. And you just kind of go through that across all possible task complexities in the distribution. And as we kind of see that like happen, you know, something that's like really top of mind for us that we kind of try to spend some time with users on is like what you don't want to do is like stop AI usage.

39:06Katelyn Lesse:Right. Like that's kind of the wrong move. And we do actually see some of our customers do that. Oftentimes, the way that AI spend has erupted inside their company has been through some kind of shadow IT. Their employees just want to use it. They find a way. They end up procuring it themselves. And before you know it, half your org has found some way to install Cloud Code. And in that world, it is kind of hard to manage because these things are, again, they're very token hungry, ultimately. And so what we try to kind of encourage our customers is you don't want to stop the innovation. If you are getting returns on top of this, you are shipping faster than ever before.

39:36you can run more operationally efficient, then those are gains.

39:41Katelyn Lesse:And so the area that we actually try to encourage people is if there is a way for you to construct, again, a strategy that allows you to design an architecture that says, given a task, assesses level of complexity. I mean, I'm effectively describing a router, but there are ways to do this that are, I think, a bit better now. And so this task comes in, has a certain level of complexity. For that level of complexity, you can define some rules. But for the most part, if it's a hard task, you should probably route that to like a big super smart model and if it's not a hard task you can route that to like cheaper models um designing that i think has a little bit of like there's a lot of technical complexity in that but it's like very very doable and we actually like encourage people to try those kinds of things i think ultimately you'll offer a rather i i think within the quad space it will like make sense it's actually one of the strategies we imagine like designing because the way that we are kind of thinking a lot of these things is like it almost feels like every month there was a new era of something.

40:33Katelyn Lesse:And if we just take a step back, like, okay, and it seems to be like really fast. And so what are the different ways that are recomposable so we can redesign very quickly for any new, whatever the cool thing is that month kind of like bit. And so this is like in that category of things where we feel like we can actually just like recompose a lot of our primitives and then design it. I think the bit that we do feel really strongly about on the model routing front is like, we are designing our platform for Claude. And we want to make sure that Claude is great at like solving all these things. So we'll like restrict to that space rather than, you know, I don't think we're that interested in saying like, okay, and then, you know, you should route to a different model or whatever.

41:10Katelyn Lesse:Makes sense.

41:11Angela Jiang:Yeah. And well, some of that too is just like, I think we have a strong belief that harnesses and just like the agentic layer should be tuned to the model family that you use it with. And so I think there was a period where people were kind of like, yeah, cool. I can like build a harness and build an agent and then just like plug in a different model underneath and they were excited about routers from that perspective and i think we started to see um like versell just did this with harness agent for example like some of these players in the space like come up a layer of abstraction and say actually like plug in the whole harness and the whole agent that's tied to a model family which makes a lot of sense and so what we could provide is a little bit better smarter like how do you mix and match the right models within the model family underneath that thing if that makes sense but yeah on the general question of token maxing costs and these sorts of things.

41:58Angela Jiang:I think we're just kind of going through what feels like a normal natural cycle for companies and figuring out how to make the best use of this technology and run their businesses really well and really effectively. And it's interesting, like before working at Anthropic as at Stripe, and we were kind of in the very reasonable era of like, we paid a lot of attention to our AWS bill. And so, you know, If someone were to have built some background job and they didn't quite configure it correctly and this thing's burning through CPU or whatever it is at any given moment and causing a big increase in spend that's not actually worth it, we have put in place the guardrails to find that and then go ask that engineer very nicely to please turn off their background job that's not within the bounds of what they should be spending for the thing they're trying to accomplish.

42:47Angela Jiang:I think those are the things with AI that people are going to start to go and figure out. And I think to Angela's point, a thing that gets dangerous is when you're kind of just like, here's a cap and you're stuck within your cap, like, ready, set, go. But I do think that encouraging innovation, encouraging people to, you know, create really excellent outcomes with this stuff and then coming in from the side and looking and saying like, OK, well, there are a few different ways we probably could have accomplished that outcome. Right. And one is like you take Opus and you run it all night and you do something crazy.

43:20Angela Jiang:And another is maybe to get a little bit smarter with the strategies that you put together in order to create that same outcome within a lower cost. And I think that's the like next layer of thinking that everyone's going to start to do.

43:31Katelyn Lesse:Very cool. Is there anything that you guys are excited about building over the next few months that you can share a hint at what might come next? Yeah, I mean, I know we said this word like 20 million times. I apologize. But we really are trying to build ways for you to compose strategies. and so that is an area that we're like trying to move into that kind of like yeah coordination layer of the abstraction and we want to start at this front because the types of problems that we see people building they are at a layer where it's like in order to get the most return on this you have to be a little clever about like what is the nature of the problem that you're solving so to give you something like concrete like when you try to solve for like let's say you want to build an agent that's like trying to do bug hunting and you could just send one off to go and do that and it's going to give you a certain level of return of possibility and then people kind of get stuck at that and they're like okay my next options are I can like make a bigger I can just like swap the model for a different probably bigger model or I could like let it run like longer and that's pretty much like the only two like levers that you have to like try to make this like bug hunting agent.

44:36Katelyn Lesse:For a lot of experimentation, when we do these kinds of things, there's like, actually, the thing, like those two, those two things are still true, but you actually have like a third lever and tends to actually do a lot more than you think it does, which is that actually, if you were to like best of end the thing, it would like give you a lot more returns. But like just to be, just saying those words are fine. And there's plenty of papers and people have published it to actually build that thing and put it into production so you can actually test it on users and see the results for yourself. That's like really, really freaking hard.

45:02Katelyn Lesse:And you end up building all these like custom harnesses, so on and so forth. or like, you know, all that stuff. But we're seeing like, this is where the alpha is and it's hard. And so like in the same very simple philosophy that we talked about at the beginning, like if it's like, gives you the return that you want and it's hard, we're gonna go try to just make it easy for you. So then you can use it to then run the experiments you actually need to run. It reminds me of when people were talking about agent swarms a year ago. It's some version of that. Yeah. Has it been a whole year? Yeah. I know, we're finally there.

45:28Katelyn Lesse:Yes. Yeah. No, I think that that's like, that's a type of strategy. Exactly. In the same way that you have like, you know, one big one that separates a bunch as another type of strategy. And I think people have thought about this maybe in the way of like human organization. I guess it could be similar, but if you take it to kind of its end state, it's actually more just like the token has a job. And I think it's this job piece that we're really indexed on. And we see a lot of returns too. And that's the thing that we want to spend time with users and the rest of the ecosystem on, on like, how can we just make that easier for folks to then experiment?

45:58Katelyn Lesse:Like we can give you like five jobs off the top of our head. And we're probably like, that's what we have internally. And if we give this out to the rest of the ecosystem, it's probably going to be like 100 ,000, 200 ,000, who knows what other combinations that people could put together.

46:10Angela Jiang:Yeah. We want to be able to keep doing this hill climbing on like, how do you get the most value, the most intelligence per dollar and just put that power in people's hands. But around the edges of that, we have these personas that have kind of just like things they have to work through in order to be able to like really deploy AI either within their companies or within their products. And that's like the sort of enterprise ready security and compliance controls and things like this. But really even just like making the platform more modular in the right ways, like being able to plug in different pieces of the solutions that we're building.

46:45Angela Jiang:Like I want to use memory for this thing over here. Right. Or whatever else it is. and having a truly excellent developer experience around that, because we spend a lot of time with enterprises who are like, okay, I have this like, walled garden, I need to figure out exactly how I can plug these solutions in. And so we're, we've got a part of our team that's innovating on things like strategies and jobs and trying to help you maximize intelligence. And they're like, that's really cool, but I can't actually use any of that for XYZ reasons. So I think solving those problems is really, really important to us.

47:14Angela Jiang:But then the other persona is, you know, the like weekend developer who's like, I want to go and build something useful for myself, right? And they're often doing that on top of our platform and on top of many other just pieces of developer platforms in the community. And I think for some of those folks, there's more that we can do to be provide solutions that are maybe more open or more hackable or whatever it might be for those folks to kind of just like go wild with what we can offer them and have this really excellent developer experience. And so I think there's a lot of stuff that maybe I would put in the category of table stakes, that I'm really excited about because I think those are the things that then unlock getting people to say, okay, yes, this thing works for me.

47:53Angela Jiang:And now I can plug in on some of the stuff that you guys are doing that's really innovative and hill-climby to get more intelligence and save costs and things like that. Wonderful. Caitlin and Angela, I feel, I mean, you were building one of the most important developer platforms in the world. And talking to the two of you over time, I just feel really optimistic that that platform is in very thoughtful hands that care about the ecosystem. So thank you for taking the time today to share what you're up to. And we look forward to what's ahead. Thanks for having us. Thank you, guys.

48:46Thank you.