Tell the Robot What You Want — Sandhya Subramani, AWS

Hello everyone. My name is Sandy and Hello everyone. My name is Sandy and meet my co-host today, Scout. This is my meet my co-host today, Scout. This is my meet my co-host today, Scout. This is my friendly rover. And one would think that friendly rover. And one would think that friendly rover. And one would think that rovers can't really think for rovers can't really think for rovers can't really think for themselves, right? We have to tell them themselves, right? We have to tell them themselves, right? We have to tell them what to do or we have to very what to do or we have to very what to do or we have to very specifically program them on how to specifically program them on how to specifically program them on how to think. But this little guy here actually think. But this little guy here actually think. But this little guy here actually has a brain and he can think for has a brain and he can think for has a brain and he can think for himself. Let me show you my screen. Oh himself. Let me show you my screen. Oh himself. Let me show you my screen. Oh no, it's going to the wrong screen. I'm no, it's going to the wrong screen. I'm no, it's going to the wrong screen. I'm going to see how I can stop this and I'm going to see how I can stop this and I'm going to see how I can stop this and I'm going to see how I can move to my going to see how I can move to my going to see how I can move to my screen. Um, screen. Um, screen. Um, give me just a second. I'm going to end give me just a second. I'm going to end give me just a second. I'm going to end show and then we get to this. So this is show and then we get to this. So this is show and then we get to this. So this is what Scout here is looking at. And Scout what Scout here is looking at. And Scout what Scout here is looking at. And Scout here has a small little brain. And Scout here has a small little brain. And Scout here has a small little brain. And Scout can understand what I'm saying in can understand what I'm saying in can understand what I'm saying in natural language. For example, if you natural language. For example, if you natural language. For example, if you can see my screen here, if I say, "Hey can see my screen here, if I say, "Hey can see my screen here, if I say, "Hey Scout," I'm going to type to him. Say, Scout," I'm going to type to him. Say, Scout," I'm going to type to him. Say, "Hey Scout, "Hey Scout, "Hey Scout, turn on your headlights turn on your headlights turn on your headlights and say hi to everyone.

he is actually going to be able to he is actually going to be able to understand and do those actions. But how understand and do those actions. But how understand and do those actions. But how is he able to do these things, right? is he able to do these things, right? is he able to do these things, right? It's going to take him a couple of It's going to take him a couple of It's going to take him a couple of seconds to think about it because how seconds to think about it because how seconds to think about it because how this has been set up is he is working on this has been set up is he is working on this has been set up is he is working on with a Raspberry Pi installed and that with a Raspberry Pi installed and that with a Raspberry Pi installed and that Raspberry Pi is actually in a small Raspberry Pi is actually in a small Raspberry Pi is actually in a small little location in New York City and little location in New York City and little location in New York City and he's got a SIM card here which is then he's got a SIM card here which is then he's got a SIM card here which is then connecting to that to that Raspberry Pi connecting to that to that Raspberry Pi connecting to that to that Raspberry Pi and then he's getting signals. So, this and then he's getting signals. So, this and then he's getting signals. So, this is via 4G connection. And so, he's like, is via 4G connection. And so, he's like, is via 4G connection. And so, he's like, "Oh, hey, on it." And if you can see, "Oh, hey, on it." And if you can see, "Oh, hey, on it." And if you can see, you know what? I'm going to tell him, you know what? I'm going to tell him, you know what? I'm going to tell him, "Spin around." And oh, no, he's actually "Spin around." And oh, no, he's actually "Spin around." And oh, no, he's actually speaking to me. He's like, "I see the speaking to me. He's like, "I see the speaking to me. He's like, "I see the stage and it's looking great. Big stage and it's looking great. Big stage and it's looking great. Big presentation screen to the left, bright presentation screen to the left, bright presentation screen to the left, bright stage lights, and rows of seats for the stage lights, and rows of seats for the stage lights, and rows of seats for the audience. audience. audience. How How many people do you see? I've never trained him to answer this I've never trained him to answer this question, but he's Let's see if he's get question, but he's Let's see if he's get question, but he's Let's see if he's get getting it. getting it. getting it. He's thinking for like a second or two.

He's thinking for like a second or two. He's thinking for like a second or two. Let me take a good look from the front Let me take a good look from the front Let me take a good look from the front camera. I see two people. One near the camera. I see two people. One near the camera. I see two people. One near the speaker monitor set up at the back of speaker monitor set up at the back of speaker monitor set up at the back of the stage and another person further the stage and another person further the stage and another person further right. Nah. spin 360 and he's also saying is this session and he's also saying is this session about to start right so he's going to about to start right so he's going to about to start right so he's going to spin 360 any second now and then he's spin 360 any second now and then he's spin 360 any second now and then he's probably going to be like oh wow I did probably going to be like oh wow I did probably going to be like oh wow I did so this little robot here is a next so this little robot here is a next so this little robot here is a next generation of robot where ah there we go generation of robot where ah there we go generation of robot where ah there we go he is spinning 360 now and he's probably he is spinning 360 now and he's probably he is spinning 360 now and he's probably going to tell me what he's seeing and he's saying let's spin. Right? So and he's saying let's spin. Right? So this new generation of robots is to it's this new generation of robots is to it's this new generation of robots is to it's different from our traditional robot different from our traditional robot different from our traditional robot training because I have given this guy a training because I have given this guy a training because I have given this guy a little brain. And what do I mean by I've little brain. And what do I mean by I've little brain. And what do I mean by I've given him a brain? I've given this robot given him a brain? I've given this robot given him a brain? I've given this robot an agentic layer and I've given it it's an agentic layer and I've given it it's an agentic layer and I've given it it's called strands agents which is an called strands agents which is an called strands agents which is an open-source framework which was built by open-source framework which was built by open-source framework which was built by AWS and I'm going to quickly go back to AWS and I'm going to quickly go back to AWS and I'm going to quickly go back to my slide deck my slide deck my slide deck we can see it right and so here what we can see it right and so here what we can see it right and so here what happens is we have these existing tools happens is we have these existing tools happens is we have these existing tools that the robot can do he can take that the robot can do he can take that the robot can do he can take certain actions by himself but only certain actions by himself but only certain actions by himself but only those actions by himself so what we can

those actions by himself so what we can those actions by himself so what we can do is we can add a layer of LLM or even do is we can add a layer of LLM or even do is we can add a layer of LLM or even better add a layer of agent to it so better add a layer of agent to it so better add a layer of agent to it so that the agent orchestrates which tool that the agent orchestrates which tool that the agent orchestrates which tool to call and how to really get the robot to call and how to really get the robot to call and how to really get the robot to start doing the things we want. So in to start doing the things we want. So in to start doing the things we want. So in traditional software with traditional AI traditional software with traditional AI traditional software with traditional AI machine uh like AI engineering we can machine uh like AI engineering we can machine uh like AI engineering we can give agents software tools. Similarly, give agents software tools. Similarly, give agents software tools. Similarly, we can give the same AI agent a hardware we can give the same AI agent a hardware we can give the same AI agent a hardware tool called a robot which has access to tool called a robot which has access to tool called a robot which has access to preset functions or programmable preset functions or programmable preset functions or programmable policies and then the agent can decide policies and then the agent can decide policies and then the agent can decide which policy to implement when. So all which policy to implement when. So all which policy to implement when. So all it takes is one robot agent for us to be it takes is one robot agent for us to be it takes is one robot agent for us to be able to do new innumerous tasks and have able to do new innumerous tasks and have able to do new innumerous tasks and have it understand what we're teaching it in it understand what we're teaching it in it understand what we're teaching it in natural language. So how do we get natural language. So how do we get natural language. So how do we get started with it? All it takes is five started with it? All it takes is five started with it? All it takes is five lines of code. This is through uh the lines of code. This is through uh the lines of code. This is through uh the agent harness called strands. And all we agent harness called strands. And all we agent harness called strands. And all we have to do is import the strands agent have to do is import the strands agent have to do is import the strands agent and call the robot tool. And we say ro and call the robot tool. And we say ro and call the robot tool. And we say ro tools equals the robot and then we say tools equals the robot and then we say tools equals the robot and then we say pick up the red cube and should be able pick up the red cube and should be able pick up the red cube and should be able to pick up a red cube assuming that the to pick up a red cube assuming that the to pick up a red cube assuming that the robot has that capability. Yeah. Now robot has that capability. Yeah. Now robot has that capability. Yeah. Now he's seen someone and he's like oh let he's seen someone and he's like oh let he's seen someone and he's like oh let me go towards that person. So he gets me go towards that person. So he gets me go towards that person. So he gets pretty excited. This guy is pretty pretty excited. This guy is pretty pretty excited. This guy is pretty special because he doesn't have just one special because he doesn't have just one special because he doesn't have just one agent. He's got three different agents.

agent. He's got three different agents. agent. He's got three different agents. All three of them are strands and all All three of them are strands and all All three of them are strands and all three of them are working three of them are working three of them are working simultaneously. One of them is the simultaneously. One of them is the simultaneously. One of them is the thinker agent and that's the part of him thinker agent and that's the part of him thinker agent and that's the part of him that's constantly thinking and assessing that's constantly thinking and assessing that's constantly thinking and assessing the environment and like what do I do the environment and like what do I do the environment and like what do I do next? And that guy's that part of his next? And that guy's that part of his next? And that guy's that part of his brain is constantly thinking. Then brain is constantly thinking. Then brain is constantly thinking. Then there's the other communication part of there's the other communication part of there's the other communication part of it where and I'm going to show you that it where and I'm going to show you that it where and I'm going to show you that in a bit, right? and I've connected him in a bit, right? and I've connected him in a bit, right? and I've connected him to my telegram app as well as to my web to my telegram app as well as to my web to my telegram app as well as to my web app. And so he is able to have a app. And so he is able to have a app. And so he is able to have a conversation with me in natural language conversation with me in natural language conversation with me in natural language and then take actions based on what I am and then take actions based on what I am and then take actions based on what I am telling him to do. Apart from him just telling him to do. Apart from him just telling him to do. Apart from him just perceiving and thinking and figuring out perceiving and thinking and figuring out perceiving and thinking and figuring out what he wants to do. And the third what he wants to do. And the third what he wants to do. And the third agent, the third type of agent that he's agent, the third type of agent that he's agent, the third type of agent that he's got access to is a voice agent. I did got access to is a voice agent. I did got access to is a voice agent. I did have to disable it because every time I have to disable it because every time I have to disable it because every time I speak, he's going to think I'm speaking speak, he's going to think I'm speaking speak, he's going to think I'm speaking to him and so he's going to keep to him and so he's going to keep to him and so he's going to keep chatting away with me and it's just not chatting away with me and it's just not chatting away with me and it's just not going to be fun because we're going to going to be fun because we're going to going to be fun because we're going to have our co-host interrupting me all the have our co-host interrupting me all the have our co-host interrupting me all the time. So, I've disabled that feature for time. So, I've disabled that feature for time. So, I've disabled that feature for the time being. But essentially all the time being. But essentially all the time being. But essentially all three of these agents work in tandem three of these agents work in tandem three of these agents work in tandem with this one robot and thereby this with this one robot and thereby this with this one robot and thereby this gives him the ability to do way more gives him the ability to do way more gives him the ability to do way more than what just what he's been trained to than what just what he's been trained to than what just what he's been trained to do more than just the policies that he's do more than just the policies that he's do more than just the policies that he's learned. Now learned. Now learned. Now what is a quick overview on this trans what is a quick overview on this trans what is a quick overview on this trans package itself? This turns package has package itself? This turns package has package itself? This turns package has more than supports more than 40 more than supports more than 40 more than supports more than 40 different robots under eight categories.

different robots under eight categories. different robots under eight categories. And all of these are just simple robot And all of these are just simple robot And all of these are just simple robot tool calls. And how is this all set up? tool calls. And how is this all set up? tool calls. And how is this all set up? Four different layers. The first one is Four different layers. The first one is Four different layers. The first one is the agent layer, the topmost one. And the agent layer, the topmost one. And the agent layer, the topmost one. And there are two parts to this. One is how there are two parts to this. One is how there are two parts to this. One is how the actions go in and the second is how the actions go in and the second is how the actions go in and the second is how it observes and the observations go up. it observes and the observations go up. it observes and the observations go up. So if you notice it's very birectional. So if you notice it's very birectional. So if you notice it's very birectional. So first when we give it an instruction So first when we give it an instruction So first when we give it an instruction we would be talking to this trans agent we would be talking to this trans agent we would be talking to this trans agent which is the agentic layer that would which is the agentic layer that would which is the agentic layer that would then decide which policy to call and the then decide which policy to call and the then decide which policy to call and the policy provider again stands agent policy provider again stands agent policy provider again stands agent supports a bunch of different policy supports a bunch of different policy supports a bunch of different policy providers and we can then train our providers and we can then train our providers and we can then train our policy based on our traditional robot policy based on our traditional robot policy based on our traditional robot training. So in our policies we would training. So in our policies we would training. So in our policies we would collect data and then we would train on collect data and then we would train on collect data and then we would train on it and we would sim create more it and we would sim create more it and we would sim create more simulation data and that policy then simulation data and that policy then simulation data and that policy then becomes a VLA model which then the robot becomes a VLA model which then the robot becomes a VLA model which then the robot would have access to strand agents would would have access to strand agents would would have access to strand agents would have access to and then it would invoke have access to and then it would invoke have access to and then it would invoke that specific policy based on the that specific policy based on the that specific policy based on the question that we're asking it or the question that we're asking it or the question that we're asking it or the command that we're giving it and that command that we're giving it and that command that we're giving it and that policy needs to sit somewhere right so policy needs to sit somewhere right so policy needs to sit somewhere right so that sits in the back end which could be that sits in the back end which could be that sits in the back end which could be your simulation environment or it could your simulation environment or it could your simulation environment or it could be a real hardware chip, your hardware be a real hardware chip, your hardware be a real hardware chip, your hardware environment. That is the back end on environment. That is the back end on environment. That is the back end on which that is the interface on which the which that is the interface on which the which that is the interface on which the policy is running. And finally, the policy is running. And finally, the policy is running. And finally, the output actually takes place in the output actually takes place in the output actually takes place in the physical hardware which is the robot.

physical hardware which is the robot. physical hardware which is the robot. And so the robot ah see so now it's And so the robot ah see so now it's And so the robot ah see so now it's responding this even if he falls down responding this even if he falls down responding this even if he falls down he's supposed to be fine. He technically he's supposed to be fine. He technically he's supposed to be fine. He technically shouldn't um he technically shouldn't uh shouldn't um he technically shouldn't uh shouldn't um he technically shouldn't uh get hurt. He should be able to pick back get hurt. He should be able to pick back get hurt. He should be able to pick back up from where he um stops. Ah, okay. So, up from where he um stops. Ah, okay. So, up from where he um stops. Ah, okay. So, I'm telling him to go back a bit. Back I'm telling him to go back a bit. Back I'm telling him to go back a bit. Back off. Let's see if he actually backs off. off. Let's see if he actually backs off. off. Let's see if he actually backs off. Um, so that is the four layers of how to Um, so that is the four layers of how to Um, so that is the four layers of how to get started with building this, right? get started with building this, right? get started with building this, right? And what's happening under the hood, And what's happening under the hood, And what's happening under the hood, like a more picturesic view of what's like a more picturesic view of what's like a more picturesic view of what's the architecture of what's going on the architecture of what's going on the architecture of what's going on under the hood. We want everything is under the hood. We want everything is under the hood. We want everything is basically strands agents on the edge as basically strands agents on the edge as basically strands agents on the edge as well as on the cloud. We want to be able well as on the cloud. We want to be able well as on the cloud. We want to be able to train the VLA and the policies on to train the VLA and the policies on to train the VLA and the policies on with using agent core. Um and we want with using agent core. Um and we want with using agent core. Um and we want that to happen on the cloud but we also that to happen on the cloud but we also that to happen on the cloud but we also want to be able to call it directly on want to be able to call it directly on want to be able to call it directly on edge so that our robot can uh execute edge so that our robot can uh execute edge so that our robot can uh execute functions and policies faster. So this functions and policies faster. So this functions and policies faster. So this is sort of like a hybrid model where a is sort of like a hybrid model where a is sort of like a hybrid model where a part of it happens on the cloud and part of it happens on the cloud and part of it happens on the cloud and another part of it happens on the edge another part of it happens on the edge another part of it happens on the edge and strands can decide when to call and strands can decide when to call and strands can decide when to call which part of it. And so this helps with which part of it. And so this helps with which part of it. And so this helps with massive amounts of training as well when massive amounts of training as well when massive amounts of training as well when it's constantly collecting information it's constantly collecting information it's constantly collecting information and it's train able to train on that and it's train able to train on that and it's train able to train on that information and learn from itself but information and learn from itself but information and learn from itself but also just execute at runtime really also just execute at runtime really also just execute at runtime really really quickly. Now, like I said, the really quickly. Now, like I said, the really quickly. Now, like I said, the agent decides what to do and the policy

agent decides what to do and the policy agent decides what to do and the policy decides how it should be done. But he's decides how it should be done. But he's decides how it should be done. But he's pretty smart. He should be able to pick pretty smart. He should be able to pick pretty smart. He should be able to pick himself back up if he's not fully fallen himself back up if he's not fully fallen himself back up if he's not fully fallen down. And he should be able to continue down. And he should be able to continue down. And he should be able to continue moving along. moving along. moving along. So, I think he's okay. Now, where does So, I think he's okay. Now, where does So, I think he's okay. Now, where does this leave us? And why is this so this leave us? And why is this so this leave us? And why is this so special? We started off with very special? We started off with very special? We started off with very traditional robots. Robots have existed traditional robots. Robots have existed traditional robots. Robots have existed since forever, right? And they've always since forever, right? And they've always since forever, right? And they've always just been programmed, pre-programmed to just been programmed, pre-programmed to just been programmed, pre-programmed to do to autom be automated and do a do to autom be automated and do a do to autom be automated and do a certain set of tasks autonomously. certain set of tasks autonomously. certain set of tasks autonomously. But there is a future in this world But there is a future in this world But there is a future in this world where this these robot policies, these where this these robot policies, these where this these robot policies, these VLA models could be so advanced that we VLA models could be so advanced that we VLA models could be so advanced that we wouldn't even need to do this. They wouldn't even need to do this. They wouldn't even need to do this. They could be as large as our large language could be as large as our large language could be as large as our large language models. So that ah wait hang on he's models. So that ah wait hang on he's models. So that ah wait hang on he's falling back again. falling back again. falling back again. I'm gonna see if I can get him to move I'm gonna see if I can get him to move I'm gonna see if I can get him to move back up.

back up. back up. Good boy. Stop. Good boy. Stop. Good boy. Stop. Then he's fallen off again. Um we get to Then he's fallen off again. Um we get to Then he's fallen off again. Um we get to a point where these large language the a point where these large language the a point where these large language the the VA models could be as large and as the VA models could be as large and as the VA models could be as large and as amazing as our larger language models amazing as our larger language models amazing as our larger language models and they know they have all the and they know they have all the and they know they have all the information in the world and we wouldn't information in the world and we wouldn't information in the world and we wouldn't even have to do this. we might just have even have to do this. we might just have even have to do this. we might just have to feed in one simple model and then we to feed in one simple model and then we to feed in one simple model and then we could give it to him and then he would could give it to him and then he would could give it to him and then he would know exactly what to do. But until that know exactly what to do. But until that know exactly what to do. But until that point where we don't have to fine-tune point where we don't have to fine-tune point where we don't have to fine-tune on top of existing VAS and existing on top of existing VAS and existing on top of existing VAS and existing policies, we can do this. And this is a policies, we can do this. And this is a policies, we can do this. And this is a stepping stone towards a future where we stepping stone towards a future where we stepping stone towards a future where we don't need to train robots anymore. So don't need to train robots anymore. So don't need to train robots anymore. So now if we wanted to do more things than now if we wanted to do more things than now if we wanted to do more things than just the tasks it's trained on, give it just the tasks it's trained on, give it just the tasks it's trained on, give it an agent and see what it can do. And so an agent and see what it can do. And so an agent and see what it can do. And so let me quickly go back to my demo and let me quickly go back to my demo and let me quickly go back to my demo and I'm going to show you how it's actually I'm going to show you how it's actually I'm going to show you how it's actually working. Okay. So this is my so this is strand Okay. So this is my so this is strand here. This is scout here. And I've been here. This is scout here. And I've been here. This is scout here. And I've been telling him to do a bunch of things. So telling him to do a bunch of things. So telling him to do a bunch of things. So I can say, "Hey, do something I can say, "Hey, do something I can say, "Hey, do something complex."

That's not complex. He's going to be That's not complex. He's going to be thinking now. Ah, he's going to fall thinking now. Ah, he's going to fall thinking now. Ah, he's going to fall off. So he's saying, "Let's spin. Full 360. So he's saying, "Let's spin. Full 360. Done. Still safely on the stage. I can Done. Still safely on the stage. I can Done. Still safely on the stage. I can see the bright stage lights and the see the bright stage lights and the see the bright stage lights and the audience seating area." All good. What's audience seating area." All good. What's audience seating area." All good. What's there? A challenge. there? A challenge. there? A challenge. >> So he's speaking. I called this my >> So he's speaking. I called this my >> So he's speaking. I called this my signature performance, >> but he's not doing anything. What are >> but he's not doing anything. What are you doing? He clearly seems to be speaking, but He clearly seems to be speaking, but what are you doing? Please do something. what are you doing? Please do something. what are you doing? Please do something. He just turned off his headlines. Cool. He just turned off his headlines. Cool. He just turned off his headlines. Cool. Okay, now he's calling. So, do you see Okay, now he's calling. So, do you see Okay, now he's calling. So, do you see it saying calling rover speak, which was it saying calling rover speak, which was it saying calling rover speak, which was the function that it called because I the function that it called because I the function that it called because I said do something complex. So now it said do something complex. So now it said do something complex. So now it spoke, but now I think it should have spoke, but now I think it should have spoke, but now I think it should have been attempting to do something and it been attempting to do something and it been attempting to do something and it fell off because it tried doing fell off because it tried doing fell off because it tried doing something. something. something. I've actually seen it do like a funky I've actually seen it do like a funky I've actually seen it do like a funky dance, like this funky dance move. But dance, like this funky dance move. But dance, like this funky dance move. But he's got a mind of his own right now.

he's got a mind of his own right now. he's got a mind of his own right now. What's going on under the hood here? What's going on under the hood here? What's going on under the hood here? Couple of things. The first thing is Couple of things. The first thing is Couple of things. The first thing is here, I can use this. What is the point here, I can use this. What is the point here, I can use this. What is the point of creating him? I can use him to create of creating him? I can use him to create of creating him? I can use him to create my data sets because I'm able to also my data sets because I'm able to also my data sets because I'm able to also manually move him. I will get him to manually move him. I will get him to manually move him. I will get him to navigate in the direction that I want navigate in the direction that I want navigate in the direction that I want him to and then I can create training him to and then I can create training him to and then I can create training episodes and I can get information on episodes and I can get information on episodes and I can get information on how he's responding and how he's how he's responding and how he's how he's responding and how he's reasoning based on the questions that I reasoning based on the questions that I reasoning based on the questions that I ask. And this is super good information ask. And this is super good information ask. And this is super good information for me to then be able to make him do a for me to then be able to make him do a for me to then be able to make him do a better job of it. So that's one part of better job of it. So that's one part of better job of it. So that's one part of this whole process and this experiment this whole process and this experiment this whole process and this experiment of getting of giving him his own of getting of giving him his own of getting of giving him his own autonomy and getting him to do things so autonomy and getting him to do things so autonomy and getting him to do things so that I can create more data but also that I can create more data but also that I can create more data but also apart from that uh this is my apart from that uh this is my apart from that uh this is my configuration. So over here under the configuration. So over here under the configuration. So over here under the hood strands agents which is your hood strands agents which is your hood strands agents which is your harness SDK is using currently anthropic harness SDK is using currently anthropic harness SDK is using currently anthropic claude opus 4.8 under the hood. So that claude opus 4.8 under the hood. So that claude opus 4.8 under the hood. So that is the brain and then this is my simple is the brain and then this is my simple is the brain and then this is my simple prompt where system prompt where I'm prompt where system prompt where I'm prompt where system prompt where I'm telling it what it's supposed to be telling it what it's supposed to be telling it what it's supposed to be doing and I'm telling it all of the doing and I'm telling it all of the doing and I'm telling it all of the rules and I'm also giving it access to rules and I'm also giving it access to rules and I'm also giving it access to all of the rules that it's already got.

all of the rules that it's already got. all of the rules that it's already got. So I'm telling it what each of these So I'm telling it what each of these So I'm telling it what each of these rules are meant for. And so that's how rules are meant for. And so that's how rules are meant for. And so that's how strand decides which tool to invoke strand decides which tool to invoke strand decides which tool to invoke based on what I'm asking it to do. And based on what I'm asking it to do. And based on what I'm asking it to do. And the voice that it's using is the one of the voice that it's using is the one of the voice that it's using is the one of open AI real time. And I've also given open AI real time. And I've also given open AI real time. And I've also given it more information for it to be able to it more information for it to be able to it more information for it to be able to like just safety and guard rails to like just safety and guard rails to like just safety and guard rails to ensure that it's doing really well. Now ensure that it's doing really well. Now ensure that it's doing really well. Now it's this is these are two of the it's this is these are two of the it's this is these are two of the agents. The other thing that it can do agents. The other thing that it can do agents. The other thing that it can do is also chat with me on Telegram. This is also chat with me on Telegram. This is also chat with me on Telegram. This is amazing because when I'm not at home is amazing because when I'm not at home is amazing because when I'm not at home and I still want to get it to speak to and I still want to get it to speak to and I still want to get it to speak to me, I can say, "Hey, scout. me, I can say, "Hey, scout. me, I can say, "Hey, scout. Who is turn around uh spin around Who is turn around uh spin around Who is turn around uh spin around analyze?" Uh-uh. Don't fall off. Analyze analyze?" Uh-uh. Don't fall off. Analyze analyze?" Uh-uh. Don't fall off. Analyze the room and tell me who is the best the room and tell me who is the best the room and tell me who is the best looking person. This is always a hit or looking person. This is always a hit or looking person. This is always a hit or miss. Don't blame me if if Scout decides miss. Don't blame me if if Scout decides miss. Don't blame me if if Scout decides to be biased. I tried playing with this to be biased. I tried playing with this to be biased. I tried playing with this because it makes me feel like Snow White because it makes me feel like Snow White because it makes me feel like Snow White um and the queen saying mirror mirror on um and the queen saying mirror mirror on um and the queen saying mirror mirror on the wall who is the fairest of them all the wall who is the fairest of them all the wall who is the fairest of them all and so let's see if Strand can give us and so let's see if Strand can give us and so let's see if Strand can give us that. So it's saying haha on it on there that. So it's saying haha on it on there that. So it's saying haha on it on there spinning around to scope the room. Is he spinning around to scope the room. Is he spinning around to scope the room. Is he spinning? He is. Looks like he's making spinning? He is. Looks like he's making spinning? He is. Looks like he's making and let's look at what he's looking at and let's look at what he's looking at and let's look at what he's looking at now. He's still looking at the stage.

now. He's still looking at the stage. now. He's still looking at the stage. This is the other miller. He's looking This is the other miller. He's looking This is the other miller. He's looking at me. Hi. Am I the prettiest? Oh, you at me. Hi. Am I the prettiest? Oh, you at me. Hi. Am I the prettiest? Oh, you flatter me. But no, turn again. Spin flatter me. But no, turn again. Spin flatter me. But no, turn again. Spin some more, please. some more, please. some more, please. He's decided not to spin more. Let's see He's decided not to spin more. Let's see He's decided not to spin more. Let's see what he says. Spin complete. Here is my analysis of Spin complete. Here is my analysis of the room. You can see my screen, right? the room. You can see my screen, right? the room. You can see my screen, right? I have no way of zooming this in. Um, I have no way of zooming this in. Um, I have no way of zooming this in. Um, spotted six to seven people total. A spotted six to seven people total. A spotted six to seven people total. A couple seated front center near the couple seated front center near the couple seated front center near the bright stage, right? A few more in the bright stage, right? A few more in the bright stage, right? A few more in the audience. One person a dark outfit on audience. One person a dark outfit on audience. One person a dark outfit on the stage. The rocking khaki jeans and the stage. The rocking khaki jeans and the stage. The rocking khaki jeans and sneakers. Thank you very much. Oh, no, sneakers. Thank you very much. Oh, no, sneakers. Thank you very much. Oh, no, no, no. The person in front of me, cocky no, no. The person in front of me, cocky no, no. The person in front of me, cocky jeans and sneakers, clearly walking with jeans and sneakers, clearly walking with jeans and sneakers, clearly walking with purpose on stage. Uh, best looking. purpose on stage. Uh, best looking. purpose on stage. Uh, best looking. Honestly, from my low angle ground view, Honestly, from my low angle ground view, Honestly, from my low angle ground view, it's tough to tell faces, but the person it's tough to tell faces, but the person it's tough to tell faces, but the person on the stage right now gets style points on the stage right now gets style points on the stage right now gets style points for the confidence stride and the for the confidence stride and the for the confidence stride and the dramatic blue curtain backdrop. Thank dramatic blue curtain backdrop. Thank dramatic blue curtain backdrop. Thank you very much. Every time I need a you very much. Every time I need a you very much. Every time I need a confidence boost, I can be the only confidence boost, I can be the only confidence boost, I can be the only person in the room and ask this guy person in the room and ask this guy person in the room and ask this guy who's the prettiest of them all, and he who's the prettiest of them all, and he who's the prettiest of them all, and he will always choose me as the answer.

will always choose me as the answer. will always choose me as the answer. [laughter] [laughter] [laughter] Thank you very much.