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AI is not gonna make you rich.
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Not the tools, not the agents, not the prompts, none of it.
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Whether you've made money with AI already or haven't made a single dollar with it, I need you to hear this.
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I've skilled AI companies past a million dollars in under six months over and over and over again.
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And I've seen exactly what makes people rich with AI and what keeps them broke.
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So in this video, I'll be walking you through how to actually get rich with AI.
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Point number one, stop selling the tool.
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AI is just a tool.
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It's like a database.
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It's like an internet connection.
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It's a thing.
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Tools don't make people rich.
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Think of it this way.
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Selling AI is like a carpenter selling hammers.
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Selling a hammer doesn't make him much more money.
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The carpenter who gets rich has a full toolkit.
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It's the person who can solve real problems by using all the tools.
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Same with AI.
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You don't need a tool.
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You need a toolkit that you know how to activate.
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And the first tool, the one to master right away is point Number two, the hammer.
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The hammer is like an LLM, an AI, right?
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You got Claude, you got Chad GPT, you got Gemini.
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And it's cool, it's powerful, it can put a nail into this piece of wood, but it is just a tool.
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The most basic and universal tool in your toolkit is the language model, but it requires you to actually know how to swing it.
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Have you ever met somebody who can't even hold a hammer?
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They're like, I'm gonna hit that nail.
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It's like, you can't even get it to contact with the nail.
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Amateurs with a hammer make more problems, create more holes, waste more resources than anything else.
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A pro, have you ever seen these guys?
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They like, it's almost like they have a nail in their teeth or behind their ear and they just go, wha-bang, and it's down.
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Wha-bang, and it's down.
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And you're like, how are you a real person?
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Wha-bang, and it's down.
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You're lucky I don't have a nail right now.
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I put it right through this wood.
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LLMs are the same.
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They're very easy to use, like a hammer, but it requires manual input but it requires the mind, the fingertips to guide it, to make it do something useful.
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Most people are using these LLMs like a fancy Google search to answer questions.
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They type, they get answers, they copy, they put in another system and they move on.
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And their output most of the time is shh.
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The way you fix that is having a prompting framework and a strategy for using it.
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And that's what I call the maps framework.
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Every great AI prompt follows this process.
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The M stands for mission, that's the first one.
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So you start with the outcome, not the task, not the reason behind the task.
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Give it direction.
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Like, what are you trying to do?
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The wrong way would be saying like, find me leads.
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A better way would be, I need 30 new customers a month, every month to hit my revenue targets.
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See how that becomes the mission?
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The second one, which is the A, which stands for ask.
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What is the task you needed to perform?
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Now, this is where we get specific.
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One clear request, not just like a bunch of ideas be clear as possible.
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So bad would be help me with leads.
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Good would be give me 40 qualified leads for my business and include their email and their cell number.
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It turns out the LLMs love to solve problems for you if you're very specific about what you want.
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The P in MAP stands for parameters.
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That's the third one.
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What is the context?
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What do you know about the mission?
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What do you know about the ask, right?
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Is it your ideal customer profile?
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It's what's worked before.
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The The more you give it, the sharper the output.
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So pro tip, if you've got a lot of stuff you wanna tell it, I like to use voice.
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I like to hit there, you hit that little button to talk to it, and you just talk and talk and talk.
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I can talk three times faster than I can type and so can you.
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The S in maps number four is the shape.
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What should the output look or sound like?
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Tell it the format.
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If you want it in a CSV spreadsheet, why would you copy and paste the output and go put it into a spreadsheet?
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Just tell it.
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Do you want bullet point?
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Do you want markdown file?
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If you ever copy and paste something from one system to another and the formatting goes away, get it to do an ASCII.
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You're welcome.
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Do you want the output to be conversational?
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Do you want it formal?
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How long do you want it?
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All of those are specifics to the shape.
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You can even give it a screenshot of what you want it to look like.
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Maybe you've seen something you like from somebody else.
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Just copy it, paste it into your chat, hit enter, and watch it do its magic.
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Just follow the maps framework and watch that output come out dialed.
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Now, most people ask at this point, well, Dan, what's the best LLM to use?
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Well, if you're a business owner and you're looking to buy back your time using AI, I created a full document with my whole AI tech stack tailored specifically for business owners.
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So if you want it, find me on Instagram and DM me the word YouTube stack and I'll send it right over.
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So you now know how to use a hammer.
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That's cool.
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But not every problem is a nail.
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Which leads me to point number three, the screwdriver.
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The screwdriver is AI automation.
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You think of like Cloud Cowork, you think of N8N, Zapier, make.com.
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Essentially, anytime you have a task that has to happen over and over again, you use a screwdriver.
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That is way different than using a hammer.
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Instead of manually prompting AI, you can actually build a workflow that once you set it up,
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the AI can be super smart and be on a schedule and repeat that task over and over again.
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That's where you start getting massive leverage in your life.
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And that is what people are willing to pay for because it solves a bigger problem.
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And the reason why it's more like a screwdriver is because the solution is more permanent.
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It's like if I have a screw and I need to put things together, once I screw it in, it's not coming apart.
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Like the whole point is for it to stay together forever.
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And once you set up an automation, it's set it and forget it.
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Set it and forget it.
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For example, I get a report sent to me every Friday in Slack
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that analyzes every call and every company
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that I'm involved in from a sales point of view that tells me how they're doing.
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Happens every time like clockwork and it keeps me on the pulse of the revenue.
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Now, how do you know if a task is worth automating?
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Well, that's what I call the rule of R.
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1. Is it repetitive?
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Is it a task that you do at least once a week?
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If you're doing it every week, then you might want to look at it as an opportunity to automate.
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And if you're doing it every day, you're definitely needing to get yourself a screwdriver.
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Number two, is it rule-based?
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Does the task have the same set of inputs and outputs every single time?
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The third is, does it generate a return?
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Does it save you more time to automate it than it takes you to manually do it every time?
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Because I'm telling you, some people are automating, they shouldn't automate.
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Don't build something that takes 60 hours to build and automate only to save you two minutes a week.
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If the answer is yes to all three of these Rs, then automate them.
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Now you got a hammer and a screwdriver of AI.
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Now this is where it gets really fun.
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What if AI didn't just do tasks for you?
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What if it took over entire workflows?
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Point number four, the power drill.
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The power drill is a gentic AI.
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If you haven't heard of things like OpenClaw, Manus, if you've been watching my videos, you've heard of it.
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Apex, one of my platforms, or even things like Perplexity Computer.
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The hammer needs swinging.
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The screwdriver, you have to turn it, right?
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The power drill, you just point it, pull the trigger, and it does the work.
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You're not doing any of the heavy lifting.
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It's a power machine.
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You're setting the direction and letting the tool take over.
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That's the power, see what I did there, of agentic AI.
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Agentic systems are supposed to just do work for you.
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So if you wanna build an app that helps you with your nutrition, you just say, build me an app that helps me with my nutrition, hit enter, and then it's done.
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Create a spreadsheet that models this scenario, enter, and it's done.
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Real agentic systems can do multiple systems, multiple workflows all over for you automatically, and it's a workflow that's complete, not a step in a process.
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So think about it.
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You have like things that gotta get done, those are steps.
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You have automation, those are steps within a process.
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You still have to set up those automations.
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With Agentic, you just say, here's the outcome I want and it just, it does it and you don't even know how it did it because it don't matter.
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So I use Apex, one of our platforms, to build a new system called AgentForge.
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It looks at a research body of all this knowledge about what's the future of AI
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and Agentic platforms and operators and humans on the loop, all the crazy stuff I'm in.
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And then identifies opportunities to build products.
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Then it creates the website, creates the ad, tests the whole thing, gets leads, even gets people to pay to be bumped up in the
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wait list to then coordinate the recruiting of the people who are going to build
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that company and have them show up and the product validated so we can launch it into the world.
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LLMs can't do that.
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Automation can't do that.
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You need an agentic system to go end to end.
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If you want to start replacing your workflows with AI agents today, this is what you got to do.
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It's called human on the loop.
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Most people have heard human in the loop, which is kind of like the automation screwdriver side where you have like a bunch of different things going on, but the human's still like kind of pushing things forward.
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Human on the loop is the complete loop is being done by an agent.
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And then the human is just there to inspect what it expects.
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No different than I have an employee that works for me.
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And I'm just making sure that the thing that I ask them to do is being done right.
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Because if you've ever had long running tasks with agents, it can start to have what's called context rot.
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their brain stops working right.
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So you need to show up, reset it, and maybe give it some guidance.
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So this is how we do it.
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Number one, pick a full workflow.
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I dare you to take a full workflow.
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Idea to a completed output and use that as your challenge workflow.
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The second is go back and use the maps framework to prompt your agent, make sure it has the mission and the S, the parameters and the shape to actually get it to do what you need it to do.
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The third step is once you get the output, do not jump in.
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I dare you not to touch it.
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Whatever your impulse is to do once the thing is done, I want you to challenge yourself to have the agent do it.
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So if it's to share it with somebody, maybe it's to review it to see if it's any good, have the agent review its own work.
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That is an advanced move that most people don't even consider.
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And the fourth is you gotta guide it.
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See, most people try to tell the AI how to do the task and it might know a way, probably a hundred ways to do it faster, better.
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So just guide it towards the outcome and let it surprise you in its decisions to getting the thing done.
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Some of you are too nice, that's the problem.
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And it's like confused, it's like, it said I was doing good, but then it's upset with me.
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No, just be like, be better.
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I trust you, you do it.
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And you'd be surprised half the time it actually is like, oh okay, I'll just get it done.
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And a pro tip is I have separate agents.
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All they do is check the work of my other agents.
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So I have like a coding critique.
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And every time there's code written, it goes to the critique.
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It writes a list of things to improve, send it back to the coder.
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And it fixed the code it wrote based on the critique.
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Separate people, just like humans.
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We have specialized knowledge.
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And if you have somebody that's like really good at finding real estate deals, but somebody else that's really good at running the numbers on the real estate deals, have those as separate agents and have this agent check its work.
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So now you got the whole toolkit, the tool for thinking, the tool for automating and the tool that runs the entire workflow.
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Yeah.
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But remember, AI tools are not what makes you rich.
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So what does?
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Point number five, the orchestrator.
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Owning the toolkit actually isn't the win.
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Knowing when and where to use it, that's going to make you money.
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See, most people fail
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because they just bounce between tools like oh my god i
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know how to use claw code oh my gosh i know
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how to use nano banana oh my gosh i know how
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to use open claw cool what does it do what problem does it solve see the orchestrator
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picks and chooses between all these three intentions and solves the right problems
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problems make you money write it down get the tattoo problems
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make you money the bigger the problems more money shows up in your bank account
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and the only way you're ever gonna get rich is if that you can sell the solution to somebody.
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How much would you pay for a hammer?
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10, 15 bucks?
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But how much would you pay a carpenter?
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They would come in and fix a massive leak in your roof and change the whole roof without you lifting a finger.
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Now we're talking thousands of dollars, 5,000, 10,000.
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See, the carpenter doesn't sell the hammer, the screwdriver, or the power drill.
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They fix a problem, which is a massive leak in your roof and give you a new one.
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That's the orchestrator.
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They use the entire toolkit to sell a solution.
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I see this happen all day long where people are like, hey, we're selling this AI solution.
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Normally it would cost them $5,000 for this.
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We can do it for 500.
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And I'm having a hard time selling it.
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And I'm like, well, if they're already used to paying 5,000, why don't you just sell the thing for 5,000?
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And if you can do it for 500, keep the difference.
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I actually think most companies shouldn't even bother saying it's AI.
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If you have a cheaper, faster, better way to do it, that's your benefit.
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The customer doesn't care.
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They just want their roof fixed.
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When's the last time you asked your carpenter, your plumber, your HVAC guy, what tool he used to do the thing?
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Now I know some of you guys said, well, just yesterday, cause you're a high fact finder.
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Not talking to you, I'm talking to normal people.
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And that's the thing.
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You have to be a director, not a doer.
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Actually teaching yourself to stop doing so much and start using the AI to get the work done.
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That's where the power comes in.
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And that's only when you start being rich.
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So stop chasing trends, Stop trying to add AI to everything and start solving real problems.
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That'll make you a lot of money.
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AI is just like the internet.
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It's just like mobile.
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These are technologies.
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These are not ways to make money.
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Always work backwards from the customer and go get paid.
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Drop a comment below and let me know what tool are you gonna add to your toolkit
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so you can level up and make more money.
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And remember, if you want a full AI tech stack, DM me YouTube stack on Instagram and I'll send it right over for free.
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And if you wanna learn how you can get dangerously smart with AI, click here and I'll see you on another side.
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Luyện nói tiếng Anh thông qua video này giúp bạn không chỉ cải thiện kỹ năng giao tiếp mà còn mở rộng kiến thức về cách sử dụng AI trong kinh doanh. Video sẽ cung cấp cho bạn cái nhìn sâu sắc về cách áp dụng các công cụ AI một cách hiệu quả và thực tế. Qua đó, bạn sẽ có thể mường tượng rõ hơn về cách vận dụng ngôn ngữ trong các tình huống cụ thể và tăng cường khả năng phát âm tiếng anh chuẩn. Việc luyện tập sẽ giúp bạn tự tin hơn khi giao lưu và trao đổi thông tin trong môi trường kinh doanh.
Cấu trúc ngữ pháp & Biểu thức trong bối cảnh
- “Stop selling the tool.” - Câu này sử dụng cấu trúc câu mệnh lệnh, thể hiện rõ ràng yêu cầu và hướng dẫn trong giao tiếp. Việc sử dụng giọng điệu mạnh mẽ như vậy giúpp người nghe chú ý và ghi nhớ thông điệp.
- “Have you ever met somebody who can’t even hold a hammer?” - Đây là câu hỏi cảm thán, mang tính gợi mở, khơi dậy sự quan tâm của người nghe và tạo liên kết với ví dụ cụ thể, từ đó giúp người nghe dễ nắm bắt hơn.
- “The most basic and universal tool in your toolkit is the language model.” - Cấu trúc này thể hiện sự nhấn mạnh vào điểm quan trọng, đồng thời sử dụng từ vựng rõ ràng để chuyển tải thông điệp. Việc kết hợp giữa từ vựng đơn giản và câu rõ nghĩa là một trong những yếu tố quan trọng giúp người học luyện nói tiếng anh hiệu quả.
Các lỗi phát âm thường gặp
Có một số từ và cụm từ trong video có thể gây khó khăn cho người học khi phát âm. Một trong những từ khóa quan trọng mà bạn cần chú ý là “AI”, trong tiếng Việt thường phát âm là [ê ai], nhưng trong tiếng Anh, phát âm có thể dễ nhầm lẫn với các âm khác. Hơn nữa, cụm từ “hammer” (cái búa) có thể gây khó khăn với âm “h” đầu tiên và “er” ở cuối. Để cải thiện, bạn có thể sử dụng phần mềm shadowing hoặc shadow speech để luyện tập cùng video, từ đó giúp củng cố phát âm tiếng anh chuẩn. Đừng quên thử áp dụng phương pháp shadowspeak để tăng cường khả năng phát âm của mình qua các tình huống thực tế!
Phương Pháp Shadowing Là Gì?
Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.