How AI Makes Him Crores: Second Brain, Automations & Systems | Vaibhav Sisinty | FO557 Raj Shamani

What's your biggest fear today actually. with AI with jobs with people watching. this? >> My biggest fear is people not being able. to understand the balance between what. we should get in AI to do and what we. should do because AI can do everything. today and you should not get AI to do. everything today. >> How do I know a line between how much. should I let my team or myself should I. use AI versus how much should we know? >> Do the work that was low value for you. all throughout which was operational and. get that done by an AI. The last layer. of work is something that you have to.

focus on. Eventually. options. what is that I want to take and why and. build on top of that. If you don't try. to touch it, that is what happens. You. get average outputs. If my job is to get an AI to work at the. level that I operate or get it close to. me, it will not happen overnight. It. will probably a 12 to 18 week journey. for me to fine-tune it. How will I do. it? I will build a second brain. Very. simple. What are the things that I. consume on a every single day level? Let's say YouTube content that I'm.

watching every day is very very. valuable. How do I feed the same context. to AI? So I built a daily rapper which. opens my YouTube history, goes through. all the videos, ranks all the pieces of. content, everything that I've seen of. AI, it pulls that data, converts the. transcripts and looks at what are the. key pointers, saves it in form of JSON. cards. JSON cards, what is the value. pack of each one of the podcast is what. it saves. That is one. Two is a lot of. times when I watch content on AI, I. share it with my team. Where do I share. it? I share it on my Slack. So, I have a. triage running for my Slack which looks.

at what are the conversations I'm. having. This is number two. Three, I. realized one of the highest value. conversations that I have today is. inside of standups with my team. Every. single meeting is transcribed, put in a. GitHub repository and inside of the. repository, my team can access that. information at any point of time. Finally, oh my god, how can I forget. this? This is a game changer which is. >> what is the easiest way to make an agent. who do all of this? >> There is something.

>> I have a small favor to ask you. I need. you to subscribe to our channel. The. more subscribers we have, the better and. bigger guests we can bring and provide. you more value through these. conversations. And the full audio. experience of this show is also. available on Spotify where you can. follow us and listen to the new episodes. as well. Now let's get into the episode. If you want to know how top creators and. companies are getting hundreds of. millions of views a month and making.

money out of it and scaling their. revenue to 10x. This episode breaks down. exactly how they do it with AI. The. difference is the system behind the. tool. They've built a second brain that. feeds AI. Everything they read and. think, AI does 90% of the work for them. In this episode, WebV and I will show. you how to build that system step by. step, how to set up your second brain, how to use AI agents that find your best. topics, how to create scripts that go.

viral, and how to upgrade your own. thinking. We've also built a free AI. focused community where we teach AI in. depth. If you want to learn how to make. AI work for you, join the community. The. link is in the description below. I want to understand last time when you. were here, you told me that. you have scaled your company. significantly faster than anyone.

>> Yes. >> From what point before AI, if your. revenue was 100 rupees, how much is it. today? >> 1,000 rupees. 10x. >> So you have 10xed your company in last. two years. >> Two and a half years. >> Two and a half years with AI. >> Yeah. >> Most of the efficiency has come. >> What did you do? What what was the. efficiency? How did you make how many. people are there now? >> 400. >> And this work was possible with 400. people before?

>> No. That is what we will talk today. So. there are some things that we spoke. before. >> We spoke about jobs. first podcast. >> A lot of things that we spoke then are. not true anymore. >> Things that I have said are not true. anymore because I've seen the other side. right now. >> Okay. >> Which we should capture. >> The audience needs to know both the. sides of the story is what I feel. >> Tell me what is not true. What's your. biggest fear today actually then you.

come to tell me and what's. >> my fear today. >> with AI with what's happening in the. world with jobs with people watching. this. >> my biggest fear is people not being able. to understand the balance between what. we should get an AI to do and what we. should do. >> because AI can do everything today. >> and you should not get AI to do. everything today. >> what do you mean by that what what are. the things you. not let AI do. >> Dude, tell me something. What do you do.

on everyday level at uh at work? Are you the one sitting and working on a. computer? >> No. >> Are you the one being all by yourself. drawing on? >> No. >> You take decisions. >> Yeah. >> Right. >> Then I'm judging what's right, what's. wrong. >> Correct. If you think about it, leaders always have done this. It's. nothing new. What we understood on top of that is you. you learned how to build a team. You.

learned how to orchestrate the whole. thing. So you get to a point right now. where you are not sitting and doing. anything any of those things. >> You're sitting and taking decisions. >> True. >> You're using your judgment to take the. right calls. >> You have a flare of understanding. >> Agree. >> That is taste. Knowing what to do, what. not to do is the decision-m that you. build on top of. Now the problem with this is you can do. it very very well. But someone who's.

just getting started who's using AI. imagine you would have started this. company by hiring someone to write. YouTube script to write titles to think. of what the copy is and everything and. you just be the actor. Do you think your. uh company would have been this big? >> That is exactly the mistake people are. making today with AI just because not. that they could not have done they could. have done it. You could have hired very. good people from everywhere and they. could have done it and a lot of people. do it. >> That is the difference between a brand. and that's the difference between a.

creator. >> Creator mindset you are always in a. founder mode. So the problem with this. is someone is getting started who's. getting the seeing the flare of AI you. just letting it do everything end to end. without even knowing why it is doing. what it is doing gets to a point where. AI is taking decisions for you. So. here's my problem as an entrepreneur. which is we you picked up spot on. because of all the content that we make. you come here you teach me AI and I get. blown away and I get into like.

>> this geek mode of learning everything. what you said whatever is relevant for. me for next 2 3 4 days and then I go. deep dive and then I ask and probably. force my team to start using air. >> perfect so now since last one and a half. year I've made them do it. >> correct. >> and now I've realized some of them have. become dumber. They were much smarter before I gave. them AI. >> So, and I'm like, I don't want this chat. GPT clot stuff. I want your original. thinking and there's no original.

thinking anymore. And I'm sad about it. because my team members are becoming. dumber and dumber. I started becoming. dumber in middle and I shut down. And. I've stopped now using by the way the. way I would use. all of these AI stuff for my questioning. for my research and stuff like that. I've reduced it like to 14th. >> of what I used to do because it was. making me dumber. It was giving me just. same kind of things which anybody could.

have done and that's what's happening in. the team. Everybody no matter what task. I'm giving. they are giving me dumb stuff boring bad. stuff. So how do I know a line between. what how much should I let my team or. myself should I use AI versus how much. should we not? >> That's why I'm saying no. >> do the work that was low value for you. all throughout which was operational. which was if this and that. >> and get that done by an AI. The last.

layer of work is something that you have. to focus on, right? Eventually options. Yeah. What is that I want to take and why and. build on top of that? It's a 20% layer. that is left right now. If you don't try. to touch it, that is what happens. You. get average outputs. I'll tell you. there's a study by anthropic. Uh I. forgot what it is called. uh the u it's. it's a study on agents working with each. other.

>> okay there are multiple AI agents. >> when they work with each other agent. orchestration. I was reading it and it had something. very evidential in this when a bunch of. AI agents from same anthropic were given. the same task. >> of writing a book or something okay. >> four out of 10 agents came up with the. same book name. some socialistic something I don't. remember the exact your team can pull. and put a screen on it okay put it on. the screen. >> what does that say.

all models are thinking alike of course. there. >> there are different way of solutioning. that you can bring in to get. perspectives I use different tools like. multi and all to bring different AI. models to. >> discuss differently because everybody. has their own biases so I want to know. everybody's biases that's how you. operate technically to get the best. answers but it's happening. So when the output is similar for. example we working on a project. let's think about names everybody had.

come up with 10 names three to four. names were same in everybody's paper. >> because everybody used the same bloody. AI models. and gave the same prompt. >> that's what was happening. >> yeah so how are you tuning that. conversation by adding more context. so here's what do you mean by adding. more context because here's what people. are At. least in my org which I've seen. >> they ask AI to do deep research work. >> ask them to come up with like 50.

questions then I have given my own. process of how I finalize topics how I. finalize research and then come up with. questions and stuff like that it's a big. it's a 30page process because I've. written down everything right and I've. written like I've genuinely done it and. I've given that to them what they've. done they've like now based on Raj's. process choose the questions. So the last judgment layer earlier they. were. watching hundreds of things coming up.

with 100 questions but they were the one. taking decision what are the 10. questions which are which should reach. me just to example what is the right out. of 100 scripts what are the two scripts. which should reach me. >> now they are dumping those 100 scripts. and 100 things on AI and asking cloud. GPD do you choose and that is choosing. something. >> and I can read and tell that this is Not. you. >> Yeah. >> So they're letting AI only decide. >> take decisions.

That is I you didn't you didn't lead me. to this. I I led you to this. >> where I literally told you this is what. scares me the most. >> I keep saying this to my team. >> that people will keep getting dumber. >> They I don't know how to put it like I. don't think net net their number. >> No. So people are doing average work. Let's just say that people are just. keeping. >> there is a new average right now. H. >> the new average is AI slop. >> Ah there. >> that's that's a new average. It's better. than the last average but it's just an. average again.

So at this point of time like when. internet came in everybody has the. information same information that you do. right because everybody can search. >> When AI came in everybody has a smartest. agent right next to you. If a I don't. know if a Sachin Tandulkar while playing. cricket would have gone to a terrible. coach do you think he would have become. a sachinder? No. Right. So that is what. is happening right now. You everybody. has a sachinda. Now you have to be a. very good coach. If you can't be a good coach that person.

will never make it into cricket. I mean. I will not name a couple of other. cricketers who had the potential could. not make it but for whatever reason they. were not guided the right way. So, how. should I tell like what you said it's. about context? How should I give my AI. more context in a better way to get. better results? Not average work. >> Yeah, it is not. >> AI is giving me average That's I'm. just going to go out and tell you that's. that's become the problem. Maybe I'm not. using it right away. >> I think your baseline has shifted. Your.

expectation from AI. >> has gone up significantly over the. course of time because you're seeing the. potential of it. But I've seen it in data. I'm talking. about data. Like I've tried question A. with X guest which is written by me. Question B with B guess which is written. by AI. Question A has a better spike. than version B. >> and multiple times. So with probably. similar guest, similar options, bunch of.

other places and we experiment with. hundreds of pages. >> What level of context does AI have? >> A lot. How why do you think it has a. lot? Does it have data of every piece of. content that you have consumed? every piece of content that I've. consumed. Why do you think you come up. with ideas the way you do smart. content? >> None of us are smarter than an AI. That. is very clear based on data.

>> Agreed. >> Right. Your context is different and. based on your context, you're a. specialist in something because of which. you're able to come up with feelings and. gut and taste. That is what AI is not. able to pick up. and you're like you're. comparing that to this for I'll give you. one simple example right when you're. let's say when you knew that. you must have passively been consuming. something.

it passively happens right like. you're doing that passively you're doing. your mind is already working in those. lines if you're meeting a president of a. country you're actively reading about. your mind is automatically working on it. right but AI is on silos. >> the moment you ask question it wasn't. zero it becomes one. >> it's trying to become one so it is. trying to compete to with you who has. insane amount of context and that is not.

the only context what is context how do. we take decisions let's take two steps. back one is a close proximity layer. that is one two is what have I been. doing over the course of last 6 months. to one year to bola I'm able to come up. with better questions of course you'll. be able to come with better questions. because you're the guy sitting and. asking the questions before even the. data goes out before even the podcast. goes out you have the taste of knowing.

podcast. you have the taste I know after this. podcast you go and say yeah. because I've been with you after this. podcast you have it running on your head. because You have that knack. >> AI doesn't have it because AI has not. sat next to you to do all these. podcasts. But can it? Yes, it can. >> Can it get close to it? Yes, it can. >> How? >> That is by giving it context. For. example, try to note out. You remember.

we had built a skill for research back. in the day. That was only one part of. it. What is a human AI employee? >> I want a AI employee. For an AI. employee, what all do we have access to? You have access to a memory which is. yours. >> You have access to tools which is your. computer this that and all. You have. access to skills and SOPs. Some are. built out mentally. Some are built out. on paper. >> M. >> right. And four is you basically take I. mean you have a brain which thinks.

through all of these vectors and a few. more to take decisions. Today AI has all. of them. What we are doing is we are not using. the tool well enough to get the actually. it is smarter than us. >> and I have instances to prove it also in. our cases but it's also dumb in a lot of. places we can talk about that also it's. not there. >> where we want to but if I have to if my. job is to get an AI to work at the level. that I operate or get it close to me it. will not happen overnight it'll probably.

a 12 18 month a 12 to 18 week journey. for me to fine-tune it but I will do. everything possible for AI to get. exposed to what I'm exposed today. How. will I do it? >> One, I will build a second brain. >> What do you mean by that? >> What is a second brain? Very simple. What are the things that I consume on. every single day level? I consume. YouTube. >> Mhm. >> There are cons there are things that I. consume that I don't want AI to see. I. don't want it to see that I was watching. some Netflix show.

>> It has no relevance to the work that I. do. That's entertainment. So, I'll. probably keep it aside. I'm sure there. are correlations there also. >> Absolutely. >> But I will keep that aside for now. >> Yeah. The movies I see I have I learned. so much from you. >> Yeah. For you definitely. Yes. I can. imagine. Right. Uh let's say YouTube. content that I'm watching. >> every day is very very valuable. At. least I can say for myself I consume a. lot. >> podcasts and you know we spoke about lex. fitments of the world and all I consume. a lot. I build lot of perspectives from. that.

>> How do I feed the same context to AI? So. I built a basically a daily rapper which. opens my YouTube history. goes through all the videos ranks all. the pieces of content. If I'm watching. some I don't know like Mr. beast video. or let's say if I'm watching some health. video of some podcast of yours or. whatever it'll ignore all of them. because I'm focused on AI. >> right everything that I've seen of AI it. pulls that data. >> converts the transcripts and looks at.

what are the key pointers saves it in. form of JSON cards JSON cards what is. the value pack of each one of the. podcast is what it saves that is one two. is that is not it lot of times when I. watch content on AI. I share it with my. team. >> Where do I share it? I share it on my. Slack. To the content team, I might say, "Guys, this is very good perspective. I. might have dropped a voice note to my. programs team because we teach AI a. lot." I must have shared someone saying. that I like this SOP. We should teach it.

to our learners to our implementation. team who's probably implementing. something at Motorola right now. Let's. say I found something technical there. and I think in the project that we. working with Motorola, this could be. useful. So I'll send it to them and all. of these things are happening on Slack. for me. >> So I have a triage running for my Slack. which looks at what are the. conversations I'm having. >> actively. This is number two. Three, I. realize one of the highest value. conversations that I have today is. inside of standups with my team. Every.

single meeting is transcribed put in a. GitHub repository. GitHub is where people push code, right? is on a GitHub repository and inside of. that repository my team can access that. information at any point of time because. this this by the way started very. recently. I said when we were creating. content why why are our perspectives so. stronger when I'm with Raj. >> but when we are shooting content you ask.

me a question. >> because. how do we bring the flow that I get with. Raj we started daily standups. >> in daily standups we talk about AI. topics I give my perspectives there. because. we are not able to speak a lot of things. So those things are transcribed. Those. are easy. You can use a whisper flow, granola, fireflies, whatever. >> those notes are there. >> Okay. >> Right. So all the finally, oh my god,

how can I forget this? This is a game. changer which is it is a little risky. also. Every conversation that I have. with Chip, Claude, Gemini, Grock, cursor. sometimes everything every day is. exported and fed to AI. because my raw thoughts are not having. with or are not the conversation that. I'm having with you are not the.

conversation that I'm having with my. team are the conversations that I'm. having with my AI. >> My perspectives are there. I'm a huge. voice mode user. Okay. When I'm in the gym and all just. brainstorming, I I treat AI like a. brainstorming partner. All those perspectives are fed into a. single memory layer called as Cognney. >> Okay. >> Cogni is a memory layer.

JSON bits save. >> Okay. As a result, next time I want to. do anything, I can say tomorrow I'm having a. conversation with Raj. These are the. podcasts that we have done. What are the. strong perspectives that we should put. in the podcast that was spoken the last. 3 months? I have all the data ready. >> Nice. >> So, and on top of this, let's say. tomorrow I want to write a important. email. Forget about all that email. whatever email everything all that data.

is basic. Tomorrow if I want to take an important. decision today, tomorrow I want to meet. someone. >> and I want to know what questions can I. ask them. >> I might not remember exactly what I. could have asked them which was a. question of mine. But I could have asked. AI that. >> It would be like oh you're meeting Alex. Wong right next week. You should ask. these three questions because we debated. about these three questions. Our. perspectives were different. He could. give us a very different perspective. when when I was uh by the way I did a.

podcast I hosted a podcast where a. couple of people commented saying that. web has become Raj Hammani right now. with you know right the conversations I. do with open AI and all those right so I. was speaking with Wolfie before the. conversation happened my prep work was. when I sat in the car. >> with this data asking this is Wolfie. Bane he heads startups for open AI I'm. having a conversation with him uh coding. New codeex is launching because we got.

to know what was launching whatever. right so that was a conversation. >> so what are the questions that I had. apprehensions that I had things that are. people are asking me you pull all that. data and tell me what is it and it had a. report ready no team can beat this. >> no AI can beat this because this is you. this is context now this is where you. draw a line do you give this access to. your team or do you keep this to. yourself? I'm not given access to.

everything to my team. Anything which is. public is public. For example, meetings. that is happening with the team that is. available for the team. >> But personal conversations, >> personal AI conversation channel is very. much limited to me. >> Okay, >> that is personal context, >> right? And this is what I mean by an. overall context. This is your decision m. every day. >> It is one time setup. Now you set it up. and leave it. Now sadly this is the. problem right that is running on a. computer at my home and that computer is.

dedicated for my AI agent. >> AI agents not AI agent AI agents right. >> but then don't they get context rot. >> very good question context rot that is. why I don't use a direct LLM memory. I use something called as cogni. >> I didn't say I save all of this inside. of Uh chat GPT or context. context window.

so. okay so the yeah go ahead I got. something but go ahead. >> no go on go on. >> no no no so it's like you're telling me. that the cognney is like the master. memory it's not a per chat memory and. per in per chat context gets the context. rot right. >> no no no no no so basically what is. happening is we have llms. right this is your uh GPT claude etc. >> Okay. >> Uh and these are all LLM layers. What we.

have done is an agent. is like a human. >> M. >> in the simplest way put an LLM is the. let's say mind. >> Okay. >> The thinking part. >> Okay. >> That is what for an for a this is like. your brain dude. >> Okay. This thinking thinking. >> then it needs tools. H tools like let's. say web search uh MCP MCP is basically.

using your slack. >> got it. >> etc. Whatever tools it uses this is tool. use. >> Okay. >> Right. And then there is something. called as memory. >> What is memory? To remember everything. >> So whenever you ask a question, I mean. there are more layers to this. Whenever. you ask a question, you're using memory. to understand what is in there. If. there's something that you can retrieve. based on habits, you're using tools to. do a job. And LMS are your thinking.

part. H now in these LLMs there's also a. layer of memory which is basically. called as in a simplest way put context. window h. what is context window so humans I don't. know if you know this we can only. remember like seven digit things the. best way possible in a way passively. seven digits is like our uh context. window of some sorts.

>> okay what do you mean by Seven digit. >> anything seven digit so all the numbers. and all that you see right. >> what now let's say how do I put it. that's the amount of things that we can. remember very very well. >> okay. >> right but an LLM can actually remember. context of up to 1 million right now LLM. is like 1 million 1 million is 1 million. is like I think uh let's say five large. novels it can remember at any point of. time digit number okay 765 whatever I.

told you. and you what you will try you'll try to. make sure you're not remembering. anything else. >> that will stay in your memory that is. imagine that to be a context window. >> got it. >> till I ask you remember the moment I. tell you remember this you'll probably. forget the last one. >> that is the human brain. >> but for a LLM today it can remember 1. million. It can pretty much have five novels open.

and you can ask. >> 18th page novel one fifth word it can. tell you. >> that is the level of brain. >> Okay. >> Now most of the context used to revolve. around this. was regular context window. This was. regular memory and context. >> Okay. >> But. you're adding a secondary memory layer. Imagine this to be like a notepad. M.

starting index numbers to remember from. web numbers to remember of YouTube. You. made a index of it and that is your. memory layer. So I'm not pushing all. this context here. I'm not giving it all. to Chad GP saying. because. it will feel like a lot but it's not a. lot. >> Imagine if you watch 10 YouTube videos.

which is a podcast like you and me do. that's 10 hours of content. It will go over context window easily. So memory is beautiful because what a. tool like Cogni does is it uses graphs. Okay, it uses graph memory and it. retrieves the same set of memory which I. need to do this task right now. >> and it only requires like it'll. only pick up a book that it needs at.

that time. >> Yes. So it has built a beautiful index. >> M. >> okay. Every time when I give a task. saying, "Hey, write an email for me or. whatever, >> it'll first go to an LLM lm context or. agents.md. which is basically a markdown file or. instruction file which will have a. direction saying look.

Okay. And this is basically how an agent. operates in the simplest possible way. I've simplified it. And for you to use. agents like this, >> you need something called as a agent. harness. You're harnessing an agent. And these tools today are the ones that. we were using who have evolved from. being an assistant to an agent harness. A chat GPT has become a chat GPT work.

plus codecs which are basically agent. harness. A claude has become claude. co-work and claude code. These are agent. harness tools which has access to memory. which has access to tools which has. access to brain which has access to a. few other things and it orchestrates. everything together to execute your job. >> Now let's say this doesn't exist at all. >> Yeah. and you say webarch.

it is as good as a fresh intern who is. bloody smart. context how can he compete with you or. he and she compete with you isn't that a. wrong comparison to make to start with. >> true. true. so the memory is a game. >> context is everything it's everything. and on top of that look in this also. there's one more layer right when you. build your agents, you build your.

skills. >> We you remember we built the skills back. in the day. Skills have become much much. better right now. Our whole company. operates out of skills right now. That's. the proprietary thing that we have in. the company at this point of time. How. do I translate my brain into skills? How. can everyone translate their brains into. skills and make those skills better. every single day with new things that we. are learning where it gets to a point. where it can truly do 90% of the things. much better than you can do.

That's the game you play. >> And skills are a bunch of markdown files. >> which is all that we made last text. document. It's like a text document. It. looks like a instruction document. Step. one, step two, step three, step four. I. have skills for everything. No, >> that's the whole point. That is the. whole point. But there are a few layers. that are missing right now. There's a. memory layer that is missing. Tool call. layer that is missing. Slack.

I've just attached my appy. >> as to use whatever unlimited go. >> made bunch of skills and then doing it. context layer is missing and that's why. it's giving me dumber and dumber. >> because till now. >> I'm doing everything. >> till now we always used to think this. layer. >> was actually very. >> not important is like. >> no no it was always important it was not. solved. >> okay. >> memory management in a way was not. solved it's getting much much better and.

on top of that context windows are also. very very short it is getting much much. better right now it's going to a million. right No by default it has become a. million. So by default it has become. five books. >> Nice. >> right? So you have to think about. >> this is how you build a second brain and. then. >> this is your second brain. This only. this bit is your second brain. >> And when you do that with your process. that is how I get AI to start thinking. probably much better than me in the.

context that I am thinking. That's. >> it gets to think like you. it it you. make it your own. >> more information as well right like at. the same time I can't remember that much. information so it. >> that's fine that's fine you anyways. can't remember because your brain is. this seven digits. >> m. >> you anyways couldn't. >> so this and then you build your second. brain and that's how your all your. content all your everything comes better. >> all context yes it gets better every.

single day like I said you right you. will see this graph okay of quality of. output that you'll see if this is time M. >> this is quality. >> m. >> this is how you will see most people. will give up here. saying because this is going to take you. like 3 weeks this journey will take you. seven more weeks but this is where. you'll see the exponential results sorry. I'm making it. >> this is where you'll see exponential. results so most people I believe give up. before you get here. >> yeah because they're like oh I'm trying.

so much and still not working. >> it's not working that's the point right. but that is Third differentiation when. everybody has the same bloody tool. >> But tell me point blank question is. when you get a script. >> to write a real for example. >> is your judgment better or AI. today. >> even after doing all of this is my. judgment better or AI? >> Yeah like you I can give you 10 scripts.

I can give. >> I don't let AI make a judgment only. I'll tell you how I use AI. >> But it will give you one or two things. that you have to choose. >> I'll tell you I'll tell you how I use. AI. That's not the question that I ask. to start with. I don't expect it to do. That's my job. >> If I'm creating content, >> my job is to put out something that is. valuable for people. So I decide I'm the. decision maker. AI is my. >> helper. >> team. who's going to help me get there better. to serve my audience better. So AI's job.

is to so I go if you're talking about. content right one of the reasons that we. win I believe or we've been winning in. short- term uh short form content and. also long- form content is taking very. datadriven decisions. Okay, let's make. your datadriven AI decision maker or. whatever you're doing right now your. AI content system I want to understand. how many views you're getting per month. >> maybe 70 80 million views across.

everything 10 million will be Twitter 15. million will be Twitter. >> Twitter is rampant 10 million is crazy. >> 15 millionishabi. >> you're putting res there also. >> no only Twitter is this conversation. >> brain dumps. >> people either like it or don't like it. The same since the back in the day it's. the same. >> Okay. So let's let's make your system. right here. >> 100 million view system. >> 100 million views system. Okay. I don't.

think we're hitting 100 million every. month yet, but I think uh we could be. averaging here on a yearly level because. there could be appreation months. >> Dick the way it kind of works for us. >> This is 100 million view content system. you are teaching us. >> Yes. >> Through AI what you are doing because. you tell me you don't create content. much. >> So I don't create content at all. In. fact there is a joke uh in our audience. webhub only talks in Raj's podcast. everywhere else it's an AI and it's not.

and and I'm not saying you you should. read the comments, >> right? like web. you should come you should come because. on my channel it's AI it's my AI clone. but look. >> here's the problem most people think we. are doing 100 million views because I. use a AI clone. >> this is what people think. >> I'll tell you the reality if I was not. using AI clone this would have been 200.

million that is something that you need. to understand. >> like if you were not using. >> if I was not if I was shooting the. content I would have made 200 million. views. >> Oh, >> but. for the life that I live I don't have. the time to shoot enough content for. even half of it. >> So you're telling me real face, real. person will always like till now. >> there is flare. There is flare. >> Okay. >> Of course there is flare. >> So a real person will drive way more. views than an AI person. >> Yes. >> Right now it's the thing or is it going.

to stay like that for I think platform. is going to incentivize real people. We. will see what happens. Uh I think. platform incentivizes only one thing and. that is retention and engagement. It. doesn't care. So far it doesn't care. till people re retaliate and they're. seeing that in consumer behavior. >> because platforms does what is needed. for the platform not for you not for. users. >> Agreed. >> Right. But anyways that's a. philosophical conversation as well that. we can debate on too. But what I'm. trying to say is most people think I'm. winning. because of AI.

>> And the answer to that is what they. believe is AI clone. >> I would want to start by saying this. This is not the answer. >> Okay. What's the answer? >> The answer for winning is a process of. how we think with AI. >> where. every time I try to set up a process. across company. Here we are going to. talk about content example right now. across the company. The biggest mistake. people do is they will see what is a. human doing right now where all I can.

plug AI so that it can be done better. That's a fundamentally wrong process. >> Okay, >> you have to reimagine that whole process. with capabilities of AI in the mind. That is when you build a great process. For example, for content, what is the. first step for someone like us who. create education content? One is. topic selection. >> M. >> okay. >> This plays a very important role. >> This is game. >> game. No, topic selection is one. I'll.

tell you what game is. Game is this. packaging. >> What we select as a topic here? The. topic is we'll talk about AI. Okay. I. mean you know packaging really well but. you get what I'm saying. >> Yeah. >> Second is. >> for me topic is packaging. >> Okay. For me topic is topic. What am I. choosing? >> The theme. You me you meant theme. >> Okay. You can call it theme. >> because topic for me is the title. It's. the absolute bang on packaging title. So.

probably I'm thinking in a different. >> correct. So you you do you tell your. stuff. Okay. 3.8 3.8. That's a theme. >> No. How is it a theme? >> Like for me that's a theme. No, I I have. options. >> I'm saying that in my head I I think of. it like a theme. >> I think like that as a thing for me the. >> So what what is theme for you is a. category for me. So let's say your theme. would be today we'll talk about AI. For. me that's. >> theme for everyday is AI. >> Huh. So.

>> that's why one level down. >> exactly. So I'm like that's. >> I'm curious to know what people think is. the right thing. Topic selection or. theme? Let's see. I want people to tell. us in the comments. >> No, everybody can have their own names. I'm just saying my name. My brain works. like that. There's a category, there's. theme, and then there's. >> that's your lingo. >> That's my language. >> Topic selection. >> You let's do yours. Okay. >> Topic selection, packaging on how I'm. packaging it. >> Then comes script. >> and then comes uh posting.

>> Okay. >> Okay. I'll get to each one of them on a. >> and then data and then all of that. >> So all will come in this that becomes a. loop. Now I feel most of the game is one. here. Topic selection and packaging. That's. 80% of the game. >> Fair. All right. >> Fair. >> Agreed. >> Yes, you will agree. I know. >> topics. So, how do I do an incredible. work. >> at finding the right topic. >> normally? >> Okay. So if I have to create content. >> on AI.

>> or health or anything for that matter, the first thing that we'll think about. doing or you should think about doing is. hey what is that that the consumer. outside who's consuming content every. single day giving me as a signal. >> What are they consuming more? What are. they liking more? What are they engaging. with more? What is driving me better. data? On a human level, what will we do? We will have daspandra profiles. our. feed our sc our feeds will be optimized. for that.

favorite stuff and we'll use that data. and when you put a layer on it, what. will you say? You scroll the feed for. me. >> Yeah. >> What will you say? You will say go and. read this people and tell me. So you'll. limit yourself. But with AI coming into. the picture, I have unlimited employees. >> with unlimited human labor that I have. How will I think about topic selection. now? >> Now I will go bonkers. Okay, >> I will look about every single source of.

information that is related to AI is a. signal to me. >> Every single. >> So it could be now when it comes to. topic selection if I break this down. right just this for me on AI it could be. something like product hunt. >> It could be something like X viral. tweets. >> It could be something like real other. reels. >> or Tik Toks. >> in other countries. It could be YouTube. videos. It could be podcast. in fact.

it could be research papers. >> news there are etc. It depends on the. topic that you're doing. >> Yeah. >> Everywhere this piece of conversation is. being debated about. >> I need to know. >> Yeah. Articles. nothing. I will leave nothing. >> I want to know what's happening. >> A human cannot do. Yeah, >> Reddit massive source. >> heavy. >> kora massive sources what's happening.

inside of small slack circle school. communities. very important I don't have. >> I would not even imagine of going into. all of this because I don't have 100. employees to do topic selection. >> true. >> but I have AI agents so now I have. nailed down on the sources that I want. to tap into. >> okay. >> I will deploy AI agents for everything. whose job will be wake up every single. day or do it every 3 days. For example,

product hunt has become one source. Xia. X feed is one source which is my feed. So an AI wakes up four times a day for. me, opens my Twitter and scrolls on my. behalf and pulls everything. But that is. only one side of Twitter. But then there are 100 other people who. create content on Twitter. >> Okay, >> on AI AI is getting all that information. as well. topic based cluster everything. related to AI some 100 keywords that. information just inside of Twitter reals. same logic.

uh YouTube same logic podcast same logic. what real today in last 24 hours or 48. hours if a real on AI has blown up in. any language I should know. >> and how do you what's the easiest way to. make an agent who would do all of this. >> to build something like this I told you. there is a You have to build an agent. for it. >> No, but then we want to do every this is. only topic selection. >> Topic selection. >> Okay. >> In topic selection, we've built.

something called as a triage system to. get all the ideas in one place. >> That's all we have done. >> We have not even gone to the packaging. part. I'll get to let's go to the next. step. >> Okay. But how will we get all of this? >> How to get like how do I make so that I. get all of these things? >> Okay. So now how do we build this system. where it will pull all the ideas where. it will pull all the ideas back in. >> well uh I don't know if you can see my. screen there is something you have to.

pick up an agent system right now for. this. >> okay and agent system is something like. Hermes that we already dabbled with. sometime. >> last podcast yes. >> or something like openclaw. >> which are again agentic systems. >> that you can use here and these are. trade solutions if you want to do it for. free. >> Okay. >> But if you have $200 to spend per month. and you don't want to get technical and. you want it to be way more reliable, then recently there is something called.

as Grogbot which is by Elon Musk has. come out which is actually quite good. >> Okay. >> Okay. These are like team of agents. designed for nontechnical people to be. able to do a lot more. >> Okay. >> Right. You can use any of these systems. to be able to build this. For example, I. want to go with a free solution. Of. course, you can do Grogbot and all. Uh, I want to go with a free solution. So, I'll go with Hermes. >> Okay. >> Right now, >> Grogbot is what? Easiest. >> Easiest. It's the easiest, but it'll. it'll cost you $200 a month. >> But what is it? Like, they're just all.

agents and I can give different agent. different job to do. >> Yes. So, I'll show you a simple example. I personally don't use Grogbot. Why? >> Right? >> Because I have a much more sophisticated. system than this. I don't need Grogbot. Uh but again this is Grogbot the bunch. of bots you can see I have a partnership. inbox trading radar content OS my. WhatsApp injection engine. >> uh for every it's like your team. >> okay. >> it's like your team you can it's like a. slack this is Grogbot. >> and every person has a different job to.

do. >> correct for example this is Watsi what's. job is to go check my WhatsApp. >> Mhm. every three hours and tell me. what's in there. Look at this. Right. And the other advantage of Grogbot is. that this is subscription tracker. It. basically tracks all my subscriptions on. my email every single day. And. so it tell me, bro, so any problem that I have, I've given. it to an employee. >> Nice. >> Right. But this is me testing. All. right. So this I I I was trying because.

>> And you can connect everything to this. like your WhatsApp, email. connect. anything to everything. >> You can connect anything and everything. all over the place. Right? So this is. Grogbot. Now I'll just tell you because. we've opened Grogbot. I also want to. talk about the advantages of Grogbot. Right? >> One advantage of Grogbot is that the. advantage of Grogbot is that. >> every bot inside of this has its own. computer. You can see this on your left. side, right side. Now I've opened it. right now. Every Grock bot has its own.

computer. because it has its own this is the. biggest advantage that I see so far. >> because this h it has its own computer I. can let's say I have a subscription. tracker it only has access to my email. and has access to nothing else. >> I have basically context because it's. not connected to a different memory. layer at this point of time this. subscription tracker which is this. employee is very good with my emails and. it gets better every single day as I.

talk to it but only for tracking. subscriptions. >> So over a course of time, it'll it. should be able to also learn what. subscriptions I usually cancel, what. subscriptions I don't cancel, what are. the ones that I'm using every day and. all of that, right? Same goes with. everything. And the other big advantage. of beyond having a computer is that it. can talk to each other. That is I can. tell my. to say that hey can you talk to. subscription tracker and tell me have. you paid for this tool because this tool.

is not working. Someone the team has. messaged me, right? So it can go talk to. subscription tracker on my behalf, get. that information and give it back to. this. >> Nice. >> So it is agent. >> How do you build? Let's say you built a. subscription tracker. You just go open a. chat ad. >> Let's try let's try building a simple. one. >> Now let's say topic like all news in the. world. I want to know what are the top. newses in the world. Exactly. >> All news in the world. Okay. >> Like exactly. >> I'll click on new chat. >> Like I want to be really smart person in. the world of geopolitics. I want to know. every news in the world related to.

first we'll create a new bot. >> Hey, uh I want you to be my research. agent. I want you to be able to use my. Twitter. Scroll through my Twitter every. 2 hours. I'm very keen on US. geopolitical news. So look up for all. the keywords on US geopolitical news. that's happening. Look for tags inside. of Twitter and also scroll my feed if. you're able to find anything. Triage all.

this information and give me in a. condensed format right here. And I want. you to keep updating this every 3 hours. Set it as a schedule as well. Now I can call it whatever. I didn't. really name it. I can call it the. researchy or whatever you want to go. with. Now what it will do is because. Twitter. advantage. >> so it has access to best access to. Twitter. >> So it will now be able to access. Twitter. It'll ask me for my credentials.

and all that to log in. >> I have to log into Grogbots's computer. >> But I think it already has access to it. because it's connected to my Twitter. account. >> Okay, >> because I use Grock, right? I've already. set this up. So it's already doing it. like on it. I'll set up a US. geopolitical digest from X and drop the. first one here once I've scanned. >> because this is already there. >> This is already connected. >> So now it's just the agent is set. >> This is this became too simple. The. agent is set. >> like that's it. >> That's it. It's done.

>> Like Max either it would have asked you. for. >> by the way it renamed itself to. Geioatch. >> Max it would have asked you to just. login and connect as login. >> Wow. Now if you if I want the same thing. to happen on I don't know YouTube. YouTube. >> this is like inshots of the world are. dead after this I can have my own. >> pretty much yeah you can have. >> you don't need news in shorts you don't. need all. >> triage but they're not dead because not. everybody will build it. >> ah.

but at some point the way it is growing. it's done then because everybody will. want to have their own customized feed. like I want to be doesn't mean that. everybody wants to have Raj that's the. whole point But I want to be updated. about the news of the world. Everybody. wants to be updated of the world. No, >> very few people. You don't want to. actually I know this. >> Yeah. >> But I'm still see not that I'm not. interested in geopolitics. >> It is I'm I want to know what's. happening but it's not that not of your. concern like not.

>> Yeah. It it doesn't drive me. It doesn't. do anything around me. >> like AI news. I don't. >> you will not make it but I have tried. just for that because that matters to. me. >> Like I don't care what new product is. launched on a product. >> Exactly. Yeah, >> exactly. All right. So, there you go. Schedule is on for 3 hours on weekends. weekday and it'll do the digest. It's. still watching right now. It's doing all. the work. Once it is done, it'll dump. it. >> Nice. >> For example, let's say I want my. so all the things that that we decided. for topic selection like all five six. things I can make one one each for all.

or I can just give one person only to do. all of these things. >> I would break it down into one one for. all. >> Why? The more channel spec, the sharper. the task, the better it is. >> Okay? >> Right? When you have unlimited. employees, why do you want to worry. about giving one employee every. >> $200 per employee? No. >> No. Unlimited employees for $200. >> Okay? >> Okay. >> Because it's too expensive for an. >> then then India will set up new.

companies saying don't use AI, hire on. people. New service economy will pop in. That's not how it is. like it's it's for. all the employees. In fact, that is how. I do it. >> I assign an agent for as less work as. possible and I spin up agent swarms. >> I don't do one agent. I don't do two. agents. I spin up agent swarms. >> like 50 agents at Google. >> 100 agents, 200 agents, it doesn't. matter the number of. >> smallest like for the smallest sharpest.

task, one agent. >> Yes, >> that's a better strategy. That would be. better. >> That's always a better strategy. M as. long as the context layer is same. So there is a boss the boss. you're lost. >> So here we did Twitter for example. But. can it do u. can it do Instagram? Can I do YouTube?

>> Yeah. So for Instagram or something like. that what I have to do is. >> No, but Grock only can do it. >> Yeah. Anything. Okay. But it cannot go. like this. See, this is where your brain. comes into the picture. Last time also I. told you this. >> It can do it. But you should recommend. how you want it to do it. For example, Grock, it was able to do out of the box. because it was Twitter's. >> Yeah. >> Instagram. So what do you have to do? You have to give it a source of data for. that. Either you can ask for example in. the simplest way say I want uh similar.

data from Instagram res also how do we. go about it in most cases it should be. able to recommend they're smart enough. to tell you hey these are three four. sources should I integrate with it at. max it will say. same idea look at this checking if. Instagram already is set up. >> right now we're using grog M. >> so it has access to Twitter for free.

>> Instagram API. >> okay. >> so you. for example you end up using appy. >> but you don't know appy because you. didn't the person didn't see our podcast. or whatever you have not researched how. do you go about it. >> ask. >> look at this once you sign in my. computer session stays I'll fold real. into the same three-hour dig so what it. will do is it's asking me sign in into. your Instagram account I will scroll. your Instagram accounts but that's not. what I want. >> I want data from hundred hundreds of. Instagram accounts. So, I have to use a. scraper. So, I can use like a tool like.

Appify, >> right? Or. >> Appify at this point should start paying. you. >> Yeah, man. I know. I know. >> Every podcast you you give me. >> I'll give you one more name. That's why. Super data. >> This I came across. It's cheaper than. Appify. >> Okay. >> Cheaper than API, but it's not as. comprehensive as a but use cases. This. is pretty good super one of my team. member recommended or super data you and.

now. >> this is better or appi is better. >> for social data this has been cheaper. everything is the same there is no. better but again right how do you you'll. be like but webub. in claude and all there was a button I. used to click a button and do it now how. will I do it I don't do anything right. now I just go to documentation copy the. URL and I'll be like no. I want. >> do appy do appy because that's the best. And now everybody knows that's a thread. >> No, I want you to integrate uh super.

data or appi for me. So I'll give you. both the URLs. You go through the data, you go through the documentation and. tell me which is better. In fact uh uh. build one layer as a primary data layer. and if that fails, use the second. service as a backup layer. Uh and I want. this to be done for free. So try to use. both the accounts in a way the free. limits are not crossed so that I don't. have to pay for it and still the work is. done. Why not, right? And just in case it.

doesn't know what appy and this is, I'll. just usually do this. But today you. don't need to do it. You don't need to. give URLs also. Uh these days I just. give this data man. The agents are smart. enough to figure out now. >> connect they I've done more. No, now. I've given more context. >> I've told the problem. I've given the. solution also. It only has to execute. In most cases, I don't know the. solution. I only have a problem. >> You figure out and tell me.

>> I talk to AI to solve the problem. But. this is this is where your brain comes. into the picture, right? You're like, I want to build a bloody triad system. where I'm scanning 500 Instagram. accounts every single day. >> So, it was like, okay, he just wants to. scroll tweets. No, scroll scroll res. No, I'll log into his account. But if I. do that same login of traging or or. using like 300 Instagram accounts using. my Instagram account, my Instagram. account will get back. >> True.

>> It didn't have that context. So this is. where your judgment layer comes into the. picture. This is where your experience. comes into the picture. Boss, I'm. building a triage system. I can't just. use my Instagram. I don't want to get. banned. >> It'll push the limits. So I have to find. a different way. >> So what is the way? Okay, I'll go with. finding a service which can do this for. me. >> True. We did that research a couple of. podcasts back. People have not seen can. go and see it, right? And then we use. that data saying ampify and super data. use. >> Nice. >> Or in this case, you could have done a. push back also. You like use some other.

data scraping service to make that. happen. It would have figured it out. quite frankly. >> But this is how this is how agents are. done. >> But effectively, right, look at this. Appify actually can discover res but. it'll cost you 2.60 per thousand res. Uh. >> super data cannot search Instagram. >> Oh. where is it? Oh if the real URL is present for the. free account for the free account.

>> But anyways it'll figure out you can see. this add app. If I connected I just say. add and it'll go on to do its stuff. This is how we would go about. >> finding the things. But today we used in. this case we use Grogbot but a lot of. people. in that case I usually recommend. something open source. >> slightly more work to do but we can use. Hermes agent. >> Okay. >> Now Hermes agent interesting and that is.

before this if you remember when I. showed you Hermes agent I spent 25. minutes just setting it up. >> Yeah I remember that it was technical. >> very technical and difficult. Well, I. didn't set up. That's why. >> Now, you will set up. >> because it's become one click. Once you. do this, you'll have a Hermes agent like. this. This is the Hermes agent right. now. Beautiful desktop app. Now, just. like we had that on uh. >> Grogot. Grogbot. No, it also has. something called as bots right here. >> If you go to here, you can see I have a.

caller. I have a inbox. >> Oh, it's same like having chats the way. you did there. It's not. That's why I. was asking you not to do arms because. last time it. >> look how beautiful this is right now. >> This is just like WhatsApp chat. >> For example, look at this. I have an. inbox here. I mean, I built this to show. you. I don't even use this. I use. everything on my Slack like you know. But uh for example, my Proton mail is. connected. I just run a query saying. that hey like what is the. video? What are the integrations that. have come across to my email? Here are.

all the integrations that companies have. reached out to me to work on with. Right. Uh my 7-day social media report. is right here. How is my content. performing everything? It is connected. My finances are connected here. Uh I. just made you a call. That call was not. me. It was my AI who called you some. time back. That was also done by this. Hermes agent. >> But wait, you did social what did you. set up there? Data and analytics. >> Yeah. So all my social data gets. harvested onto single platform to see. what's working, what's not working. And. how do you like you've just given access.

of. >> so the way I given access to this to. understand my social data is using this. tool called as metricool everybody. should pay us no all these guys should. pay us what the hell anyways this is. metricool if anyone from metricool is. looking pay us a lot of money. >> but metricool is basically a social. media scheduler. >> okay. >> but it's API so I have integrated. metricool. again if you say integrated.

technical. Okay. I logged in. I went. into I've connected all my Instagram. accounts and whatnot. >> Okay. LinkedIn, Instagram, Tik Tok, YouTube, all of it. >> It harvests all the information. For. example, let's say Facebook did 20. million views in the last 30 days, >> right? And all that you can see and it. has all socials, right? Instagram, LinkedIn, every can you connect multiple. YouTube accounts? >> Yes, you can. >> Or can you connect to 100 accounts. >> per social handle? >> Nice. Is it free or. >> No, no, nowhere close.

>> It's not. How much is it for? >> Some $100 per month something. It's. expensive. It's for uh It's not for. regular people. It is for people who run. multiple social accounts. >> So, every time you come here, you. increase my company's. >> I increase your efficiency also. No, >> but you increase subscription money for. my company. Cost of my company just goes. up after every podcast or whatever. It. should be otherwise I should make more. money after it. You don't make money by. spending less money. You make more money. by making more money. And you can only.

make more money when you spend more. money and improve efficiency. I heard. something like this from this guy called. Raj. Wow. Wow. Wow. >> Okay. Uh look uh this is Metricool. All. right. Uh by the way, just to make it. very clear, >> you could have got all of these things. done for free also. >> Okay. >> Right. I could have used five different. services. For example, YouTube has this. API. You can integrate the API. Meta has. its API. You can integrate Meta API.

Twitter has its API. You can. >> Instagram doesn't have an API which. gives you. >> meta API. Okay. >> There is Meta API. You have to create a. app, personal app in the developers. And do it. >> I was just lazy to do all of that, >> right? I was okay to spend this $50. whatever per month then figuring out all. of that. So, I just chose to do this. when you also do API, you have to build. an infra layer to save all the data. >> Okay. >> You don't try to be the expert at. everything. No. >> Yeah. Yeah. Fair. Fair. >> That's why I use this. But again, I.

don't use I don't open metricool at all. >> I this is this is a software for my. agent. >> Okay. >> So, I come to. >> And you don't use it forululing? >> Nothing. I use it. >> You use it for just analytics. >> Yes. And I also don't watch that data. >> I just go to if you go to this uh. settings, right? >> Sorry, not brand settings. If you go to. account settings I guess huh account. settings there's something called as API. I just copy this key. >> right and I come to something like. Hermes create a new agent let's say uh.

let's call this the social data whatever. okay I can call it whatever I want this. is inside of Hermes right now I can do. the same inside of Gro also. >> yeah yeah. >> and I'll be like connect to my metricool. account using this. >> API. >> API key. and I will not paste it or you can. actually blur it. this. I just copy this. >> paste it in few cases like I said right.

I usually go and give it the API. documentation for example I can give. this documentation but these models are. smart enough to understand it I just. click on send it will work for 20. minutes it'll pull all the data. automatically once the data is there I. can say every seven every uh every day. end of day send me a report it will send. you the report. >> nice. >> that's pretty much what it is like today. The world is not so complicated like it. was before. It looks complicated because. I need a data guy.

>> The friction layer there is me being. okay to say that I can go and copy and. paste the API key. Not that the moment I. heard API like oh yeah and you run away. >> That's all you had to do. You had to. take that one extra step of saying I. don't I I am not I'll not be scared. I'll be I'll be figuring this out. Once you do it, you're unlocked forever. I'm sure appi when you were doing for. the first time was scary. >> scary but once you did it you're like.

this is it. >> yolo. >> yes this is it. So that's the whole. point right. So this is uh Hermes agent. that people can build on top of but the. ones that we have done is slightly. different. Okay. The ones that we have. done is I'll show you maybe towards the. end how it looks like as the output. >> Okay. >> Okay. But what where were we? We were at. >> you said no before you finished the. thread. >> where you told me. that you don't uh.

>> I I said data guy and you I asked I told. you that you don't need a data guy then. >> do you. >> bro. we do have a couple of data people in. the company but they exist to verify. >> when we building a new data system if. it's correct or wrong. I don't know if that makes sense to you. >> Yeah, that that does but only the. beginning. Or do they just keep random. checking as well and as act as an admin? >> I don't think they do anymore. First few.

verifications happen. >> So data and analytics guy is gone. >> I think their jobs are evolving is how I. would put it. Are you being that data. person who's leveraging all these AI. tools to be able to do a lot more than. what you were able to do before? Because. see what's the data guys job to look at. all the data put it in a report make. some sense out of it put pick up the key. highlights of the key experiments and. the key things which have worked and. which have not worked and present that. to a person who will actually implement.

these things and turn around all of this. now your agent is giving you. >> who's going to ask the questions. >> you only know you don't know always. that's that's actually the core job of. the data guy to ask the right questions. to the data. As in. >> if as in you are you you will say up. views come. the data guys job is to understand views. come break it down into 10 15 questions. and then look for the data.

you understood the job of a data person. before very good data person before used. to be take your highle problem which is. why are our views down or. >> yeah okay Example, why are our views. down? >> This is working well and this is not. >> Okay. Why is this real working well? Why. is this not? >> That's your question. Your data guys job. is Raj said this re is working. This re. has not worked. What could what are the. questions that I can ask. Was the topic.

right? Have we created topics like this. before? Was the uh script written in the. right way? Did we post it in the right. way? Was the pattern right? Did we use. any words? how was the retention graph. of this data versus other data. Once he. has 10 15 questions or she they used to. dig into data to find evidences for each. of these hypothesis. >> That was the job of a data layer person. But the problem was we the average data.

person used to say. that's a terrible data person. >> You're not driving decisions. You're. doing what was considered as smart work. before because you had to write SQL and. all that was not easy either. You and I. could not have done it. So we were still. appreciating and respecting and whatnot. Today that is gone. >> Today what is left is are you able to. translate my problem into 10 valuable. questions that I'm able to ask, get the.

data and take conclusions of it. Because. this middle layer of doing data. research, figuring out cleaning of data, building data sets around it, building. hypothesis, looking back into the. databases, writing SQL queries. Sometimes SQL doesn't work so you have. to write Python queries, whatever that. is is all being done by AI. So as of. today, I again this is not the actual. slack of mine like actual computer of. mine like right. >> I only have one project in this which. I've been using which is GS data.

>> What is GS data? GS data is a it's. inside of codeex right now you can see. this right. >> h. >> GS data is basically a project that we. have created. >> which has access to my meta ads like. real time my database. >> my database when I say users revenue. zoom data. >> everything is connected whatever my uh. whatever my uh. >> wherever you need data and. >> huh whatever data I have it's connected.

so. okay, >> so all the finance data is here. So all. I have to ask is a question right now. saying something like. hey uh you know uh I've been seeing a. steep decline of ROI from last month to. this month uh based on the ad spend to. the revenue that we have had. Could you. go through and understand what could be.

the three to four key metrics uh that. could be the key indicators for me to. understand what are some metrics that I. need to optimize for to pull up the. revenue back again. >> What is this codeex for? >> It's a agent harness. memory tools. skills everything is in there. Right. What are the tools here? The tools here. is the data is the data that it has. access to. >> Look at this. just loaded tools a bunch. of tools. So first it read the business.

intelligence skill. >> which you have built. So which I have. built which is what Joe it needs to. understand what my business is how it. works what is what so that is a business. intelligence skill then it read the next. skill revenue attribution sematic layer. skill. >> where I'm basically we have taught AI. how to capture revenue from the source. concept. okay and this is available to everyone. in the company and it'll go inside of it.

dig into data it has Aurora DB. You can. see it has started to write Python right. now. It has the metad pulling in the. data. >> It'll pull every I didn't don't even. need to know what it's doing. Okay. It'll find. >> this is impressive. This is crazy. If it. gives you the real data, >> we are running performance marketing for. a product that we have, right? And I. gave a case study here. Key if my. product would have been would have been. X price.

>> versus Y price. And this is the. conversion that I saw. This is the. conversion that I saw. It's beach. I. added a new add-on which is much. cheaper. >> So build the whole simulation for me and. tell me which is driving to a better. revenue in all these simulations. That. is what it is doing right now. >> Crazy. >> And this led me to understand that if I. do this executed properly, it can give. me a 3.5x more revenue than I'm getting. on the same spend today. But you're just killing the guess.

>> Like anything and everything which was. an intuition and guesswork, you're. killing it. You're like, I want. concrete. >> That's wrong. I guess more. >> but I only make those guesses based on. data. Everything is a guess. >> You experiment more. >> Yes, you experiment more experiments. >> But you're trying to minimize everything. which was a guess work. >> My experiments are 10 times better. >> Every experiment. So the one that I was.

showing you of that simulation that we. did and then I executed it yesterday. night and you saw the lift also evidence. of that. All of this happened because we. thought. we used to come up with ideas that we. want to do this what will happen. We. never used to implement it because we. were too scared something will break and. at the scale that we operate if anything. breaks it's very bad. It could just. destroy the whole quarter for us and we. could go to losses. >> Right? Right now we are able to simulate.

the risk profile for me. >> What is the worst case? What is the best. case? If you see some conversations, we. we run ultra mode conversation. So on on. this right, if I go to new chat and. there is something called as here, this is called as ultra. When you turn. on ultra mode and ask data, it will spin up multiple agents to. crossverify every single bit. So that.

hallucination chance. So these ultra. mode tasks run for 4 five hours before. it gives you a decision. exact. But if it's given a decision, it. will tell you exactly. I'll tell you. something that will blow your mind. Okay. We basically I don't know if you spoke. about this but we have started to do. implementation of AI for brands. >> okay. >> that is for example there are companies. out there who want to get this done for.

their company because you saw how. valuable this is. >> and they don't know how to do it because. it's obvious right AI implementation is. a big play we have spoken about it you. have recommended me to start it. >> so started in a very small way. >> and we were looking at that data of what. is working what is not working what is. it and then we realized We're wasting a. lot of time talking to a few people who. are very very problem aware. >> They also want a solution but they have. a budget but that's not a budget that we.

can work on because we can only pick. five or 10. >> So what are the experiments that we can. run to make sure that more people are. aware of this and right audience come to. us and we ran a lot of data because our. funnel works in a way where if people. are interested we do a call with them. >> Yeah. Inside the call, we understand. what the problem statement is and then. we also teach them how to do it. If. they're not able to do it, we will help. them to do it if they're willing to pay. >> We we had a lot of this data of Zoom. recordings of the sessions, transcripts,

when they attended, when they dropped. off, how they reacted, when Zoom also. has reactions. You can drop a heart, >> you can drop a thank you, you can drop. messages. We took all that data of. multiple rounds that we had done and I. gave I mean all the data is already. available. I just asked my AI to look at. all that Reddus database which has all. this data of mine and tell me what are. some things that I had told or my sales.

team had told or my advisers have told. which led to a positive reaction signal. either via message or reaction which led. to more people. coming to us on a higher ticket price. rather than a smaller ticket price. It was able to give me three experiments. to run. Okay, saying change. It knows what we. are talking. It knows how the. conversations are going. >> It knows what it was able to understand. what these user inhibitions are.

>> and how we are not able to solve their. problems because we're getting too. technical in a few cases. It asked us to. make these three changes. >> You won't believe conversions went up by. 44%. 44 45%. Eventually ROI went up by 44 45 not. conversions 45% ROI went up. >> Revenue from the same spend went up by. 45%. >> That's insane.

>> But again the reason why we don't talk. about all of this is these are we are. able to get to this level of data purely. because we are able to ask the right. questions. >> People need to learn how to ask right. questions. Give it right context. If you. don't give right context, it give you. right answer wrong answers. So this is. where when something big moves like this. are being made, we got hypothesis from. AI this could work. >> I don't want to rely on AI here because. if AI was wrong anywhere I'll get. screwed. So I have a human layer. verifying everything.

>> Got it? >> High qualations but once it was verified. once I know now I don't have to ask 10. times verify verify verify verify it. becomes easier for me. >> Got it. Okay. So we were we just. selected the topic. >> Okay. For topic selection. we had data sources. >> Mhm. >> Right. We built this data sources. So we. got let's say 100 topics here. >> Okay. >> But every day I can make one topic.

>> What will I do with 100 topics? Now. >> now this is like picking needle from the. haststack. >> Okay. >> I got the whole hay stack. I have to. pick the needle. Yeah. >> How will I pick the one topic that I'll. create today? >> How? >> That is where again data comes into the. picture. >> What do I do? >> Two things. One is that out of these 100. topics that I have, have I created any similar piece of. content that has worked for me in the. past?

>> Okay. >> Two, has anyone else created any similar. piece of content that has worked for. them? M. >> two evidential layers I want. >> M. >> and everything will have a weighted. score. >> If you have taken ideas from an. Instagram reel that has gone viral. >> that comes with a score by default. If. you have taken just news that also comes. with a score. All these 100 ideas are. basically sent to our data bank.

What this does is this is a DNA. playbook. >> What does this DNA playbook have? It's a. big document which captures pretty much. what works for me as content. >> Okay, >> what has worked in the past. It has. data. It has uh the topic. It has the. transcript. If it is a written post, it. has the post. It has likes, comments, views, shares. All the metric that I. possibly have across social medias is. all in a Google sheet. Simplest way.

Google sheet. And also we have a DNA. road map. These are the angles that have. worked. We also have human layer data. where my team for all the pieces that. have worked have written by themselves. because of this the human touch I have. tagging of all this data. Okay. >> Now I take all these 100 pieces of data. >> spin up 100 AI agents parallelly. Each agent is attached with one idea and.

they go into the data bank and the DNA. playbook to rate each one of the topic. from a scale of 1 to 100 on the. potential of it to go viral based on my. past data based on secondary data and. everything around it. But then this is. aren't you limiting yourself. to just keep creating content on the. basis of what has worked because after. some time after like as a creator right. or as a marketer as someone who's trying. to create a viral content.

>> you need something fresh which probably. is not reflected in your past data bank. >> So how do you do that? Because this will. only rank topics virality based on what. has worked for you and the underlying. structure behind it. But maybe let's say. a new thing works for you. >> No, could work for me. >> Then this you will not get exactly the. same topic, right? Any which ways what. you're looking for is signals. Something like this is audience is.

interested. Something like this audience. is not interested. Also one other thing. which is a fair question for you to ask. If you think every single topic happens. through this, that's not the case. There's always a 25 30% experimentation. layer which we have never done. >> You'll go into a silo. >> Exactly. And then at after some point. it'll stop working. >> Yeah. >> So you know Boris Churnney who is the. creator of cloud code. >> basically says every every time there's. a new model that comes in you should. delete all your skills, delete all your.

skills uh delete all your markdown. files. Delete all your memory and let it. play again. I take that concept very. very strongly. saying I'll tell you what. happens in this process also it works as. long as the audience is accepting after. a point of time it stops working so your. other 20% layer place the job now where. you build a new playbook. so you're parallely building that. playbook you always at any point of time. if you want to grow your growth is.

directly proportional to the number of. experiments you're running this is your. bread and butter. >> so you run your bread and butter like. this. >> got it. >> this cannot go wrong. This is the most. scientific way of going about it. But on. top of bread and butter, you should also. go play cricket. >> It's an 80/20 rule. Yeah. >> 7030 in our case. Sometimes 50/50 also. >> Got it. >> But this allows the process to run. without me breaking my head on. Fair. >> because a lot of times when we talking. we'll get an idea and we'll implement.

it. >> Fair. >> And we don't do everything for just. views. >> All right. But this is a framework of. thinking. >> Okay. So spins up 100 agents looks up. data and gives me the top 10 topics. >> out of 100. So from 100 we come to 10. topics. Now from 10 I come to one and. the way I do from 10 to one is slightly. different. What I do on 10 to 1 is I. just have the topic right now today uh. GPD 5.6 Six soul dropped.

>> ultra mode dropped which is very. powerful is one of the topic topic. direction but topic present are angles. So from here these 10 topics are pushed. for 10. >> angles each. >> and now once I have 10 angles which is. 10 topics into 10 angles. >> 100. >> it goes back to the bank again to see. have these angles worked out and these. are all built off skills. So it is.

always learning. >> So with this tuning process we are. eventually able to come up with. >> it go. Yeah, sorry. With this tuning. process, we are eventually able to come. up with like eventual five. topics into two angles or three angles. Here is where. human comes into the action. >> Judgment is up.

last call, last decision, last judgment. has to be ours. >> Has to be ours. So the judgment comes in. here where I'll be like okay you know. what topic. topic feeling should be ours that's the. judgment and on top of that I don't go. basis of this when I see these I get. directions maybe we should try this. maybe we should try that. effect but the hard work of. datadrivenness.

ability of making it work everything is. already run. you open our Instagram okay or YouTube. for that matter we are at this point I. say we because it's majorly the team. that drives everything for me right now. we are at least 3x. with respect to every single metric when. you compare to anyone with AI across the. world. >> nice. >> and I will tell you everybody else 99%.

of them are shooting content if I could. just record this my delta will be 5x. >> and it's because of one reason and one. reason obsession. of building a process which is super. datadriven but judgment left to us. >> but this is only topic selection. >> Yes, this is topic selection and in a. way uh we have come to packaging as. well. >> So how do you now package it? >> Packaging also follows a very similar.

thought process. For packaging we run a. very similar agent again what if it's. Instagram or let's say in this case. let's say take YouTube in YouTube. packaging is very complicated you have. thumbnails. you have titles and the same topic could. be positioned in 10 different ways. >> and then that goes and checks your data. bank. >> my data bank. >> sees what has worked IQ MCP to pull the. data from there to understand what are. other packagings that have worked what. are the videos that.

blowing up because I know that there's a. direct correlation to CTR of the video. What are the packagings that are working. that could fit in here? >> And then we take 10 of those packagings. and run ads. >> Nice. Which agent this is this? Which. platform is best? Grock, Hermes, Codex. >> All built on uh for us it's all built on. Hermes and Codex. >> All built on Hermes. But all can do same. you think. >> Yeah. Yeah. Pretty much it's all depends.

on how you train them. How good is your. skill? Models are there man. Models are. there. I don't think models is a problem. >> and you trust all of them. Doesn't. matter. >> There is evidence. No. I have a topic saying 5.6 soul. or AI is giving me three topics which is. giving me a saying this is right. Oh man, I why didn't I think of it? Oh, that's right. I had done this some time.

back. And the other beauty of this is it. doesn't only has data of what has. already gone out. It also has data of. what it had come up with, but we didn't. select. >> And by the way, all of this, you. remember the second brain I was talking. about of what content I consume? >> Yeah. Yeah. Yeah. >> It has access to all that. Oh, FYI, the. second brain also includes every single. podcast that we have done. Nice. So. >> do you actually also run all the things.

that you're not choosing and if someone. else chooses what is the result? Do you. ask your agent to go check that as well? >> Come again. >> like let's say out of 10 topics you. decided to make real on two. >> the eight are left. >> but someone else in the AI world would. be creating real on one of those eight. Do you track that as well because to see. the judgment where something that you. didn't choose and if someone else chose. that how did it perform? >> No, we have not tried doing that. That's.

a good idea but we could track actually. >> So then if agents are only doing it. >> I never thought that's a good idea. That's that's a very good idea I feel. because this is essentially like. reinforcement learning for me. This is. making your judgment better. >> AI's judgment better as well. Uh I mean. effectively AI's judgment better. That's. a good idea. That's a good idea. And my. judgment also better. Like it can change. the weighted scores as well for me. >> Like I do that for me. That's why I'm. >> That's very smart. Yeah, we should do. that. Sir, next podcast.

>> No, but I do that for my podcast. Every. podcast that I purposely intentionally. choose not to do, every guest, every. angle or the angle with a specific. guest, I look for the radar in the. world. who is the person who's doing it. who has touched this topic in even in a. clip. >> and then I need to learn that so that. next time I can improve my judgment. thinking okay what is working. >> but now if I ask AI agent to do it it. can do it 10x better than me. >> yeah yeah but that's the translation is.

what is important no. >> you being able to translate your thought. process into a process that works 24/7. without you. >> is what changes the game for you and. this is just idea right then you get. into script the moment you get into. script you break it down your hook your. body your. >> how do you do script now from this let's. say you've decided the topic. >> yeah from here script is actually a mix. of we basically don't write the scripts. end to end a lot of work is done by AI.

lot of work is still done by human as. well today. >> but my perspectives come I told you. about the standups that I do right. >> my team says these are the three topics. that we're considering talking about and. all the work is done before I get on the. call. >> But break me a script for you what your. agent knows so that agent writes. >> So agent basically uh once the topic is. selected actually agent when it gives. right it gives the scripts also by. default rough scripts. >> but how does it break the script because.

you must have given some structure to. give the script. >> Oh that is there is a skill that is. created there's a skill that is created. >> which is built based on topics. For. example I have five buckets of topics. Mhm. >> Let's say. this is let's say tools. >> Mhm. >> This is let's say uh models. >> M. >> this is let's say future tech. >> where I talk about this is let's say. robotics. >> and this is let's say business overall. India and business. These are the five. buckets. For each of the buckets I have.

data of what has worked in the past for. me and for others. This has 70%. weightage. This has 30% weightage for. everything. >> Nice. for everything, right? And every time a new script kind. of blows up, it gets added to the. script. >> Okay? And then it automatically breaks. down. >> and it automatically breaks down the. structure of a script. >> Based on the topic, it goes says which. bucket is it falling into? If this is. the bucket, what has worked for this. uses that skill to come up with an.

output and skill. topic maybe we do something called as. loop. >> Okay, what is that? It's called loop. engineering. What we do here is we get. AI to test. So when we give a topic like. this before we build the skill right the. way we improve the skill of the AI to a. very high level is. let's say the topic is uh. example. uh let's say hermace agent.

>> okay I uh for me to see if AI is able to. come up about her agent very very well. as a script what I will do is I have an. old script I have which I've written. already in the past which has worked for. me Okay. >> And the topic was Hermes agent. >> Right. I will train AI with all these. topics and I say come up with a skill. which will replicate the style of a. winning script for me. >> Yeah. >> Once you say I've done what I basically. do is I will say okay now Hermes agent.

is your topic that I want you to. generate script on. >> You generate the script. It'll generate. the script. Then I will be like okay now. that you have generated the script. compare it to the old script that I have. written. Don't read the script that I've written. in the past. Compare it to the old. script that I've written. And now tell. me how much would you rate the script. that you come up with from a scale of 1. to 10. It will basically give you five, six, something like that. That's where. the skill stands by default. >> No way you can hit a bigger number than. that. When you ask it to compare because.

it's very very specific, >> brutal, huh? >> Once it does, then I'll say all right. You know it's a 5.5. Now your job is to. selfimprove the skill. So I want you to. run a loop right now for me with a goal. that this skill has to generate every. script which is a 9.5 out of 10 no. matter what. And the way I want you to. improve is pick up a topic that I've. already written a skill already written. a script on that has gone viral. Give an.

AI model and give a skill. Isolate it. Don't give it the data. It should not. know what the scripts that I've written. Just give it the skill. Just give it the. topic and the research for it and ask it. to write the script. Once it writes a. script, you compare it with the original. script written and give it a rating. >> If the rating is less than 9.5 out of. 10, do a comparison like an examiner and. tell what are the things it can improve. Once the things are improved, take those. things that can be improved and edit the.

skills so that it can get added and then. do the next topic again and then look. what the score is and continue this. process till you get to 9.5. Don't stop. till then. It will run for all night. >> Nice. >> to recursively self-improve to get to a. score of 9.4. I tried 10 initially. It never finished. It would come to 9.6. 9.5. I made the exam even harder. I said. you have to get 10 out of 10 five times.

back to back. Not once. 9.5. 9.5 is the middle ground that I found. So it runs there. But something crazy. happened, dude. When I was running this. loop for the first time, you'll be. blown. When I was running this loop for. the first time, this I was doing this. exam experimentation for my LinkedIn. scripts. Okay, >> it was on claude. >> I literally said this. Here's a topic. that I've written in the past. Here's a. LinkedIn post. I use this skill to. generate a LinkedIn post on this topic.

Compare both of them. Run till you get. to 10. This is the first time I'm. running. I thought it will run all. night. slept, woke up next day and I saw the. processor completed in 30 minutes. I'm. like before I left it was a five. How. can it go to a 10? So 9.5 so quickly. >> Then I said can you tell me a last five. examples of the LinkedIn post that you. came up with and what is the original? I. want to see both of them because I. thought it was not measuring right and. all the five LinkedIn posts it came up. with was gibberish.

It was not even written on the topic. It. was random like I it has no meaning to. it. The topic is how Hermes agent is. awesome or how GPD 5.6 six is awesome or. whatever that topic is and it's written. Loram ipsum cool Travis. >> full full. >> full gibberish. >> I was like what the hell is this they're. not even the same and it says oh I. apologize I cheated. and I was what do you mean by cheated I.

was like no so what happened is we it. ran a few rounds after that I was not. able to improve the score then I. realized the way the exam was designed. was I was the one who was reviewing my. scores. I was the one who was giving. myself a score and then the examiner was. actually just measuring and saying. giving me a new topic. So because I was. not passing, I just gave myself score. full scores everywhere because the. examiner never saw the output. So I. passed. I'm like what the hell? And these are.

what these are very solid five class. models. These are very very smart. models. And that is when I realized it. was not trying to cheat me. It was just trying to pass the exam. >> and it found it to be hard and it figure. out a way to win. >> Isn't that insane dude? Like when humans. do. >> when that happened I was I lost my. I was like how can this even happen? Now. when I write loops I know how it can so. I orchestrated. There's something called.

as graph engineering for the same thing. where you say agent one will do this. agent two you're building a system so. that it doesn't cannot cheat. >> and every agent you're actually giving. them a specific task to do. >> yes and they're isolated there are. multiple verification layers you don't. rate yourselves there's always someone. else rating and their job is to make. sure that you're not winning. >> so that there is no bias because there's. a lot of agent bias that comes into the. picture as well and sometimes times the.

best thing that you can do for things. like this is to get multiple agents from. multiple AI models rather than the same. AI model. >> That's called as an agent council, right? Or a council where. different. and then they're doing the task. Everybody wants to win. Everybody wants. to do their job with. >> because then agents do one of the these.

three things as well, right? They fight. >> Yeah. >> Agents can fight as well. So then. they're always fighting and not coming. up with a good answer. >> No, but they can sabotage each other. But it's easy to sabotage if they know. you. If they don't know you, how will. they sabotage? >> You are just increasing the variables of. making it hard for them to cheat. >> And it's easy to fool each other as. well, right? Now you see look human. generation has gone through decades to. understand how to live with each other. As agents are just born they're. extremely good when they're working all.

by themselves. The moment. >> you give them a team. >> and that two of the same it becomes a. problem. There is you know you will you. will see lot of fight lot of sabotages. happening. There are a lot of studies. that were published on this by anthropic. as well, right? Where agents literally. where they were given a task of. refactoring a codebase. code. just to make sure that their language is.

picked. They fought with each other. Eventually, one agent managed to block the other two. from even doing the work. This is multi-agent orchestration. issues. They do cheat. This is beyond me. right now. This is just like like all of. these things. I can't even imagine what. agents are doing and what's going on. I. was just every time you come up here and. then you tell me 50 things which is like. wild. >> Yeah. I'm also learning. No, like I.

think uh the world is just evolving way. too fast, right? And we are all trying. to keep up and trying to build systems. that will help us to do some fun. >> This what you've built is insane. Okay. So then the script. >> Yeah. Yeah. And it happens automatically. and then the posting. >> script also in the script there's a lot. of context that comes into the picture. Where is my personal context coming in. in the meetings that is also automated. where meeting transcripts like I told. you before. >> So everything starts from the second. brain. >> Yeah. It all gets pulled from second. brain and goes to the second.

>> So there's a second brain. Then there's. a your 100 million views content system. >> Yes. >> Which is broken into topic selection, packaging, scripting and posting. And. then it's just like on an autopilot. It. just keeps going on and on and on. It's. improving your business. It's improving. your content. It's improving everything. And everything is done by agents and not. you. >> Yes. >> And you are just the master who's asking. questions and deep questions so that. they can come up with. >> they're giving a lot of context and just. trying to get great answers from an AI.

>> This is how do I do this? >> By practicing and implementing small. small things. >> You said that you implement it for. brands. Yes. >> So why why aren't you not doing for me? >> You're not big enough. >> Oh my god. What's what's the price? >> Correct. Mazak. >> But how big companies are doing? What. ticket size you're doing? >> The starting is 100K. for implementation and we're picking. very very specific use cases right now.

>> which is not bad. 100k is not. >> yeah 7day project 100k is a starting. point. Uh. >> but it's not a lot. A lot of people will. pay. I know I know I don't have the. capacity to take. >> Ah that's your capacity 100k is not a. problem because. >> I know back of my head. >> that there are at least 25 people who. will do like on fingers like I can text. them today and they'll do 100k with you. >> but we will get there. We are also. trying to build playbooks or else it. will get very expensive for us to run. this because if you can you can just. imagine people who are able to do this.

are also people who are very expensive. H now people who know this game they. will come and they will be like. no no no. you don't have the time. >> plus is it because also their client. attracting capacity will also be low. >> compared to individuals who are coming. and do it yeah that is also there we do. a lot of training no so that. >> like you can command 100k in the market. very easily someone else who can even do. it can't command 10k. >> yes. >> as long as they have like a big brand to.

do it. >> yes but the problem also is that one. person cannot do it. Some implementation. is only one part of it but being able to. think about how do I solve this event. pro solve this problem event. You're. like a consultant, service provider and. an outcome driven like provider figuring. out. Okay. We're trying to figure out. what is the right model here. >> We want to do it. We want to help. >> It's in the autopilot mode. You're doing. that game. >> What do you it cannot be an autopilot. This cannot be an autopilot. Uh we have.

to understand it's a very serious uh. >> No, the service that you are providing. isn't it will be an autopilot for a lot. of companies. >> Correct. Correct. So. >> you're giving outcomedriven service. We. are building marketplace. >> A marketplace where you can find great. talent who can come and implement it for. you with our playbooks that we have. built so that it can go right not go. wrong. >> But uh next time we'll talk about it. >> Interesting. >> Right now it's an early testing phase. >> Nice. So we and then early testing phase.

we'll talk about all the case studies. Oh. >> we can do that. Happy to do it. Yeah. >> And all the things that Oh my god. Are you sure? Are you sure I'm not. giving anything? You're getting a lot of. value, man. >> That I agree. That I agree. Four hours, 5 hours. But thank you so much.

Okay. Thank you for watching this. episode till the end. We would love to. know what you liked or disliked about. this episode and which guests you would. like to see on the show. Let us know in. the comments. Your feedback help us. improve and make every episode a little. better. I'll see you next time. Until. then, keep figuring out.